example of research problem in quantitative research

45 Research Problem Examples & Inspiration

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A research problem is an issue of concern that is the catalyst for your research. It demonstrates why the research problem needs to take place in the first place.

Generally, you will write your research problem as a clear, concise, and focused statement that identifies an issue or gap in current knowledge that requires investigation.

The problem will likely also guide the direction and purpose of a study. Depending on the problem, you will identify a suitable methodology that will help address the problem and bring solutions to light.

Research Problem Examples

In the following examples, I’ll present some problems worth addressing, and some suggested theoretical frameworks and research methodologies that might fit with the study. Note, however, that these aren’t the only ways to approach the problems. Keep an open mind and consult with your dissertation supervisor!

Psychology Problems

1. Social Media and Self-Esteem: “How does prolonged exposure to social media platforms influence the self-esteem of adolescents?”

• Theoretical Framework : Social Comparison Theory
• Methodology : Longitudinal study tracking adolescents’ social media usage and self-esteem measures over time, combined with qualitative interviews.

2. Sleep and Cognitive Performance: “How does sleep quality and duration impact cognitive performance in adults?”

• Theoretical Framework : Cognitive Psychology
• Methodology : Experimental design with controlled sleep conditions, followed by cognitive tests. Participant sleep patterns can also be monitored using actigraphy.

3. Childhood Trauma and Adult Relationships: “How does unresolved childhood trauma influence attachment styles and relationship dynamics in adulthood?

• Theoretical Framework : Attachment Theory
• Methodology : Mixed methods, combining quantitative measures of attachment styles with qualitative in-depth interviews exploring past trauma and current relationship dynamics.

4. Mindfulness and Stress Reduction: “How effective is mindfulness meditation in reducing perceived stress and physiological markers of stress in working professionals?”

• Theoretical Framework : Humanist Psychology
• Methodology : Randomized controlled trial comparing a group practicing mindfulness meditation to a control group, measuring both self-reported stress and physiological markers (e.g., cortisol levels).

5. Implicit Bias and Decision Making: “To what extent do implicit biases influence decision-making processes in hiring practices?

• Theoretical Framework : Cognitive Dissonance Theory
• Methodology : Experimental design using Implicit Association Tests (IAT) to measure implicit biases, followed by simulated hiring tasks to observe decision-making behaviors.

6. Emotional Regulation and Academic Performance: “How does the ability to regulate emotions impact academic performance in college students?”

• Theoretical Framework : Cognitive Theory of Emotion
• Methodology : Quantitative surveys measuring emotional regulation strategies, combined with academic performance metrics (e.g., GPA).

7. Nature Exposure and Mental Well-being: “Does regular exposure to natural environments improve mental well-being and reduce symptoms of anxiety and depression?”

• Theoretical Framework : Biophilia Hypothesis
• Methodology : Longitudinal study comparing mental health measures of individuals with regular nature exposure to those without, possibly using ecological momentary assessment for real-time data collection.

8. Video Games and Cognitive Skills: “How do action video games influence cognitive skills such as attention, spatial reasoning, and problem-solving?”

• Theoretical Framework : Cognitive Load Theory
• Methodology : Experimental design with pre- and post-tests, comparing cognitive skills of participants before and after a period of action video game play.

9. Parenting Styles and Child Resilience: “How do different parenting styles influence the development of resilience in children facing adversities?”

• Theoretical Framework : Baumrind’s Parenting Styles Inventory
• Methodology : Mixed methods, combining quantitative measures of resilience and parenting styles with qualitative interviews exploring children’s experiences and perceptions.

10. Memory and Aging: “How does the aging process impact episodic memory , and what strategies can mitigate age-related memory decline?

• Theoretical Framework : Information Processing Theory
• Methodology : Cross-sectional study comparing episodic memory performance across different age groups, combined with interventions like memory training or mnemonic strategies to assess potential improvements.

Education Problems

11. Equity and Access : “How do socioeconomic factors influence students’ access to quality education, and what interventions can bridge the gap?

• Theoretical Framework : Critical Pedagogy
• Methodology : Mixed methods, combining quantitative data on student outcomes with qualitative interviews and focus groups with students, parents, and educators.

12. Digital Divide : How does the lack of access to technology and the internet affect remote learning outcomes, and how can this divide be addressed?

• Theoretical Framework : Social Construction of Technology Theory
• Methodology : Survey research to gather data on access to technology, followed by case studies in selected areas.

13. Teacher Efficacy : “What factors contribute to teacher self-efficacy, and how does it impact student achievement?”

• Theoretical Framework : Bandura’s Self-Efficacy Theory
• Methodology : Quantitative surveys to measure teacher self-efficacy, combined with qualitative interviews to explore factors affecting it.

14. Curriculum Relevance : “How can curricula be made more relevant to diverse student populations, incorporating cultural and local contexts?”

• Theoretical Framework : Sociocultural Theory
• Methodology : Content analysis of curricula, combined with focus groups with students and teachers.

15. Special Education : “What are the most effective instructional strategies for students with specific learning disabilities?

• Theoretical Framework : Social Learning Theory
• Methodology : Experimental design comparing different instructional strategies, with pre- and post-tests to measure student achievement.

16. Dropout Rates : “What factors contribute to high school dropout rates, and what interventions can help retain students?”

• Methodology : Longitudinal study tracking students over time, combined with interviews with dropouts.

17. Bilingual Education : “How does bilingual education impact cognitive development and academic achievement?

• Methodology : Comparative study of students in bilingual vs. monolingual programs, using standardized tests and qualitative interviews.

18. Classroom Management: “What reward strategies are most effective in managing diverse classrooms and promoting a positive learning environment?

• Theoretical Framework : Behaviorism (e.g., Skinner’s Operant Conditioning)
• Methodology : Observational research in classrooms , combined with teacher interviews.

19. Standardized Testing : “How do standardized tests affect student motivation, learning, and curriculum design?”

• Theoretical Framework : Critical Theory
• Methodology : Quantitative analysis of test scores and student outcomes, combined with qualitative interviews with educators and students.

20. STEM Education : “What methods can be employed to increase interest and proficiency in STEM (Science, Technology, Engineering, and Mathematics) fields among underrepresented student groups?”

• Theoretical Framework : Constructivist Learning Theory
• Methodology : Experimental design comparing different instructional methods, with pre- and post-tests.

21. Social-Emotional Learning : “How can social-emotional learning be effectively integrated into the curriculum, and what are its impacts on student well-being and academic outcomes?”

• Theoretical Framework : Goleman’s Emotional Intelligence Theory
• Methodology : Mixed methods, combining quantitative measures of student well-being with qualitative interviews.

22. Parental Involvement : “How does parental involvement influence student achievement, and what strategies can schools use to increase it?”

• Theoretical Framework : Reggio Emilia’s Model (Community Engagement Focus)
• Methodology : Survey research with parents and teachers, combined with case studies in selected schools.

23. Early Childhood Education : “What are the long-term impacts of quality early childhood education on academic and life outcomes?”

• Theoretical Framework : Erikson’s Stages of Psychosocial Development
• Methodology : Longitudinal study comparing students with and without early childhood education, combined with observational research.

24. Teacher Training and Professional Development : “How can teacher training programs be improved to address the evolving needs of the 21st-century classroom?”

• Theoretical Framework : Adult Learning Theory (Andragogy)
• Methodology : Pre- and post-assessments of teacher competencies, combined with focus groups.

25. Educational Technology : “How can technology be effectively integrated into the classroom to enhance learning, and what are the potential drawbacks or challenges?”

• Theoretical Framework : Technological Pedagogical Content Knowledge (TPACK)
• Methodology : Experimental design comparing classrooms with and without specific technologies, combined with teacher and student interviews.

Sociology Problems

26. Urbanization and Social Ties: “How does rapid urbanization impact the strength and nature of social ties in communities?”

• Theoretical Framework : Structural Functionalism
• Methodology : Mixed methods, combining quantitative surveys on social ties with qualitative interviews in urbanizing areas.

27. Gender Roles in Modern Families: “How have traditional gender roles evolved in families with dual-income households?”

• Theoretical Framework : Gender Schema Theory
• Methodology : Qualitative interviews with dual-income families, combined with historical data analysis.

28. Social Media and Collective Behavior: “How does social media influence collective behaviors and the formation of social movements?”

• Theoretical Framework : Emergent Norm Theory
• Methodology : Content analysis of social media platforms, combined with quantitative surveys on participation in social movements.

29. Education and Social Mobility: “To what extent does access to quality education influence social mobility in socioeconomically diverse settings?”

• Methodology : Longitudinal study tracking educational access and subsequent socioeconomic status, combined with qualitative interviews.

30. Religion and Social Cohesion: “How do religious beliefs and practices contribute to social cohesion in multicultural societies?”

• Methodology : Quantitative surveys on religious beliefs and perceptions of social cohesion, combined with ethnographic studies.

31. Consumer Culture and Identity Formation: “How does consumer culture influence individual identity formation and personal values?”

• Theoretical Framework : Social Identity Theory
• Methodology : Mixed methods, combining content analysis of advertising with qualitative interviews on identity and values.

32. Migration and Cultural Assimilation: “How do migrants negotiate cultural assimilation and preservation of their original cultural identities in their host countries?”

• Theoretical Framework : Post-Structuralism
• Methodology : Qualitative interviews with migrants, combined with observational studies in multicultural communities.

33. Social Networks and Mental Health: “How do social networks, both online and offline, impact mental health and well-being?”

• Theoretical Framework : Social Network Theory
• Methodology : Quantitative surveys assessing social network characteristics and mental health metrics, combined with qualitative interviews.

34. Crime, Deviance, and Social Control: “How do societal norms and values shape definitions of crime and deviance, and how are these definitions enforced?”

• Theoretical Framework : Labeling Theory
• Methodology : Content analysis of legal documents and media, combined with ethnographic studies in diverse communities.

35. Technology and Social Interaction: “How has the proliferation of digital technology influenced face-to-face social interactions and community building?”

• Theoretical Framework : Technological Determinism
• Methodology : Mixed methods, combining quantitative surveys on technology use with qualitative observations of social interactions in various settings.

Nursing Problems

36. Patient Communication and Recovery: “How does effective nurse-patient communication influence patient recovery rates and overall satisfaction with care?”

• Methodology : Quantitative surveys assessing patient satisfaction and recovery metrics, combined with observational studies on nurse-patient interactions.

37. Stress Management in Nursing: “What are the primary sources of occupational stress for nurses, and how can they be effectively managed to prevent burnout?”

