example of research paper in quantitative research

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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.

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Example of a Quantitative Research Paper

Posted by Rene Tetzner | Sep 4, 2021 | How To Get Published | 0 |

Example of a Quantitative Research Paper for Students & Researchers This example of a quantitative research paper is designed to help students and other researchers who are learning how to write about their work. The reported research observes the behaviour of restaurant customers, and example paragraphs are combined with instructions for logical argumentation. Authors are encouraged to observe a traditional structure for organising quantitative research papers, to formulate research questions, working hypotheses and investigative tools, to report results accurately and thoroughly, and to present thoughtful interpretation and logical discussion of evidence.

The structure of the example and the nature of its contents follow the recommendations of the   Publication Manual of the American Psychological Association . This APA style calls for parenthetical author–date citations in the paper’s main text (with page numbers when material is quoted) and a final list of complete references for all sources cited, so I have given a few sample references here. Content has been kept as simple as possible to focus attention on the way in which the paper presents the research process and its results. As is the case in many research projects, the more the author learns and thinks about the topic, the more complex the issues become, and here the researcher discusses a hypothesis that proved incorrect. An APA research paper would normally include additional elements such as an abstract, keywords and perhaps tables, figures and appendices similar to those referred to in the example. These elements have been eliminated for brevity here, so do be sure to check the APA   Manual   (or any other guidelines you are following) for the necessary instructions.

Surprises at a Local “Family” Restaurant: Example Quantitative Research Paper

A quantitative research paper with that title might start with a paragraph like this:

Quaintville, located just off the main highway only five miles from the university campus, may normally be a sleepy community, but recent plans to close the only fast-food restaurant ever to grace its main street have been met with something of a public outcry. Regular clients argue that Pudgy’s Burgers fills a vital function and will be sorely missed. As the editor of the  Quaintville Times  would have it, “good old Pudgy’s is the only restaurant in Quaintville where a working family can still get a decent meal for a fair buck, and a comfortable place to eat it too, out of the winter wind where the kids can run about and play a bit” (Chapton, 2017, p. A3). On the other hand, the most outspoken of Quaintville residents in favour of the planned closure look forward to the eradication of a local eyesore and tend to consider the restaurant more of “a hazard than a benefit to the health of some of our poorest families” (“Local dive,” 2017, p. 1).

Following this opening a brief introduction to published scholarship and other issues associated with the problem would be appropriate, so here the researcher might add a paragraph or two discussing:

• A selection of recently published studies that investigate the effect of inexpensive fast-food restaurants on the health of low-income families, especially their children (Shunts, 2013; Whinner, 2015). • Fast-food restaurants that have responded to criticism about the quality of their food by offering healthy menu items. This could be enhanced with evidence that when such choices are available, they are rare purchases for many families (Parkson, 2016), particularly in small towns and rural areas (Shemble, 2017). • The interesting trend in several independent studies suggesting that families form a much smaller portion of the clientele of fast-food restaurants than anticipated.

Explaining how the current research is related to the published scholarship as well as the specific problem is vital. Here, for instance, the author might be thinking that Pudgy’s, which has healthy menu items as well as the support of so many long-term residents, will prove an exception to the trends revealed by other studies. Research questions and hypotheses should be constructed to articulate and explore that idea. Research questions, for instance, could be developed from that claim in the   Quaintville Times   as well as from the published scholarship:

• Do families constitute the majority of Pudgy’s regular clientele? • Does the restaurant offer a decent family meal for a fair price? • Do families linger in the restaurant’s comfort and warmth?> • Do children use the indoor play area provided by the restaurant?

Working hypotheses can be constructed by anticipating answers to these questions. The example paper assumes a simple hypothesis something along the lines of “Families do indeed constitute the majority of Pudgy’s clientele.” The exact opposite supposition would work as well – “Families do not constitute the majority of Pudgy’s clientele” – and so would hypotheses exploring and combining other aspects of the situation, such as “Pudgy’s healthy menu options and indoor play area are positive and appealing considerations for families” or “The comfortable atmosphere of Pudgy’s with its play area makes it much more than a restaurant for local families.”

The exact wording of your questions and hypotheses will ultimately depend on your focus and aims, but certain terms, concepts and categories may require definition to ensure precision in communicating your ideas to readers. Here, for instance, exactly what is meant by ‘a family,’ ‘a decent meal,’ ‘a fair price’ and even ‘comfortable’ could be briefly but carefully defined. A general statement about your understanding of how the current research will explore the problem, answer your questions and test your hypotheses is usually required as well, setting the stage for the more detailed Method section that follows. This statement might be something as simple as “I intend to observe the restaurant’s customers over a two-month period with the objective of learning about Pudgy’s clientele and measuring the use and value of the establishment for local families.” On the other hand, outlining your research might require a paragraph or two of introductory discussion.

Method Whether a brief general statement or a longer explanation of how the research will proceed appears among your introductory material, it is in the Method section that you should report exactly what you did to conduct your investigation, explain the conditions and controls you applied to increase the reliability and value of your research, and reveal any difficulties you encountered. For example:

My observations took place at Pudgy’s Burgers in January and February of 2018. Each session was approximately four hours long, and I aimed to obtain an equivalent number of observations for all opening hours of the week (the restaurant’s hours are listed in Table 1), but course requirements made this difficult. Tuesday and Thursday afternoons are therefore underrepresented, and observations from 1:00 pm to 5:00 pm on two consecutive Tuesdays (6 and 13 February) are the work of my classmate, Jake Jenkins. Without his assistance, I could not have met my objective of gathering observations for every opening hour of the week at least twice (Table 2 outlines the overall pattern of observation sessions). Serving staff at the restaurant assure me that I have now “seen ‘em all,” so I believe my observations have resulted in a representative sampling of local customers over two months when that “winter wind” has been especially busy about its work.

To avoid detection by the customers I was observing and the possibility of altering their behaviour, I obtained permission from Pudgy’s manager, Mr Jobson, to sit at the staff table in a dark and quiet corner of the restaurant where clients never go. This table is labelled in the plan of Pudgy’s Burgers and its grounds that I have included as Figure 1. From there I could see the customers both at the service counter and at their tables, but they could not see me, at least not clearly, and if they did, they paid me no more attention than they did the restaurant employees. From the staff table I could also see the row of indoor park-style children’s toys running down the north wall of windows, as well as the take out lane and the people waiting in their cars.

A Method section often features subheadings to separate and present particularly important aspects of the research methodology, such as the Customer Fact Sheet developed and used by the author of this study.

