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- v.37(16); 2022 Apr 25
A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles
Edward barroga.
1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.
Glafera Janet Matanguihan
2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.
The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.
INTRODUCTION
Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6
It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4
There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.
DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES
A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5
On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4
Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8
Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12
CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES
Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13
There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10
TYPES OF RESEARCH QUESTIONS AND HYPOTHESES
Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .
Quantitative research questions | Quantitative research hypotheses |
---|---|
Descriptive research questions | Simple hypothesis |
Comparative research questions | Complex hypothesis |
Relationship research questions | Directional hypothesis |
Non-directional hypothesis | |
Associative hypothesis | |
Causal hypothesis | |
Null hypothesis | |
Alternative hypothesis | |
Working hypothesis | |
Statistical hypothesis | |
Logical hypothesis | |
Hypothesis-testing | |
Qualitative research questions | Qualitative research hypotheses |
Contextual research questions | Hypothesis-generating |
Descriptive research questions | |
Evaluation research questions | |
Explanatory research questions | |
Exploratory research questions | |
Generative research questions | |
Ideological research questions | |
Ethnographic research questions | |
Phenomenological research questions | |
Grounded theory questions | |
Qualitative case study questions |
Research questions in quantitative research
In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .
Quantitative research questions | |
---|---|
Descriptive research question | |
- Measures responses of subjects to variables | |
- Presents variables to measure, analyze, or assess | |
What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training? | |
Comparative research question | |
- Clarifies difference between one group with outcome variable and another group without outcome variable | |
Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)? | |
- Compares the effects of variables | |
How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells? | |
Relationship research question | |
- Defines trends, association, relationships, or interactions between dependent variable and independent variable | |
Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic? |
Hypotheses in quantitative research
In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .
Quantitative research hypotheses | |
---|---|
Simple hypothesis | |
- Predicts relationship between single dependent variable and single independent variable | |
If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered. | |
Complex hypothesis | |
- Foretells relationship between two or more independent and dependent variables | |
The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable). | |
Directional hypothesis | |
- Identifies study direction based on theory towards particular outcome to clarify relationship between variables | |
Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects. | |
Non-directional hypothesis | |
- Nature of relationship between two variables or exact study direction is not identified | |
- Does not involve a theory | |
Women and men are different in terms of helpfulness. (Exact study direction is not identified) | |
Associative hypothesis | |
- Describes variable interdependency | |
- Change in one variable causes change in another variable | |
A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable). | |
Causal hypothesis | |
- An effect on dependent variable is predicted from manipulation of independent variable | |
A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient. | |
Null hypothesis | |
- A negative statement indicating no relationship or difference between 2 variables | |
There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2). | |
Alternative hypothesis | |
- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables | |
The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2). | |
Working hypothesis | |
- A hypothesis that is initially accepted for further research to produce a feasible theory | |
Dairy cows fed with concentrates of different formulations will produce different amounts of milk. | |
Statistical hypothesis | |
- Assumption about the value of population parameter or relationship among several population characteristics | |
- Validity tested by a statistical experiment or analysis | |
The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2. | |
There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan. | |
Logical hypothesis | |
- Offers or proposes an explanation with limited or no extensive evidence | |
If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less. | |
Hypothesis-testing (Quantitative hypothesis-testing research) | |
- Quantitative research uses deductive reasoning. | |
- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses. |
Research questions in qualitative research
Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15
There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .
Qualitative research questions | |
---|---|
Contextual research question | |
- Ask the nature of what already exists | |
- Individuals or groups function to further clarify and understand the natural context of real-world problems | |
What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems) | |
Descriptive research question | |
- Aims to describe a phenomenon | |
What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities? | |
Evaluation research question | |
- Examines the effectiveness of existing practice or accepted frameworks | |
How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility? | |
Explanatory research question | |
- Clarifies a previously studied phenomenon and explains why it occurs | |
Why is there an increase in teenage pregnancy (phenomenon) in Tanzania? | |
Exploratory research question | |
- Explores areas that have not been fully investigated to have a deeper understanding of the research problem | |
What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic? | |
Generative research question | |
- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions | |
How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative? | |
Ideological research question | |
- Aims to advance specific ideas or ideologies of a position | |
Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care? | |
Ethnographic research question | |
- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings | |
What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis? | |
Phenomenological research question | |
- Knows more about the phenomena that have impacted an individual | |
What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual) | |
Grounded theory question | |
- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups | |
What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed? | |
Qualitative case study question | |
- Assesses a phenomenon using different sources of data to answer “why” and “how” questions | |
- Considers how the phenomenon is influenced by its contextual situation. | |
How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan? |
Qualitative research hypotheses | |
---|---|
Hypothesis-generating (Qualitative hypothesis-generating research) | |
- Qualitative research uses inductive reasoning. | |
- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis. | |
- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach. |
Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15
Hypotheses in qualitative research
Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1
FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES
Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14
The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14
As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.
Variables | Unclear and weak statement (Statement 1) | Clear and good statement (Statement 2) | Points to avoid |
---|---|---|---|
Research question | Which is more effective between smoke moxibustion and smokeless moxibustion? | “Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.” | 1) Vague and unfocused questions |
2) Closed questions simply answerable by yes or no | |||
3) Questions requiring a simple choice | |||
Hypothesis | The smoke moxibustion group will have higher cephalic presentation. | “Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group. | 1) Unverifiable hypotheses |
Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group. | 2) Incompletely stated groups of comparison | ||
Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.” | 3) Insufficiently described variables or outcomes | ||
Research objective | To determine which is more effective between smoke moxibustion and smokeless moxibustion. | “The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.” | 1) Poor understanding of the research question and hypotheses |
2) Insufficient description of population, variables, or study outcomes |
a These statements were composed for comparison and illustrative purposes only.
b These statements are direct quotes from Higashihara and Horiuchi. 16
Variables | Unclear and weak statement (Statement 1) | Clear and good statement (Statement 2) | Points to avoid |
---|---|---|---|
Research question | Does disrespect and abuse (D&A) occur in childbirth in Tanzania? | How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania? | 1) Ambiguous or oversimplistic questions |
2) Questions unverifiable by data collection and analysis | |||
Hypothesis | Disrespect and abuse (D&A) occur in childbirth in Tanzania. | Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania. | 1) Statements simply expressing facts |
Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania. | 2) Insufficiently described concepts or variables | ||
Research objective | To describe disrespect and abuse (D&A) in childbirth in Tanzania. | “This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.” | 1) Statements unrelated to the research question and hypotheses |
2) Unattainable or unexplorable objectives |
a This statement is a direct quote from Shimoda et al. 17
The other statements were composed for comparison and illustrative purposes only.
CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES
To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .
Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.
Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12
In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.
EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES
- EXAMPLE 1. Descriptive research question (quantitative research)
- - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
- “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
- RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
- EXAMPLE 2. Relationship research question (quantitative research)
- - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
- “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
- Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
- EXAMPLE 3. Comparative research question (quantitative research)
- - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
- “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
- RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
- STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
- EXAMPLE 4. Exploratory research question (qualitative research)
- - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
- “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
- EXAMPLE 5. Relationship research question (quantitative research)
- - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
- “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23
EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES
- EXAMPLE 1. Working hypothesis (quantitative research)
- - A hypothesis that is initially accepted for further research to produce a feasible theory
- “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
- “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
- EXAMPLE 2. Exploratory hypothesis (qualitative research)
- - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
- “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
- “Conclusion
- Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
- EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
- “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
- Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
- EXAMPLE 4. Statistical hypothesis (quantitative research)
- - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
- “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
- “Statistical Analysis
- ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27
EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS
- EXAMPLE 1. Background, hypotheses, and aims are provided
- “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
- “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
- “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
- EXAMPLE 2. Background, hypotheses, and aims are provided
- “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
- “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
- EXAMPLE 3. Background, aim, and hypothesis are provided
- “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
- “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
- “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30
Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.
Disclosure: The authors have no potential conflicts of interest to disclose.
Author Contributions:
- Conceptualization: Barroga E, Matanguihan GJ.
- Methodology: Barroga E, Matanguihan GJ.
- Writing - original draft: Barroga E, Matanguihan GJ.
- Writing - review & editing: Barroga E, Matanguihan GJ.
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Qualitative vs Quantitative Research: Differences, Examples, and Methods
There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.
Table of Contents
Qualitative v s Quantitative Research
Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.
Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .
What Are the Differences Between Qualitative and Quantitative Research
Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.
Understanding meanings, exploring ideas, behaviors, and contexts, and formulating theories | Generating and analyzing numerical data, quantifying variables by using logical, statistical, and mathematical techniques to test or prove hypothesis | |
Limited sample size, typically not representative | Large sample size to draw conclusions about the population | |
Expressed using words. Non-numeric, textual, and visual narrative | Expressed using numerical data in the form of graphs or values. Statistical, measurable, and numerical | |
Interviews, focus groups, observations, ethnography, literature review, and surveys | Surveys, experiments, and structured observations | |
Inductive, thematic, and narrative in nature | Deductive, statistical, and numerical in nature | |
Subjective | Objective | |
Open-ended questions | Close-ended (Yes or No) or multiple-choice questions | |
Descriptive and contextual | Quantifiable and generalizable | |
Limited, only context-dependent findings | High, results applicable to a larger population | |
Exploratory research method | Conclusive research method | |
To delve deeper into the topic to understand the underlying theme, patterns, and concepts | To analyze the cause-and-effect relation between the variables to understand a complex phenomenon | |
Case studies, ethnography, and content analysis | Surveys, experiments, and correlation studies |
Data Collection Methods
There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:
Qualitative Research Data Collection
- Interviews
- Focus g roups
- Content a nalysis
- Literature review
- Observation
- Ethnography
Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.
Quantitative Research Data Collection
- Surveys/ q uestionnaires
- Experiments
- Secondary data analysis
- Structured o bservations
- Case studies
- Tests and a ssessments
Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.
Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.
Qualitative vs Quantitative Research Outcomes
Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs. Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.
When to Use Qualitative vs Quantitative Research Approach
The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.
Qualitative research approach
Qualitative research approach is used under following scenarios:
- To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.
- Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.
- Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.
Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”
This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?
Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.
Quantitative research approach
Quantitative research approach is used under following scenarios:
- Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.
- Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.
- Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.
Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”
Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.
Mixed methods approach
In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.
Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.
How to Analyze Qualitative and Quantitative Data
When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.
Analyzing qualitative data
Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:
- Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.
- Coding: Data can be arranged in categories based on themes/concepts using coding.
- Theme development: Utilize higher-level organization to group related codes into broader themes.
- Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.
- Reporting: Present findings with quotes or excerpts to illustrate key themes.
Analyzing quantitative data
Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:
- Processing raw data: Check missing values, outliers, or inconsistencies in raw data.
- Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.
- Exploratory data analysis: Usage of visuals to deduce patterns and trends.
- Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).
- Interpretation: Analyze results considering significance and practical implications.
- Validation: Data validation through replication or literature review.
- Reporting: Present findings by means of tables, figures, or graphs.
Benefits and limitations of qualitative vs quantitative research
There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:
Benefits of qualitative research
- Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.
- Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.
- Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.
- Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.
- Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.
Limitations of qualitative research
- Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.
- Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.
- Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.
- Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.
- Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.
Benefits of quantitative research
- Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.
- Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.
- Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.
- Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.
- Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.
Limitations of quantitative research
- Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.
- Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.
- Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.
- Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .
- Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.
Frequently asked questions
- What is the difference between qualitative and quantitative research?
Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.
- What are the types of qualitative research?
Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.
- What are the types of quantitative research?
Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.
- Can you give me examples for qualitative and quantitative research?
Qualitative Research Example:
Research Question: What are the experiences of parents with autistic children in accessing support services?
Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.
Quantitative Research Example:
Research Question: What is the correlation between sleep duration and academic performance in college students?
Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.
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The main difference between quantitative and qualitative research is the type of data they collect and analyze.
Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.
- Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
- Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.
On This Page:
What Is Qualitative Research?
Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.
Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.
Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)
Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).
Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human. Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).
Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.
Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.
Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.
Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.
Qualitative Methods
There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .
The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.
The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)
Here are some examples of qualitative data:
Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.
Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.
Unstructured interviews : generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.
Diaries or journals : Written accounts of personal experiences or reflections.
Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.
Qualitative Data Analysis
Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.
Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .
For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .
Key Features
- Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
- Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
- The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
- The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
- The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.
Limitations of Qualitative Research
- Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
- The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
- Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
- The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.
Advantages of Qualitative Research
- Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
- Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
- Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
- Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.
What Is Quantitative Research?
Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.
The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.
Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.
Quantitative Methods
Experiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.
For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).
Experimental methods limit how research participants react to and express appropriate social behavior.
Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.
There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:
Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .
The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.
Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.
This data can be analyzed to identify brain regions involved in specific mental processes or disorders.
For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.
The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms.
Quantitative Data Analysis
Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.
Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).
- Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
- The research aims for objectivity (i.e., without bias) and is separated from the data.
- The design of the study is determined before it begins.
- For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
- Research is used to test a theory and ultimately support or reject it.
Limitations of Quantitative Research
- Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
- Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
- Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
- Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.
Advantages of Quantitative Research
- Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
- Useful for testing and validating already constructed theories.
- Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
- Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
- Hypotheses can also be tested because of statistical analysis (Antonius, 2003).
Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.
Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.
Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.
Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.
Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.
Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.
Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.
Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage
Further Information
- Mixed methods research
- Designing qualitative research
- Methods of data collection and analysis
- Introduction to quantitative and qualitative research
- Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
- Qualitative research in health care: Analysing qualitative data
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Quantitative vs. Qualitative Research in Psychology
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Quantitative Research Methods
Qualitative research methods.
- How They Relate
In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena—things that happen because of and through human behavior—are especially difficult to grasp with typical scientific models.
At a Glance
Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.
- Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
- Quantitative research involves collecting and evaluating numerical data.
This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.
Qualitative Research vs. Quantitative Research
In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.
Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:
- Self-reports , like surveys or questionnaires
- Observation (often used in experiments or fieldwork)
- Implicit attitude tests that measure timing in responding to prompts
Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.
However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.
Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.
Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.
Used to develop theories
Takes a broad, complex approach
Answers "why" and "how" questions
Explores patterns and themes
Used to test theories
Takes a narrow, specific approach
Answers "what" questions
Explores statistical relationships
Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."
The scientific method follows this general process. A researcher must:
- Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
- Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
- Develop experiments to manipulate the variables
- Collect empirical (measured) data
- Analyze data
Quantitative methods are about measuring phenomena, not explaining them.
Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.
These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.
Basic Assumptions
Quantitative methods assume:
- That the world is measurable
- That humans can observe objectively
- That we can know things for certain about the world from observation
In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.
As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .
Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.
Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.
Correlation and Causation
A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:
- The study was a true experiment.
- The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
- The dependent variable can be measured through a ratio or a scale.
So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.
Pitfalls of Quantitative Research
Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?
As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.
Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.
Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.
While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."
Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.
These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.
Qualitative Approaches
There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:
- Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
- Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
- Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
- Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.
Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.
Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.
There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.
Interpretation
Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).
The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.
Relationship Between Qualitative and Quantitative Research
It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.
These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.
For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).
After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.
By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.
Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.
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By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.
Reference management. Clean and simple.
Qualitative vs. quantitative research - what’s the difference?
What is quantitative research?
What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.
Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.
Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.
The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".
To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.
With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?
With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.
In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.
Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.
Some methods to collect this data include:
- Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
- Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
- Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
- Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
- Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
- Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
- Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.
Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.
The easiest way to describe qualitative research is that it answers the question " why ".
Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.
Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.
Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.
We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.
Some of these data collection methods include:
- Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
- Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
- Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
- Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
- Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
- Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.
Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:
- Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
- Qualitative research allows you to understand concepts or experiences.
Let's look at how you'll use these approaches in a research project a bit closer:
- Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
- Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
- Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
- Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.
Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.
This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.
So, to answer the initial question, the approach you use is up to you. However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.
No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.
Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.
To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.
Considering the above, you can use tools, applications, and programming languages like R to calculate:
- The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
- The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
- Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
- The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.
Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.
Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.
Some of the most common ways to analyze qualitative data include:
- Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️ Learn how to do a content analysis in the guide.
- Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️ Learn how to do a thematic analysis in this guide.
- Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.