• Methodology : Mixed methods, combining quantitative measures of stress and burnout with qualitative interviews exploring personal experiences and coping mechanisms.

38. Hand Hygiene Compliance: “How effective are different interventions in improving hand hygiene compliance among nursing staff, and what are the barriers to consistent hand hygiene?”

• Methodology : Experimental design comparing hand hygiene rates before and after specific interventions, combined with focus groups to understand barriers.

39. Nurse-Patient Ratios and Patient Outcomes: “How do nurse-patient ratios impact patient outcomes, including recovery rates, complications, and hospital readmissions?”

• Methodology : Quantitative study analyzing patient outcomes in relation to staffing levels, possibly using retrospective chart reviews.

40. Continuing Education and Clinical Competence: “How does regular continuing education influence clinical competence and confidence among nurses?”

• Methodology : Longitudinal study tracking nurses’ clinical skills and confidence over time as they engage in continuing education, combined with patient outcome measures to assess potential impacts on care quality.

Communication Studies Problems

41. Media Representation and Public Perception: “How does media representation of minority groups influence public perceptions and biases?”

• Theoretical Framework : Cultivation Theory
• Methodology : Content analysis of media representations combined with quantitative surveys assessing public perceptions and attitudes.

42. Digital Communication and Relationship Building: “How has the rise of digital communication platforms impacted the way individuals build and maintain personal relationships?”

• Theoretical Framework : Social Penetration Theory
• Methodology : Mixed methods, combining quantitative surveys on digital communication habits with qualitative interviews exploring personal relationship dynamics.

43. Crisis Communication Effectiveness: “What strategies are most effective in managing public relations during organizational crises, and how do they influence public trust?”

• Theoretical Framework : Situational Crisis Communication Theory (SCCT)
• Methodology : Case study analysis of past organizational crises, assessing communication strategies used and subsequent public trust metrics.

44. Nonverbal Cues in Virtual Communication: “How do nonverbal cues, such as facial expressions and gestures, influence message interpretation in virtual communication platforms?”

• Theoretical Framework : Social Semiotics
• Methodology : Experimental design using video conferencing tools, analyzing participants’ interpretations of messages with varying nonverbal cues.

45. Influence of Social Media on Political Engagement: “How does exposure to political content on social media platforms influence individuals’ political engagement and activism?”

• Theoretical Framework : Uses and Gratifications Theory
• Methodology : Quantitative surveys assessing social media habits and political engagement levels, combined with content analysis of political posts on popular platforms.

Before you Go: Tips and Tricks for Writing a Research Problem

This is an incredibly stressful time for research students. The research problem is going to lock you into a specific line of inquiry for the rest of your studies.

So, here’s what I tend to suggest to my students:

• Start with something you find intellectually stimulating – Too many students choose projects because they think it hasn’t been studies or they’ve found a research gap. Don’t over-estimate the importance of finding a research gap. There are gaps in every line of inquiry. For now, just find a topic you think you can really sink your teeth into and will enjoy learning about.
• Take 5 ideas to your supervisor – Approach your research supervisor, professor, lecturer, TA, our course leader with 5 research problem ideas and run each by them. The supervisor will have valuable insights that you didn’t consider that will help you narrow-down and refine your problem even more.
• Trust your supervisor – The supervisor-student relationship is often very strained and stressful. While of course this is your project, your supervisor knows the internal politics and conventions of academic research. The depth of knowledge about how to navigate academia and get you out the other end with your degree is invaluable. Don’t underestimate their advice.

I’ve got a full article on all my tips and tricks for doing research projects right here – I recommend reading it:

• 9 Tips on How to Choose a Dissertation Topic

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 101 Class Group Name Ideas (for School Students)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 19 Top Cognitive Psychology Theories (Explained)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 119 Bloom’s Taxonomy Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ All 6 Levels of Understanding (on Bloom’s Taxonomy)

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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 .

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 .

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 .

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 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.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

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.

• How to create a survey

Quantitative Research 2 – Formulating a Research Problem

• 4 minute read

Formulating a Research Problem

Welcome to our article discussing how to formulate a research problem. Strictly on their own, research problems are meaningless. Because of this, they must always be related to a specific topic that one wants to study. A research problem should be formulated using questions that are used to describe the given topic and from which you can then deduce certain hypotheses.

• Problem –   Lack of customers at a café
• Research question –  Are customers satisfied with the services at the café?
• Hypothesis –  If customers are dissatisfied with services at the café, they will not come there.

We will continue on towards the units for correctly formulating a research problem, which are:

• Decomposing the topic (breaking down the topic into individual elements)
• Variable types*

Decomposing the Topic

Decomposition—the division of a topic into its component elements—is closely connected with the correct creation of research questions. Thanks to decomposition, you can put together “specifying” questions, with which you will describe the research problem better and then resolve it more successfully. Take care not to ask too many such questions, because they can make your research problem too tangled. Always try to focus only on the main areas and describe those briefly!

• Problem —Lack of customer interest in a travel agency
• Research question —Are our clients satisfied with the travel agency’s services?
• Are clients satisfied with our sales agents?
• Are clients satisfied with our transport?
• Are clients satisfied with the trips themselves?

Decomposing a topic is also decisive for going on to correctly compose a hypothesis on the current state of the research problem and write questions for respondents.

You could say that a hypothesis is a proposed prerequisite for the current state of the “project”—a prerequisite that you are trying to confirm or deny with your research. Forming hypotheses is the next-to-last step towards designing the survey itself. Forming a hypothesis comes after getting to know the problem, defining the research question, and decomposing that question.

When forming hypotheses, it is always appropriate to start from available and relevant data and predefined research questions. Then you just need to make use of this information to form hypotheses that you want to confirm or deny.

• Problem: After the car repair shop was reconstructed, fewer people went there.
• Research question: Are customers satisfied with the shop’s services?
• Are customers satisfied with the new repair prices?
• Are customers satisfied with the waiting time for repairs, which has increased since the reconstruction?
• Customers are avoiding the car repair shop due to the increased price for repairs.
• Customers are avoiding the car repair shop due to the now-increased waiting time.

Examples of defined hypotheses:

• Example 1: A restaurant owner believes that his customers are extremely satisfied with the quality of the restaurant’s food. He will confirm or deny this belief through research.
• Example 2: A library is visited by university students. The director believes that higher education positively influences the frequency of library visits. She will confirm or deny this belief through research.
• Example 3: A company’s owners believe that customers would appreciate the option to make purchases over the internet. He will confirm or deny this belief through research.

Variable Types

In quantitative research, a variable means a property within a research question that can take on different values .

Question: How old are you? (this question contains a property that can take on different values )

• Value – 10-20
• Value – 21-40
• Value – 41-60
• Value – 61+

Variables are mainly used in questionnaires that are then statistically evaluated and edited into the form of graphs.

Before you start creating your questionnaire ,  you should know that various types of variables exist, and they are not the same. Variables are classified into three groups by the values they can take on:

• Interval (cardinal) – The value is a number, which you can compare with other numbers easily and determine by how much they differ. Age and pay belong in this category.
• Nominal – Nominal values are generally expressed in words. These include, for example, gender or marital status (male/female, single/married).
• Ordinal – Ordinal values may also be expressed in words, but unlike nominal values, they can be put in order. However, the amount by which they differ cannot be determined precisely. An example would be level of education (high school / university).

The next piece in this series covers sample selection, which is the last step before the actual process of asking respondents questions.

If you have any questions, suggestions, or remarks (on this series or otherwise), please don’t hesitate to contact us via   Facebook , Twitter , G+ or  e-mail .

• Variable —a property that you are measuring, which can be expressed via specific values
• Decomposition —the dividing of a topic or area into components
• Hypothesis —the prerequisite for research (can be confirmed or denied)
• Respondent —a survey participant who answers questions

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What is a Problem Statement? [with examples]

• 5 minute read

Table of Contents

The statement of the problem is one of the first things that a colleague or potential client will read. With the vastness of the information available at one’s fingertips in the online9 world, your work may have just a few seconds to draw in a reader to take a deeper look at your proposal before moving on to the next option. It explains quickly to the reader, the problem at hand, the need for research, and how you intend to do it.

A strong, clear description of the problem that drew you to your research has to be straightforward, easy to read and, most important, relevant. Why do you care about this problem? How can solving this problem impact the world? The problem statement is your opportunity to explain why you care and what you propose to do in the way of researching the problem.

A problem statement is an explanation in research that describes the issue that is in need of study . What problem is the research attempting to address? Having a Problem Statement allows the reader to quickly understand the purpose and intent of the research. The importance of writing your research proposal cannot be stressed enough. Check for more information on Writing a Scientific Research Project Proposal .

It is expected to be brief and concise , and should not include the findings of the research or detailed data . The average length of a research statement is generally about one page . It is going to define the problem, which can be thought of as a gap in the information base. There may be several solutions to this gap or lack of information, but that is not the concern of the problem statement. Its purpose is to summarize the current information and where a lack of knowledge may be presenting a problem that needs to be investigated .

The purpose of the problem statement is to identify the issue that is a concern and focus it in a way that allows it to be studied in a systematic way . It defines the problem and proposes a way to research a solution, or demonstrates why further information is needed in order for a solution to become possible.

What is Included in a Problem Statement?

Besides identifying the gap of understanding or the weakness of necessary data, it is important to explain the significance of this lack.

-How will your research contribute to the existing knowledge base in your field of study?

-How is it significant?

-Why does it matter?

Not all problems have only one solution so demonstrating the need for additional research can also be included in your problem statement. Once you identify the problem and the need for a solution, or for further study, then you can show how you intend to collect the needed data and present it.

How to Write a Statement of Problem in Research Proposal

It is helpful to begin with your goal. What do you see as the achievable goal if the problem you outline is solved? How will the proposed research theoretically change anything? What are the potential outcomes?

Then you can discuss how the problem prevents the ability to reach your realistic and achievable solution. It is what stands in the way of changing an issue for the better. Talk about the present state of affairs and how the problem impacts a person’s life, for example.

It’s helpful at this point to generally layout the present knowledge and understanding of the subject at hand, before then describing the gaps of knowledge that are currently in need of study. Your problem statement is a proposed solution to address one of these gaps.

A good problem statement will also layout the repercussions of leaving the problem as it currently stands. What is the significance of not addressing this problem? What are the possible future outcomes?

Example of Problem Statement in Research Proposal

If, for example , you intended to research the effect of vitamin D supplementation on the immune system , you would begin with a review of the current knowledge of vitamin D’s known function in relation to the immune system and how a deficiency of it impacts a person’s defenses.