The Customer Fact Sheet Recording thorough and equivalent information about every Pudgy’s customer I observed was crucial for quantifying and analysing the results of my study. I therefore prepared a Customer Fact Sheet (included as Appendix I at the end of this paper) for gathering key pieces of information and recording observations about each individual, couple or group who purchased food or beverages. This sheet ensured that vital details such as date, weather conditions, time of arrival, eat in or take out order, number in party, approximate age of individuals, food purchased, food consumed, healthy choices, amount spent, who paid, dessert or extra beverage, children playing, interaction with other children and families, time of departure and other important details were recorded in every case. The Customer Fact Sheet proved particularly helpful when my classmate performed observations for me and was invaluable for evaluating the data I collected. I initially hoped to complete at least 500 of these Customer Fact Sheets and was pleased to increase that number by 100 for a total of 600 or an average of just over 10 per day over the 59 days of the study.

Notice in the three example paragraphs for the Method section that clear references to Tables 1 & 2, Figure 1 and Appendix I are provided to let readers know when and why these extra elements are relevant and helpful. Be sure also to include in your description of methods any additional approaches or sources of information that should be considered part of your research procedures, such as:

• Receipt information about customer purchases provided by the restaurant manager. • Conversations with restaurant servers who might confirm family relationships and estimated ages or tell you what was eaten and what was not by particular customer groups. • The analysis you performed to make sense of your results, such as counting customers, meals and behaviours and working out percentages and averages overall as well as for certain categories in order to answer the research questions.

Results The Results section is where you report what you discovered during your research, including the findings that do not support your hypothesis (or hypotheses) as well as those that do. Returning to your research questions to indicate exactly how the data you gathered answers them is an excellent way to stay focused and enable the selectivity that may be necessary to meet length requirements or maintain a clear line of argumentation. A Results section for the Pudgy’s research project might start like this:

The results of my investigation were both surprising and more complex than I had anticipated. I asked whether families constituted the majority of Pudgy’s clientele and assumed they did, but my research shows that they do not (see Figure 2 for information on customer categories). Even when the loosest definition of family as explained in my introduction is applied, only slightly over 25% (152) of the 600 Customer Fact Sheets record family visits to the restaurant. Among them fathers alone with their children are the most frequent patrons (68 Customer Fact Sheets or nearly 45% of the family category). The only day of the week on which families approach 50% of the restaurant’s customers is Sunday, particularly in the afternoon, when family groups account for 48% of the total customers averaged over the eight Sundays of observation. On all other days of the week, individual customers are the most frequent patrons, with their numbers hovering around 50% on most days. Single men visit the restaurant more often than any other customers and constitute as much as 61% of the clientele on a few weekday evenings.

The report of results might then continue by providing information about other categories of customer, what different types of customers ate and did, and any additional results that help answer the other research questions posed in the introductory paragraphs. Major trends revealed by the data should be reported, and both content and writing style should be clear and factual. Interpretation and discussion are best saved for the Discussion section except in those rare instances when guidelines indicate that research results and discussion should be combined in a single section.  Although you will need to inform readers about any mathematical or statistical analysis of your raw data if you have not already done so in the Method section, the raw data itself is usually not appropriate for a short research paper. Selecting the most convincing and relevant evidence as the focus is, however, and the raw data can usually be made available via a university’s website or a journal’s online archives for expert readers and future researchers.

Discussion The Discussion section of a quantitative paper is where you interpret your research results and discuss their implications. Here the hypotheses as well as the research questions established in the introductory material are important. Were your primary suppositions confirmed by your results or not? Be precise and concise as you discuss your findings, but keep in mind that matters need not be quite as black and white or as strictly factual as they were in the Results section. Your ideas and argument should be soundly based on the data you collected, of course, but the Discussion is the place for describing complexities and expressing uncertainties as well as offering interpretations and explanations. The following opening briefly restates primary findings, picks up other important threads from the Results section and sets the stage for discussing the complexities involved in assessing the true value of Pudgy’s to the Quaintville community:

Although I had anticipated that families constitute the majority of Pudgy’s clientele, the evidence gathered over two months of observation does not support this supposition. In fact, individuals are the most frequent customers, with groups of teenagers running a close second. These teenagers are often in the restaurant when families are and they sometimes sit on the indoor toys instead of at the plastic tables and chairs, which I can confirm as extremely uncomfortable. On a few occasions the presence of teenagers appeared to intimidate the children and prevent them from playing on the facilities intended for them. In accordance with Parkson (2016) and Shemble (2017), my research also showed that most families who eat at Pudgy’s do not choose the healthier low-fat menu items, with the limited number and extremely high prices of these items offering little incentive. The few parents who make healthy choices for themselves and their children often do not insist upon the children eating those items, adding waste (of both food and money) to the problem. Furthermore, although Pudgy’s prices for their more traditional fast-food items are the lowest in town, at least two of the restaurants in Quaintville offer equivalent meals for similar prices and far healthier ones for just a little more.

The claim, then, in the  Quaintville Times  that “good old Pudgy’s is the only restaurant in Quaintville where a working family can still get a decent meal for a fair buck, and a comfortable place to eat it too, out of the winter wind where the kids can run about and play a bit” (Chapton, 2017, p.A3) is revealed as more sentiment than fact. It would be equally erroneous, however, to insist that Pudgy’s Burgers has no value for the local community or to call it more of “a hazard…to the health of some of our poorest families” (“Local dive,” 2017, p.1) than any other restaurants serving burgers and chips in Quaintville. Indeed, I suspect those “poorest families” very rarely visit local restaurants at all, but my observations have revealed a great deal about who does eat at Pudgy’s, what they do when they are there and what kind of value the establishment actually has for Quaintville residents.

The discussion could then continue with information about the customers, behaviours and other issues that render the findings more complex and the restaurant more valuable to the community than the primary results noted above may indicate:

• Perhaps the restaurant serves a vital function as a social gathering place for all those single customers. Do they usually remain alone or do they meet up with others to linger and talk over coffee or lunch? • Do the teenagers who gather at Pudgy’s have an alternative place to meet out of the cold? In towns without recreation centres or other facilities for teens, restaurants with informal, open-door policies can be vital. Where might those teenagers go or what might they be doing were Pudgy’s not there? • Even though the evidence showed that families are not the most frequent customers, you may want to consider the value the restaurant has for the families who do use it. Those single fathers are certainly worthy of some attention, for instance, and perhaps family groups occasionally met up with other families, ate together and then lingered for dessert and talk as their children enjoyed the toys. This would be worth discussing too. • Less measurable considerations viewed through a qualitative research lens may be helpful as well, but the data collected through observations should support such discussions. Remember as you analyse your data, reflect on your findings, determine their meaning and develop your argument that it is important to keep the limitations of your methodology and thus of your results and their implications clearly in mind.