We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:
| Focuses on testing hypotheses. Can also be used to determine general facts about a topic. | Focuses on developing an idea or hypotheses. Can also be used to gain a deeper understanding into specific topics. |
| Analysis is mainly done through mathematical or statistical analytics. | Analysis is more interpretive and involves summarizing and categorizing topics or themes and interpreting data. |
| Data is typically expressed in numbers, graphs, tables, or other numerical formats. | Data is generally expressed in words or text. |
| Requires a reasonably large sample size to be reliable. | Requires smaller sample sizes with only a few respondents. |
| Data collection is focused on closed-ended questions. | Data collection is focused on open-ended questions to extract the opinions and views on a particular subject. |
Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.
3 examples of qualitative research would be:
- Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
- Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.
3 examples of quantitative research include:
- Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
- Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.
The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".
The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".
Qualitative vs Quantitative Research 101
A plain-language explanation (with examples).
By: Kerryn Warren (PhD, MSc, BSc) | June 2020
So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.
However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.
In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.
Qualitative vs Quantitative: Overview
- Qualitative analysis 101
- Quantitative analysis 101
- How to choose which one to use
- Data collection and analysis for qualitative and quantitative research
- The pros and cons of both qualitative and quantitative research
- A quick word on mixed methods
Qualitative Research 101: The Basics
The bathwater is hot.
Let us unpack that a bit. What does that sentence mean? And is it useful?
The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .
Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences by systematically coding and analysing the data .
With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.
In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.
But that’s not the only way to analyse bathwater, of course…
Quantitative Research 101: The Basics
The bathwater is 45 degrees Celsius.
Now, what does this mean? How can this be used?
I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .
If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).
With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…
In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:
Hypothesis: Men prefer bathing in lower temperature water than women do.
This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.
So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .
Need a helping hand?
Qualitative vs Quantitative Research: Which one should you use?
And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research.
If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.
On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.
Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.
Let’s look at an example.
Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:
- How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
- What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
- Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).
The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.
Qualitative vs Quantitative Research: Data collection and analysis
The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.
A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.
To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.
On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.
So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.
Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.
There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.
Qualitative vs Quantitative Research: The pros & cons on both sides
Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.
Let’s start with qualitative “pros”
Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.
Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.
There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.
Qualitative research also has its “cons”:
A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?
But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?
Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.
Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.
Now let’s turn to quantitative “pros”:
Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.
Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.
Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.
Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.
But there are also quantitative “cons”:
Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…
It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.
Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.
Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.
Mixed methods: a happy medium?
Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .
In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.
A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!
Recap: Qualitative vs Quantitative Research
So, just to recap what we have learned in this post about the great qual vs quant debate:
- Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
- Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
- Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
- Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
- Mixed methods can be a happy medium, but should be used purposefully.
- Bathwater temperature is a contentious and severely under-studied research topic.
Psst... there’s more!
This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...
It was helpful
thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena
Waoo! Simplifies language. I have read this several times and had probs. Today it is very clear. Bravo
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- Qualitative vs Quantitative Research | Examples & Methods
Qualitative vs Quantitative Research | Examples & Methods
Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.
When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.
Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.
Table of contents
The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.
Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.
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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).
Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).
However, some methods are more commonly used in one type or the other.
Quantitative data collection methods
- Surveys : List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
- Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
- Observations: Observing subjects in a natural environment where variables can’t be controlled.
Qualitative data collection methods
- Interviews : Asking open-ended questions verbally to respondents.
- Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
- Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
- Literature review : Survey of published works by other authors.
A rule of thumb for deciding whether to use qualitative or quantitative data is:
- Use quantitative research if you want to confirm or test something (a theory or hypothesis)
- Use qualitative research if you want to understand something (concepts, thoughts, experiences)
For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.
Quantitative research approach
You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’
You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.
Qualitative research approach
You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’
Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.
Mixed methods approach
You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.
It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.
Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.
Analysing quantitative data
Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.
Applications such as Excel, SPSS, or R can be used to calculate things like:
- Average scores
- The number of times a particular answer was given
- The correlation or causation between two or more variables
- The reliability and validity of the results
Analysing qualitative data
Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.
Some common approaches to analysing qualitative data include:
- Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
- Thematic analysis : Closely examining the data to identify the main themes and patterns
- Discourse analysis : Studying how communication works in social contexts
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
The research methods you use depend on the type of data you need to answer your research question .
- If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
- If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
- If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.
There are various approaches to qualitative data analysis , but they all share five steps in common:
- Prepare and organise your data.
- Review and explore your data.
- Develop a data coding system.
- Assign codes to the data.
- Identify recurring themes.
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
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Qualitative vs quantitative research.
13 min read You’ll use both quantitative and qualitative research methods to gather data in your research projects. So what do qualitative and quantitative mean exactly, and how can you best use them to gain the most accurate insights?
What is qualitative research?
Qualitative research is all about language, expression, body language and other forms of human communication. That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people feel the way they do, why do they act in a certain way, what opinions do they have and what motivates them?
Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.
Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data.
This qualitative data is called unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyze this structured data to assist computers and the human brain?
Free eBook: Quantitative and qualitative research design
What is quantitative research?
Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.
Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.
It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.
As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.
1. Data collection
Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.
Qualitative data collection methods | Quantitative data collection methods |
---|---|
Gathered from focus groups, in-depth interviews, case studies, expert opinion, observation, audio recordings, and can also be collected using surveys. | Gathered from surveys, questionnaires, polls, or from secondary sources like census data, reports, records and historical business data. |
Uses and open text survey questions | Intended to be as close to objective as possible. Understands the ‘human touch’ only through quantifying the OE data that only this type of research can code. |
2. Data analysis
Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.
Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.
Qualitative data analysis | Quantitative data analysis |
---|---|
Results are categorized, summarized and interpreted using human language and perception, as well as logical reasoning | Results are analyzed mathematically and statistically, without recourse to intuition or personal experience. |
Fewer respondents needed, each providing more detail | Many respondents needed to achieve a representative result |
3. Strengths and weaknesses
When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.
Qualitative approach | Quantitative approach |
---|---|
Can be used to help formulate a theory to be researched by describing a present phenomenon | Can be used to test and confirm a formulated theory |
Results typically expressed as text, in a report, presentation or journal article | Results expressed as numbers, tables and graphs, relying on numerical data to tell a story. |
Less suitable for scientific research | More suitable for scientific research and compatible with most standard statistical analysis methods |
Harder to replicate, since no two people are the same | Easy to replicate, since what is countable can be counted again |
Less suitable for sensitive data: respondents may be biased or too familiar with the pro | Ideal for sensitive data as it can be anonymized and secured |
Qualitative vs quantitative – the role of research questions
How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.
You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.
As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.
For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.
Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.
Here’s how to evaluate your research question and decide which method to use:
- Qualitative research:
Use this if your goal is to understand something – experiences, problems, ideas.
For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.
- Quantitative research:
Use this if your goal is to test or confirm a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.
For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.
Qualitative and quantitative research combined?
Do you always have to choose between qualitative or quantitative data?
In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain insights around a topic and propose a hypothesis. Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.
For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.
In response, the supermarket might pilot a children’s clothing range. Targeted quantitative research could then reveal whether or not those stores selling children’s clothes achieve higher customer satisfaction scores and a rise in profits for clothing.
Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.
Qualitative vs quantitative question types
As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.
Qualitative data survey questions
There are fewer survey question options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.
Open text ‘Other’ box (can be used with multiple choice questions)
Text box (space for short written answer)
Essay box (space for longer, more detailed written answers)
Quantitative data survey questions
These questions will yield quantitative data – i.e. a numerical value.
Net Promoter Score (NPS)
Likert Scale
Radio buttons (respondents choose just one option)
Check boxes (respondents can choose multiple options)
Sliding scale
Star rating
Analyzing data (quantitative or qualitative) using technology
We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for quantitative data are now used for interpreting non numerical data too.
A rtificial intelligence programs can now be used to analyze open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.
Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organizations.
The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.
Qualitative or quantitative – which is better for data analysis?
Historically, quantitative data was much easier to analyze than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.
That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.
Related resources
Qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, request demo.
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The differences between qualitative and quantitative research methods
Last updated
15 January 2023
Reviewed by
Two approaches to this systematic information gathering are qualitative and quantitative research. Each of these has its place in data collection, but each one approaches from a different direction. Here's what you need to know about qualitative and quantitative research.
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- The differences between quantitative and qualitative research
The main difference between these two approaches is the type of data you collect and how you interpret it. Qualitative research focuses on word-based data, aiming to define and understand ideas. This study allows researchers to collect information in an open-ended way through interviews, ethnography, and observation. You’ll study this information to determine patterns and the interplay of variables.
On the other hand, quantitative research focuses on numerical data and using it to determine relationships between variables. Researchers use easily quantifiable forms of data collection, such as experiments that measure the effect of one or several variables on one another.
- Qualitative vs. quantitative data collection
Focusing on different types of data means that the data collection methods vary.
Quantitative data collection methods
As previously stated, quantitative data collection focuses on numbers. You gather information through experiments, database reports, or surveys with multiple-choice answers. The goal is to have data you can use in numerical analysis to determine relationships.
Qualitative data collection methods
On the other hand, the data collected for qualitative research is an exploration of a subject's attributes, thoughts, actions, or viewpoints. Researchers will typically conduct interviews , hold focus groups, or observe behavior in a natural setting to assemble this information. Other options include studying personal accounts or cultural records.
- Qualitative vs. quantitative outcomes
The two approaches naturally produce different types of outcomes. Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions.
On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships. Put another way, qualitative research investigates why something happens, while quantitative research looks at what happens.
- How to analyze qualitative and quantitative data
Because the two research methods focus on different types of information, analyzing the data you've collected will look different, depending on your approach.
Analyzing quantitative data
As this data is often numerical, you’ll likely use statistical analysis to identify patterns. Researchers may use computer programs to generate data such as averages or rate changes, illustrating the results in tables or graphs.
Analyzing qualitative data
Qualitative data is more complex and time-consuming to process as it may include written texts, videos, or images to study. Finding patterns in thinking, actions, and beliefs is more nuanced and subject to interpretation.
Researchers may use techniques such as thematic analysis , combing through the data to identify core themes or patterns. Another tool is discourse analysis , which studies how communication functions in different contexts.
- When to use qualitative vs. quantitative research
Choosing between the two approaches comes down to understanding what your goal is with the research.
Qualitative research approach
Qualitative research is useful for understanding a concept, such as what people think about certain experiences or how cultural beliefs affect perceptions of events. It can help you formulate a hypothesis or clarify general questions about the topic.
Quantitative research approach
On the other hand, quantitative research verifies or tests a hypothesis you've developed, or you can use it to find answers to those questions.
Mixed methods approach
Often, researchers use elements of both types of research to provide complex and targeted information. This may look like a survey with multiple-choice and open-ended questions.
- Benefits and limitations
Of course, each type of research has drawbacks and strengths. It's essential to be aware of the pros and cons.
Qualitative studies: Pros and cons
This approach lets you consider your subject creatively and examine big-picture questions. It can advance your global understanding of topics that are challenging to quantify.
On the other hand, the wide-open possibilities of qualitative research can make it tricky to focus effectively on your subject of inquiry. It makes it easier for researchers to skew the data with social biases and personal assumptions. There’s also the tendency for people to behave differently under observation.
It can also be more difficult to get a large sample size because it's generally more complex and expensive to conduct qualitative research. The process usually takes longer, as well.
Quantitative studies: Pros and cons
The quantitative methodology produces data you can communicate and present without bias. The methods are direct and generally easier to reproduce on a larger scale, enabling researchers to get accurate results. It can be instrumental in pinning down precise facts about a topic.
It is also a restrictive form of inquiry. Researchers cannot add context to this type of data collection or expand their focus in a different direction within a single study. They must be alert for biases. Quantitative research is more susceptible to selection bias and omitting or incorrectly measuring variables.
- How to balance qualitative and quantitative research
Although people tend to gravitate to one form of inquiry over another, each has its place in studying a subject. Both approaches can identify patterns illustrating the connection between multiple elements, and they can each advance your understanding of subjects in important ways.
Understanding how each option will serve you will help you decide how and when to use each. Generally, qualitative research can help you develop and refine questions, while quantitative research helps you get targeted answers to those questions. Which element do you need to advance your study of the subject? Can both of them hone your knowledge?
Open-ended vs. close-ended questions
One way to use techniques from both approaches is with open-ended and close-ended questions in surveys. Because quantitative analysis requires defined sets of data that you can represent numerically, the questions must be close-ended. On the other hand, qualitative inquiry is naturally open-ended, allowing room for complex ideas.
An example of this is a survey on the impact of inflation. You could include both multiple-choice questions and open-response questions:
1. How do you compensate for higher prices at the grocery store? (Select all that apply)
A. Purchase fewer items
B. Opt for less expensive choices
C. Take money from other parts of the budget
D. Use a food bank or other charity to fill the gaps
E. Make more food from scratch
2. How do rising prices affect your grocery shopping habits? (Write your answer)
We need qualitative and quantitative forms of research to advance our understanding of the world. Neither is the "right" way to go, but one may be better for you depending on your needs.
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Quantitative and Qualitative Research
- Quantitative vs. Qualitative Research
- Find quantitative or qualitative research in CINAHL
- Find quantitative or qualitative research in PsycINFO
- Relevant book titles
Mixed Methods Research
As its name suggests, mixed methods research involves using elements of both quantitative and qualitative research methods. Using mixed methods, a researcher can more fully explore a research question and provide greater insight.
What is Empirical Research?
Empirical research is based on observed and measured phenomena. Knowledge is extracted from real lived experience rather than from theory or belief.
IMRaD: Scholarly journals sometimes use the "IMRaD" format to communicate empirical research findings.
Introduction: explains why this research is important or necessary. Provides context ("literature review").
Methodology: explains how the research was conducted ("research design").
Results: presents what was learned through the study ("findings").
Discussion: explains or comments upon the findings including why the study is important and connecting to other research ("conclusion").
What is Quantitative Research?
Quantitative research gathers data that can be measured numerically and analyzed mathematically. Quantitative research attempts to answer research questions through the quantification of data.
Indicators of quantitative research include:
contains statistical analysis
large sample size
objective - little room to argue with the numbers
types of research: descriptive studies, exploratory studies, experimental studies, explanatory studies, predictive studies, clinical trials
What is Qualitative Research?
Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups.
Indicators of qualitative research include:
interviews or focus groups
small sample size
subjective - researchers are often interpreting meaning
methods used: phenomenology, ethnography, grounded theory, historical method, case study
Video: Empirical Studies: Qualitative vs. Quantitative
This video from usu libraries walks you through the differences between quantitative and qualitative research methods. (5:51 minutes) creative commons attribution license (reuse allowed) https://youtu.be/rzcfma1l6ce.
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This site is compliant with the W3C-WAI Web Content Accessibility Guidelines HOFSTRA UNIVERSITY Hempstead, NY 11549-1000 (516) 463-6600 © 2000-2009 Hofstra University Product OverviewSurveyMonkey is built to handle every use case and need. Explore our product to learn how SurveyMonkey can work for you. SurveyMonkeyGet data-driven insights from a global leader in online surveys. IntegrationsIntegrate with 100+ apps and plug-ins to get more done. SurveyMonkey FormsBuild and customize online forms to collect info and payments. SurveyMonkey GeniusCreate better surveys and spot insights quickly with built-in AI. Market Research SolutionsPurpose-built solutions for all of your market research needs. Financial ServicesSee more industries, customer experience, human resources, see more roles. Online Polls Registration FormsEmployee feedback, event feedback, customer satisfaction, see more use cases. Contact Sales Net Promoter ScoreMeasure customer satisfaction and loyalty for your business. Learn what makes customers happy and turn them into advocates. Website FeedbackGet actionable insights to improve the user experience. Contact InformationCollect contact information from prospects, invitees, and more. Event RegistrationEasily collect and track RSVPs for your next event. Find out what attendees want so that you can improve your next event. Employee EngagementUncover insights to boost engagement and drive better results. Meeting FeedbackGet feedback from your attendees so you can run better meetings. 360-degree employee evaluationUse peer feedback to help improve employee performance. Course EvaluationCreate better courses and improve teaching methods. University Instructor EvaluationLearn how students rate the course material and its presentation. Product TestingFind out what your customers think about your new product ideas. See all templatesResource center. Best practices for using surveys and survey data Curiosity at Work BlogOur blog about surveys, tips for business, and more. Help CenterTutorials and how to guides for using SurveyMonkey. How top brands drive growth with SurveyMonkey.