You would describe the ideal environment in the body when there is a sufficient level of vitamin D. Then, begin to identify the problems associated with vitamin D deficiency and the difficulty of raising the level through supplementation, along with the consequences of that deficiency. Here you are beginning to identify the problem of a common deficiency and the current difficulty of increasing the level of vitamin D in the blood.

At this stage, you may begin to identify the problem and narrow it down in a way that is practical to a research project. Perhaps you are proposing a novel way of introducing Vitamin D in a way that allows for better absorption by the gut, or in a combination with another product that increases its level in the blood.

Describe the way your research in this area will contribute to the knowledge base on how to increase levels of vitamin D in a specific group of subjects, perhaps menopausal women with breast cancer. The research proposal is then described in practical terms.

How to write a problem statement in research?

Problem statements differ depending on the type and topic of research and vary between a few sentences to a few paragraphs.

However, the problem statement should not drag on needlessly. Despite the absence of a fixed format, a good research problem statement usually consists of three main parts:

Context: This section explains the background for your research. It identifies the problem and describes an ideal scenario that could exist in the absence of the problem. It also includes any past attempts and shortcomings at solving the problem.

Significance: This section defines how the problem prevents the ideal scenario from being achieved, including its negative impacts on the society or field of research. It should include who will be the most affected by a solution to the problem, the relevance of the study that you are proposing, and how it can contribute to the existing body of research.

Solution: This section describes the aim and objectives of your research, and your solution to overcome the problem. Finally, it need not focus on the perfect solution, but rather on addressing a realistic goal to move closer to the ideal scenario.

Here is a cheat sheet to help you with formulating a good problem statement.

1. Begin with a clear indication that the problem statement is going to be discussed next. You can start with a generic sentence like, “The problem that this study addresses…” This will inform your readers of what to expect next.

2. Next, mention the consequences of not solving the problem . You can touch upon who is or will be affected if the problem continues, and how.

3. Conclude with indicating the type of research /information that is needed to solve the problem. Be sure to reference authors who may have suggested the necessity of such research.

This will then directly lead to your proposed research objective and workplan and how that is expected to solve the problem i.e., close the research gap.

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The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

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How to Write a Statement of the Problem in Research

Table of Contents

The problem statement is a foundation of academic research writing , providing a precise representation of an existing gap or issue in a particular field of study.

Crafting a sharp and focused problem statement lays the groundwork for your research project.

• It highlights the research's significance .
• Emphasizes its potential to influence the broader academic community.
• Represents the initial step for you to make a meaningful contribution to your discipline.

Therefore, in this article, we will discuss what is a statement of the problem in research and how to craft a compelling research problem statement.

What is a research problem statement?

A research problem statement is a concise, clear, and specific articulation of a gap in current knowledge that your research aims to bridge. It not only sets forth the scope and direction of your research but also establishes its relevance and significance.

Your problem statement in your research paper aims to:

• Define the gap : Clearly identify and articulate a specific gap or issue in the existing knowledge.
• Provide direction : Serve as a roadmap, guiding the course of your research and ensuring you remain focused.
• Establish relevance : Highlight the importance and significance of the problem in the context of your field or the broader world.
• Guide inquiry :  Formulate the research questions or hypotheses you'll explore.
• Communicate intent : Succinctly convey the core purpose of your research to stakeholders, peers, and any audience.
• Set boundaries : Clearly define the scope of your research to ensure it's focused and achievable.

When should you write a problem statement in research?

Initiate your research by crafting a clear problem statement. This should be done before any data collection or analysis, serving as a foundational anchor that clearly identifies the specific issue you aim to address.

By establishing this early on, you shape the direction of your research, ensuring it targets a genuine knowledge gap.

Furthermore, an effective and a concise statement of the problem in research attracts collaborators, funders, and supporters, resonating with its clarity and purpose. Remember, as your research unfolds, the statement might evolve, reflecting new insights and staying pertinent.

But how do you distinguish between a well-crafted problem statement and one that falls short?

Effective vs. ineffective research problem statements

Imagine a scenario where medical researchers aim to tackle a new strain of virus. Their effective problem statement wouldn't merely state the existence of the virus. Instead, it would delve into the specifics — the regions most affected, the demographics most vulnerable, and the current limitations in medical interventions.

Whereas an ineffective research problem statement is vague, overly broad, or ambiguous, failing to provide a clear direction for the research. It may not be rooted in existing literature, might lack clarity on its significance, or could be framed in a way that makes the research objectives unachievable or irrelevant.

To understand it better, let's consider the topic of “Remote work and employee productivity.”

Effective problem statement

“Over the past decade, there has been a 70% increase in organizations adopting remote work policies. While some studies suggest remote work enhances employee productivity, others indicate potential declines due to distractions at home.

However, there’s a lack of comprehensive research examining the specific factors in a remote environment that influence productivity. This study aims to identify and analyze these factors, providing organizations with actionable insights to optimize remote work policies.”

Why is this statement of a problem in research effective?

• Specificity : The statement provides a clear percentage to highlight the rise in remote work.
• Context : It acknowledges existing research and the conflicting findings.
• Clear gap identification : It points out the lack of comprehensive research on specific factors affecting productivity in remote work.
• Purpose : The statement concludes with a clear aim for the research.

Ineffective problem statement

"People are working from home a lot now, especially since there are so many internet tools. Some say it's good; others say it's not that great. This research will just look into the whole work-from-home thing and see what's up."

Why is this statement of a problem in research ineffective?

• Informal language : Phrases like "what's up" and "the whole work-from-home thing" are not suitable for academic writing.
• Vagueness : The statement doesn't provide any specific data or context about the rise of remote work.
• Lack of clear focus : It's unclear what aspect of remote work the research will address.
• Ambiguous purpose : The statement doesn't specify the research's objectives or expected outcomes.

After gaining an understanding of what an effective research problem statement looks like, let's dive deeper into how to write one.

How to write a problem statement in research?

Drafting your research problem statement at the onset of your research journey ensures that your research remains anchored. That means by defining and articulating the main issue or challenge you intend to address at the very beginning of your research process; you provide a clear focus and direction for the entire study.

Here's a detailed guide to how you can write an effective statement of the problem in research.

Identify the research area : Before addressing a specific problem, you need to know the broader domain or field of your study. This helps in contextualizing your research and ensuring it aligns with existing academic disciplines.

Example: If you're curious about the effects of digital technology on human behavior, your broader research area might be Digital Sociology or Media Studies.

Conduct preliminary literature review : Familiarize yourself with existing research related to your topic. This will help you understand what's already known and, more importantly, identify gaps or unresolved questions in the existing knowledge. This step also ensures you're advancing upon existing work rather than replicating it.

Example: Upon reviewing literature on digital technology and behavior, you find many studies on social media's impact on youth but fewer on its effects on the elderly.

Read how to conduct an effective literature review .

Define the specific problem : After thoroughly reviewing the literature, pinpoint a particular issue that your research will address. Ensure that this chosen issue is not only of substantial importance in its field but also realistically approachable given your resources and expertise. To define it precisely, you might consider:

• Highlighting discrepancies or contradictions in existing literature.
• Emphasizing the real-world implications of this gap.
• Assessing the feasibility of exploring this issue within your means and timeframe.

Example: You decide to investigate how digital technology, especially social media, affects the mental well-being of the elderly, given the limited research in this area.

Articulate clearly and concisely : Your problem statement should be straightforward and devoid of jargon. It needs to convey the essence of your research issue in a manner that's understandable to both experts and non-experts.

Example: " The impact of social media on the mental well-being of elderly individuals remains underexplored, despite the growing adoption of digital technology in this age group. "

Highlight the significance : Explain why your chosen research problem matters. This could be due to its real-world implications, its potential to fill a knowledge gap or its relevance to current events or trends.

Example: As the elderly population grows and becomes more digitally connected, understanding the psychological effects of social media on this demographic could inform digital literacy programs and mental health interventions.

Ensure feasibility : Your research problem should be something you can realistically study, given your resources, timeframe, and expertise. It's essential to ensure that you can gather data, conduct experiments, or access necessary materials or participants.

Example: You plan to survey elderly individuals in local community centers about their social media usage and perceived mental well-being, ensuring you have the means to reach this demographic.

Seek feedback : Discuss your preliminary problem statement with peers, mentors, or experts in the field. They can provide insights, point out potential pitfalls, or suggest refinements.

Example: After discussing with a gerontologist, you decide to also consider the role of digital training in moderating the effects of social media on the elderly.

Refine and Revise : Based on feedback and further reflection, revise and improve your problem statement. This iterative process ensures clarity, relevance, and precision.

Example: Your refined statement reads: Despite the increasing digital connectivity of the elderly, the effects of social media on their mental well-being, especially in the context of digital training, remain underexplored.

By following these detailed steps, you can craft a research problem statement that is both compelling and academically rigorous.

Having explored the details of crafting a research problem statement, it's crucial to distinguish it from another fundamental element in academic research: the thesis statement.

Difference between a thesis statement and a problem statement

While both terms are central to research, a thesis statement presents your primary claim or argument, whereas a problem statement describes the specific issue your research aims to address.

Think of the thesis statement as the conclusion you're driving towards, while the problem statement identifies a specific gap in current knowledge.

For instance, a problem statement might highlight the rising mental health issues among teenagers, while the thesis statement could propose that increased screen time is a significant contributor.

Refer to the comparison table between what is a thesis and a problem statement in the research below:

Common mistakes to avoid in writing statement of the problem in research

Mistakes in the research problem statement can lead to a domino effect, causing misalignment in research objectives, wasted resources, and even inconclusive or irrelevant results.

Recognizing and avoiding these pitfalls not only strengthens the foundation of your research but also ensures that your efforts concede impactful insights.

Here's a detailed exploration of frequent subjective, qualitative, quantitative and measurable mistakes and how you can sidestep them.

Being too broad or too narrow

A problem statement that's too broad can lack focus, making it challenging to derive specific research questions or objectives. Conversely, a statement that's too narrow might limit the scope of your research or make it too trivial.

Example of mistake: "Studying the effects of diet on health" is too broad, while "Studying the effects of eating green apples at 3 pm on heart health" is overly narrow.

You can refine the scope based on preliminary research. The correct way to write this problem statement will be "Studying the effects of a high-fiber diet on heart health in adults over 50." This statement is neither too broad nor too narrow, and it provides a clear direction for the research.