Offering recommendations is also standard in the Discussion section of a quantitative research paper, and here recommendations might be particularly useful if the franchise had not yet finalised its decision about closing Pudgy’s and was actively seeking community feedback. The researcher might suggest that Pudgy’s could better serve families by increasing the number of healthy food items on the menu, offering these for more affordable prices and making an effort to keep the teenagers off the children’s toys. Finally, the last part of a Discussion usually provides concluding comments, so summarising your key points and clearly articulating the main messages you want your readers to take away with them are essential. In some organisational templates, Conclusions are offered in a separate final section of the paper instead of at the end of the Discussion, so always check the guidelines.

References These references follow APA style, but since special fonts may not display properly in all online situations, please note that the titles of books and the names and volume numbers of journals are (and should be) in italic font. The list represents a sample only; a paper the length of the one posited in this example would almost certainly mention, discuss and list more than half a dozen studies and sources.

Chapton, D. (2017, September 29). Will Quaintville lose its favourite family restaurant?  Quaintville Times , pp. A1, A3. Local dive sees last days. (2017, Autumn).  Quaintville Community Newsletter , pp. 1–2. Shemble, M. (2017). Is anyone really eating healthy fast food in rural towns?  Country Food & Families ,  14 , 12–23. Shunts, P. (2013). The true cost of high-fat fast food for low-income families.  Journal of Family Health & Diet ,  37 , 3–19. Parkson, L. (2016). Family diets, fast foods and unhealthy choices. In S. Smith & J. Jones (eds.),  Modern diets and family health  (pp. 277–294). Philadelphia, PA: The Family Press. Whinner, N. (2015). Healthy families take time: The impact of fatty fast foods on child health.  Journal of Family Health & Diet ,  39 , 31–43.

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Example of a Quantitative Research Paper for Students & Researchers

This example of a quantitative research paper is designed to help students and other r esearchers who are learning how to write about their work. The reported research obs erves the behaviour of restaurant customers, and example paragraphs are combined with instructions for logical argumentation. Authors are encouraged to observe a traditional structure for organising quantitative research papers, to formulate research que stions, working hypotheses and investigative tools, to report results accurately and thor oughly, and to present thoughtful interpretation and logical discussion of evidence.

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Food industry, the world over, is witnessing unprecedented increase in the number of multinational enterprises. These multinational enterprises, when deciding to expand their operations to a new country, have to make a choice between following uniform business strategies as in their home country or modify their strategies to suit the host country socioeconomic and political environment. Given the economic cost of modification of business strategies, the choice has widespread implications for the sustainability of multinational enterprises. The present paper argues that this decision-making is particularly critical in the case of multinational food enterprises because of large scale variability in food habits across countries and even within a country. Drawing from case studies of three multinational food enterprises in India, the paper points out that, in order to operate successfully in their host countries, the multinational food enterprises must adopt Glocalized strategies in marketing, product development, advertisement etc.

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Food is an important aspect of social culture and has a close relationship with economic development. The Chinese food culture has the characteristics of inheritability and development, and throughout the history of Chinese food culture, it has maintained its momentum of development since its primitive society. Neither the change of dynasty nor the change of social system has had a profound influence on it, and the philosophy of supplying enough food to people and food being the top priority was very popular. Eating was a top priority for people in China. Long ago, Confucius said that the desire for food and sex is part of human nature. As such, in the Chinese culture food became the priority. Because of the attention to diet, Chinese people would, when they had leisure time or abundant raw materials, work out a variety of food. Chinese cooking is flexible, which is characterized by saying that there is no fixed taste and what is delicious is valued. The beauty of food is one of the important roots of Chinese aesthetics, which inspires people with the stimulation of eating. Triggering art inspiration is the inevitable result of Chinese food culture pursuing complete and beautiful color, fragrance, taste, shape, and utensils. It makes food culture a comprehensive art containing multiple cultural connotations of diet, diet mentality, beautiful utensils and etiquette, food enjoyment and eating. Chinese foods have not only exquisite craftsmanship and rich nutrition, but also elegant and graceful names, which are literary and romantic, poetic and fancy. Food functions to not only satiate people’s hunger; it has also become an integral aspect of life enjoyment, which represents an essential component of food anthropology. Food anthropologists stress that changes in people’s eating habits not only depend on the local food culture, which may be specific to a given region, but also varies with economic development in different regions. Food anthropology, as a sub branch of applied anthropology, adapts anthropological theories and methods to study food industry, food culture, food consumption and food commerce. Seminal work in this regard has been provided by scholars and consultants in the field of food anthropology. This book describes the anthropological studies on Chinese foodways, outlines the Chinese food anthropology basic theories and methods. Anthropology in China is still at its development stage in China, while food anthropology is just at its initial stages of development. Nevertheless, China’s economic and social development, especially in ethnic minority regions in Western China, needs the theoretical guidance of some disciplines, including food anthropology, economic anthropology and business anthropology. At the same time, it has provided opportunities to develop food anthropology with the Chinese characteristics. Therefore, when Chinese scholars are learning and adopting Western food anthropology theories and methodologies, they must innovate and develop the related theories and methodologies with Chinese characteristics, so that they can better serve the well-off of the entire society.

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Home » 500+ Quantitative Research Titles and Topics

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
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• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
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• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
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• The effect of social media on interpersonal communication skills
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• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
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• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
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• The correlation between early childhood education and math achievement
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• The effect of social media on misinformation
• The impact of green energy policies on economic growth
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• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
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• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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How to Write an APA Methods Section | With Examples

Published on February 5, 2021 by Pritha Bhandari . Revised on June 22, 2023.

The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods .

In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample , measures, and procedures used.

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Table of contents

Structuring an apa methods section.

Participants

Example of an APA methods section

Other interesting articles, frequently asked questions about writing an apa methods section.

The main heading of “Methods” should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles .

To structure your methods section, you can use the subheadings of “Participants,” “Materials,” and “Procedures.” These headings are not mandatory—aim to organize your methods section using subheadings that make sense for your specific study.

Note that not all of these topics will necessarily be relevant for your study. For example, if you didn’t need to consider outlier removal or ways of assigning participants to different conditions, you don’t have to report these steps.

The APA also provides specific reporting guidelines for different types of research design. These tell you exactly what you need to report for longitudinal designs , replication studies, experimental designs , and so on. If your study uses a combination design, consult APA guidelines for mixed methods studies.

Detailed descriptions of procedures that don’t fit into your main text can be placed in supplemental materials (for example, the exact instructions and tasks given to participants, the full analytical strategy including software code, or additional figures and tables).

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Begin the methods section by reporting sample characteristics, sampling procedures, and the sample size.

Participant or subject characteristics

When discussing people who participate in research, descriptive terms like “participants,” “subjects” and “respondents” can be used. For non-human animal research, “subjects” is more appropriate.