Qualitative vs. quantitative research: What's the difference?Are surveys qualitative or quantitative? Turns out, they can be both. Learn when and how to use them, then explore our expert survey templates to run your own research. Quantitative and qualitative research are complementary methods used in surveys to gather results that are both wide-reaching and deep. Choosing the method depends on the type of data and insights you aim to capture to serve your research goals. Qualitative data provides details and context to better understand individual responses, while quantitative data can supply the cumulative results you need to prove the general ideas or hypotheses of your research. To get the best results from these methods in your surveys, it’s important to understand the differences between them. Let’s dive in. The difference between quantitative and qualitative researchExamples of when to use qualitative vs. quantitative research, 3 tips for combining quantitative and qualitative research, the benefits of quantitative vs. qualitative data, examples of qualitative and quantitative questions, what's the difference between quantitative and qualitative research. Quantitative research uses numbers and statistical methods to make predictions or identify patterns, while qualitative research dives into ideas and understanding through descriptive data like observations and interviews. Quantitative researchQuantitative research is designed to collect numerical data that can be used to measure variables. The resulting quantitative data should be structured and statistical to present objective and conclusive findings, relying on systematically analyzed data collection. Qualitative researchQualitative research is designed to explain the “why” behind research findings. With less of an emphasis on statistics and structured data, it provides an in-depth understanding of human behaviors, motivations, and emotions through text-based information. Qualitative research methods usually involve first-hand observation, such as interviews or focus groups. This type of market research is usually conducted in natural settings, meaning that researchers study things as they are without experiments and control groups. While qualitative approaches bring depth of understanding to your research questions, it can make the results harder to analyze . Qualitative data collects information that seeks to describe a topic more than measure it. This type of research measures opinions, views, and attributes vs. hard numbers that would be presented in a graph or a chart. In essence, qualitative and quantitative research methods shine light on different survey objectives. Quantitative data can help you see the big picture. Qualitative data adds the details and can also give a human voice to your survey results. Let’s break down how to use each method in a research project.
Launch a quantitative research study with SurveyMonkey audience and reach a sample audience that matches your needs. In a world of Big Data, there’s a wealth of statistics and figures that form the strong foundation on which decisions can rest. But that foundation is incomplete without the information collected from real people that gives the numbers meaning. Therefore, these two research methods don’t conflict with each other—they actually work much better as a team. Here’s our top tips for how to combine the two research methods:
Gaining both types of insight can provide a comprehensive understanding of research subjects; however, if you've already decided you need to know the "why" vs. the "what" (or vice versa), it may be simpler to choose just one research method. Let's break it down one more time: Quantitative data (what):
Qualitative data (why):
Our customer satisfaction survey template includes some good examples of how qualitative and quantitative questions can work together to provide you a complete view of how your business is doing. Quantitative questions:How long have you been a customer of our company?
How likely are you to purchase any of our products again?
Qualitative follow-up question:
The following is another example from our employee engagement survey . When you make a mistake, how often does your supervisor respond constructively?
Qualitative question:
Now that you know the definition of qualitative and quantitative data and the differences between these two research methods, you can better understand how to combine them, or zero-in on one. Put them to work in your next project with one of our expert written survey templates. We’ve got templates for all types of questions. Check out our library of expert-designed survey templates . Discover more resourcesUnderstand your target market to fuel explosive brand growthBrand marketing managers can use this toolkit to understand your target audience, grow your brand, and prove ROI. 3 ways to win in a new marketing eraHow to adapt to marketing trends using customer feedback. What is word-of-mouth marketing and how to use itWord-of-mouth marketing instills customer loyalty and decreases customer acquisition costs. Learn how to generate and leverage WOM marketing here. AI in Marketing Statistics: How Marketers Are Using AI in 2024Discover how industry professionals are using AI in marketing and dive into SurveyMonkey research statistics on AI usage in the marketing field. Ready to send and analyze your first survey?SurveyMonkey can help you choose whether to collect qualitative or quantitative data to get the best results. App Directory Vision and Mission SurveyMonkey Together Diversity, Equity & Inclusion Health Plan Transparency in Coverage Office Locations Terms of Use Privacy Notice California Privacy Notice Acceptable Uses Policy Security Statement GDPR Compliance Email Opt-In Accessibility Cookies Notice Facebook Surveys Survey Template Scheduling Polls Google Forms vs. SurveyMonkey Employee Satisfaction Surveys Free Survey Templates Mobile Surveys How to Improve Customer Service AB Test Significance Calculator NPS Calculator Questionnaire Templates Event Survey Sample Size Calculator Writing Good Surveys Likert Scale Survey Analysis 360 Degree Feedback Education Surveys Survey Questions NPS Calculation Customer Satisfaction Survey Questions Agree Disagree Questions Create a Survey Online Quizzes Qualitative vs Quantitative Research Customer Survey Market Research Surveys Survey Design Best Practices Margin of Error Calculator Questionnaire Demographic Questions Training Survey Offline Survey 360 Review Template
Qualitative vs Quantitative Research: Key Differences & QuestionsWritten by: Orana Velarde There’s a common saying in business that goes, “Know your customer.” And yes, it's crucial for many situations, such as customer experience, sales targets, marketing and communication. But how can you truly know your customer? One way is by conducting qualitative and quantitative research. Qualitative and quantitative research go hand in hand to help you successfully grasp what your customer's pain points are, what they expect from your brand and what their life situations are. The answers and results from the research are critical to making fundamental decisions for your business and your brand. In this guide, you’ll learn how qualitative and quantitative research differ, how to formulate questions for each type, how to conduct the research and how to present the results. Plus, you’ll discover Visme Forms, the ideal way to engage your customers with your research questions. Table of ContentsWhat is qualitative research, what is quantitative research, qualitative vs quantitative research.
Qualitative research is an investigation method that explores the “whys” and “hows” of human behavior and experiences concerning a specific situation. Instead of focusing on numbers and statistics, it dives deep into people’s thoughts, feelings and perspectives. Through methods such as interviews, observation and text analysis, researchers can uncover the nuances and complexities of any situation. Quantitative research is a systematic investigation method that uses statistical and mathematical techniques to analyze numerical data. This approach involves collecting data through methods like surveys and experiments with close-ended questions. The data is then analyzed using statistical tools to conclude patterns, trends and relationships between variables. Qualitative vs quantitative research methods differ in their purpose but go hand in hand when you're looking to fully understand a situation. They complement each other to give researchers a wide view of the data they need to analyze critical information. You should conduct qualitative research if you’re looking to understand concepts, thoughts and experiences. On the other hand, you should conduct quantitative research when you want to confirm or test a hypothesis or theory. That said, what is a common goal of qualitative and quantitative research? Both research methods help you gain a deeper understanding of a phenomenon or topic by generating knowledge through the collection and analysis of data. They’re complementary to each other and, therefore, have a common goal. Here are some qualitative vs quantitative research examples: Made with Visme Infographic Maker Qualitative Research Examples
Quantitative Research Examples
Advantages and Disadvantages of Qualitative ResearchQualitative business research is crucial for understanding the customer pain points, goals and expectations concerning your business. Conducting qualitative research within your business can help improve sales and marketing funnels, create better-oriented touchpoints and even improve your employees' experiences. Here are some of the main advantages of qualitative research:
Some disadvantages of qualitative research include the following:
Advantages and Disadvantages of Quantitative ResearchIn business, quantitative research is the backbone of all statistical decision-making. Conducting quantitative research in your business is critical for determining its performance in terms of sales, distribution margins, profit and loss, ROI and demographic expansion. These are the major advantages of quantitative research
Some of the disadvantages of quantitative research include the following:
Examples of Qualitative & Quantitative QuestionsTo conduct a comprehensive study, researchers must ask questions using surveys, feedback forms, tests and experiments. When it comes to qualitative vs quantitative research questions, there’s a clear distinction. Qualitative questions aim to understand behavior and thought, while quantitative questions aim to prove hypotheses and gain numerical data. Here are some qualitative and quantitative examples of questions to get you started. Qualitative Research QuestionsQualitative research questions are typically answered in sentences, long-form answers or rating systems. Start the research with a broad question and then work your way toward the details. Here are some broad question examples:
Next, here are some detailed questions to research the first broad question:
This customer service survey asks the customer about their overall shopping experience through several steps, including a rating system and personal thoughts. Quantitative Research QuestionsQuantitative research begins with one broad question that expands into detailed numerical questions. Here are some sample broad quantitative research questions.
Here are some questions to delve deeper into the first broad quantitative research question above:
Conduct & Analyze Qualitative & Quantitative DataLet's take a look at how to conduct and analyze qualitative and quantitative data. Prepare For The Research ProcessBefore conducting any research, you must set a goal. It’s at this point that you set the broad question that you will then expand on for more detailed data. For this to work, you have to brainstorm with your team or colleagues. Visme’s online whiteboard makes it easy to collaborate with your team during the brainstorming process . Use sticky notes to come up with possible broad questions and then pick the best ones. Next, draft the questions you’ll ask participants and put together more detailed questions that pertain to your main question. Once all the questions are finalized, start building the form, survey or worksheet you’ll use to gather your data. Here’s an example whiteboard you can use for a brainstorming session with your team: Gather DataThere are two ways to gather the research data; in-person or digital (optionally anonymous). In-Person ResearchIn-person research, like interviews, focus groups, observation and experiments, is more suited for qualitative research. Some examples include:
For more efficient in-person data collection, provide researchers with a digital interactive document where they can record their findings. This can also come in handy when you need to recount important data when creating a grant proposal . Use this workbook template to create a checklist-style document with your research questions and notes. You can create this digital document on Visme and access it via a link on a tablet, mobile device or laptop. The digital worksheet should include informational interactive hotspots to help testers ask better, more nuanced qualitative questions. Digital (Anonymous) ResearchDigital forms and surveys work for quantitative and qualitative research questions and can have combined questions to get a broader grasp of the situation. Typically, the first questions are quantitative and then progress to qualitative. With a Visme subscription, you have access to an interactive online form builder that lets you create digital research forms in a variety of styles. Create pop-up forms that collect names and emails quickly or customer feedback forms that take several steps to fill out. With the same tool, you can create lead generation forms , registration forms and contact forms . The example below is a feedback form for a software training program. The questions are both quantitative and qualitative, separated into steps. When you create quantitative and qualitative research forms with Visme, you can design and publish forms that convert 2X better than traditional forms. These forms come equipped with customizable animations and 3D characters that support your brand message so you can easily personalize them to match your buyer persona or ideal customer profile . Choose from a wide variety of form templates by use case and style it to match your brand. First, you have to publish your form to gather data. From within Visme Forms, you can share a live link or copy an embed code and insert your form into a webpage or Visme project in seconds. Regardless of how it’s shared, a Visme form is 100% responsive and will adjust to screen size dynamically. Data collected through Visme forms is stored natively in your form analytics dashboard. Track views, demographics, emails and form entries in an easy-to-read layout. You can also collect and store your data through multiple integrations , like Mailchimp, HubSpot and Google Sheets. Analyze The DataTo analyze quantitative data, you typically use statistical methods to identify patterns and trends. You can calculate measures and descriptive statistics such as mean, median, mode, standard deviation and range to summarize the central tendency and variability of your data. Conduct inferential statistics tests, such as t-tests, ANOVA, correlation analysis, or regression analysis, to test hypotheses, relationships, or differences between groups. Visualize your data using graphs, charts and histograms to identify patterns, trends and outliers. To analyze qualitative data, you can use various techniques, such as coding, categorization and synthesis, to identify themes and patterns. Some popular tools include SPSS, SAS, R and Stata. Qualitative data consists of non-numerical information, such as text, images, or observations, and is analyzed using thematic, content, or narrative analysis. By coding the data systematically, you can identify key themes, patterns, or categories within it. Use popular tools like NVivo, ATLAS.ti, MAXQDA and Dedoose. Present The DataOne of the most important aspects of qualitative and quantitative research is presenting the data. Sharing the results with your team, higher-ups and other stakeholders is critical for growth and decision-making. Once you have all the data collected, create a research presentation or research report and share it with relevant stakeholders. Use one of Visme’s document or presentation templates to build a comprehensive report from which to share your results and analysis. This research report template example includes data visualizations and text blurbs about quantitative and qualitative research results. Alternatively, you can save time creating your report using the Visme AI Report Writer , which takes a text prompt and generates a report draft in a style of your choosing. To create other reports or documents to share your qualitative and quantitative data, the AI Document Generator will generate any type of document you need. While working on the report together, use the workflow feature to assign specific sections to their stakeholders. Ask them to add their survey data to charts, graphs, maps and tables by connecting to a spreadsheet or filling it in manually. But what is the best way to showcase qualitative vs quantitative data?Quantitative data is best represented with data visualizations , like charts, graphs and tables. Creating reports with Visme allows you to make your data beautiful, regardless of whether it's qualitative or quantitative. When you create reports with Visme, your data can be static or live through a connection to a live Google Sheet or Google Analytics data capture. Then take advantage of the AI Writer to help you write a concise and descriptive text that highlights your research analysis. This AI writing tool can help you summarize long analysis copy, rephrase complicated sentences or edit grammar mistakes. Once you’re done, sharing your forms, surveys and reports is easy. Visme Forms are embeddable on websites, emails and even other Visme projects. Reports can be shared as digital flipbook documents, interactive experiences, static PDFs or printed booklets. You can even select singular data visualizations from the report and share them on social media via the Visme social media scheduler . Here’s an example of how a report is viewed when shared as a live Visme link. When distributing qualitative and quantitative research reports, share them as digital Visme links. This allows you to track who opens them and from where. Know if all stakeholders have seen the report and easily remind those who haven’t. You can track your report’s performance with Visme’s analytics dashboard . Get inspired by Andrew Kitchner , CEO of New Wave Solutions, an employee surveying company. Andrew and his team use Visme to present surveying reports in what they call The Return to Work Sentiment Report. Andrew KitchnerCEO of New Wave Solutions Improve Your Research Activities & Content with VismeDon’t work on qualitative and quantitative research alone; do it with your team as a collaborative effort. Create a dedicated Visme workspace where you can brainstorm, plan, and create surveys, polls and forms to conduct qualitative and quantitative research. Finally, create data reports together so that all relevant departments can double-check the information. Use Visme’s form builder to create engaging forms that make qualitative and quantitative research easier and more effective. Personalize them and set their actions to match your brand style. Take advantage of integrated 3D characters, animations and other interactive elements to give your form personality and a unique feel. Get started with Visme Forms today and uncover your customers’ (or your employees’) pain points, expectations and goals. You’ll see improvement in your decision-making process in no time. Create beautiful forms that engage & convert.Trusted by leading brands Recommended content for you:Create Beautiful Forms That Convert.Improve your data collection from emails, leads, to surveys and more, by using beautifully designed forms that convert up 2X better. About the AuthorOrana is a multi-faceted creative. She is a content writer, artist, and designer. She travels the world with her family and is currently in Istanbul. Find out more about her work at oranavelarde.com
What Is Qualitative vs. Quantitative Study?Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the “how” and “why” of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the “when” and “where,” utilizing tools like surveys, experiments, and statistical models to collect and analyze numerical data. In This Article:What is qualitative research, what is quantitative research.