Using unnecessary jargon or technical language

While academic writing often involves academic terms, overloading your problem statement with jargon can alienate readers and obscure the actual problem.

Example of Mistake: "Examining the diurnal variations in macronutrient ingestion vis-à-vis metabolic homeostasis."

To ensure it’s not complicated, you can simplify and clarify. "Examining how daily changes in nutrient intake affect metabolic balance" conveys the same idea more accessible.

Not emphasizing the "Why" of the problem

It's not enough to state a problem; you must also convey its significance. Why does this problem matter? What are the implications of not addressing it?

Example of Mistake: "Many students are not engaging with online learning platforms."

You can proceed with the approach of highlighting the significance here. "Many students are not engaging with online learning platforms, leading to decreased academic performance and widening educational disparities."

Circular reasoning and lack of relevance

Your problem statement should be grounded in existing research or observed phenomena. Avoid statements that assume what they set out to prove or lack a clear basis in current knowledge.

Example of Mistake: "We need to study X because not enough research has been done on X."

Instead, try grounding your statement based on already-known facts. "While several studies have explored Y, the specific impact of X remains unclear, necessitating further research."

Being overly ambitious

While it's commendable to aim high, your problem statement should reflect a challenge that's achievable within your means, timeframe, and resources.

Example of Mistake: "This research will solve world hunger."

Here, you need to be realistic and focused. "This research aims to develop sustainable agricultural techniques to increase crop yields in arid regions."

By being mindful of these common mistakes, you can craft a problem statement that is clear, relevant and sets a solid foundation for your research.

Over-reliance on outdated data

Using data that is no longer relevant can mislead the direction of your research. It's essential to ensure that the statistics or findings you reference are current and pertinent to the present scenario.

Example of Mistake: "According to a 1995 study, only 5% of the population uses the internet for daily tasks."

You always cross-check the dates and relevance of the data you're using. For a contemporary study on internet usage, you'd want to reference more recent statistics.

Not specifying the sample size or demographic

A problem statement should be clear about the population or sample size being studied, especially when making generalizations or claims.

Example of Mistake: "People prefer online shopping to in-store shopping."

Here, you would benefit from specifying the demographic or sample size when presenting data to avoid overgeneralization. " In a survey of 1,000 urban residents aged 18-35, 70% expressed a preference for online shopping over in-store shopping. "

Ignoring conflicting data

Cherry-picking data that supports your hypothesis while ignoring conflicting data can lead to a biased problem statement.

Example of Mistake: "Research shows that all students benefit from online learning."

You’ve to ensure a balanced view by considering all relevant data, even if it contradicts your hypothesis. " While many studies highlight the advantages of online learning for students, some research points to challenges such as decreased motivation and lack of face-to-face interaction. "

Making unsubstantiated predictions

Projecting future trends without solid data can weaken the credibility of your problem statement.

Example of Mistake: "The demand for electric cars will increase by 500% in the next year."

Base your predictions on current trends and reliable data sources, avoiding hyperbolic or unsupported claims. " With the current growth rate and recent advancements in battery technology, there's potential for a significant rise in the demand for electric cars. "

Wrapping Up

A well-crafted problem statement ensures that your research is focused, relevant, and contributes meaningfully to the broader academic community.

However, the consequences of an incorrect or poorly constructed problem statement can be severe. It can lead to misdirected research efforts, wasted resources, compromised credibility, and even ethical concerns. Such pitfalls underscore the importance of dedicating time and effort to craft a precise and impactful problem statement.

So, as you start your research journey , remember that a well-defined problem statement is not just a starting point; it guides your entire research journey, ensuring clarity, relevance, and meaningful contributions to your field.

Frequently Asked Questions

A problem statement is a clear, concise and specific articulation of a gap in current knowledge that your research aims to bridge.

The Problem Statement should highlight existing gaps in current knowledge and also the significance of the research. It should also include the research question and purpose of the research.

Clear articulation of the problem and establishing relevance; Working thesis (methods to solve the problem); Purpose and scope of study — are the 3 parts of the problem statement.

While the statement of the problem articulates and delineates a particular research problem, Objectives designates the aims, purpose and strategies to address the particular problem.

Here’s an example — “The study aims to identify and analyze the specific factors that impact employee productivity, providing organizations with actionable insights to optimize remote work policies.”

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What is a Problem Statement in Research? How to Write It with Examples

The question, “What is a research problem statement?” is usually followed by “Why should I care about problem statements, and how can it affect my research?” In this article, we will try to simplify the concept so that you not only grasp its meaning but internalize its importance and learn how to craft a problem statement.

To put it simply, a “problem statement” as the name implies is any statement that describes a problem in research. When you conduct a study, your aim as a researcher is to answer a query or resolve a problem. This learned information is then typically disseminated by writing a research paper that details the entire process for readers (both for experts and the general public). To better grasp this concept, we’ll try to explain what a research problem statement is from the viewpoint of a reader. For the purpose of clarity and brevity the topic is divided into subsections.

Table of Contents

What is a research problem?

A research problem is a clearly defined issue in a particular field of study that requires additional investigation and study to resolve. Once identified, the problem can be succinctly stated to highlight existing knowledge gaps, the importance of solving the research problem, and the difference between a current situation and an improved state.

But why is it important to have a research problem ready? Keep in mind that a good research problem helps you define the main concepts and terms of research that not only guide your study but help you add to or update existing literature. A research problem statement should ideally be clear, precise, and tangible enough to assist you in developing a framework for establishing the objectives, techniques, and analysis of the research project. Hence, any research project, if it is to be completed successfully,  must start with a well-defined research problem.

What is research problem statement?

A research problem statement in research writing is the most crucial component of any study, which the researcher must perfect for a variety of reasons, including to get funding and boost readership. We’ve already established that a research article’s “research problem” is a sentence that expresses the specific problem that the research is addressing. But first, let’s discuss the significance of the problem statement in research and how to formulate one, using a few examples.

Do you recall the thoughts that went through your head the last time you read a study article? Have you ever tried to quickly scan the introduction or background of the research article to get a sense of the context and the exact issue the authors were attempting to address through the study? Were you stuck attempting to pinpoint the key sentence(s) that encapsulates the background and context of the study, the motivation behind its initial conduct, and its goals? A research problem statement is the descriptive statement which conveys the issue a researcher is trying to address through the study with the aim of informing the reader the context and significance of performing the study at hand . The research problem statement is crucial for researchers to focus on a particular component of a vast field of study, and for readers to comprehend the significance of the research. A well-defined problem allows you to create a framework to develop research objectives or hypotheses.

Now that we are aware of the significance of a problem statement in research, we can concentrate on creating one that is compelling. Writing a problem statement is a fairly simple process; first, you select a broad topic or research area based on your expertise and the resources at your disposal. Then, you narrow it down to a specific research question or problem relevant to that area of research while keeping the gaps in existing knowledge in mind. To give you a step-by-step instruction on how to write a problem statement for research proposal we’ve broken the process down into sections discussing individual aspects.

When to write a problem statement?

The placement of the research problem in the research project is another crucial component when developing a problem statement. Since the research problem statement is fundamental to writing any research project, it is best to write it at the start of the research process, before experimental setup, data collection, and analysis. Without identifying a specific research problem, you don’t know what exactly you are trying to address through the research so it would not be possible for you to set up the right conditions and foundation for the research project.

It is important to describe the research problem statement at the beginning of the research process to guide the research design and methodology. Another benefit of having a clear and defined research problem early on is that it helps researchers stay on track and focus on the problem at hand without deviating into other trajectories. Writing down the research problem statement also ensures that the current study is relevant, fitting, and fills a knowledge gap. However, note that a research statement can be refined or modified as the research advances and new information becomes available. This could be anything from further deconstructing a specific query to posing a fresh query related to the selected topic area. In fact, it is common practice to revise the problem statement in research to maintain specificity and clarity and to allow room to reflect advancement in the research field.

Bonus point:

A well-defined research problem statement that is referenced in the proper position in the research proposal/article is crucial to effectively communicate the goal and significance of the study to all stakeholders concerned with the research. It piques the reader’s interest in the research area, which can advance the work in several ways and open up future partnerships and even employment opportunities for authors.

What does a research problem statement include?

If you have to create a problem statement from scratch, follow the steps/important aspects listed below to create a well-defined research problem statement.

• Describe the wide-ranging research topics

To put things in perspective, it is important to first describe the background of the research issue, which derives from a broad area of study or interest that the research project is concerned with.

• Talk about the research problem/issue

As mentioned earlier, it’s important to state the problem or issues that the research project seeks to address in a clear, succinct manner, preferably in a sentence or two to set the premise of the entire study.

• Emphasize the importance of the issue

After defining the problem your research will try to solve, explain why it’s significant in the larger context and how your study aims to close the knowledge gap between the current state of knowledge and the ideal scenario.

• Outline research questions to address the issue

Give a brief description of the list of research questions your study will use to solve the problem at hand and explain how these will address various components of the problem statement.

• Specify the key goals of the research project

Next, carefully define a set of specific and measurable research objectives that the research project aims to address.

• Describe the experimental setup

Be sure to include a description of the experimental design, including the intended sample (population/size), setting, or context in the problem statement.

• Discuss the theoretical framework

Mention the numerous theoretical ideas and precepts necessary to comprehend the study issue and guide the research activity in this section.

• Include the research methodology

To provide a clear and concise research framework, add a brief description of the research methodologies, including collection and analysis of data, which will be needed to address the research questions and objectives.

Characteristics of a research problem statement

It is essential for a research statement to be clear and concise so that it can guide the development of the research project. A good research statement also helps other stakeholders in comprehending the scope and relevance of the research, which could further lead to opportunities for collaboration or exploration. Here is a list of the key characteristics of a research problem that you should keep in mind when writing an effective research problem statement.

• The “need” to resolve the issue must be present.

It is not enough to choose a problem in your area of interest and expertise; the research problem should have larger implications for a population or a specific subset. Unless the significance of the research problem is elaborated in detail, the research is not deemed significant. Hence, mentioning the “need” to conduct the research in the context of the subject area and how it will create a difference is of utmost importance.

• The research problem needs to be presented rationally and clearly

The research statement must be written at the start and be simple enough for even researchers outside the subject area to understand. The two fundamental elements of a successful research problem statement are clarity and specificity. So, check and rewrite your research problem statement if your peers have trouble understanding it. Aim to write in a straightforward manner while addressing all relevant issues and coherent arguments.