Specify all relevant demographic characteristics of your participants. This may include their age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Depending on your study topic, other characteristics like educational or immigration status or language preference may also be relevant.

Be sure to report these characteristics as precisely as possible. This helps the reader understand how far your results may be generalized to other people.

The APA guidelines emphasize writing about participants using bias-free language , so it’s necessary to use inclusive and appropriate terms.

Sampling procedures

Outline how the participants were selected and all inclusion and exclusion criteria applied. Appropriately identify the sampling procedure used. For example, you should only label a sample as random  if you had access to every member of the relevant population.

Of all the people invited to participate in your study, note the percentage that actually did (if you have this data). Additionally, report whether participants were self-selected, either by themselves or by their institutions (e.g., schools may submit student data for research purposes).

Identify any compensation (e.g., course credits or money) that was provided to participants, and mention any institutional review board approvals and ethical standards followed.

Sample size and power

Detail the sample size (per condition) and statistical power that you hoped to achieve, as well as any analyses you performed to determine these numbers.

It’s important to show that your study had enough statistical power to find effects if there were any to be found.

Additionally, state whether your final sample differed from the intended sample. Your interpretations of the study outcomes should be based only on your final sample rather than your intended sample.

Write up the tools and techniques that you used to measure relevant variables. Be as thorough as possible for a complete picture of your techniques.

Primary and secondary measures

Define the primary and secondary outcome measures that will help you answer your primary and secondary research questions.

Specify all instruments used in gathering these measurements and the construct that they measure. These instruments may include hardware, software, or tests, scales, and inventories.

• To cite hardware, indicate the model number and manufacturer.
• To cite common software (e.g., Qualtrics), state the full name along with the version number or the website URL .
• To cite tests, scales or inventories, reference its manual or the article it was published in. It’s also helpful to state the number of items and provide one or two example items.

Make sure to report the settings of (e.g., screen resolution) any specialized apparatus used.

For each instrument used, report measures of the following:

• Reliability : how consistently the method measures something, in terms of internal consistency or test-retest reliability.
• Validity : how precisely the method measures something, in terms of construct validity  or criterion validity .

Giving an example item or two for tests, questionnaires , and interviews is also helpful.

Describe any covariates—these are any additional variables that may explain or predict the outcomes.

Quality of measurements

Review all methods you used to assure the quality of your measurements.

These may include:

• training researchers to collect data reliably,
• using multiple people to assess (e.g., observe or code) the data,
• translation and back-translation of research materials,
• using pilot studies to test your materials on unrelated samples.

For data that’s subjectively coded (for example, classifying open-ended responses), report interrater reliability scores. This tells the reader how similarly each response was rated by multiple raters.

Report all of the procedures applied for administering the study, processing the data, and for planned data analyses.

Data collection methods and research design

Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how you collected the necessary data.

Describe all procedures you applied in administering surveys, tests, physical recordings, or imaging devices, with enough detail so that someone else can replicate your techniques. If your procedures are very complicated and require long descriptions (e.g., in neuroimaging studies), place these details in supplementary materials.

To report research design, note your overall framework for data collection and analysis. State whether you used an experimental, quasi-experimental, descriptive (observational), correlational, and/or longitudinal design. Also note whether a between-subjects or a within-subjects design was used.

For multi-group studies, report the following design and procedural details as well:

• how participants were assigned to different conditions (e.g., randomization),
• instructions given to the participants in each group,
• interventions for each group,
• the setting and length of each session(s).

Describe whether any masking was used to hide the condition assignment (e.g., placebo or medication condition) from participants or research administrators. Using masking in a multi-group study ensures internal validity by reducing research bias . Explain how this masking was applied and whether its effectiveness was assessed.

Participants were randomly assigned to a control or experimental condition. The survey was administered using Qualtrics (https://www.qualtrics.com). To begin, all participants were given the AAI and a demographics questionnaire to complete, followed by an unrelated filler task. In the control condition , participants completed a short general knowledge test immediately after the filler task. In the experimental condition, participants were asked to visualize themselves taking the test for 3 minutes before they actually did. For more details on the exact instructions and tasks given, see supplementary materials.

Data diagnostics

Outline all steps taken to scrutinize or process the data after collection.

This includes the following:

• Procedures for identifying and removing outliers
• Data transformations to normalize distributions
• Compensation strategies for overcoming missing values

To ensure high validity, you should provide enough detail for your reader to understand how and why you processed or transformed your raw data in these specific ways.

Analytic strategies

The methods section is also where you describe your statistical analysis procedures, but not their outcomes. Their outcomes are reported in the results section.

These procedures should be stated for all primary, secondary, and exploratory hypotheses. While primary and secondary hypotheses are based on a theoretical framework or past studies, exploratory hypotheses are guided by the data you’ve just collected.

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This annotated example reports methods for a descriptive correlational survey on the relationship between religiosity and trust in science in the US. Hover over each part for explanation of what is included.

The sample included 879 adults aged between 18 and 28. More than half of the participants were women (56%), and all participants had completed at least 12 years of education. Ethics approval was obtained from the university board before recruitment began. Participants were recruited online through Amazon Mechanical Turk (MTurk; www.mturk.com). We selected for a geographically diverse sample within the Midwest of the US through an initial screening survey. Participants were paid USD $5 upon completion of the study.

A sample size of at least 783 was deemed necessary for detecting a correlation coefficient of ±.1, with a power level of 80% and a significance level of .05, using a sample size calculator (www.sample-size.net/correlation-sample-size/).

The primary outcome measures were the levels of religiosity and trust in science. Religiosity refers to involvement and belief in religious traditions, while trust in science represents confidence in scientists and scientific research outcomes. The secondary outcome measures were gender and parental education levels of participants and whether these characteristics predicted religiosity levels.

Religiosity

Religiosity was measured using the Centrality of Religiosity scale (Huber, 2003). The Likert scale is made up of 15 questions with five subscales of ideology, experience, intellect, public practice, and private practice. An example item is “How often do you experience situations in which you have the feeling that God or something divine intervenes in your life?” Participants were asked to indicate frequency of occurrence by selecting a response ranging from 1 (very often) to 5 (never). The internal consistency of the instrument is .83 (Huber & Huber, 2012).

Trust in Science

Trust in science was assessed using the General Trust in Science index (McCright, Dentzman, Charters & Dietz, 2013). Four Likert scale items were assessed on a scale from 1 (completely distrust) to 5 (completely trust). An example question asks “How much do you distrust or trust scientists to create knowledge that is unbiased and accurate?” Internal consistency was .8.

Potential participants were invited to participate in the survey online using Qualtrics (www.qualtrics.com). The survey consisted of multiple choice questions regarding demographic characteristics, the Centrality of Religiosity scale, an unrelated filler anagram task, and finally the General Trust in Science index. The filler task was included to avoid priming or demand characteristics, and an attention check was embedded within the religiosity scale. For full instructions and details of tasks, see supplementary materials.