What’s the Difference Between a Qualitative and Quantitative Study?Analyzing qualitative and quantitative data, when to use qualitative or quantitative research, develop your research skills at national university. Qualitative and quantitative data are broad categories covering many research approaches and methods. While both share the primary aim of knowledge acquisition, quantitative research is numeric and objective, seeking to answer questions like when or where. On the other hand, qualitative research is concerned with subjective phenomena that can’t be numerically measured, like how different people experience grief. Having a firm grounding in qualitative and quantitative research methodologies will become especially important once you begin work on your dissertation or thesis toward the end of your academic program. At that point, you’ll need to decide which approach best aligns with your research question, a process that involves working closely with your Dissertation Chair. Keep reading to learn more about the difference between quantitative vs. qualitative research, including what research techniques they involve, how they approach the task of data analysis, and some strengths — and limitations — of each approach. We’ll also briefly examine mixed-method research, which incorporates elements of both methodologies. Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research, qualitative research is more exploratory, drawing upon data sources such as photographs, journal entries, video footage, and interviews. These features stand in stark contrast to quantitative research, as we’ll see throughout the remainder of this article. Quantitative research tackles questions from different angles compared to qualitative research. Instead of probing for subjective meaning by asking exploratory “how?” and “why?” questions, quantitative research provides precise causal explanations that can be measured and communicated mathematically. While qualitative researchers might visit subjects in their homes or otherwise in the field, quantitative research is usually conducted in a controlled environment. Instead of gaining insight or understanding into a subjective, context-dependent issue, as is the case with qualitative research, the goal is instead to obtain objective information, such as determining the best time to undergo a specific medical procedure. How Does Qualitative and Quantitative Research Differ?How are the approaches of quantitative and qualitative research different? In qualitative studies, data is usually gathered in the field from smaller sample sizes, which means researchers might personally visit participants in their own homes or other environments. Once the research is completed, the researcher must evaluate and make sense of the data in its context, looking for trends or patterns from which new theories, concepts, narratives, or hypotheses can be generated. Quantitative research is typically carried out via tools (such as questionnaires) instead of by people (such as a researcher asking interview questions). Another significant difference is that, in qualitative studies, researchers must interpret the data to build hypotheses. In a quantitative analysis, the researcher sets out to test a hypothesis. Both qualitative and quantitative studies are subject to rigorous quality standards. However, the research techniques utilized in each type of study differ, as do the questions and issues they hope to address or resolve. In quantitative studies, researchers tend to follow more rigid structures to test the links or relationships between different variables, ideally based on a random sample. On the other hand, in a qualitative study, not only are the samples typically smaller and narrower (such as using convenience samples), the study’s design is generally more flexible and less structured to accommodate the open-ended nature of the research. Below are a few examples of qualitative and quantitative research techniques to help illustrate these differences further. Sources of Quantitative ResearchSome example methods of quantitative research methods or sources include, but are not limited to, the following:
The following section will cover some examples of qualitative research methods for comparison, followed by an overview of mixed research methods that blend components of both approaches. Sources of Qualitative ResearchResearchers can use numerous qualitative methods to explore a topic or gain insight into an issue. Some sources of, or approaches to, qualitative research include the following examples:
Examples of Research Questions Best Suited for Qualitative vs. Quantitative MethodsQualitative research questions:.
Quantitative Research Questions:
These examples illustrate how qualitative research delves into the depth and context of human experiences, while quantitative research focuses on measurable data and statistical analysis. Mixed Methods ResearchIn addition to the purely qualitative and quantitative research methods outlined above, such as conducting focus groups or performing meta-analyses, it’s also possible to take a hybrid approach that merges qualitative and quantitative research aspects. According to an article published by LinkedIn , “Mixed methods research avoids many [of the] criticisms” that have historically been directed at qualitative and quantitative research, such as the former’s vulnerability to bias, by “canceling the effects of one methodology by including the other methodology.” In other words, this mixed approach provides the best of both worlds. “Mixed methods research also triangulates results that offer higher validity and reliability.” If you’re enrolled as a National University student, you can watch a video introduction to mixed-method research by logging in with your student ID. Our resource library also covers qualitative and quantitative research methodologies and a video breakdown of when to use which approach. When it comes to quantitative and qualitative research, methods of collecting data differ, as do the methods of organizing and analyzing it. So what are some best practices for analyzing qualitative and quantitative data sets, and how do they call for different approaches by researchers? How to Analyze Qualitative DataBelow is a step-by-step overview of how to analyze qualitative data.
How to Analyze Quantitative DataThere are numerous approaches to analyzing quantitative data. Some examples include cross-tabulation, conjoint analysis, gap analysis, trend analysis, and SWOT analysis, which refers to Strengths, Weaknesses, Opportunities, and Threats. Whichever system or systems you use, there are specific steps you should take to ensure that you’ve organized your data and analyzed it as accurately as possible. Here’s a brief four-step overview.
Simply knowing the difference between quantitative and qualitative research isn’t enough — you also need an understanding of when each approach should be used and under what circumstances. For that, you’ll need to consider all of the comparisons we’ve made throughout this article and weigh some potential pros and cons of each methodology. Pros and Cons of Qualitative ResearchQualitative research has numerous strengths, but the research methodology is only more appropriate for some projects or dissertations. Here are some strengths and weaknesses of qualitative research to help guide your decision:
Pros and Cons of Quantitative ResearchQuantitative research also comes with drawbacks and benefits, depending on what information you aim to uncover. Here are a few pros and cons to consider when designing your study.
If you dream of making a scientific breakthrough and contributing new knowledge that revolutionizes your field, you’ll need a strong foundation in research, from how it’s conducted and analyzed to a clear understanding of professional ethics and standards. By pursuing your degree at National University, you build stronger research skills and countless other in-demand job skills. With flexible course schedules, convenient online classes , scholarships and financial aid , and an inclusive military-friendly culture, higher education has never been more achievable or accessible. At National University, you’ll find opportunities to challenge and hone your research skills in more than 75 accredited graduate and undergraduate programs and fast-paced credential and certificate programs in healthcare, business, engineering, computer science, criminal justice, sociology, accounting, and more. Contact our admissions office to request program information, or apply to National University online today . Learn More About Our University and ScholarshipsJoin our email list!
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You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching. In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity. The higher the content validity, the more accurate the measurement of the construct. If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question. Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level. When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure. For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test). On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover. A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives. Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants. Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random. Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample . This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research . Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias . Snowball sampling is best used in the following cases:
The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language. Reproducibility and replicability are related terms.
Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups. The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ). Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection. A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study. The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population. Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample. On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data. Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants. However, in convenience sampling, you continue to sample units or cases until you reach the required sample size. In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population. A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population. Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics. Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population . A systematic review is secondary research because it uses existing research. You don’t collect new data yourself. The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment . An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups . It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests. While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise. Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance. Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method. Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface. Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests. You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity . When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research. Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity. Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity. There are two subtypes of construct validity.
Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting. The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects. Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation. You can think of naturalistic observation as “people watching” with a purpose. A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable. In statistics, dependent variables are also called:
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study. Independent variables are also called:
As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses. Overall, your focus group questions should be:
A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups . Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups . Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes. This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly. The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee. There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions. A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:
An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic. Unstructured interviews are best used when:
The four most common types of interviews are:
Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research . In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data. Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions. Deductive reasoning is also called deductive logic. There are many different types of inductive reasoning that people use formally or informally. Here are a few common types:
Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down. Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions. In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories. Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions. Inductive reasoning is also called inductive logic or bottom-up reasoning. A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data). Triangulation can help:
But triangulation can also pose problems:
There are four main types of triangulation :
Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript. However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively. Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process. Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication. In general, the peer review process follows the following steps:
Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason. You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it. Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way. Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research. Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem. Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic. Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors. Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry. Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data. For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do. After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values. Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset. These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid. Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these. Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities. Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured. In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing. Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud. These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure. Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations . You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos. You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals. Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe. Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication. Scientists and researchers must always adhere to a certain code of conduct when collecting data from others . These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity. In multistage sampling , you can use probability or non-probability sampling methods . For a probability sample, you have to conduct probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study. Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame. But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples . These are four of the most common mixed methods designs :
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings. Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation. In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage. This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from. No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes. To find the slope of the line, you’ll need to perform a regression analysis . Correlation coefficients always range between -1 and 1. The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions. The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation. These are the assumptions your data must meet if you want to use Pearson’s r :
Quantitative research designs can be divided into two main categories:
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs. A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions. The priorities of a research design can vary depending on the field, but you usually have to specify:
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data. Questionnaires can be self-administered or researcher-administered. Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording. Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions. You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects. Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly. Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires. The third variable and directionality problems are two main reasons why correlation isn’t causation . The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them. While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy . Controlled experiments establish causality, whereas correlational studies only show associations between variables.
In general, correlational research is high in external validity while experimental research is high in internal validity . A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables. A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables. Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables. A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research . A correlation reflects the strength and/or direction of the association between two or more variables.
Random error is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables . You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible. Systematic error is generally a bigger problem in research. With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out. Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying. Random and systematic error are two types of measurement error. Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement). Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are). On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.
The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent. Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term. The difference between explanatory and response variables is simple:
In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
Depending on your study topic, there are various other methods of controlling variables . There are 4 main types of extraneous variables :
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable. In a factorial design, multiple independent variables are tested. If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions. Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful . Advantages:
Disadvantages:
While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .
Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects. In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions. In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions. The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group. Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable. In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic. To implement random assignment , assign a unique number to every member of your study’s sample . Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups. Random selection, or random sampling , is a way of selecting members of a population for your study’s sample. In contrast, random assignment is a way of sorting the sample into control and experimental groups. Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study. In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. “Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables. Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest. Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity . If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable . A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes. Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships. Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds. If something is a mediating variable :
A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related. A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship. There are three key steps in systematic sampling :
Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling . Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups. For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups. You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying. Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure. For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions. In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment). Once divided, each subgroup is randomly sampled using another probability sampling method. Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area. However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole. There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. The clusters should ideally each be mini-representations of the population as a whole. If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied, If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling. The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey. Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset. Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment . Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings. A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned. Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity . If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.
Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment . A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups). For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables. An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways. Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution. Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them. The type of data determines what statistical tests you should use to analyze your data. A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined. To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement. In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports). The process of turning abstract concepts into measurable variables and indicators is called operationalization . There are various approaches to qualitative data analysis , but they all share five steps in common:
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis . There are five common approaches to qualitative research :
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance. Operationalization means turning abstract conceptual ideas into measurable observations. For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations. Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure. When conducting research, collecting original data has significant advantages:
However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable. Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations. There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization. In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables. In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable . In statistical control , you include potential confounders as variables in your regression . In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables. A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables. To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists. Yes, but including more than one of either type requires multiple research questions . For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question. You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable . To ensure the internal validity of an experiment , you should only change one independent variable at a time. No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both! You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable. In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included. Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling . Probability sampling means that every member of the target population has a known chance of being included in the sample. Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling . Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias . Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias. Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people. A sampling error is the difference between a population parameter and a sample statistic . A statistic refers to measures about the sample , while a parameter refers to measures about the population . Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible. Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable. There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect. The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings). The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures. Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study . Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research. Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it. Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long. The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study . Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies. Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.
There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition . Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors. In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question . The research methods you use depend on the type of data you need to answer your research question .
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship. A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable. In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact. Discrete and continuous variables are two types of quantitative variables :
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age). Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips). You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results . You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect . In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design . Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:
When designing the experiment, you decide:
Experimental design is essential to the internal and external validity of your experiment. I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables . External validity is the extent to which your results can be generalized to other contexts. The validity of your experiment depends on your experimental design . Reliability and validity are both about how well a method measures something:
If you are doing experimental research, you also have to consider the internal and external validity of your experiment. A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives. Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ). In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section . In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods. Ask our teamWant to contact us directly? No problem. We are always here for you.