• The research issue is supported by facts and evidence

Before you begin writing the problem statement, you must collect all relevant information available to gain a better understanding of the research topic and existing gaps. A thorough literature search will give you an idea about the current situation and the specific questions you need to ask to close any knowledge gaps. This will also prevent you from asking the questions or identifying issues that have already been addressed. Also, the problem statement should be based on facts and data and should not depend upon hypothetical events.

• The research problem should generate more research questions

Ideally, the research problem should be such that it helps advance research and encourage more questions. The new questions could be specific to the research that highlights different components or aspects of the problem. These questions must also aid in addressing the problem in a more comprehensive manner which provides a solid foundation for the research study.

• The research problem should be tangible

The research issue should be concrete, which means that the study project’s budget and time constraints should be met. The research problem should not call for any actions and experiments that are impractical or outside of your area of competence.

To summarize the main characteristics of a research problem statement, it must:

• Address the knowledge gap
• Be current and relevant
• Aids in advancing the field
• Support future research
• Be tangible and should suit researcher’s time and interest
• Be based on facts and data

  How to write a problem statement in research proposal

The format of a problem statement might vary based on the nature and subject of the research; there is no set format. It is typically written in clear, concise sentences and can range from a few sentences to a few pages. Three considerations must be made when formulating a problem statement for a research proposal:

• Context: The research problem statement needs to be created in the right setting with sufficient background information on the research topic. Context makes it easier to distinguish between the current state and the ideal one in which the issue would not exist. In this section, you can also include instances of any prior attempts and significant roadblocks to solving the problem.
• Relevance: The main goal of the researcher here is to highlight the relevance of the research study. Explain how the research problem affects society or the field of research and, if the study is conducted to mitigate the issue, what an ideal scenario would look like. Who your study will most affect if the issue is resolved and how it can impact future research are other arguments that might be made in this section.
• Strategy: Be sure to mention the goals and objectives of your research, and your approach to solve the problem. The purpose of this section is to lay out the research approach for tackling various parts of the research subject.

Examples of problem statement in research proposal

To put what we learned into practice, let’s look at an example of a problem statement in a research report. Suppose you decide to conduct a study on the topic of attention span of different generations. After a thorough literature search you concluded that the attention span of university students is reducing over generations compared to the previous one, even though there are many websites and apps to simplify tasks and make learning easy . This decrease in attention span is attributed to constant exposure to digital content and multiple screens.

In this scenario, the problem statement could be written as – “The problem this study addresses is the lack of regulative measures to control consumption of digital content by young university students, which negatively impacts their attention span”. The research’s goals and objectives, which may employ strategies to increase university students’ attention span by limiting their internet exposure, can then be described in more detail in subsequent paragraphs.

Frequently asked questions

What is a problem statement.

A problem statement is a succinct and unambiguous overview of the research issue that the study is trying to solve.

What is the difference between problem statement and thesis statement?

A problem statement is different from a thesis statement in that the former highlights the main points of a research paper while emphasizing the hypothesis, whilst the latter identifies the issue for which research is being done.

Why is a problem statement needed in a research proposal?

A problem statement identifies the specific problem that the researchers are trying to solve through their research. It is necessary to establish a framework for the project, focus the researcher’s attention, and inform stakeholders of the study’s importance.

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How to Write Quantitative Research Questions: Types With Examples

Has it ever happened that you conducted a quantitative research study and found out the results you were expecting are quite different from the actual results?

This could happen due to many factors like the unpredictable nature of respondents, errors in calculation, research bias, etc. However, your quantitative research usually does not provide reliable results when questions are not written correctly.

We get it! Structuring the quantitative research questions can be a difficult task.

Hence, in this blog, we will share a few bits of advice on how to write good quantitative research questions. We will also look at different types of quantitative research questions along with their examples.

Let’s start:

How to Write Quantitative Research Questions?

When you want to obtain actionable insight into the trends and patterns of the research topic to make sense of it, quantitative research questions are your best bet.

Being objective in nature, these questions provide you with detailed information about the research topic and help in collecting quantifiable data that can be easily analyzed. This data can be generalized to the entire population and help make data-driven and sound decisions.

Respondents find it easier to answer quantitative survey questions than qualitative questions. At the same time, researchers can also analyze them quickly using various statistical models.

However, when it comes to writing the quantitative research questions, one can get a little overwhelmed as the entire study depends on the types of questions used.

There is no “one good way” to prepare these questions. However, to design well-structured quantitative research questions, you can follow the 4-steps approach given below:

1. Select the Type of Quantitative Question

The first step is to determine which type of quantitative question you want to add to your study. There are three types of quantitative questions:

• Descriptive
• Comparative 
• Relationship-based

This will help you choose the correct words and phrases while constructing the question. At the same time, it will also assist readers in understanding the question correctly.

2. Identify the Type of Variable

The second step involves identifying the type of variable you are trying to measure, manipulate, or control. Basically, there are two types of variables:

• Independent variable (a variable that is being manipulated)
• Dependent variable (outcome variable)

If you plan to use descriptive research questions, you have to deal with a number of dependent variables. However, where you plan to create comparative or relationship research questions, you will deal with both dependent and independent variables.

3. Select the Suitable Structure

The next step is determining the structure of the research question. It involves:

• Identifying the components of the question. It involves the type of dependent or independent variable and a group of interest (the group from which the researcher tries to conclude the population).
• The number of different components used. Like, as to how many variables and groups are being examined.
• Order in which these are presented. For example, the independent variable before the dependent variable or vice versa.

4. Draft the Complete Research Question

The last step involves identifying the problem or issue that you are trying to address in the form of complete quantitative survey questions . Also, make sure to build an exhaustive list of response options to make sure your respondents select the correct response. If you miss adding important answer options, then the ones chosen by respondents may not be entirely true.

Want to create a quantitative research survey hassle-free? Explore our library of 1,000,000+ readymade questions.

Types of Quantitative Research Questions With Examples

Quantitative research questions are generally used to answer the “who” and “what” of the research topic. For quantitative research to be effective, it is crucial that the respondents are able to answer your questions concisely and precisely. With that in mind, let’s look in greater detail at the three types of formats you can use when preparing quantitative market research questions.

1. Descriptive 

Descriptive research questions are used to collect participants’ opinions about the variable that you want to quantify. It is the most effortless way to measure the particular variable (single or multiple variables) you are interested in on a large scale. Usually, descriptive research questions begin with “ how much,” “how often,” “what percentage,” “what proportion,” etc.

Examples of descriptive research questions include:

2. Comparative

Comparative research questions help you identify the difference between two or more groups based on one or more variables. In general, a comparative research question is used to quantify one variable; however, you can use two or more variables depending on your market research objectives.

Comparative research questions examples include:

3. Relationship-based

Relationship research questions are used to identify trends, causal relationships, or associations between two or more variables. It is not vital to distinguish between causal relationships, trends, or associations while using these types of questions. These questions begin with “What is the relationship” between independent and dependent variables, amongst or between two or more groups.

Relationship-based quantitative questions examples include:

Ready to Write Your Quantitative Research Questions?

So, there you have it. It was all about quantitative research question types and their examples. By now, you must have figured out a way to write quantitative research questions for your survey to collect actionable customer feedback.

Now, the only thing you need is a good survey maker tool , like ProProfs Survey Maker , that will glide your process of designing and conducting your surveys . You also get access to various survey question types, both qualitative and quantitative, that you can add to any kind of survey along with professionally-designed survey templates .

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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How to Write a Statement of a Problem in Research with Steps

Published by Grace Graffin at August 11th, 2021 , Revised On October 3, 2023

Research is a systematic investigation to find new techniques, products or processes to solve problems. Apart from being systematic, research is empirical in nature: it’s based on observations and measurement of those observations.

It’s what comes before the development. Impacts and policies that are born in society are borne out of the research.

The most important step to perform any research is to identify a problem that needs to be solved. Therefore, it is necessary to define a research problem before starting the actual research process. Once a research problem has been identified, the next step is to write a problem statement.

Philosopher Kaoru Ishikawa said: “You will have a problem half-solved by defining it correctly on the first day.”

This quote perfectly reflects the importance of a problem statement in research. Before writing a problem statement, it is essential to pinpoint a specific problem, the difficulties you can expect to face as you try to solve it and the research gaps you aim to fill with your research.

The last part—how your research aims to fill a gap in the existing literature—will act as a springboard to the solution(s) that policy makers, for instance, might eventually take to solve that problem.

Filling a gap, therefore, is very important towards solving an existing problem.

What is a Problem Statement?

A problem statement is a clear and concise description of an issue or challenge that needs to be addressed. It typically outlines the existing gap between the current state (what currently is) and the desired state (what should be). Crafting a well-defined problem statement is critical for problem-solving, research, or project planning, as it serves as a guidepost and sets the direction for the subsequent steps.

Research Problem and Research Method – A Cyclical Process

The type of research strategy used in research determines whether you will be analysing theoretical problems to add value to existing knowledge, discussing practical issues to become an agent of change for an organisation or industry or looking at both aspects in relation to any given problem.

However, the kind of problem you aim to tackle with your research, to begin with, will also help you narrow down which research design , method or strategy to opt for.

This is therefore a cyclical process. Your research aim guides your research design can help you focus on a specific kind of research gap/problem.

However, generally, your research will focus on one or the other.

Here is all you need to know about how to write a statement of the problem in research, also called problem statement by some research writers .

Why do you Need a Statement of the Problem, to Begin with?

You need a statement of the problem to transform a generalised problem into a well-defined, brief, targeted statement to perform research in the decision-making process. The problem statement helps the researcher to identify the purpose of the ongoing research.

The problem statement in the dissertation is the pillar of the introduction chapter through which the reader can understand the research questions and scope of the project. If you do not define the problem statement properly, the results might become unmanageable.

Writing Problem Statement for a Business or Organisation

In the business world, problem statements provide the basis for the enhancement and refinement of projects. Without identifying and understanding the problem, it will be hard to find and effectively implement solutions.

A stand-alone document that solely provides an in-depth and detailed problem statement is usually the answer for organisations and businesses when it becomes imperative to find the solution to a problem.

Writing Problem Statement for Academic Research

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Problem Statement – How to Write it

Ask yourself the following questions before writing the problem statement:

• What is wrong in the research area/subarea XYZ?
• Where did it happen?
• When did it happen?
• To what extent (how much)?
• I know that because…(evidence)

‘What’ always defines the defect of the problem at hand and explains why it matters? ‘Where’ defines the geological location of the problem. ‘When’ defines the history and the pattern of the problem, the goal of the stated problem and the scope of research.