For this correlational study , we assessed our primary hypothesis of a relationship between religiosity and trust in science using Pearson moment correlation coefficient. The statistical significance of the correlation coefficient was assessed using a t test. To test our secondary hypothesis of parental education levels and gender as predictors of religiosity, multiple linear regression analysis was used.

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.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

In your APA methods section , you should report detailed information on the participants, materials, and procedures used.

• Describe all relevant participant or subject characteristics, the sampling procedures used and the sample size and power .
• Define all primary and secondary measures and discuss the quality of measurements.
• Specify the data collection methods, the research design and data analysis strategy, including any steps taken to transform the data and statistical analyses.

You should report methods using the past tense , even if you haven’t completed your study at the time of writing. That’s because the methods section is intended to describe completed actions or research.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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Research Methodologies

Quantitative research methodologies.

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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

• What are the relationships between two or more variables? 
• What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

• Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
• Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

• Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
• Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

• Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
• Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

• Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
• Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

• Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Quantitative Research Essay Examples

A quantitative research essay analyzes numerical data in the form of trends, opinions, or efficiency results. This academic writing genre requires you to generalize the figures across a broad group of people and make a relevant conclusion. The possible research methods comprise questionnaires, polls, and surveys, but the results shall be processed with computational techniques and statistics.

For instance, a paper on organized crime in Texas will calculate the number of offenses committed over a given period and compare the findings with the same period in the past.

Below we’ve gathered dozens of quantitative essay examples to help you brainstorm ideas. You will surely find here a couple of papers that meet your needs.

48 Best Quantitative Research Essay Examples

Audit report for the university of alabama system.

• Subjects: Economics Financial Reporting

Decision Making: Starbucks Transformational Experience

• Subjects: Business Case Study
• Words: 2003

Business Problem Matrix and Research Question Hypotheses

• Subjects: Sciences Statistics
• Words: 2058

Asians Seeking U.S. Education

• Subjects: Education Education Theories
• Words: 3014

Facial Feedback Hypothesis

• Subjects: Psychological Principles Psychology
• Words: 2206

User Satisfaction and Service Quality in Academic Libraries: Use of LibQUAL+

• Subjects: Education Education System
• Words: 4019

Predicting Unemployment Rates to Manage Inventory

• Subjects: Business Management
• Words: 2141

Fuel Consumption for Cars Made in the US and Japan

• Subjects: Business Industry

Theoretical Stock Prices

• Subjects: Economics Investment

Supply Chain Design: Honda Gulf

• Words: 2999

Using Smartphones in Learning

• Subjects: Tech & Engineering Technology in Education
• Words: 6084

Action Research in Science Education

• Subjects: Education Writing & Assignments
• Words: 1199

Introduction to Nursing Research

• Subjects: Health & Medicine Healthcare Research

Students’ Perception of a Mobile Application for College Course

• Words: 1500

Carbon Fiber Reinforced Polymer Application

• Subjects: Construction Design
• Words: 1440

An Evaluation of the Suitability of ‘New Headway- Intermediate’ by Liz & John Soars

• Subjects: Education Pedagogical Approaches
• Words: 3456

Parenting Variables in Antenatal Education

• Subjects: Family Planning Health & Medicine
• Words: 1211

Sustained Organisational Learning Methods

• Words: 1416

The Achievement of Millennium Development Goals in India

• Subjects: International Relations Politics & Government

Green Energy Brand Strategy: Chinese E-Car Consumer Behaviour

• Subjects: Business Strategy
• Words: 3378

Binomial Logistic Regression

• Subjects: Math Sciences

Odds Ratio in Logistic Regression

Local food production in malaysia.

• Words: 1625

The Indian Agriculture Sector

• Subjects: Agriculture Sciences
• Words: 1662

Beer Market Trends in the UK

• Subjects: Business Marketing
• Words: 1374

Waste Management in Australia

• Subjects: Environment Recycling
• Words: 1851

Health and Environment in Abu Dhabi

• Subjects: Air Pollution Environment
• Words: 3126

The Relations Between Media and School Violence

• Subjects: Sociology Violence
• Words: 2832

BlackBerry Management Perspectives

• Words: 2831

Apple Inc. Equity Valuation

• Words: 3729

Zara Fashions’ Supply Chain

• Words: 6066

Ashtead Group Plc Financial Accounting

• Words: 5733

Independent Samples t-test with SPSS

Exploring reliability and validity.

• Subjects: Psychological Issues Psychology

Sustaining Australia’s Rate of Economic Growth

• Subjects: Economic Systems & Principles Economics
• Words: 1356

E-Cig Project and Price Customization

• Subjects: Business Marketing Project

Public Relations and Customer Loyalty

• Subjects: Branding Business
• Words: 2118

Game-based Learning and Simulation in a K-12 School in the United Arab Emirates

• Subjects: Education Pedagogy
• Words: 3683

The Issue of Muslims’ Immigration to Australia

• Subjects: Immigration Sociology
• Words: 3492

The Target Company

• Subjects: Business Company Analysis
• Words: 4066

The Effect of Social Media on Today’s Youth

• Subjects: Entertainment & Media Social Media Issues
• Words: 2165

Heineken Company in the US market

• Words: 1275

International Communication in Saudi Arabia

• Subjects: Communications Sociology
• Words: 1390

Jewish Life in North America

• Subjects: Sociological Issues Sociology
• Words: 1788

Impact of Gambling on the Bahamian Economy

• Subjects: Economics Influences on Political Economy
• Words: 3871

International Marketing Plan for Tata Nano

• Subjects: Business Financial Marketing
• Words: 5299

Home Based and Community Based Services (HCBS)

• Subjects: Health & Medicine Healthcare Institution

Case of Ski Pro Corporation

• Subjects: Business Company Missions

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Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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Exploring Quantitative Biology: A Guide to Research Topics

Welcome to the fascinating world of quantitative biology, where biology, math, and technology blend to help us understand life better. Whether you’re a student, a science enthusiast, or just curious about how biology works at a deeper level, this guide will break down the key research areas in simple terms. Quantitative biology is all about using numbers, patterns, and computer models to figure out how living things behave, and we’re going to explore some of its most exciting topics. Let’s dive in!

What is Quantitative Biology?

Table of Contents

At its core, quantitative biology is the use of mathematical models, statistics, and computational tools to understand biological systems. It combines biology with math, providing a quantitative approach to solving biological problems. Whether predicting how a disease spreads or understanding genetic mutations, quantitative biology allows researchers to gain insights that would be impossible without the power of numbers.

For instance, imagine you’re studying how bacteria develop antibiotic resistance. Using mathematical models, you can predict how quickly resistance will spread in a population, helping scientists develop better treatments.