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Scribbr’s Plagiarism Checker is powered by elements of Turnitin’s Similarity Checker , namely the plagiarism detection software and the Internet Archive and Premium Scholarly Publications content databases . The add-on AI detector is powered by Scribbr’s proprietary software. The Scribbr Citation Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero. You can find all the citation styles and locales used in the Scribbr Citation Generator in our publicly accessible repository on Github . Chapter 4. Finding a Research Question and Approaches to Qualitative ResearchWe’ve discussed the research design process in general and ways of knowing favored by qualitative researchers. In chapter 2, I asked you to think about what interests you in terms of a focus of study, including your motivations and research purpose. It might be helpful to start this chapter with those short paragraphs you wrote about motivations and purpose in front of you. We are now going to try to develop those interests into actual research questions (first part of this chapter) and then choose among various “traditions of inquiry” that will be best suited to answering those questions. You’ve already been introduced to some of this (in chapter 1), but we will go further here. Developing a Research QuestionResearch questions are different from general questions people have about the social world. They are narrowly tailored to fit a very specific issue, complete with context and time boundaries. Because we are engaged in empirical science and thus use “data” to answer our questions, the questions we ask must be answerable by data. A question is not the same as stating a problem. The point of the entire research project is to answer a particular question or set of questions. The question(s) should be interesting, relevant, practical, and ethical. Let’s say I am generally interested in the problem of student loan debt. That’s a good place to start, but we can’t simply ask, General question: Is student loan debt really a problem today? How could we possibly answer that question? What data could we use? Isn’t this really an axiological (values-based) question? There are no clues in the question as to what data would be appropriate here to help us get started. Students often begin with these large unanswerable questions. They are not research questions. Instead, we could ask, Poor research question: How many people have debt? This is still not a very good research question. Why not? It is answerable, although we would probably want to clarify the context. We could add some context to improve it so that the question now reads, Mediocre research question: How many people in the US have debt today? And does this amount vary by age and location? Now we have added some context, so we have a better idea of where to look and who to look at. But this is still a pretty poor or mediocre research question. Why is that? Let’s say we did answer it. What would we really know? Maybe we would find out that student loan debt has increased over time and that young people today have more of it. We probably already know this. We don’t really want to go through a lot of trouble answering a question whose answer we already have. In fact, part of the reason we are even asking this question is that we know (or think) it is a problem. Instead of asking what you already know, ask a question to which you really do not know the answer. I can’t stress this enough, so I will say it again: Ask a question to which you do not already know the answer . The point of research is not to prove or make a point but to find out something unknown. What about student loan debt is still a mystery to you? Reviewing the literature could help (see chapter 9). By reviewing the literature, you can get a good sense of what is still mysterious or unknown about student loan debt, and you won’t be reinventing the wheel when you conduct your research. Let’s say you review the literature, and you are struck by the fact that we still don’t understand the true impact of debt on how people are living their lives. A possible research question might be, Fair research question: What impact does student debt have on the lives of debtors? Good start, but we still need some context to help guide the project. It is not nearly specific enough. Better research question: What impact does student debt have on young adults (ages twenty-five to thirty-five) living in the US today? Now we’ve added context, but we can still do a little bit better in narrowing our research question so that it is both clear and doable; in other words, we want to frame it in a way that provides a very clear research program: Optimal research question: How do young adults (ages twenty-five to thirty-five) living in the US today who have taken on $30,000 or more in student debt describe the impact of their debt on their lives in terms of finding/choosing a job, buying a house, getting married, and other major life events? Now you have a research question that can be answered and a clear plan of how to answer it. You will talk to young adults living in the US today who have high debt loads and ask them to describe the impacts of debt on their lives. That is all now in the research question. Note how different this very specific question is from where we started with the “problem” of student debt. Take some time practicing turning the following general questions into research questions:
Hint: Step back from each of the questions and try to articulate a possible underlying motivation, then formulate a research question that is specific and answerable. It is important to take the time to come up with a research question, even if this research question changes a bit as you conduct your research (yes, research questions can change!). If you don’t have a clear question to start your research, you are likely to get very confused when designing your study because you will not be able to make coherent decisions about things like samples, sites, methods of data collection, and so on. Your research question is your anchor: “If we don’t have a question, we risk the possibility of going out into the field thinking we know what we’ll find and looking only for proof of what we expect to be there. That’s not empirical research (it’s not systematic)” ( Rubin 2021:37 ). Researcher Note How do you come up with ideas for what to study? I study what surprises me. Usually, I come across a statistic that suggests something is common that I thought was rare. I tend to think it’s rare because the theories I read suggest it should be, and there’s not a lot of work in that area that helps me understand how the statistic came to be. So, for example, I learned that it’s common for Americans to marry partners who grew up in a different class than them and that about half of White kids born into the upper-middle class are downwardly mobile. I was so shocked by these facts that they naturally led to research questions. How do people come to marry someone who grew up in a different class? How do White kids born near the top of the class structure fall? —Jessi Streib, author of The Power of the Past and Privilege Lost What if you have literally no idea what the research question should be? How do you find a research question? Even if you have an interest in a topic before you get started, you see the problem now: topics and issues are not research questions! A research question doesn’t easily emerge; it takes a lot of time to hone one, as the practice above should demonstrate. In some research designs, the research question doesn’t even get clearly articulated until the end of data collection . More on that later. But you must start somewhere, of course. Start with your chosen discipline. This might seem obvious, but it is often overlooked. There is a reason it is called a discipline. We tend to think of “sociology,” “public health,” and “physics” as so many clusters of courses that are linked together by subject matter, but they are also disciplines in the sense that the study of each focuses the mind in a particular way and for particular ends. For example, in my own field, sociology, there is a loosely shared commitment to social justice and a general “sociological imagination” that enables its practitioners to connect personal experiences to society at large and to historical forces. It is helpful to think of issues and questions that are germane to your discipline. Within that overall field, there may be a particular course or unit of study you found most interesting. Within that course or unit of study, there may be an issue that intrigued you. And finally, within that issue, there may be an aspect or topic that you want to know more about. When I was pursuing my dissertation research, I was asked often, “Why did you choose to study intimate partner violence among Native American women?” This question is necessary, and each time I answered, it helped shape me into a better researcher. I was interested in intimate partner violence because I am a survivor. I didn’t have intentions to work with a particular population or demographic—that came from my own deep introspection on my role as a researcher. I always questioned my positionality: What privileges do I hold as an academic? How has public health extracted information from institutionally marginalized populations? How can I build bridges between communities using my position, knowledge, and power? Public health as a field would not exist without the contributions of Indigenous people. So I started hanging out with them at community events, making friends, and engaging in self-education. Through these organic relationships built with Native women in the community, I saw that intimate partner violence was a huge issue. This led me to partner with Indigenous organizations to pursue a better understanding of how Native survivors of intimate partner violence seek support. —Susanna Y. Park, PhD, mixed-methods researcher in public health and author of “How Native Women Seek Support as Survivors of Intimate Partner Violence: A Mixed-Methods Study” One of the most exciting and satisfying things about doing academic research is that whatever you end up researching can become part of the body of knowledge that we have collectively created. Don’t make the mistake of thinking that you are doing this all on your own from scratch. Without even being aware of it, no matter if you are a first-year undergraduate student or a fourth-year graduate student, you have been trained to think certain questions are interesting. The very fact that you are majoring in a particular field or have signed up for years of graduate study in a program testifies to some level of commitment to a discipline. What we are looking for, ideally, is that your research builds on in some way (as extension, as critique, as lateral move) previous research and so adds to what we, collectively, understand about the social world. It is helpful to keep this in mind, as it may inspire you and also help guide you through the process. The point is, you are not meant to be doing something no one has ever thought of before, even if you are trying to find something that does not exactly duplicate previous research: “You may be trying to be too clever—aiming to come up with a topic unique in the history of the universe, something that will have people swooning with admiration at your originality and intellectual precociousness. Don’t do it. It’s safer…to settle on an ordinary, middle-of-the-road topic that will lend itself to a nicely organized process of project management. That’s the clever way of proceeding.… You can always let your cleverness shine through during the stages of design, analysis, and write-up. Don’t make things more difficult for yourself than you need to do” ( Davies 2007:20 ). Rubin ( 2021 ) suggests four possible ways to develop a research question (there are many more, of course, but this can get you started). One way is to start with a theory that interests you and then select a topic where you can apply that theory. For example, you took a class on gender and society and learned about the “glass ceiling.” You could develop a study that tests that theory in a setting that has not yet been explored—maybe leadership at the Oregon Country Fair. The second way is to start with a topic that interests you and then go back to the books to find a theory that might explain it. This is arguably more difficult but often much more satisfying. Ask your professors for help—they might have ideas of theories or concepts that could be relevant or at least give you an idea of what books to read. The third way is to be very clever and select a question that already combines the topic and the theory. Rubin gives as one example sentencing disparities in criminology—this is both a topic and a theory or set of theories. You then just have to figure out particulars like setting and sample. I don’t know if I find this third way terribly helpful, but it might help you think through the possibilities. The fourth way involves identifying a puzzle or a problem, which can be either theoretical (something in the literature just doesn’t seem to make sense and you want to tackle addressing it) or empirical (something happened or is happening, and no one really understands why—think, for example, of mass school shootings). Once you think you have an issue or topic that is worth exploring, you will need to (eventually) turn that into a good research question. A good research question is specific, clear, and feasible . Specific . How specific a research question needs to be is somewhat related to the disciplinary conventions and whether the study is conceived inductively or deductively. In deductive research, one begins with a specific research question developed from the literature. You then collect data to test the theory or hypotheses accompanying your research question. In inductive research, however, one begins with data collection and analysis and builds theory from there. So naturally, the research question is a bit vaguer. In general, the more closely aligned to the natural sciences (and thus the deductive approach), the more a very tight and specific research question (along with specific, focused hypotheses) is required. This includes disciplines like psychology, geography, public health, environmental science, and marine resources management. The more one moves toward the humanities pole (and the inductive approach), the more looseness is permitted, as there is a general belief that we go into the field to find what is there, not necessarily what we imagine we are looking for (see figure 4.2). Disciplines such as sociology, anthropology, and gender and sexuality studies and some subdisciplines of public policy/public administration are closer to the humanities pole in this sense. Regardless of discipline and approach, however, it is a good idea for beginning researchers to create a research question as specific as possible, as this will serve as your guide throughout the process. You can tweak it later if needed, but start with something specific enough that you know what it is you are doing and why. It is more difficult to deal with ambiguity when you are starting out than later in your career, when you have a better handle on what you are doing. Being under a time constraint means the more specific the question, the better. Questions should always specify contexts, geographical locations, and time frames. Go back to your practice research questions and make sure that these are included. Clear . A clear research question doesn’t only need to be intelligible to any reader (which, of course, it should); it needs to clarify any meanings of particular words or concepts (e.g., What is excessive force?). Check all your concepts to see if there are ways you can clarify them further—for example, note that we shifted from impact of debt to impact of high debt load and specified this as beginning at $30,000. Ideally, we would use the literature to help us clarify what a high debt load is or how to define “excessive” force. Feasible . In order to know if your question is feasible, you are going to have to think a little bit about your entire research design. For example, a question that asks about the real-time impact of COVID restrictions on learning outcomes would require a time machine. You could tweak the question to ask instead about the long-term impacts of COVID restrictions, as measured two years after their end. Or let’s say you are interested in assessing the damage of opioid abuse on small-town communities across the United States. Is it feasible to cover the entire US? You might need a team of researchers to do this if you are planning on on-the-ground observations. Perhaps a case study of one particular community might be best. Then your research question needs to be changed accordingly. Here are some things to consider in terms of feasibility:
In addition to these practicalities, you will also want to consider the research question in terms of what is best for you now. Are you engaged in research because you are required to be—jumping a hurdle for a course or for your degree? If so, you really do want to think about your project as training and develop a question that will allow you to practice whatever data collection and analysis techniques you want to develop. For example, if you are a grad student in a public health program who is interested in eventually doing work that requires conducting interviews with patients, develop a research question and research design that is interview based. Focus on the practicality (and practice) of the study more than the theoretical impact or academic contribution, in other words. On the other hand, if you are a PhD candidate who is seeking an academic position in the future, your research question should be pitched in a way to build theoretical knowledge as well (the phrasing is typically “original contribution to scholarship”). The more time you have to devote to the study and the larger the project, the more important it is to reflect on your own motivations and goals when crafting a research question (remember chapter 2?). By “your own motivations and goals,” I mean what interests you about the social world and what impact you want your research to have, both academically and practically speaking. Many students have secret (or not-so-secret) plans to make the world a better place by helping address climate change, pointing out pressure points to fight inequities, or bringing awareness to an overlooked area of concern. My own work in graduate school was motivated by the last of these three—the not-so-secret goal of my research was to raise awareness about obstacles to success for first-generation and working-class college students. This underlying goal motivated me to complete my dissertation in a timely manner and then to further continue work in this area and see my research get published. I cared enough about the topic that I was not ready to put it away. I am still not ready to put it away. I encourage you to find topics that you can’t put away, ever. That will keep you going whenever things get difficult in the research process, as they inevitably will. On the other hand, if you are an undergraduate and you really have very little time, some of the best advice I have heard is to find a study you really like and adapt it to a new context. Perhaps you read a study about how students select majors and how this differs by class ( Hurst 2019 ). You can try to replicate the study on a small scale among your classmates. Use the same research question, but revise for your context. You can probably even find the exact questions I used and ask them in the new sample. Then when you get to the analysis and write-up, you have a comparison study to guide you, and you can say interesting things about the new context and whether the original findings were confirmed (similar) or not. You can even propose reasons why you might have found differences between one and the other. Another way of thinking about research questions is to explicitly tie them to the type of purpose of your study. Of course, this means being very clear about what your ultimate purpose is! Marshall and Rossman ( 2016 ) break down the purpose of a study into four categories: exploratory, explanatory, descriptive, and emancipatory ( 78 ). Exploratory purpose types include wanting to investigate little-understood phenomena, or identifying or discovering important new categories of meaning, or generating hypotheses for further research. For these, research questions might be fairly loose: What is going on here? How are people interacting on this site? What do people talk about when you ask them about the state of the world? You are almost (but never entirely) starting from scratch. Be careful though—just because a topic is new to you does not mean it is really new. Someone else (or many other someones) may already have done this exploratory research. Part of your job is to find this out (more on this in “What Is a ‘Literature Review’?” in chapter 9). Descriptive purposes (documenting and describing a phenomenon) are similar to exploratory purposes but with a much clearer goal (description). A good research question for a descriptive study would specify the actions, events, beliefs, attitudes, structures, and/or processes that will be described. Most researchers find that their topic has already been explored and described, so they move to trying to explain a relationship or phenomenon. For these, you will want research questions that capture the relationships of interest. For example, how does gender influence one’s understanding of police brutality (because we already know from the literature that it does, so now we are interested in understanding how and why)? Or what is the relationship between education and climate change denialism? If you find that prior research has already provided a lot of evidence about those relationships as well as explanations for how they work, and you want to move the needle past explanation into action, you might find yourself trying to conduct an emancipatory study. You want to be even more clear in acknowledging past research if you find yourself here. Then create a research question that will allow you to “create opportunities and the will to engage in social action” ( Marshall and Rossman 2016:78 ). Research questions might ask, “How do participants problematize their circumstances and take positive social action?” If we know that some students have come together to fight against student debt, how are they doing this, and with what success? Your purpose would be to help evaluate possibilities for social change and to use your research to make recommendations for more successful emancipatory actions. Recap: Be specific. Be clear. Be practical. And do what you love. Choosing an Approach or TraditionQualitative researchers may be defined as those who are working with data that is not in numerical form, but there are actually multiple traditions or approaches that fall under this broad category. I find it useful to know a little bit about the history and development of qualitative research to better understand the differences in these approaches. The following chart provides an overview of the six phases of development identified by Denzin and Lincoln ( 2005 ): Table 4.1. Six Phases of Development