‘How much’ defines the trend of the problem as to how many objects are facing the same defect and to what extent. The last part, ‘I know this because…’, will help the researcher identify the standard(s) that he must meet.

Step 1: Understanding the Problem

The problem statement should provide a clear and concise background to the research problem you are investigating. Before starting your research , review the literature about the specific problem and find a gap to fill with your own research.

Practical Research Problem Statement

If you are doing experimental research , you can identify problems by talking to people working in a relevant field, studying research reports, and reviewing previous research. Here are some examples of practical research problems:

• A problem that hinders the efficiency of a company
• An institutional process that needs interventions
• An area of concern in your field/sub-field of interest
• Members of a society facing a specific difficulty

The problem statement should focus on the details related to the problem, such as:

• When and where was the problem observed?
• Who is/are affected by it?
• What research has been conducted and what practical steps have been taken to resolve the problem?

Example of Practical Research Problem Statement

The production of a company is low for the months of July and August every year. Initial research has been conducted by the company, which revealed poor production in July and August is due to the unavailability of local raw material.

The company has made some effective attempts at engaging the local suppliers to ensure an uninterrupted supply of the raw material, but these efforts are yet to have any significant impact on the production levels.

Theoretical Research Problem Statement

According to USC Libraries, “A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study…theoretical framework must demonstrate an understanding of theories and concepts…relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.”

The theoretical research indirectly contributes to the change by identifying the problem, expanding knowledge and improving understanding. The researcher can find a specific problem by brainstorming the topic and reviewing already published theories and research.

When writing a problem statement based on a theoretical research problem , it is important to recognise the historical, geographical, social and scientific background. Here are the elements of the theoretical problem statement framework that you should consider:

• What are the facts about the problem?
• Does the problem relate to a certain geographical area or time period?
• How is the problem discussed and explained in the existing literature?

Example of Theoretical Research Problem Statement

The production of a company is low for July and August every year. Initial research has been conducted by the company, which revealed poor production in July and August is due to the unavailability of local raw material. The company has made some effective attempts to engage the local suppliers to ensure an uninterrupted raw material supply. Still, these efforts are yet to have any significant impact on the production levels.

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Step 2 – Show why it’s Important and Relevant

By discussing the importance of the problem under investigation, you are demonstrating the relevance of your research. However, this does not mean that you will end up discovering something unimaginable or extraordinary.

The objective here is to clearly state how and why your research problem is relevant in your chosen area of study and why it requires further research.

As indicated previously, practical research deals with a problem affecting society, social group, firm or organisation on a broader scale. To elaborate on why it is important to solve this problem and why your research is significant, you could consider the following questions:

• What will be the consequences if the problem remains unsolved?
• Who do these consequences have the most implications for?
• What is the wider relevance of the problem being investigated?

Low production in July and August negatively affects the company’s marketing capital, thereby becoming an area of deep concern for the directors and stakeholders. The marketing budget cut in July and August is hindering its ability to promote its products uninterruptedly.

Addressing this problem will have practical benefits for the company and help establish the reasons for disruption in raw material supply.

The relevance of all theoretical issues may not be too obvious, even though most theoretical problems do have practical implications. Here are some questions for you to ponder to establish the importance of your research problem:

• Will your research help to advance understanding of the topic under investigation?
• Are there any benefits of you resolving the problem for other researchers who wish to explore this topic further in the future?
• What are the direct or indirect implications (s) of the problem you are trying to solving?

The new forms of employment such as freelance, contract-based work and zero-hour work arrangements are recognised as either a manipulative last option or a flexible active choice. It is necessary to conduct comprehensive qualitative research to uncover why fresh graduates take up these types of employment in the gig economy. There is a need to advance more vigorous concepts relating to instability and flexibility in modern forms of employment from employees’ perspectives, which will also help shape future policies.

Also see: How to Write the Abstract for Dissertation

Step 3 – Declaring the Problem

Before you jump on to state your research’s problem statements, it’s important to devote a sentence or two to let your readers know the precise, narrowed-down research problem you will be discussing about.

For language clarity purposes, here are some strong opening statements to achieve this step:

• Recently, there has been growing interest in …
• The possibility of…has generated wide interest in …
• The development of…is a classic problem in…
• The development of…has led to the hope that …
• The…has become a favourite topic for analysis …
• Knowledge of…has great importance for …
• The study of…has become an important aspect of …
• A central issue in…is…
• The…has been extensively studied in recent years.
• Many investigators have recently turned to …
• The relationship between…has been investigated by many researchers.
• Many recent studies have found out…

Step 4 – Establishing Aim and Objectives

The last step in writing a problem statement is to provide a framework for solving the problem. This will help you, the researcher, stay focused on your research aims and not stray; it will also help you readers keep in mind the reason as to why you conducted this study, to begin with.

A good problem statement does not provide the exact solution to any problem. Rather, it focuses more on how to effectively understand or tackle a problem by establishing the possible causes.

The aim of a research study is its end goal or overall purpose. Following are some examples of how you can craft your research aim statements:

• This research study aims to investigate…
• This paper is aimed at exploring…
• This research aims to identify…

On the other hand, objectives are the smaller steps that a researcher must take to address the aim of the research. Once you have laid out the research problem your research will deal with, it’s important to next mention the how behind that. Objectives are mostly imperative statements, often beginning with transitive verbs like ‘to analyse,’ ‘to investigate,’ etc.

Some more examples are:

• Statistical analysis will be conducted to determine…
• Both quantitative and qualitative research methods will be employed to probe…
• Face-to-face interviews will be carried out with the participants to establish…

Practical Research Aim and Objectives

This project aims to identify the causes of disturbed supply of raw material in the region, which resulted in low production for the company in July and August. This will be achieved by conducting interviews and surveys with the suppliers to understand why the supply is unpredictable in those two months and what can be done to ensure orderliness. Practical experiments will also be conducted to observe the effectiveness of proposed solutions.

Theoretical Research Aim and Objectives

This study aims to understand and unearth the experiences of fresh graduates in the modern economy. The sample population will participate in this study through qualitative research methods, which are expected to provide a deeper insight into the perceptions and motives of these fresh graduates working as freelancers and contract-based employees. The data collected from this exercise and the existing literature on the topic will be analysed in statistical analysis software.

TIP: Search the common themes of the problem statement in your field of research before writing a problem statement.

Also see: Argumentative Essay Writing Service

Problem Statement versus Significance of the Study

Even though both may sound similar, the statement of the problem and the significance of your study are going to be different. The latter does develop upon and from the former, though.

The problem statement tells your readers what’s wrong, whereas the significance of the study will tell them how your research contributed to that problem. You can’t have a significance of a study without mentioning the problem statement first.

Furthermore, signifying your study implies mentioning 4 key points related to it:

• How your study will further develop the theory behind the existing problem
• Practical solutions that might be implemented to solve the problem (especially in field research work)
• Whether your study or research will pave way for innovative methods to solve the existing problem.
• How your study can help in policy making and implementation, impact studies, etc.

Problem statement in research is the description of an existing issue that needs to be addressed. The problem statement is a focal point of any research and a bridge between the  literature review  and the  research methodology .

Problem statement often has three elements; the problem itself, the method of solving the problem, and the purpose. There are five aspects of every problem: What, Where, When, to what extent, and what defects you know about the topic. Here is an  example of a problem statement in a research proposal  for your better understanding.

If you wish to know more about how to start your research process, then you might want to take a look at the “ Starting the Research Process ” section on our website, which has several articles relating to a  research problem , problem statement, research aim and objectives, and  research proposal .

ResearchProspect is a UK-registered business that offers academic support and assistance to students across the globe. Our writers can help you with individual chapters of your dissertation or the full dissertation writing service , no matter how urgent or complex your requirements might be.

Frequently Asked Questions

Is it necessary to write a problem statement.

Yes, the most important step to perform any research is to identify a problem that needs to be solved. Therefore, it is necessary to define a research problem before starting the actual research process .

How is a problem statement different from a problem statement written for an organisation?

In the business world, problem statements provide the basis for the enhancement and refinement of projects. Whereas, in academic research, A problem statement helps researchers understand and realise organised the significance of a research problem .

What is a practical research problem?

Doing experimental research can identify problems by talking to people working in a relevant field, studying research reports, and reviewing previous research. 

What is a theoretical research problem?

A theoretical research problem is when the researcher finds a specific problem by brainstorming and reviewing already published theories and research.

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If your dissertation includes many abbreviations, it would make sense to define all these abbreviations in a list of abbreviations in alphabetical order.

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Problem statement overview.

The dissertation problem needs to be very focused because everything else from the dissertation research logically flows from the problem. You may say that the problem statement is the very core of a dissertation research study. If the problem is too big or too vague, it will be difficult to scope out a purpose that is manageable for one person, given the time available to execute and finish the dissertation research study.

Through your research, your aim is to obtain information that helps address a problem so it can be resolved. Note that the researcher does not actually solve the problem themselves by conducting research but provides new knowledge that can be used toward a resolution. Typically, the problem is solved (or partially solved) by practitioners in the field, using input from researchers.

Given the above, the problem statement should do three things :

• Specify and describe the problem (with appropriate citations)
• Explain the consequences of NOT solving the problem
• Explain the knowledge needed to solve the problem (i.e., what is currently unknown about the problem and its resolution – also referred to as a gap )

What is a problem?

The world is full of problems! Not all problems make good dissertation research problems, however, because they are either too big, complex, or risky for doctorate candidates to solve. A proper research problem can be defined as a specific, evidence-based, real-life issue faced by certain people or organizations that have significant negative implications to the involved parties.

Example of a proper, specific, evidence-based, real-life dissertation research problem:

“Only 6% of CEOs in Fortune 500 companies are women” (Center for Leadership Studies, 2019).

Specific refers to the scope of the problem, which should be sufficiently manageable and focused to address with dissertation research. For example, the problem “terrorism kills thousands of people each year” is probably not specific enough in terms of who gets killed by which terrorists, to work for a doctorate candidate; or “Social media use among call-center employees may be problematic because it could reduce productivity,” which contains speculations about the magnitude of the problem and the possible negative effects.

Evidence-based here means that the problem is well-documented by recent research findings and/or statistics from credible sources. Anecdotal evidence does not qualify in this regard. Quantitative evidence is generally preferred over qualitative ditto when establishing a problem because quantitative evidence (from a credible source) usually reflects generalizable facts, whereas qualitative evidence in the form of research conclusions tend to only apply to the study sample and may not be generalizable to a larger population. Example of a problem that isn’t evidence-based: “Based on the researcher’s experience, the problem is that people don’t accept female leaders;” which is an opinion-based statement based on personal (anecdotal) experience.