Why is Quantitative Biology Important?

Quantitative biology plays a vital role in modern science. By blending biological science with quantitative methods, researchers can:

• Understand Complex Biological Systems : From individual cells to entire ecosystems.
• Predict Outcomes : Such as how a disease spreads or how an ecosystem responds to environmental changes.
• Innovate in Medicine and Technology : For example, designing new drugs or genetically engineering crops.
• Make Sense of Large Datasets : With advances in technology, scientists have more data than ever, and quantitative biology helps analyze it.

Key Research Topics in Quantitative Biology

1. systems biology: the blueprint of life.

Systems biology is a key branch of quantitative biology that examines how different parts of a biological system interact to create its overall behavior. It studies biological networks—how genes, proteins, and cells communicate with one another. Using computational modeling, scientists simulate these interactions and predict what might happen if one part of the system changes.

For example, understanding how cancer spreads requires studying how cells interact and multiply. Systems biology helps researchers identify which proteins or genes are involved in these processes, enabling the development of targeted therapies.

Why It Matters:

• Helps in developing new treatments for diseases.
• Provides insights into how cells and organisms function as a whole.

Example Research Question:

• How does a specific protein impact the way cells communicate during growth?

2. Bioinformatics and Genomics: Decoding DNA

Bioinformatics is a field of quantitative biology that applies computational modeling to the study of DNA and genetic data. It plays a central role in genomics, the study of an organism’s entire genetic makeup. Scientists use bioinformatics tools to analyze vast amounts of DNA and gene data, helping them find connections between genes and diseases.

For example, researchers use DNA analysis to identify mutations linked to conditions like diabetes or cancer. The data generated from sequencing entire genomes is immense, and bioinformatics is essential for making sense of it.

• Helps in finding the genetic basis of diseases.
• Enables the development of personalized medicine based on a person’s DNA.
• What genetic mutations are responsible for certain inherited diseases?

3. Population Genetics: Evolution in Action

Population genetics is the study of how gene frequencies change in a population over time. It examines how natural selection, mutations, and genetic drift shape populations’ genetic makeup. Using mathematical models, population geneticists can predict how traits evolve and spread in a group of organisms.

For instance, a population of animals might adapt to a changing environment by developing thicker fur for colder climates. Population genetics helps scientists understand the genetic diversity that drives these changes.

• Helps in conservation efforts by studying how species adapt to environmental changes.
• Provides insights into how diseases or traits evolve within populations.
• How do environmental changes influence the evolution of genetic traits in a population?

4. Biophysics: The Physics Behind Life

Biophysics combines physics with biology to understand the physical principles governing biological processes. It focuses on the molecular dynamics of proteins, DNA, and other cellular components. Scientists use biophysics to study how proteins fold, how cells transmit signals, and how forces within cells affect their behavior.

One crucial area in biophysics is studying protein structure. When proteins fold incorrectly, it can lead to diseases like Alzheimer’s. Understanding these physical processes allows researchers to develop drugs that stabilize proteins and prevent misfolding.

• Helps in understanding diseases caused by misfolded proteins, such as Alzheimer’s and Parkinson’s.
• Provides insights into how cells function on a molecular level.
• How do proteins fold, and what causes them to misfold in diseases?

5. Quantitative Ecology: Modeling Nature

In quantitative ecology, researchers use mathematical tools and environmental modeling to study ecosystems. By simulating how species interact with their environment and each other, ecologists can predict changes in biodiversity due to factors like climate change, pollution, or habitat destruction.

For example, if a new predator is introduced into an ecosystem, it can dramatically alter the populations of prey species. Quantitative ecology models help scientists understand these dynamics and develop strategies to protect endangered species.

• Helps in conservation efforts by modeling how species and ecosystems respond to changes.
• Provides tools for managing ecosystems and protecting biodiversity.
• How does climate change affect the biodiversity of an ecosystem?

6. Neuroscience and Brain Networks: Understanding the Brain

Neuroscience focuses on understanding the structure and function of the brain, and quantitative biology plays a big role here. By studying brain networks and neural circuits, scientists can map out how neurons interact and how information flows through the brain. Neuroscience uses computational models to understand how these networks change when we learn or suffer from disorders like epilepsy.

For instance, researchers use quantitative models to simulate how neural circuits adapt during learning processes, providing insights into memory formation and decision-making.

• Helps in developing new treatments for brain disorders.
• Provides insights into how the brain functions and learns.
• How do neural circuits in the brain adapt when we learn something new?
• 200+ Unique And Interesting Biology Research Topics For Students In 2023
• 200+ Experimental Quantitative Research Topics For STEM Students In 2023

7. Synthetic Biology: Building New Life

Synthetic biology is an exciting field of biotechnology in which researchers design and create new biological systems or organisms. Using principles from genetic engineering, scientists can modify or build DNA sequences to produce new functions, like bacteria that break down plastic or plants that grow faster.

For instance, synthetic biology has been used to engineer yeast cells that can produce medicines like insulin. This type of research is paving the way for sustainable solutions to medical and environmental problems.

• Offers new solutions to environmental and medical challenges.
• Enables the development of genetically modified organisms (GMOs) with useful traits.
• How can we engineer bacteria to produce new antibiotics?

8. Epidemiology and Infectious Disease Modeling: Preventing Outbreaks

In epidemiology, researchers study how diseases spread within populations. By using disease modeling, scientists can predict outbreaks and design public health strategies to prevent the spread of infectious diseases. These models take into account factors like transmission rates, immunity, and social behavior.

For example, during the COVID-19 pandemic, epidemiologists used models to forecast how the virus would spread and what measures, like social distancing, could slow its progression. Public health officials rely on these models to make informed decisions.

• Helps governments and public health officials prepare for and control disease outbreaks.
• Provides insights into the effectiveness of vaccines and other interventions.
• How can we predict the spread of the next pandemic?

How Quantitative Biology Impacts Our Lives

Quantitative biology might sound technical, but it affects everyone. From better healthcare (through personalized medicine and disease modeling) to conservation efforts (by protecting species and ecosystems), the insights from this field shape the world we live in. Whether scientists are predicting how a virus spreads or figuring out how to grow more food in a changing climate, quantitative biology helps tackle global challenges.

Table: Key Research Areas in Quantitative Biology

Conclusion: The Future of Quantitative Biology

As technology continues to advance, quantitative biology will become even more important in solving real-world problems. Whether you’re interested in medicine, ecology, genetics, or any other field, quantitative biology offers exciting opportunities to make a meaningful impact on society . It’s a field that continues to grow, offering new ways to understand and influence the living world.