There are other ways one could present the history as well. Feminist theory and methodologies came to the fore in the 1970s and 1980s and had a lot to do with the internal critique of more positivist approaches. Feminists were quite aware that standpoint matters—that the identity of the researcher plays a role in the research, and they were ardent supporters of dismantling unjust power systems and using qualitative methods to help advance this mission. You might note, too, that many of the internal disputes were basically epistemological disputes about how we know what we know and whether one’s social location/position delimits that knowledge. Today, we are in a bountiful world of qualitative research, one that embraces multiple forms of knowing and knowledge. This is good, but it means that you, the student, have more choice when it comes to situating your study and framing your research question, and some will expect you to signal the choices you have made in any research protocols you write or publications and presentations. Creswell’s ( 1998 ) definition of qualitative research includes the notion of distinct traditions of inquiry: “Qualitative research is an inquiry process of understanding based on distinct methodological traditions of inquiry that explore a social or human problem. The research builds complex, holistic pictures, analyzes words, reports detailed views of informants , and conducted the study in a natural setting” (15; emphases added). I usually caution my students against taking shelter under one of these approaches, as, practically speaking, there is a lot of mixing of traditions among researchers. And yet it is useful to know something about the various histories and approaches, particularly as you are first starting out. Each tradition tends to favor a particular epistemological perspective (see chapter 3), a way of reasoning (see “ Advanced: Inductive versus Deductive Reasoning ”), and a data-collection technique. There are anywhere from ten to twenty “traditions of inquiry,” depending on how one draws the boundaries. In my accounting, there are twelve, but three approaches tend to dominate the field. EthnographyEthnography was developed from the discipline of anthropology, as the study of (other) culture(s). From a relatively positivist/objective approach to writing down the “truth” of what is observed during the colonial era (where this “truth” was then often used to help colonial administrators maintain order and exploit people and extract resources more effectively), ethnography was adopted by all kinds of social science researchers to get a better understanding of how groups of people (various subcultures and cultures) live their lives. Today, ethnographers are more likely to be seeking to dismantle power relations than to support them. They often study groups of people that are overlooked and marginalized, and sometimes they do the obverse by demonstrating how truly strange the familiar practices of the dominant group are. Ethnography is also central to organizational studies (e.g., How does this institution actually work?) and studies of education (e.g., What is it like to be a student during the COVID era?). Ethnographers use methods of participant observation and intensive fieldwork in their studies, often living or working among the group under study for months at a time (and, in some cases, years). I’ve called this “deep ethnography,” and it is the subject of chapter 14. The data ethnographers analyze are copious “field notes” written while in the field, often supplemented by in-depth interviews and many more casual conversations. The final product of ethnographers is a “thick” description of the culture. This makes reading ethnographies enjoyable, as the goal is to write in such a way that the reader feels immersed in the culture. There are variations on the ethnography, such as the autoethnography , where the researcher uses a systematic and rigorous study of themselves to better understand the culture in which they find themselves. Autoethnography is a relatively new approach, even though it is derived from one of the oldest approaches. One can say that it takes to heart the feminist directive to “make the personal political,” to underscore the connections between personal experiences and larger social and political structures. Introspection becomes the primary data source. Grounded TheoryGrounded Theory holds a special place in qualitative research for a few reasons, not least of which is that nonqualitative researchers often mistakenly believe that Grounded Theory is the only qualitative research methodology . Sometimes, it is easier for students to explain what they are doing as “Grounded Theory” because it sounds “more scientific” than the alternative descriptions of qualitative research. This is definitely part of its appeal. Grounded Theory is the name given to the systematic inductive approach first developed by Glaser and Strauss in 1967, The Discovery of Grounded Theory: Strategies for Qualitative Research . Too few people actually read Glaser and Strauss’s book. It is both groundbreaking and fairly unremarkable at the same time. As a historical intervention into research methods generally, it is both a sharp critique of positivist methods in the social sciences (theory testing) and a rejection of purely descriptive accounts-building qualitative research. Glaser and Strauss argued for an approach whose goal was to construct (middle-level) theories from recursive data analysis of nonnumerical data (interviews and observations). They advocated a “constant comparative method” in which coding and analysis take place simultaneously and recursively. The demands are fairly strenuous. If done correctly, the result is the development of a new theory about the social world. So why do I call this “fairly unremarkable”? To some extent, all qualitative research already does what Glaser and Strauss ( 1967 ) recommend, albeit without denoting the processes quite so specifically. As will be seen throughout the rest of this textbook, all qualitative research employs some “constant comparisons” through recursive data analyses. Where Grounded Theory sets itself apart from a significant number of qualitative research projects, however, is in its dedication to inductively building theory. Personally, I think it is important to understand that Glaser and Strauss were rejecting deductive theory testing in sociology when they first wrote their book. They were part of a rising cohort who rejected the positivist mathematical approaches that were taking over sociology journals in the 1950s and 1960s. Here are some of the comments and points they make against this kind of work: Accurate description and verification are not so crucial when one’s purpose is to generate theory. ( 28 ; further arguing that sampling strategies are different when one is not trying to test a theory or generalize results) Illuminating perspectives are too often suppressed when the main emphasis is verifying theory. ( 40 ) Testing for statistical significance can obscure from theoretical relevance. ( 201 ) Instead, they argued, sociologists should be building theories about the social world. They are not physicists who spend time testing and refining theories. And they are not journalists who report descriptions. What makes sociologists better than journalists and other professionals is that they develop theory from their work “In their driving efforts to get the facts [research sociologists] tend to forget that the distinctive offering of sociology to our society is sociological theory, not research description” ( 30–31 ). Grounded Theory’s inductive approach can be off-putting to students who have a general research question in mind and a working hypothesis. The true Grounded Theory approach is often used in exploratory studies where there are no extant theories. After all, the promise of this approach is theory generation, not theory testing. Flying totally free at the start can be terrifying. It can also be a little disingenuous, as there are very few things under the sun that have not been considered before. Barbour ( 2008:197 ) laments that this approach is sometimes used because the researcher is too lazy to read the relevant literature. To summarize, Glaser and Strauss justified the qualitative research project in a way that gave it standing among the social sciences, especially vis-à-vis quantitative researchers. By distinguishing the constant comparative method from journalism, Glaser and Strauss enabled qualitative research to gain legitimacy. So what is it exactly, and how does one do it? The following stages provide a succinct and basic overview, differentiating the portions that are similar to/in accordance with qualitative research methods generally and those that are distinct from the Grounded Theory approach: Step 1. Select a case, sample, and setting (similar—unless you begin with a theory to test!). Step 2. Begin data collection (similar). Step 3. Engage data analysis (similar in general but specificity of details somewhat unique to Grounded Theory): (1) emergent coding (initial followed by focused), (2) axial (a priori) coding , (3) theoretical coding , (4) creation of theoretical categories; analysis ends when “theoretical saturation ” has been achieved. Grounded Theory’s prescriptive (i.e., it has a set of rules) framework can appeal to beginning students, but it is unnecessary to adopt the entire approach in order to make use of some of its suggestions. And if one does not exactly follow the Grounded Theory rulebook, it can mislead others if you tend to call what you are doing Grounded Theory when you are not: Grounded theory continues to be a misunderstood method, although many researchers purport to use it. Qualitative researchers often claim to conduct grounded theory studies without fully understanding or adopting its distinctive guidelines. They may employ one or two of the strategies or mistake qualitative analysis for grounded theory. Conversely, other researchers employ grounded theory methods in reductionist, mechanistic ways. Neither approach embodies the flexible yet systematic mode of inquiry, directed but open-ended analysis, and imaginative theorizing from empirical data that grounded theory methods can foster. Subsequently, the potential of grounded theory methods for generating middle-range theory has not been fully realized ( Charmaz 2014 ). PhenomenologyWhere Grounded Theory sets itself apart for its inductive systematic approach to data analysis, phenomenologies are distinct for their focus on what is studied—in this case, the meanings of “lived experiences” of a group of persons sharing a particular event or circumstance. There are phenomenologies of being working class ( Charlesworth 2000 ), of the tourist experience ( Cohen 1979 ), of Whiteness ( Ahmed 2007 ). The phenomenon of interest may also be an emotion or circumstance. One can study the phenomenon of “White rage,” for example, or the phenomenon of arranged marriage. The roots of phenomenology lie in philosophy (Husserl, Heidegger, Merleau-Ponty, Sartre) but have been adapted by sociologists in particular. Phenomenologists explore “how human beings make sense of experience and transform experience into consciousness, both individually and as shared meaning” ( Patton 2002:104 ). One of the most important aspects of conducting a good phenomenological study is getting the sample exactly right so that each person can speak to the phenomenon in question. Because the researcher is interested in the meanings of an experience, in-depth interviews are the preferred method of data collection. Observations are not nearly as helpful here because people may do a great number of things without meaning to or without being conscious of their implications. This is important to note because phenomenologists are studying not “the reality” of what happens at all but an articulated understanding of a lived experience. When reading a phenomenological study, it is important to keep this straight—too often I have heard students critique a study because the interviewer didn’t actually see how people’s behavior might conflict with what they say (which is, at heart, an epistemological issue!). In addition to the “big three,” there are many other approaches; some are variations, and some are distinct approaches in their own right. Case studies focus explicitly on context and dynamic interactions over time and can be accomplished with quantitative or qualitative methods or a mixture of both (for this reason, I am not considering it as one of the big three qualitative methods, even though it is a very common approach). Whatever methods are used, a contextualized deep understanding of the case (or cases) is central. Critical inquiry is a loose collection of techniques held together by a core argument that understanding issues of power should be the focus of much social science research or, to put this another way, that it is impossible to understand society (its people and institutions) without paying attention to the ways that power relations and power dynamics inform and deform those people and institutions. This attention to power dynamics includes how research is conducted too. All research fundamentally involves issues of power. For this reason, many critical inquiry traditions include a place for collaboration between researcher and researched. Examples include (1) critical narrative analysis, which seeks to describe the meaning of experience for marginalized or oppressed persons or groups through storytelling; (2) participatory action research, which requires collaboration between the researcher and the research subjects or community of interest; and (3) critical race analysis, a methodological application of Critical Race Theory (CRT), which posits that racial oppression is endemic (if not always throughout time and place, at least now and here). Do you follow a particular tradition of inquiry? Why? Shawn Wilson’s book, Research Is Ceremony: Indigenous Research Methods , is my holy grail. It really flipped my understanding of research and relationships. Rather than thinking linearly and approaching research in a more canonical sense, Wilson shook my world view by drawing me into a pattern of inquiry that emphasized transparency and relational accountability. The Indigenous research paradigm is applicable in all research settings, and I follow it because it pushes me to constantly evaluate my position as a knowledge seeker and knowledge sharer. Autoethnography takes the researcher as the subject. This is one approach that is difficult to explain to more quantitatively minded researchers, as it seems to violate many of the norms of “scientific research” as understood by them. First, the sample size is quite small—the n is 1, the researcher. Two, the researcher is not a neutral observer—indeed, the subjectivity of the researcher is the main strength of this approach. Autoethnographies can be extremely powerful for their depth of understanding and reflexivity, but they need to be conducted in their own version of rigor to stand up to scrutiny by skeptics. If you are skeptical, read one of the excellent published examples out there—I bet you will be impressed with what you take away. As they say, the proof is in the pudding on this approach. Advanced: Inductive versus Deductive ReasoningThere has been a great deal of ink shed in the discussion of inductive versus deductive approaches, not all of it very instructive. Although there is a huge conceptual difference between them, in practical terms, most researchers cycle between the two, even within the same research project. The simplest way to explain the difference between the two is that we are using deductive reasoning when we test an existing theory (move from general to particular), and we are using inductive reasoning when we are generating theory (move from particular to general). Figure 4.2 provides a schematic of the deductive approach. From the literature, we select a theory about the impact of student loan debt: student loan debt will delay homeownership among young adults. We then formulate a hypothesis based on this theory: adults in their thirties with high debt loads will be less likely to own homes than their peers who do not have high debt loads. We then collect data to test the hypothesis and analyze the results. We find that homeownership is substantially lower among persons of color and those who were the first in their families to graduate from college. Notably, high debt loads did not affect homeownership among White adults whose parents held college degrees. We thus refine the theory to match the new findings: student debt loads delay homeownership among some young adults, thereby increasing inequalities in this generation. We have now contributed new knowledge to our collective corpus. The inductive approach is contrasted in figure 4.3. Here, we did not begin with a preexisting theory or previous literature but instead began with an observation. Perhaps we were conducting interviews with young adults who held high amounts of debt and stumbled across this observation, struck by how many were renting apartments or small houses. We then noted a pattern—not all the young adults we were talking to were renting; race and class seemed to play a role here. We would then probably expand our study in a way to be able to further test this developing theory, ensuring that we were not seeing anomalous patterns. Once we were confident about our observations and analyses, we would then develop a theory, coming to the same place as our deductive approach, but in reverse. A third form of reasoning, abductive (sometimes referred to as probabilistic reasoning) was developed in the late nineteenth century by American philosopher Charles Sanders Peirce. I have included some articles for further reading for those interested. Among social scientists, the deductive approach is often relaxed so that a research question is set based on the existing literature rather than creating a hypothesis or set of hypotheses to test. Some journals still require researchers to articulate hypotheses, however. If you have in mind a publication, it is probably a good idea to take a look at how most articles are organized and whether specific hypotheses statements are included. Table 4.2. Twelve Approaches. Adapted from Patton 2002:132-133.
Further ReadingsThe following readings have been examples of various approaches or traditions of inquiry: Ahmed, Sara. 2007. “A Phenomenology of Whiteness.” Feminist Theory 8(2):149–168. Charlesworth, Simon. 2000. A Phenomenology of Working-Class Experience . Cambridge: Cambridge University Press.* Clandinin, D. Jean, and F. Michael Connelly. 2000. Narrative Inquiry: Experience and Story in Qualitative Research . San Francisco: Jossey-Bass. Cohen, E. 1979. “A Phenomenology of Tourist Experiences.” Sociology 13(2):179–201. Cooke, Bill, and Uma Kothari, eds. 2001. Participation: The New Tyranny? London: Zed Books. A critique of participatory action. Corbin, Juliet, and Anselm Strauss. 2008. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 3rd ed. Thousand Oaks, CA: SAGE. Crabtree, B. F., and W. L. Miller, eds. 1999. Doing Qualitative Research: Multiple Strategies . Thousand Oaks, CA: SAGE. Creswell, John W. 1997. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Thousand Oaks, CA: SAGE. Glaser, Barney G., and Anselm Strauss. 1967. The Discovery of Grounded Theory: Strategies for Qualitative Research . New York: Aldine. Gobo, Giampetro, and Andrea Molle. 2008. Doing Ethnography . Thousand Oaks, CA: SAGE. Hancock, Dawson B., and Bob Algozzine. 2016. Doing Case Study Research: A Practical Guide for Beginning Research . 3rd ed. New York: Teachers College Press. Harding, Sandra. 1987. Feminism and Methodology . Bloomington: Indiana University Press. Husserl, Edmund. (1913) 2017. Ideas: Introduction to Pure Phenomenology . Eastford, CT: Martino Fine Books. Rose, Gillian. 2012. Visual Methodologies . 3rd ed. London: SAGE. Van der Riet, M. 2009. “Participatory Research and the Philosophy of Social Science: Beyond the Moral Imperative.” Qualitative Inquiry 14(4):546–565. Van Manen, Max. 1990. Researching Lived Experience: Human Science for an Action Sensitive Pedagogy . Albany: State University of New York. Wortham, Stanton. 2001. Narratives in Action: A Strategy for Research and Analysis . New York: Teachers College Press. Inductive, Deductive, and Abductive Reasoning and Nomothetic Science in GeneralAliseda, Atocha. 2003. “Mathematical Reasoning vs. Abductive Reasoning: A Structural Approach.” Synthese 134(1/2):25–44. Bonk, Thomas. 1997. “Newtonian Gravity, Quantum Discontinuity and the Determination of Theory by Evidence.” Synthese 112(1):53–73. A (natural) scientific discussion of inductive reasoning. Bonnell, Victoria E. 1980. “The Uses of Theory, Concepts and Comparison in Historical Sociology.” C omparative Studies in Society and History 22(2):156–173. Crane, Mark, and Michael C. Newman. 1996. “Scientific Method in Environmental Toxicology.” Environmental Reviews 4(2):112–122. Huang, Philip C. C., and Yuan Gao. 2015. “Should Social Science and Jurisprudence Imitate Natural Science?” Modern China 41(2):131–167. Mingers, J. 2012. “Abduction: The Missing Link between Deduction and Induction. A Comment on Ormerod’s ‘Rational Inference: Deductive, Inductive and Probabilistic Thinking.’” Journal of the Operational Research Society 63(6):860–861. Ormerod, Richard J. 2010. “Rational Inference: Deductive, Inductive and Probabilistic Thinking.” Journal of the Operational Research Society 61(8):1207–1223. Perry, Charner P. 1927. “Inductive vs. Deductive Method in Social Science Research.” Southwestern Political and Social Science Quarterly 8(1):66–74. Plutynski, Anya. 2011. “Four Problems of Abduction: A Brief History.” HOPOS: The Journal of the International Society for the History of Philosophy of Science 1(2):227–248. Thompson, Bruce, and Gloria M. Borrello. 1992. “Different Views of Love: Deductive and Inductive Lines of Inquiry.” Current Directions in Psychological Science 1(5):154–156. Tracy, Sarah J. 2012. “The Toxic and Mythical Combination of a Deductive Writing Logic for Inductive Qualitative Research.” Qualitative Communication Research 1(1):109–141. A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community. A person who introduces the researcher to a field site’s culture and population. Also referred to as guides. Used in ethnography . A form of research and a methodological tradition of inquiry in which the researcher uses self-reflection and writing to explore personal experiences and connect this autobiographical story to wider cultural, political, and social meanings and understandings. “Autoethnography is a research method that uses a researcher's personal experience to describe and critique cultural beliefs, practices, and experiences” ( Adams, Jones, and Ellis 2015 ). The philosophical framework in which research is conducted; the approach to “research” (what practices this entails, etc.). Inevitably, one’s epistemological perspective will also guide one’s methodological choices, as in the case of a constructivist who employs a Grounded Theory approach to observations and interviews, or an objectivist who surveys key figures in an organization to find out how that organization is run. One of the key methodological distinctions in social science research is that between quantitative and qualitative research. The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages. See coding frame and codebook. A later stage coding process used in Grounded Theory in which data is reassembled around a category, or axis. A later stage-coding process used in Grounded Theory in which key words or key phrases capture the emergent theory. The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted. Achieving saturation is often used as the justification for the final sample size. A methodological tradition of inquiry that focuses on the meanings held by individuals and/or groups about a particular phenomenon (e.g., a “phenomenology of whiteness” or a “phenomenology of first-generation college students”). Sometimes this is referred to as understanding “the lived experience” of a particular group or culture. Interviews form the primary tool of data collection for phenomenological studies. Derived from the German philosophy of phenomenology (Husserl 1913; 2017). The number of individuals (or units) included in your sample A form of reasoning which employs a “top-down” approach to drawing conclusions: it begins with a premise or hypothesis and seeks to verify it (or disconfirm it) with newly collected data. Inferences are made based on widely accepted facts or premises. Deduction is idea-first, followed by observations and a conclusion. This form of reasoning is often used in quantitative research and less often in qualitative research. Compare to inductive reasoning . See also abductive reasoning . A form of reasoning that employs a “bottom-up” approach to drawing conclusions: it begins with the collection of data relevant to a particular question and then seeks to build an argument or theory based on an analysis of that data. Induction is observation first, followed by an idea that could explain what has been observed. This form of reasoning is often used in qualitative research and seldom used in qualitative research. Compare to deductive reasoning . See also abductive reasoning . An “interpretivist” form of reasoning in which “most likely” conclusions are drawn, based on inference. This approach is often used by qualitative researchers who stress the recursive nature of qualitative data analysis. Compare with deductive reasoning and inductive reasoning . A form of social science research that generally follows the scientific method as established in the natural sciences. In contrast to idiographic research , the nomothetic researcher looks for general patterns and “laws” of human behavior and social relationships. Once discovered, these patterns and laws will be expected to be widely applicable. Quantitative social science research is nomothetic because it seeks to generalize findings from samples to larger populations. Most qualitative social science research is also nomothetic, although generalizability is here understood to be theoretical in nature rather than statistical . Some qualitative researchers, however, espouse the idiographic research paradigm instead. Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted. This website does not fully support Internet Explorer. For a better experience, please consider using a modern browser such as Chrome , Firefox , or Edge . Qualitative vs. Quantitative Research: What’s the Difference?There are two distinct types of data collection and study: qualitative and quantitative. Although both provide an analysis of data, they differ in their approach and the type of data they collect. Awareness of these approaches can help researchers construct their study and data collection methods. In This Article: What Is the Difference Between Qualitative vs. Quantitative Research?Qualitative vs. quantitative outcomes, benefits and limitations of qualitative vs. quantitative research, how to analyze qualitative vs. quantitative data, become a qualitative or quantitative researcher. Because qualitative and quantitative studies collect different types of data, their data collection methods differ considerably. Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society. Qualitative Research: Data Collection for Your Doctorate DegreeQualitative research methods include gathering and interpreting non-numerical data. The following are some sources of qualitative data 1 :