Real-life means that a problem exists regardless of whether research is conducted or not. This means that “lack of knowledge” or “lack of research” cannot be used as the problem for a dissertation study because it’s an academic issue or a gap; and not a real-life problem experienced by people or organizations.  Example of a problem that doesn’t exist in real life: “There is not enough research on the reasons why people distrust minority healthcare workers.” This type of statement also reveals the assumption that people actually do mistrust minority healthcare workers; something that needs to be supported by actual, credible evidence to potentially work as an underlying research problem.

What are consequences?

Consequences are negative implications experienced by a group of people or organizations, as a result of the problem. The negative effects should be of a certain magnitude to warrant research. For example, if fewer than 1% of the stakeholders experience a negative consequence of a problem and that consequence only constitutes a minor inconvenience, research is probably not warranted. Negative consequences that can be measured weigh stronger than those that cannot be put on some kind of scale.

In the example above, a significant negative consequence is that women face much larger barriers than men when attempting to get promoted to executive jobs; or are 94% less likely than men to get to that level in Corporate America.

What is a gap?

To establish a complete basis for a dissertation research study, the problem has to be accompanied by a gap . A gap is missing knowledge or insights about a particular issue that contributes to the persistence of the problem. We use gaps to “situate” new research in the existing literature by adding to the knowledge base in the business research field, in a specific manner (determined by the purpose of the research). Identifying gaps requires you to review the literature in a thorough fashion, to establish a complete understanding of what is known and what isn’t known about a certain problem.  In the example from above about the underrepresentation of female CEOs, a gap may be that male-dominated boards have not been studied extensively in terms of their CEO hiring decisions, which might then warrant a study of such boards, to uncover implicit biases and discriminatory practices against female candidates.

How to Write a Problem Statement

How to write a problem statement.

• Here is one way to construct a problem section (keep in mind you have a 250-300 word limit, but you can write first and edit later):

It is helpful to begin the problem statement with a sentence :  “The problem to be addressed through this study is… ”  Then, fill out the rest of the paragraph with elaboration of that specific problem, making sure to “document” it, as NU reviewers will look for research-based evidence that it is indeed a problem (emphasis also on timeliness of the problem, supported by citations within the last 5 years).

Next, write a paragraph explaining the consequences of NOT solving the problem. Who will be affected? How will they be affected? How important is it to fix the problem? Again, NU reviewers will want to see research-based citations and statistics that indicate the negative implications are significant.

In the final paragraph, you will explain what information (research) is needed in order to fix the problem. This paragraph shows that the problem is worthy of doctoral-level research. What isn’t known about the problem? Ie, what is the gap? Presumably, if your problem and purpose are aligned, your research will try to close or minimize this gap by investigating the problem. Have other researchers investigated the issue? What has their research left unanswered?

• Another way to tackle the Statement of the Problem:

The Statement of the Problem section is a very clear, concise identification of the problem. It must stay within the template guidelines of 250-300 words but more importantly, must contain four elements as outlined below. A dissertation worthy problem should be able to address all of the following points:

-->identification of the problem itself--what is "going wrong" (Ellis & Levy, 2008)

-->who is affected by the problem

-->the consequences that will result from a continuation of the problem

-->a brief discussion of 1) at least 3 authors’ research related to the problem; and 2)   their stated suggestion/recommendation for further research related to the problem

Use the following to work on the Statement of the Problem by first outlining the section as follows:

1. One clear, concise statement that tells the reader what is not working, what is “going wrong”. Be specific and support it with current studies.

2. Tell who is affected by the problem identified in #1. 

3. Briefly tell what will happen if the problem isn’t addressed.

4. Find at least 3 current studies and write a sentence or two for each study that

i. briefly discusses the author(s)’ work, what they studied, and

ii. state their recommendation for further research about the problem

• Finally, you can follow this simple 3-part outline when writing the statement of the problem section:

Your problem statement is a short (250-300 words), 3 paragraph section, in which you

• Explain context and state problem (“the problem is XYZ”), supported by statistics and/or recent research findings
• Explain the negative consequences of the problem to stakeholders, supported by statistics and/or recent research findings
• Explain the gap in the literature.

Example of a problem statement that follows the 3-part outline (295 words):

The problem to be addressed by this study is the decline of employee well-being for followers of novice mid-level managers and the corresponding rise in employee turnover faced by business leaders across the financial services industry (Oh et al., 2014).  Low levels of employee well-being are toxic for morale and result in expensive turnover costs, dysfunctional work environments, anemic corporate cultures, and poor customer service (Compdata, 2018; Oh et al., 2014).  According to Ufer (2017), the financial services industry suffers from one of the highest turnover rates among millennial-aged employees in all industries in the developed world, at 18.6% annually.  Starkman (2015) reported that 50% of those surveyed in financial services were not satisfied with a single one of the four key workplace aspects: job, firm, pay or career path. 

Low levels of employee well-being interrupt a financial services’ company’s ability to deliver outstanding customer service in a world increasingly dependent on that commodity (Wladawsky-Berger, 2018).Mid-level managers play an essential role in support of the success of many of top businesses today (Anicich & Hirsh, 2017). 

The current body of literature does not adequately address the well-being issue in the financial services industry from the follower’s perspective (Uhl-Bien, Riggio, Lowe, & Carsten, 2014). Strategic direction flows top-down from senior executives and passes through mid-level leadership to individual contributors at more junior grades.  The mid-level managers’ teams are tasked with the achievement of core tasks and the managers themselves are expected to maintain the workforce’s morale, motivation and welfare (Anicich & Hirsh, 2017).  Unless industry leaders better understand the phenomenon of employee well-being from the follower perspective and its role in positioning employees to provide a premium client experience, they may be handicapped from preserving their most significant principal market differentiator: customer service (Wladawsky-Berger, 2018). 

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

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How to structure quantitative research questions

There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps : (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2) Identifying the different types of variables you are trying to measure, manipulate and/or control, as well as any groups you may be interested in; (3) Selecting the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved; and (4) Writing out the problem or issues you are trying to address in the form of a complete research question. In this article, we discuss each of these four steps , as well as providing examples for the three types of quantitative research question you may want to create: descriptive , comparative and relationship-based research questions .

• STEP ONE: Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
• STEP TWO: Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
• STEP THREE: Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
• STEP FOUR: Write out the problem or issues you are trying to address in the form of a complete research question

STEP ONE Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create

The type of quantitative research question that you use in your dissertation (i.e., descriptive , comparative and/or relationship-based ) needs to be reflected in the way that you write out the research question; that is, the word choice and phrasing that you use when constructing a research question tells the reader whether it is a descriptive, comparative or relationship-based research question. Therefore, in order to know how to structure your quantitative research question, you need to start by selecting the type of quantitative research question you are trying to create: descriptive, comparative and/or relationship-based.

STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in

Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure , manipulate and/or control . If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable , should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal , dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables , which you need to understand to create quantitative research questions.

To provide a brief explanation; a variable is not only something that you measure , but also something that you can manipulate and control for. In most undergraduate and master's level dissertations, you are only likely to measure and manipulate variables. You are unlikely to carry out research that requires you to control for variables, although some supervisors will expect this additional level of complexity. If you plan to only create descriptive research questions , you may simply have a number of dependent variables that you need to measure. However, where you plan to create comparative and/or relationship-based research questions , you will deal with both dependent and independent variables . An independent variable (sometimes called an experimental or predictor variable ) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable ). For example, if we were interested in investigating the relationship between gender and attitudes towards music piracy amongst adolescents , the independent variable would be gender and the dependent variable attitudes towards music piracy . This example also highlights the need to identify the group(s) you are interested in. In this example, the group of interest are adolescents .

Once you identifying the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in, it is possible to start thinking about the way that the three types of quantitative research question can be structured . This is discussed next.

STEP THREE Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved

The structure of the three types of quantitative research question differs, reflecting the goals of the question, the types of variables, and the number of variables and groups involved. By structure , we mean the components of a research question (i.e., the types of variables, groups of interest), the number of these different components (i.e., how many variables and groups are being investigated), and the order that these should be presented (e.g., independent variables before dependent variables). The appropriate structure for each of these quantitative research questions is set out below:

Structure of descriptive research questions

• Structure of comparative research questions
• Structure of relationship-based research questions

There are six steps required to construct a descriptive research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the group(s) you are interested in; (4) decide whether dependent variable or group(s) should be included first, last or in two parts; (5) include any words that provide greater context to your question; and (6) write out the descriptive research question. Each of these steps is discussed in turn:

Choose your starting phrase

Identify and name the dependent variable

Identify the group(s) you are interested in

Decide whether the dependent variable or group(s) should be included first, last or in two parts

Include any words that provide greater context to your question

Write out the descriptive research question

FIRST Choose your starting phrase

You can start descriptive research questions with any of the following phrases:

How many? How often? How frequently? How much? What percentage? What proportion? To what extent? What is? What are?

Some of these starting phrases are highlighted in blue text in the examples below:

How many calories do American men and women consume per day?

How often do British university students use Facebook each week?

What are the most important factors that influence the career choices of Australian university students?

What proportion of British male and female university students use the top 5 social networks?

What percentage of American men and women exceed their daily calorific allowance?

SECOND Identify and name the dependent variable

All descriptive research questions have a dependent variable. You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .

The first two examples highlight that while the name of the dependent variable is the same, namely daily calorific intake , the way that this dependent variable is written out differs in each case.

THIRD Identify the group(s) you are interested in

All descriptive research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .

What are the most important factors that influence the career choices of Australian university students ?

The examples illustrate the difference between the use of a single group (e.g., British university students ) and multiple groups (e.g., American men and women ).

FOURTH Decide whether the dependent variable or group(s) should be included first, last or in two parts

Sometimes it makes more sense for the dependent variable to appear before the group(s) you are interested in, but sometimes it is the opposite way around. The following examples illustrate this, with the group(s) in green text and the dependent variable in blue text :

Group 1st; dependent variable 2nd:

How often do British university students use Facebook each week ?

Dependent variable 1st; group 2nd:

Sometimes, the dependent variable needs to be broken into two parts around the group(s) you are interested in so that the research question flows. Again, the group(s) are in green text and the dependent variable is in blue text :

How many calories do American men and women consume per day ?

Of course, you could choose to restructure the question above so that you do not have to split the dependent variable into two parts. For example:

How many calories are consumed per day by American men and women ?