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9 Quantitative Research Methods With Real Client Examples

• June 21, 2021
• Tallwave Team

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Quantitative research is essential to developing a clear understanding of consumer engagement and how to increase satisfaction.

Primary Quantitative Research Methods

When it comes to quantitative research, many people often confuse this type of research with the methodology. The research type refers to style of research while the data collection method can be different.

Research types

These are the primary types of quantitative research used by businesses today.

• Survey research: Ideally when conducting survey research businesses will use a statistically relevant sample to understand the sentiments and actions of a large group of people. This could be their current customers or consumers who fit into their ideal demographic.
• Correlational research: Correlational research compares two variables to come to a conclusion about whether there is a relationship between the two. Keep in mind that correlation does not always imply causation, which is to say you need to account for external variables that could cause an apparent relationship.
• Experimental research: This form of research takes a scientific approach, testing a hypothesis by manipulating certain variables to understand what changes this could cause. In these experiments, there is a control group and a manipulated group.

Also read:  6 Factors Influencing Customer Behaviors in 2021

Data collection methods

Launching the above research requires creating a plan to collect data. After all, quantitative research relies on data. Here are the common primary data collection methods for quantitative research.

• Surveys: A common approach to collecting data is using a survey. This is ideal especially if the business can obtain a statistically relevant sample from their responses. Surveys are often conducted through web or email questionnaires.
• Interviews: Yes, interviews can be used to obtain quantitative data. While this form of data collection is typically associated with qualitative research, interviewers can ask a standard set of questions to collate formal, quantitative data.
• Documentation review: With an increasing amount of business occurring digitally, there is more documentation now than ever before to help inform quantitative conclusions. Businesses can assess website metrics such as return visits, time on page or even use a pixel to track customer movement across websites. They can also view how many times their app has been opened and actions users have taken on their platform to determine customer engagement.

Secondary research can be helpful when formulating a plan for obtaining primary quantitative data. It can help narrow areas of focus or illuminate key challenges.

Secondary Quantitative Research Methods

Secondary data is information that is already collected and not necessarily exclusive to the company but still relevant when understanding overall industry and marketplace trends. Here are a few examples of secondary data:

• Government reports: Government research can indicate potential regulatory roadblocks, customer pain points and future opportunities. For example, a fitness company might use government data that shows an increase in use of outdoor running trials to develop a new product used to meet that specific use case.
• Survey-based secondary data: Polls or surveys that have been conducted for a primary use could be reused for secondary purposes. This could include survey data obtained by other companies or governments.
• Academic research: Research that has been previously conducted and published in peer-reviewed journals can help inform trends and consumer behavior, even if it doesn’t apply to a company’s specific customers.

Secondary research can be helpful when formulating a plan for obtaining primary quantitative data. It can help narrow areas of focus or illuminate key challenges. It can also help when it comes to interpreting primary data, especially when trying to understand the relationship between two variables of correlated data.

Also read:  The What, Why, & How of Customer Behavior Analysis

Real Examples of Quantitative Research

We regularly use quantitative research to help our clients understand where they can best add value to increase customer engagement. Here are three examples of quantitative research in motion.

Example 1: Leading food distribution company

We helped a leading food distribution company identify changes in the needs and values of their restaurant clients as a result of COVID-19. This helped inform opportunities to become more valuable partners.

The research plan involved creating a survey that was emailed to clients. The questions were specific and numeric. For example, respondents were asked what percentage of their weekly spend was used with the food distribution company. They were also asked to assign a percentage to the way their food ordering had changed during COVID-19 and to rate their satisfaction with the food distribution company.

The results showed changes that had occurred for clients of the food distribution company as a result of the unique stressors of the pandemic. We were able to determine changes in weekly food supply and customer count as well as menu adaptations and purchase behavior.

Example 2: Leading credit card company

Our work with a leading credit card company required us to understand what current travel card members valued about the rewards program and their preferred communication method for booking travel in order to create an omnichannel servicing strategy and ideal customer journey.

Through an online survey of younger cardholders, the target demographic for this project, we asked questions such as length of card membership, total spend and the number of annual leisure trips in addition to more specific questions that showed how members get inspiration for trip planning and where they research.

The results highlighted ways to overcome resistance to pricing by proving more value. It also illuminated ways to make the benefits of membership more tangible to card holders and how to influence travelers in the early stages of planning their journey.

Example 3: Internal research report

We’re in the business of drinking our own champagne, so to speak, which is why we conducted our own quantitative research aimed at understanding the consumer trends that were spurred by the pandemic and how these will transform behaviors in the future.

There’s no question that new customer experiences emerged from the pandemic. Think of offerings such as “buy online, pickup in store (BOPIS),” or blended restaurant meals that are cooked at home. We wanted to understand how consumers truly felt about these new experiences and which they were likely to continue using even after restrictions were lifted. We also wanted to know more about the changing expectations for branded communication and how all of these pieces of the puzzle fit together to create consumer engagement. Our method of data collection was a survey.

Our research led us to develop insights we could use to inform our customers in their decision making. For example, we found convenience is paramount for consumers who are seeking out hybrid experiences such as BOPIS to take the best of both worlds. We also found many of these changes are permanent as consumers embraced new experiences that made their lives easier.

We regularly use quantitative research to help our clients understand where they can best add value to increase customer engagement.

The Bottom Line

Quantitative research is essential to developing a clear understanding of consumer engagement and how to increase satisfaction. Though online surveys are one of the most common methods for obtaining data, research isn’t limited to this strategy. It’s important to use whatever strategies are within your scope to constantly evaluate new trends and consumer behaviors that could significantly impact your offerings. The results can show you how to re-engage customers and drive loyalty.

Interested in partnering with us to learn more about your customers needs, wants, and behaviors to inform future experience design? Contact us today !

Tallwave Headquarters

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For business inquiries, contact Ed Borromeo, Tallwave Partner at [email protected]

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A systematic review of aspect-based sentiment analysis: domains, methods, and trends

• Open access
• Published: 17 September 2024
• Volume 57 , article number  296 , ( 2024 )

Cite this article

You have full access to this open access article

• Yan Cathy Hua   ORCID: orcid.org/0000-0001-9155-9667 1 ,
• Paul Denny   ORCID: orcid.org/0000-0002-5150-9806 1 ,
• Jörg Wicker   ORCID: orcid.org/0000-0003-0533-3368 1 &
• Katerina Taskova   ORCID: orcid.org/0000-0002-3217-7877 1  

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.