In the course of a qualitative study, the researcher may conduct interviews or focus groups to collect data that is not available in existing documents or records. To allow freedom for varied or unexpected answers, interviews and focus groups may be unstructured or semi-structured. An unstructured or semi-structured format allows the researcher to pose open-ended questions and follow wherever the responses lead. The responses provide a comprehensive perspective on each individual’s experiences, which are then compared with those of other participants in the study. Quantitative Research: Data Collection for Your Doctorate DegreeQuantitative studies, in contrast, require different data collection methods. These methods include compiling numerical data to test causal relationships among variables. Some forms of data collection for this type of study include 1 :
The above collection methods yield data that lends itself to numerical analysis. Questionnaires in this case have a multiple-choice format to generate countable answers, such as “yes” or “no,” which can be turned into quantifiable data. One of the factors distinguishing qualitative from quantitative studies is the nature of the intended outcome. Qualitative researchers seek to learn from details of the testimonies of those they are studying. Over the course of a study, conclusions are drawn by compiling, comparing and evaluating the participants’ feedback and input. Qualitative research is often focused on answering the “why” behind a phenomenon, correlation or behavior. In contrast, quantitative data are analyzed numerically to develop a statistical picture of a trend or connection. Such statistical results may shed light on cause-and-effect relationships, and they may either confirm or disprove the study’s original hypothesis. Whether positive or negative, the outcome can enrich understanding of a subject and spark action. Quantitative research is often focused on answering the questions of “what” or “how” in regards to a phenomenon, correlation or behavior. Another difference between qualitative and quantitative research lies in their advantages and limitations. Each form of research has benefits and shortcomings. Researchers must consider their hypotheses and what forms of data collection and analysis are likely to produce the most relevant findings. Benefits of Qualitative ResearchThere are some significant benefits of qualitative research that should be considered when evaluating the difference between qualitative and quantitative research. The qualitative method allows for creativity, varied interpretations and flexibility. The scope of the research project can change as more information is gathered. Limitations of Qualitative ResearchQualitative studies are more subjective in their results and interpretation than are quantitative studies. The expertise and perspective of the researcher may strongly influence the interpretation of results and the conclusions reached, because personal bias can be hard to manage. In addition, qualitative studies often test a smaller sample size due to the costs and efforts associated with qualitative data collection methods. 1 Benefits of Quantitative ResearchThe similarities of qualitative and quantitative research do not encompass their respective benefits, because each approach has unique advantages. For example, unlike qualitative studies, quantitative studies produce objective data, and their results can be clearly communicated through statistics and numbers. Quantitative studies can be quickly analyzed with the benefit of data computing software. Limitations of Quantitative ResearchYet, although objectivity is a benefit of the quantitative method, this approach can be viewed as a more restrictive form of study. Participants cannot tailor their responses or add context. Furthermore, statistical analysis requires a large data sample, which calls for a large pool of participants. 1 Another of the similarities of qualitative and quantitative research is that both look for patterns in the data they collect that point to a relationship between elements. Both qualitative and quantitative data are instrumental in supporting existing theories and developing new ones. Ultimately, the researcher must determine which kind of research best serves the goals of their study. Analyzing Qualitative DataBecause qualitative data doesn’t allow for numerical data analysis, any analytical approach must be developed with care and caution. Here are a few different methods of qualitative data analysis, as follows:
Analyzing Quantitative DataThe question of how to analyze quantitative data is slightly more straightforward compared to the various approaches for qualitative data. When working with quantitative data, doctoral researchers will generally review the collected data and organize it into visual elements, such as charts and graphs. The data can be evaluated using either descriptive or inferential statistics. Descriptive statistics provide an avenue for describing the population or data set. Inferential statistics can be used to generalize results, as well as to project future trends or predictions about a larger dataset or population. Some researchers choose to adhere to and hone a single methodological approach throughout their time as doctoral learners — or in their profession. Research skills are critical in a variety of careers. If you have a desire to conduct research, a qualitative or quantitative doctoral degree can support your initiative. Throughout your program, you will learn methods for constructing a qualitative or quantitative study and producing written research findings. Interested in starting your doctoral journey? Grand Canyon University has a wide variety of qualitative and quantitative programs and resources to help you. Fill out the form on this page to get started. 1 Mcleod, S. (2023, May 10). Qualitative vs quantitative research: methods & data analysis. Simply Psychology. Retrieved in May 2023. Approved by the dean of the College of Doctoral Studies on Oct. 2, 2023. The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.
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Written by: Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights. IntroductionIn the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts. This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis. Read More: Exploring 5 Statistical Data Analysis Techniques with Real-World Examples Defining Qualitative DataQualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject. This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups. Characteristics of Qualitative Data
Examples of Qualitative Data
Defining Quantitative DataQuantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data. Characteristics of Quantitative Data
Examples of Quantitative Data
Key Differences Between Qualitative and Quantitative DataUnderstanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions: When to Use Qualitative DataQualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice: Exploratory ResearchWhen investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses. Understanding ContextQualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis. Gaining Insights into Attitudes and BehavioursWhen the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights. Developing TheoriesQualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook. When to Use Quantitative DataQuantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice: Testing HypothesesWhen researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses. Measuring VariablesQuantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys. Generalising FindingsWhen the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions. Identifying Patterns and TrendsQuantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development. Integrating Qualitative and Quantitative DataWhile qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights. Benefits of IntegrationIntegrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.
Examples of Integration
Challenges and Considerations While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type: Challenges of Qualitative DataThe challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data: Subjectivity and BiasOne of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data. Data OverloadQualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked. Lack of StructureQualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data. Time-Consuming NatureQualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers. Challenges of Quantitative DataQuantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations. Limits in Capturing ComplexityQuantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours. Chances for MisinterpretationNumbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data. Influence of Measurement ErrorsDue to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis. Lack of ContextQuantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied. Sample Size LimitationsSmall sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations. Confirmation BiasResearchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights. In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives. By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making. As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike. Frequently Asked QuestionsWhat is the primary difference between qualitative and quantitative data. The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses. When Should I Use Qualitative Data in My Research?Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important. Can Qualitative and Quantitative Data Be Used Together?Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights. 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83 Qualitative Research Questions & ExamplesFree Website Traffic Checker Discover your competitors' strengths and leverage them to achieve your own success Qualitative research questions help you understand consumer sentiment. They’re strategically designed to show organizations how and why people feel the way they do about a brand, product, or service. It looks beyond the numbers and is one of the most telling types of market research a company can do. The UK Data Service describes this perfectly, saying, “The value of qualitative research is that it gives a voice to the lived experience .” Read on to see seven use cases and 83 qualitative research questions, with the added bonus of examples that show how to get similar insights faster with Similarweb Research Intelligence. What is a qualitative research question?A qualitative research question explores a topic in-depth, aiming to better understand the subject through interviews, observations, and other non-numerical data. Qualitative research questions are open-ended, helping to uncover a target audience’s opinions, beliefs, and motivations. How to choose qualitative research questions?Choosing the right qualitative research questions can be incremental to the success of your research and the findings you uncover. Here’s my six-step process for choosing the best qualitative research questions.
Types of qualitative research questionsFor a question to be considered qualitative, it usually needs to be open-ended. However, as I’ll explain, there can sometimes be a slight cross-over between quantitative and qualitative research questions. Open-ended questionsThese allow for a wide range of responses and can be formatted with multiple-choice answers or a free-text box to collect additional details. The next two types of qualitative questions are considered open questions, but each has its own style and purpose.
Closed-ended questionsThese ask respondents to choose from a predetermined set of responses, such as “On a scale of 1-5, how satisfied are you with the new product?” While they’re traditionally quantitative, adding a free text box that asks for extra comments into why a specific rating was chosen will provide qualitative insights alongside their respective quantitative research question responses.
Qualitative research question examplesThere are many applications of qualitative research and lots of ways you can put your findings to work for the success of your business. Here’s a summary of the most common use cases for qualitative questions and examples to ask. Qualitative questions for identifying customer needs and motivationsThese types of questions help you find out why customers choose products or services and what they are looking for when making a purchase.
Qualitative research questions to enhance customer experienceUse these questions to reveal insights into how customers interact with a company’s products or services and how those experiences can be improved.
Qualitative research question example for customer experience
First, I’m going to do a website market analysis of the banking credit and lending market in the finance sector to get a clearer picture of industry benchmarks. Here, I can view device preferences across any industry or market instantly. It shows me the device distribution for any country across any period. This clearly answers the question of how mobile dominate my target audience is , with 59.79% opting to access site via a desktop vs. 40.21% via mobile I then use the trends section to show me the exact split between mobile and web traffic for each key player in my space. Let’s say I’m about to embark on a competitive campaign that targets customers of Chase and Bank of America ; I can see both their audiences are highly desktop dominant compared with others in their space . Qualitative question examples for developing new products or servicesResearch questions like this can help you understand customer pain points and give you insights to develop products that meet those needs.
Qualitative research question example for service development
Using the market analysis element of Similarweb Digital Intelligence, I select my industry or market, which I’ve kept as banking and credit. A quick click into marketing channels shows me which channels drive the highest traffic in my market. Taking direct traffic out of the equation, for now, I can see that referrals and organic traffic are the two highest-performing channels in this market. Similarweb allows me to view the specific referral partners and pages across these channels. Looking closely at referrals in this market, I’ve chosen chase.com and its five closest rivals . I select referrals in the channel traffic element of marketing channels. I see that Capital One is a clear winner, gaining almost 25 million visits due to referral partnerships. Next, I get to see exactly who is referring traffic to Capital One and the total traffic share for each referrer. I can see the growth as a percentage and how that has changed, along with an engagement score that rates the average engagement level of that audience segment. This is particularly useful when deciding on which new referral partnerships to pursue. Once I’ve identified the channels and campaigns that yield the best results, I can then use Similarweb to dive into the various ad creatives and content that have the greatest impact. These ads are just a few of those listed in the creatives section from my competitive website analysis of Capital One. You can filter this list by the specific campaign, publishers, and ad networks to view those that matter to you most. You can also discover video ad creatives in the same place too. In just five minutes ⏰
Qualitative questions to determine pricing strategiesCompanies need to make sure pricing stays relevant and competitive. Use these questions to determine customer perceptions on pricing and develop pricing strategies to maximize profits and reduce churn.
Get Faster Answers to Qualitative Research Questions with Similarweb Today Qualitative research question example for determining pricing strategies
Here, I’m using Capital One as an example site. I can see trending pages on their site showing the largest increase in page views. Other filters include campaign, best-performing, and new–each of which shows you page URLs, share of traffic, and growth as a percentage. This page is particularly useful for staying on top of trending topics , campaigns, and new content being pushed out in a market by key competitors. Qualitative research questions for product development teamsIt’s vital to stay in touch with changing consumer needs. These questions can also be used for new product or service development, but this time, it’s from the perspective of a product manager or development team.
Qualitative questions examples to understand customer segmentsMarket segmentation seeks to create groups of consumers with shared characteristics. Use these questions to learn more about different customer segments and how to target them with tailored messaging.
Qualitative research question example for understanding customer segments
This example shows me Bank of America’s social media distribution, with YouTube , Linkedin , and Facebook taking the top three spots, and accounting for almost 80% of traffic being driven from social media. When doing any type of market research, it’s important to benchmark performance against industry averages and perform a social media competitive analysis to verify rival performance across the same channels. Qualitative questions to inform competitive analysisOrganizations must assess market sentiment toward other players to compete and beat rival firms. Whether you want to increase market share , challenge industry leaders , or reduce churn, understanding how people view you vs. the competition is key.
Qualitative research question example for competitive analysis
Using the audience interests element of Similarweb website analysis, you can view the cross-browsing behaviors of a website’s audience instantly. You can see a matrix that shows the percentage of visitors on a target site and any rival site they may have visited. With the Similarweb audience overlap feature, view the cross-visitation habits of an audience across specific websites. In this example, I chose chase.com and its four closest competitors to review. For each intersection, you see the number of unique visitors and the overall proportion of each site’s audience it represents. It also shows the volume of unreached potential visitors. Here, you can see a direct comparison of the audience loyalty represented in a bar graph. It shows a breakdown of each site’s audience based on how many other sites they have visited. Those sites with the highest loyalty show fewer additional sites visited. From the perspective of chase.com, I can see 47% of their visitors do not visit rival sites. 33% of their audience visited 1 or more sites in this group, 14% visited 2 or more sites, 4% visited 3 or more sites, and just 0.8% viewed all sites in this comparison. How to answer qualitative research questions with SimilarwebSimilarweb Research Intelligence drastically improves market research efficiency and time to insight. Both of these can impact the bottom line and the pace at which organizations can adapt and flex when markets shift, and rivals change tactics. Outdated practices, while still useful, take time . And with a quicker, more efficient way to garner similar insights, opting for the fast lane puts you at a competitive advantage. With a birds-eye view of the actions and behaviors of companies and consumers across a market , you can answer certain research questions without the need to plan, do, and review extensive qualitative market research . Wrapping up Qualitative research methods have been around for centuries. From designing the questions to finding the best distribution channels, collecting and analyzing findings takes time to get the insights you need. Similarweb Digital Research Intelligence drastically improves efficiency and time to insight. Both of which impact the bottom line and the pace at which organizations can adapt and flex when markets shift. Similarweb’s suite of digital intelligence solutions offers unbiased, accurate, honest insights you can trust for analyzing any industry, market, or audience.
Are quantitative or qualitative research questions best? Both have their place and purpose in market research. Qualitative research questions seek to provide details, whereas quantitative market research gives you numerical statistics that are easier and quicker to analyze. You get more flexibility with qualitative questions, and they’re non-directional. What are the advantages of qualitative research? Qualitative research is advantageous because it allows researchers to better understand their subject matter by exploring people’s attitudes, behaviors, and motivations in a particular context. It also allows researchers to uncover new insights that may not have been discovered with quantitative research methods. What are some of the challenges of qualitative research? Qualitative research can be time-consuming and costly, typically involving in-depth interviews and focus groups. Additionally, there are challenges associated with the reliability and validity of the collected data, as there is no universal standard for interpreting the results. by Liz March Digital Research Specialist Liz March has 15 years of experience in content creation. She enjoys the outdoors, F1, and reading, and is pursuing a BSc in Environmental Science. Related Posts Importance of Market Research: 9 Reasons Why It’s Crucial for Your BusinessAudience Segmentation: Definition, Importance & TypesGeographic Segmentation: Definition, Pros & Cons, Examples, and MoreDemographic Segmentation: The Key To Transforming Your Marketing StrategyUnlocking Consumer Behavior: What Makes Your Customers Tick?Customer Segmentation: Expert Tips on Understanding Your AudienceWondering what similarweb can do for your business. Give it a try or talk to our insights team — don’t worry, it’s free!