When deciding whether the dependent variable or group(s) should be included first or last, and whether the dependent variable should be broken into two parts, the main thing you need to think about is flow : Does the question flow? Is it easy to read?

FIFTH Include any words that provide greater context to your question

Sometimes the name of the dependent variable provides all the explanation we need to know what we are trying to measure. Take the following examples:

In the first example, the dependent variable is daily calorific intake (i.e., calories consumed per day). Clearly, this descriptive research question is asking us to measure the number of calories American men and women consume per day. In the second example, the dependent variable is Facebook usage per week. Again, the name of this dependent variable makes it easy for us to understand that we are trying to measure the often (i.e., how frequently; e.g., 16 times per week) British university students use Facebook.

However, sometimes a descriptive research question is not simply interested in measuring the dependent variable in its entirety, but a particular component of the dependent variable. Take the following examples in red text :

In the first example, the research question is not simply interested in the daily calorific intake of American men and women, but what percentage of these American men and women exceeded their daily calorific allowance. So the dependent variable is still daily calorific intake, but the research question aims to understand a particular component of that dependent variable (i.e., the percentage of American men and women exceeding the recommend daily calorific allowance). In the second example, the research question is not only interested in what the factors influencing career choices are, but which of these factors are the most important.

Therefore, when you think about constructing your descriptive research question, make sure you have included any words that provide greater context to your question.

SIXTH Write out the descriptive research question

Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the group(s) you are interested in, and (4) any potential joining words ? you can write out the descriptive research question in full. The example descriptive research questions discussed above are written out in full below:

In the section that follows, the structure of comparative research questions is discussed.

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• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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.

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.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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.

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Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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Quantitative Research Design

Ai generator.

Quantitative research design is a systematic approach used to investigate phenomena by collecting and analyzing numerical data. It involves the use of structured tools such as surveys, experiments, and statistical analysis to quantify variables and identify patterns, relationships, and cause-and-effect dynamics. This Research design emphasizes objectivity and replicability, allowing researchers to generalize findings across larger populations. By focusing on measurable data, quantitative research design aims to provide precise and reliable results.

What is Quantitative Research Design?

Quantitative research design is a structured method of inquiry that focuses on quantifying data and analyzing it using statistical techniques. This approach involves collecting numerical data through various tools such as surveys, experiments, and questionnaires to identify patterns, relationships, and causal effects. The design emphasizes objectivity, allowing researchers to generalize findings to larger populations.

Types of Quantitative Research Design

1. Descriptive Research Design

• Purpose: To describe characteristics of a population or phenomenon.
• Methods: Surveys, observational studies, case studies.
• Example: Measuring the prevalence of a particular health behavior in a community.

2. Correlational Research Design

• Purpose: To identify and measure the relationship between two or more variables without manipulating them.
• Methods: Surveys, archival data analysis.
• Example: Examining the relationship between study habits and academic performance.

3. Experimental Research Design

• Purpose: To determine cause-and-effect relationships by manipulating one or more independent variables and measuring their effect on dependent variables.
• Methods: Randomized controlled trials, laboratory experiments.
• Example: Testing the efficacy of a new drug by randomly assigning participants to treatment and control groups.

4. Quasi-Experimental Research Design

• Purpose: To estimate causal relationships when random assignment is not possible.
• Methods: Non-randomized control groups, pre-test/post-test designs.
• Example: Evaluating the impact of an educational intervention in different schools where random assignment is not feasible.

5. Cross-Sectional Research Design

• Purpose : To collect data at a single point in time to provide a snapshot of a population or phenomenon.
• Methods : Census surveys, sample surveys.
• Example : Surveying a population to assess the current prevalence of smoking.

6. Longitudinal Research Design

• Purpose : To collect data from the same subjects over an extended period to observe changes and developments.
• Methods : Panel studies, cohort studies.
• Example : Following a group of students over several years to track their academic progress.

7. Comparative Research Design

• Purpose : To compare two or more groups or variables to identify similarities and differences.
• Methods : Case-control studies, cross-national studies.
• Example : Comparing educational outcomes between students from different countries.

8. Meta-Analysis

• Purpose : To combine data from multiple studies to draw more robust conclusions about a research question.
• Methods : Systematic reviews, statistical aggregation.
• Example : Aggregating data from various studies to assess the overall effectiveness of a particular therapy.

9. Secondary Data Analysis

• Purpose : To use existing datasets collected by other researchers or organizations to answer new research questions or test different hypotheses.
• Methods : Government surveys, social surveys, market research data.
• Example : Analyzing national health survey data to investigate trends in obesity rates.

10. Archival Research

• Purpose : To analyze existing data collected for purposes other than the current study to uncover long-term trends and patterns.
• Methods : Historical documents, government records, public databases.
• Example : Examining historical voting records to understand changes in political participation over time.

Quantitative Research Design Methods

Surveys involve administering questionnaires or structured interviews to gather data from a sample of participants. Surveys can be implemented through different channels, such as conducting them in person, over the phone, via mail, or utilizing online platforms. Researchers use various question types, such as multiple-choice, Likert scales, or rating scales, to collect quantitative data on attitudes, opinions, behaviors, and demographics.

2. Experiments

Experiments involve manipulating one or more independent variables and measuring their effects on dependent variables. To compare outcomes, participants are assigned randomly to various groups, including control and experimental groups. Experimental designs allow researchers to establish cause-and-effect relationships by controlling for confounding factors.

3. Observational Studies

Observational studies involve systematically observing and recording behavior, events, or phenomena in natural settings. Researchers can use structured or unstructured quantitative observation methods, depending on the research objectives. Quantitative data can be collected by counting the frequency of specific behaviors or by using coding systems to categorize and analyze observed data.

4. Archival Research

Archival research involves analyzing existing data collected for purposes other than the current study. Researchers may use historical documents, government records, public databases, or organizational records to extract data through quantitative research. Archival research allows for large-scale data analysis and can provide insights into long-term trends and patterns.

5. Secondary Data Analysis

Similar to archival research, secondary data analysis involves using existing datasets that were collected by other researchers or organizations. Researchers analyze the data to answer new research questions or test different hypotheses. Secondary data sources can include government surveys, social surveys, or market research data.

6. Content Analysis

Content analysis is a method used to analyze textual or visual data to identify patterns, themes, or relationships. Researchers code and categorize the content of documents, interviews, articles, or media sources. The coded data is then quantified and statistically analyzed to draw conclusions. Content analysis can be both qualitative and quantitative, depending on the approach used.

7. Psychometric Testing

Psychometric testing involves the development and administration of tests or scales to measure psychological constructs, such as intelligence, personality traits, or attitudes. Researchers use statistical techniques to analyze the test data, such as factor analysis, reliability analysis, or item response theory.

Difference between Quantitative Research Design and Qualitative Research Design

How to Find Quantitative Research Design

Finding a quantitative research design involves several steps to ensure that the chosen method is suitable for your research question and objectives. Here’s a step-by-step guide:

1. Define Your Research Question

• Identify the Problem : Clearly define the problem or phenomenon you want to study.
• Specify Objectives : Determine what you aim to achieve with your research.

2. Literature Review

• Search Existing Research : Look for existing studies related to your topic in academic journals, books, and databases.
• Identify Gaps : Note any gaps in the current literature that your research could fill.

3. Choose a Suitable Research Design

• Descriptive Design : If your goal is to describe characteristics or functions, consider surveys or observational studies.
• Correlational Design : To explore relationships between variables, use correlational methods.
• Experimental Design : For establishing cause-and-effect relationships, conduct experiments with control and experimental groups.
• Quasi-Experimental Design : When random assignment isn’t feasible, use quasi-experimental designs.
• Longitudinal Design : If you need to study changes over time, choose a longitudinal approach.
• Cross-Sectional Design : For a snapshot of a population at a single point in time, use cross-sectional surveys.

4. Develop Your Hypothesis

• Formulate Hypotheses : Based on your research question, develop clear and testable hypotheses.

5. Select Your Sample

• Define the Population : Determine the population from which you will draw your sample.
• Sampling Methods : Choose an appropriate sampling method (random, stratified, cluster, etc.) to ensure representativeness.

6. Choose Data Collection Methods

• Surveys : Use questionnaires or structured interviews.
• Experiments : Design experiments with independent and dependent variables.
• Observations : Use structured or unstructured observation methods.
• Archival Research : Analyze existing records or databases.
• Secondary Data Analysis : Use pre-existing datasets for new analysis.

7. Design the Data Collection Instrument

• Create Surveys/Questionnaires : Develop questions that are clear and unbiased.
• Design Experimental Protocols : Outline the procedures for conducting experiments.
• Develop Observation Checklists : List specific behaviors or events to observe and record.

8. Pilot Testing

• Test Instruments : Conduct a pilot test to identify any issues with your data collection instruments or procedures.
• Refine Methods : Make necessary adjustments based on feedback from the pilot test.

9. Data Collection

• Implement Your Design : Collect data according to your chosen methods and protocols.
• Ensure Accuracy : Follow ethical guidelines and ensure accurate data recording.

10. Data Analysis

• Statistical Techniques : Use appropriate statistical methods to analyze your data.
• Software Tools : Utilize software such as SPSS, R, or Excel for data analysis.

FAQ’s

What is the purpose of quantitative research.

The purpose is to quantify variables, test hypotheses, and identify patterns, relationships, or causal effects through numerical data analysis.

What is a descriptive research design?

Descriptive research design aims to describe characteristics or functions of a population or phenomenon, without establishing cause-and-effect relationships.

What is correlational research design?

Correlational research design examines the relationship between two or more variables to determine if a connection exists, without implying causation.

What is causal-comparative research design?

Causal-comparative research design seeks to identify cause-and-effect relationships by comparing different groups based on varying independent variables.

What is an experimental research design?

Experimental research design involves manipulating one variable to determine its effect on another variable, establishing a cause-and-effect relationship.

What are independent and dependent variables?

Independent variables are manipulated to observe their effect on dependent variables, which are measured to see if they change due to the manipulation.

Why is random sampling important in quantitative research?

Random sampling ensures each member of a population has an equal chance of being selected, enhancing the generalizability of results.

What is the role of hypothesis in quantitative research?

A hypothesis is a testable prediction about the relationship between variables, guiding the direction and focus of the study.

What are common data collection methods in quantitative research?

Common methods include surveys, questionnaires, structured interviews, and standardized tests.

What is the significance of statistical analysis in quantitative research?

Statistical analysis helps interpret numerical data, identify trends, and test hypotheses, providing a basis for drawing conclusions.

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