How to Create a Survey Qualitative vs quantitative questions: What you need to knowSurveys are a great way for organizations to gather feedback from customers, employees, partners, and other stakeholders. With this information in hand, an organization is in a better position to make decisions about the future of the business. However, designing a survey is no easy task. Determining the right questions to ask to get the data you’re looking for actually takes a good deal of thought. And you need the right survey tools to help you gather that data. One of the most important aspects to consider when crafting a survey is whether to use qualitative vs quantitative questions. In this article, we cover what these types of questions are, why they’re useful, and when and how to use them effectively in surveys. Qualitative vs quantitative questions: Key differencesQualitative and quantitative questions in surveys are both effective ways of getting information from a target audience, but the type of information they provide is fundamentally different. As the name suggests, quantitative questions offer answers that either are numeric or lend themselves to statistical analysis. For example, the question, “How often do you do your grocery shopping?” is a quantitative question that can help an organization determine how many of their customers shop at specific intervals, such as every three days, five days, or seven days. Another example of a quantitative question is, “Do you prefer the blue sweater or the red sweater?” The answers to that question can be represented by the percentage of customers who chose each option. Here are other examples:
Qualitative questions, on the other hand, aren’t as easy to analyze as quantitative questions because their answers have subtler, more subjective meanings. Qualitative questions focus on the why or how of a topic and elicit deeper and more detailed responses. For example, the qualitative question, “How would you describe your last customer service experience with our business?” can draw a variety of responses from customers, ranging from one-word answers to multi-paragraph recaps. Whether the replies are short or long, understanding their significance requires you to analyze the sentiment of responses and identify overall patterns in them. Another example of a qualitative question is, “If you could improve our product, what would you change?” The answers to this question could range from design features to marketing slogans to a complete overhaul, so they may be difficult or impossible to quantify.
Benefits of quantitative survey questionsWhy should an organization consider using quantitative questions in their surveys? There are many advantages to this question type:
Benefits of qualitative survey questionsWhile qualitative questions result in answers that are hard to quantify, they still provide the organization with useful insights. Here are some advantages this question type can deliver:
When to use quantitative survey questions“Organizations should use quantitative results to identify patterns and trends in data sets,” says Olivia Tsang, growth marketing director at Fraction , a mortgage lender helping homeowners tap into their home equity. “Companies can collect accurate data with quantitative questions to make predictions and inferences. Additionally, quantitative data can be analyzed and interpreted using statistical analysis if further assessment is required.” Here are other examples of situations when it’s best to use quantitative questions in a survey:
When to use qualitative survey questionsSince qualitative questions often provide unexpected answers and deep insights, there are certain situations in which these types of survey questions work best. “Organizations should use qualitative results to know people’s experiences, opinions, and perspectives about a product or service,” says Tsang. “Companies can collect subjective, behavioral-based answers when using qualitative data.” Here are situations in which an organization should consider using qualitative questions:
Qualitative vs quantitative questions: The benefits of a blendWhile quantitative questions and qualitative questions differ greatly from one another, they can be equally helpful when you use them strategically in a survey. Many surveys include both types of questions. Often, they’re integrated through such techniques as following a quantitative question with a qualitative question on the same topic. “The lines between qualitative (no closed set of answers) and quantitative (a closed text or numeric answer set with, at most, an ‘Other/Specify’ option) have rightfully been blurred by online methods, including forums, bulletin boards, collaborative editing, virtual white boards, and more,” notes Laurie Gelb, freelance consultant at Profit by Change , a marketing and health outcomes research company. “Since decisions need to be made quickly and we typically need the understanding provided by both qualitative and quantitative,” Gelb continues, “the best studies often combine the two, whether with a one-time set of stimuli, interval pings, or asynchronous data collection. “Thoughtful question order and pacing — allowing the respondent time and space to impart structured matters of fact and opinion, [which the surveyor strategically integrates, as appropriate] — allow a survey project to add maximum value. For example, instead of asking ‘What else?’ or ‘Do you have any other comments?’ it may be appropriate to ask, ‘If you became president of this firm tomorrow, what would be your first move?’” Jotform: The solution to your qualitative and quantitative question needsWhether an organization is using quantitative questions, qualitative questions, or a combination of both in their surveys, Jotform is the ideal survey maker for businesses of all sizes and types. It offers hundreds of survey templates , so a lot of the hard work is already done. Organizations can use Jotform’s drag-and-drop survey builder to customize a survey to their exact requirements, choosing from multiple quantitative and qualitative question types to include in the questionnaire. In addition, Jotform integrates with many enterprise solutions, such as customer relationship management (CRM) and email marketing tools, making it easy to connect the survey responses to the business as a whole. It enables organizations to create surveys that result in valuable insights — regardless of whether they come from qualitative or quantitative questions. 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Qualitative vs. quantitative data analysis: How do they differ?Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction. What is qualitative data?Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1 What is quantitative data?Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2 Key difference between qualitative and quantitative dataIt's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing. Data Types and NatureExamples of qualitative data types in learning analytics:
Examples of quantitative data types:
Methods of CollectionQualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data. Qualitative research methods Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:
Quantitative research methods Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:
Analysis techniquesQualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis. Qualitative data analysis methods Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3 Quantitative analysis techniques The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4 Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4 Qualitative and quantitative research toolsFrom the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types. Qualitative research software: NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5 ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6 SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7 R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8 Applications in Educational ResearchBoth quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions. Master Data Analysis with an M.S. in Learning Sciences From SMUWhether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals. For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.
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Quantitative Research Questionnaire – Types & ExamplesPublished by Alvin Nicolas at August 20th, 2024 , Revised On August 21, 2024 Research is usually done to provide solutions to an ongoing problem. Wherever the researchers see a gap, they tend to launch research to enhance their knowledge and to provide solutions to the needs of others. If they want to research from a subjective point of view, they consider qualitative research. On the other hand, when they research from an objective point of view, they tend to consider quantitative research. There’s a fine line between subjectivity and objectivity. Qualitative research, related to subjectivity, assesses individuals’ personal opinions and experiences, while quantitative research, associated with objectivity, collects numerical data to derive results. However, the best medium to collect data in quantitative research is a questionnaire. Let’s discuss what a quantitative research questionnaire is, its types, methods of writing questions, and types of survey questions. By thoroughly understanding these key essential terms, you can efficiently create a professional and well-organised quantitative research questionnaire. What is a Quantitative Research Questionnaire?Quantitative research questionnaires are preferably used during quantitative research. They are a well-structured set of questions designed specifically to gather specific, close-ended participant responses. This allows the researchers to gather numerical data and obtain a deep understanding of a particular event or problem. As you know, qualitative research questionnaires contain open-ended questions that allow the participants to express themselves freely, while quantitative research questionnaires contain close-ended and specific questions, such as multiple-choice and Likert scales, to assess individuals’ behaviour. Quantitative research questionnaires are usually used in research in various fields, such as psychology, medicine, chemistry, and economics. Let’s see how you can write quantitative research questions by going through some examples:
Types of Quantitative Research Questions With ExamplesAfter learning what a quantitative research questionnaire is and what quantitative research questions look like, it’s time to thoroughly discuss the different types of quantitative research questions to explore this topic more. Dichotomous QuestionsDichotomous questions are those with a margin for only two possible answers. They are usually used when the answers are “Yes/No” or “True/False.” These questions significantly simplify the research process and help collect simple responses. Example: Have you ever visited Istanbul? Multiple Choice QuestionsMultiple-choice questions have a list of possible answers for the participants to choose from. They help assess people’s general knowledge, and the data gathered by multiple-choice questions can be easily analysed. Example: Which of the following is the capital of France? Multiple Answer QuestionsMultiple-answer questions are similar to multiple-choice questions. However, there are multiple answers for participants to choose from. They are used when the questions can’t have a single, specific answer. Example: Which of the following movie genres are your favourite? Likert Scale QuestionsLikert scale questions are used when the preferences and emotions of the participants are measured from one extreme to another. The scales are usually applied to measure likelihood, frequency, satisfaction, and agreement. The Likert scale has only five options to choose from. Example: How satisfied are you with your job? Semantic Differential QuestionsSimilar to Likert scales, semantic differential questions are also used to measure the emotions and attitudes of participants. The only difference is that instead of using extreme options such as strongly agree and strongly disagree, opposites of a particular choice are given to reduce bias. Example: Please rate the services of our company. Rank Order QuestionsRank-order questions are usually used to measure the preferences and choices of the participants efficiently. In this, multiple choices are given, and participants are asked to rank them according to their perspective. This helps to create a good participant profile. Example: Rank the given books according to your interest. Matrix QuestionsMatrix questions are similar to Likert scales. In Likert scales, participants’ responses are measured through separate questions, while in matrix questions, multiple questions are compiled in a single row to simplify the data collection method efficiently. Example: Rate the following activities that you do in daily life. How To Write Quantitative Research Questions?Quantitative research questions allow researchers to gather empirical data to answer their research problems. As we have discussed the different types of quantitative research questions above, it’s time to learn how to write the perfect quantitative research questions for a questionnaire and streamline your research process. Here are the steps to follow to write quantitative research questions efficiently. Step 1: Determine the Research GoalsThe first step in writing quantitative research questions is to determine your research goals. Determining and confirming your research goals significantly helps you understand what kind of questions you need to create and for what grade. Efficiently determining the research goals also reduces the need for further modifications in the questionnaire. Step 2: Be Mindful About the VariablesThere are two variables in the questions: independent and dependent. It is essential to decide what would be the dependent variable in your questions and what would be the independent. It significantly helps to understand where to emphasise and where not. It also reduces the probability of additional and vague questions. Step 3: Choose the Right Type of QuestionIt is also important to determine the right type of questions to add to your questionnaire. Whether you want Likert scales, rank-order questions, or multiple-answer questions, choosing the right type of questions will help you measure individuals’ responses efficiently and accurately. Step 4: Use Easy and Clear LanguageAnother thing to keep in mind while writing questions for a quantitative research questionnaire is to use easy and clear language. As you know, quantitative research is done to measure specific and simple responses in empirical form, and using easy and understandable language in questions makes a huge difference. Step 5: Be Specific About The TopicAlways be mindful and specific about your topic. Avoid writing questions that divert from your topic because they can cause participants to lose interest. Use the basic terms of your selected topic and gradually go deep. Also, remember to align your topic and questions with your research objectives and goals. Step 6: Appropriately Write Your QuestionsWhen you have considered all the above-discussed things, it’s time to write your questions appropriately. Don’t just haste in writing. Think twice about the result of a question and then consider writing it in the questionnaire. Remember to be precise while writing. Avoid overwriting. Step 7: Gather Feedback From PeersWhen you have finished writing questions, gather feedback from your researcher peers. Write down all the suggestions and feedback given by your peers. Don’t panic over the criticism of your questions. Remember that it’s still time to make necessary changes to the questionnaire before launching your campaign. Step 8: Refine and Finalise the QuestionsAfter gathering peer feedback, make necessary and appropriate changes to your questions. Be mindful of your research goals and topic. Try to modify your questions according to them. Also, be mindful of the theme and colour scheme of the questionnaire that you decided on. After refining the questions, finalise your questionnaire. Types of Survey Questionnaires in Quantitative ResearchQuantitative research questionnaires have close-ended questions that allow the researchers to measure accurate and specific responses from the participants. They don’t contain open-ended questions like qualitative research, where the response is measured by interviews and focus groups. Good combinations of questions are used in the quantitative research survey . However, here are the types of surveys in quantitative research: Descriptive SurveyThe descriptive survey is used to obtain information about a particular variable. It is used to associate a quantity and quantify research variables. The questions associated with descriptive surveys mostly start with “What is” and “How much”. Example: A descriptive survey to measure how much money children spend to buy toys. Comparative SurveyA comparative survey is used to establish a comparison between one or more dependable variables and two or more comparison groups. This survey aims to form a comparative relation between the variables under study. The structure of the question in a comparative survey is, “What is the difference in [dependable variable] between [two or more groups]?”. Example: A comparative survey on the difference in political awareness between Eastern and Western citizens. Relationship-Based SurveyRelationship-based survey is used to understand the relationship or association between two or more independent and dependent variables. Cause and effect between two or more variables is measured in the relationship-based survey. The structure of questions in a relationship-based survey is, “What is the relation [between or among] [independent variable] and [dependable variable]?”. Example: What is the relationship between education and lifestyle in America? Advantages & Disadvantages of Questionnaires in Quantitative ResearchQuantitative research questionnaires are an excellent tool to collect data and information about the responses of individuals. Quantitative research comes with various advantages, but along with advantages, it also has its disadvantages. Check the table below to learn about the advantages and disadvantages of a quantitative research questionnaire.
Quantitative Research Questionnaire ExampleHere is an example of a quantitative research questionnaire to help you get the idea and create an efficient and well-developed questionnaire for your research:
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When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...
Unlike quantitative research, qualitative research isn't about finding causal relationships between variables. So although qualitative research questions might touch on topics that involve one variable influencing another, or looking at the difference between things, finding and quantifying those relationships isn't the primary objective ...
Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys ...
The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test ...
At a Glance. Psychologists rely on quantitative and quantitative research to better understand human thought and behavior. Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions. Quantitative research involves collecting and evaluating numerical data.
The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs.
The first question asks for a ready-made solution, and is not focused or researchable. The second question is a clearer comparative question, but note that it may not be practically feasible. For a smaller research project or thesis, it could be narrowed down further to focus on the effectiveness of drunk driving laws in just one or two countries.
This is an important cornerstone of the scientific method. Quantitative research can be pretty fast. The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average.
This type of research can be used to establish generalisable facts about a topic. Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research. Qualitative research is expressed in words. It is used to understand concepts, thoughts or experiences.
It can help add a 'why' element to factual, objective data. Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data. This qualitative data is called unstructured data by researchers.
Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions. On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships.
Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the research process, how research questions are set up, how existing theories are built upon, what research methods are employed, and how the findings are unveiled to the readers. You may adopt conventional methods ...
Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups. Indicators of qualitative research include:
Qualitative data provides details and context to better understand individual responses, while quantitative data can supply the cumulative results you need to prove the general ideas or hypotheses of your research. To get the best results from these methods in your surveys, it's important to understand the differences between them. Let's ...
Qualitative research is about people's thoughts, feelings and perspectives, while quantitative research concentrates on demographic, statistical and numerical data. Quantitative and qualitative data involve asking the right questions in a survey or form. Quantitative questions are simple questions with definite answers, while qualitative ...
It explores the "how" and "why" of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the "when" and "where," utilizing tools like surveys, experiments, and ...
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It's a research strategy that can help you enhance the validity and credibility of your findings. Triangulation is mainly used in qualitative research, but it's also commonly applied in quantitative research.
Finding a Research Question and Approaches to Qualitative Research We've discussed the research design process in general and ways of knowing favored by qualitative researchers. In chapter 2, I asked you to think about what interests you in terms of a focus of study, including your motivations and research purpose. ... Quantitative ...
Quantitative research is often focused on answering the questions of "what" or "how" in regards to a phenomenon, correlation or behavior. Benefits and Limitations of Qualitative vs. Quantitative Research. Another difference between qualitative and quantitative research lies in their advantages and limitations.
A qualitative evaluation explores non-numerical responses, while a quantitative evaluation only uses numbers to support or discredit a hypothesis. For instance, a researcher gathers qualitative data through a series of interviews, asking participants different questions about the research topic. Afterward, they group similar responses into ...
While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights.
Qualitative research questions help you understand consumer sentiment. They're strategically designed to show organizations how and why people feel the way they do about a brand, product, or service. It looks beyond the numbers and is one of the most telling types of market research a company can do. The UK Data Service describes this ...
There are many advantages to this question type: Simpler analysis: It's easier to analyze the answers to quantitative (as opposed to qualitative) questions, enabling an organization to analyze a huge number of survey responses. Better mobile user experience: Qualitative questions require typing in words or sentences, while quantitative ...
Distribute surveys with open-ended questions; Quantitative research methods. Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as: Surveys with close-ended questions that gather numerical data like birthdates or ...
As you know, qualitative research questionnaires contain open-ended questions that allow the participants to express themselves freely, while quantitative research questionnaires contain close-ended and specific questions, such as multiple-choice and Likert scales, to assess individuals' behaviour.
Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies. Coghlan, D ...
While many quantitative methods are utilized in package design research, sometimes we overlook the importance of the softer side of research—the qualitative techniques. When a new package is to be designed, or an old one redesigned, the process should begin with qualitative research so that the package design work is informed by an ...
Job Summary: The National Research Network Manager will lead organizational management for our National Research Network for Children and Youth with Special Health Care Needs under direction from, and in collaboration with, the division chief and the network's co-leads. This network pursues a portfolio of multisite quantitative and qualitative research studies in close collaboration with the ...