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1 Introduction

In the digital era, a vast amount of online opinionated text is generated daily through which people express views and feelings (i.e. sentiment) towards certain subjects, such as user reviews, social media posts, and open-ended survey question responses (Kumar and Gupta 2021 ). Understanding the sentiment of these opinionated text data is essential for gaining insights into people’s preferences and behaviours and supporting decision-making across a wide variety of domains (Sharma and Shekhar 2020 ; Wankhade et al 2022 ; Tubishat et al 2021 ; García-Pablos et al 2018 ; Poria et al 2016 ). The analyses of opinionated text usually aim at answering questions such as “ What subjects were mentioned? ”, “ What did people think of (a specific subject)? ”, and “ How are the subjects and/or opinions distributed across the sample? ” (e.g. (Dragoni et al 2019 ; Krishnakumari and Sivasankar 2018 ; Fukumoto et al 2016 ; Zarindast et al 2021 )). These objectives, along with today’s enormous volume of digital opinionated text, require an automated solution for identifying, extracting and classifying the subjects and their associated opinions from the raw text. Aspect-based sentiment analysis (ABSA) is one such solution.

1.1 Review focus and research questions

This work presents a systematic literature review (SLR) of existing ABSA studies with a large-scale sample and quantitative results. We focus on trends and high-level patterns instead of methodological details that were well covered by the existing surveys mentioned above. We aim to benefit both ABSA newcomers by introducing the basics of the topic, as well as existing ABSA researchers by sharing perspectives and findings that are useful to the ABSA community and can only be obtained beyond the immediate research tasks and technicalities.

We seek to answer the following sets of research questions (RQs):

RQ1. To what extent is ABSA research and its dataset resources dominated by the commercial (especially the product and service review) domain? What proportion of ABSA research focuses on other domains and dataset resources?

RQ2. What are the most common ABSA problem formulations via subtask combinations, and what proportion of ABSA studies only focus on a specific subtask?

RQ3. What is the trend in the ABSA solution approaches over time? Are linguistic and traditional machine-learning approaches still in use?

This review makes a number of unique contributions to the ABSA research field: (1) It is one of the largest scoped SLRs on ABSA, with a main review and a Phase-2 targeted review of a combined 727 primary studies published in 2008–2024, selected from 8550 search results without time constraint. (2) To our knowledge, it is the first SLR that systematically examines the ABSA data resource distribution in relation to research application domains and methodologies; and (3) Our review methodology adopted an innovative automatic filtering process based on PDF-mining, which enhanced screening quality and reliability. Our quantitative results not only revealed trends in nearly two decades of ABSA research literature but also highlighted potential systemic issues that could limit the development of future ABSA research.

1.2 Organisation of this review

In Sect.  2 (“Background”), we introduce ABSA and highlight the motivation and uniqueness of this review. Section  3 (“Methods”) outlines our SLR procedures, and Sect.  4 (“Results”) answers the research questions with the SLR results. We then discuss the key findings and acknowledge limitations in Sects.  5 and 6 (“Discussion” and “Conclusion”).

For those interested in more details, Appendix A provides an in-depth introduction to ABSA and its subtasks. Appendix B describes the full details of our Methods, and additional figures from the Results are provided in Appendix C .

2 Background

2.1 absa: a fine-grained sentiment analysis.

Aspect-based sentiment analysis (ABSA) is a sub-field of Sentiment Analysis (SA), which is a core task of natural language processing (NLP). SA, also known as “opinion mining” (García-Pablos et al 2018 ; Poria et al 2016 ; Liang et al 2022 ; López and Arco 2019 ; Tran et al 2020 ), solves the problem of identifying and classifying given text corpora’s affect or sentiment orientation (Akhtar et al 2020 ; Tubishat et al 2021 ) into polarity categories (e.g. “positive, neutral, negative”) (Brauwers and Frasincar 2023 ; Hu and Liu 2004a ), intensity/strength scores (e.g. from 1 to 5) (Wang et al 2017 ), or other categories. The “ identifying the subjects of opinions ” part of the quest relates to the granularity of SA. Traditional SA mostly focuses on document- or sentence-level sentiment and thus assumes a single subject of opinions (Nazir et al 2022b ; Liu et al 2020 ). In recent decades, the explosion of online opinion text has attracted increasing interest in distilling more targeted insights on specific entities or their aspects within each sentence through finer-grained SA (Nazir et al 2022b ; Liu et al 2020 ; Akhtar et al 2020 ; You et al 2022 ; Ettaleb et al 2022 ). This is the problem ABSA aims to solve.

2.2 ABSA and its subtasks

ABSA involves identifying the sentiments toward specific entities or their attributes, called aspects . These aspects can be explicitly mentioned in the text or implied from the context (“implicit aspects”), and can be grouped into aspect categories (Nazir et al 2022a ; Akhtar et al 2020 ; Maitama et al 2020 ; Xu et al 2020b ; Chauhan et al 2019 ; Akhtar et al 2018 ). Appendix  A.1 presents a more detailed definition of ABSA, including its key components and examples.

A complete ABSA solution as described above traditionally involves a combination of subtasks, with the fundamental ones (Li et al 2022a ; Huan et al 2022 ; Li et al 2020 ; Fei et al 2023b ; Pathan and Prakash 2022 ) being Aspect (term) Extraction (AE), Opinion (term) Extraction (OE), and Aspect-Sentiment Classification (ASC), or in an aggregated form via Aspect-Category Detection (ACD) and Aspect Category Sentiment Analysis (ACSA).

The choice of subtasks in an ABSA solution reflects both the problem formulation and, to a large extent, the technologies and resources available at the time. The solutions to these fundamental ABSA subtasks evolved from pure linguistic and statistical solutions to the dominant machine learning (ML) approaches (Maitama et al 2020 ; Cortis and Davis 2021 ; Liu et al 2020 ; Federici and Dragoni 2016 ), usually with multiple subtask models or modules orchestrated in a pipeline (Li et al 2022b ; Nazir and Rao 2022 ). More recently, the rise of multi-task learning brought an increase in End-to-end (E2E) ABSA solutions that can better capture the inter-task relations via shared learning (Liu et al 2024 ), and many only involve a single model that provides the full ABSA solution via one composite task (Huan et al 2022 ; Li et al 2022b ; Zhang et al 2022b ). The most typical composite ABSA tasks include Aspect-Opinion Pair Extraction (AOPE) (Nazir and Rao 2022 ; Li et al 2022c ; Wu et al 2021 ), Aspect-Polarity Co-Extraction (APCE) (Huan et al 2022 ; He et al 2019 ), Aspect-Sentiment Triplet Extraction (ASTE) (Huan et al 2022 ; Li et al 2022b ; Du et al 2021 ; Fei et al 2023b ), and Aspect-Sentiment Quadruplet Extraction/Prediction (ASQE/ASQP) (Zhang et al 2022a ; Lim and Buntine 2014 ; Zhang et al 2021a , 2024a ). We provide a more detailed introduction to ABSA subtasks in Appendix  A.2 .

2.3 The context- and domain-dependency challenges

The nature and the interconnection of its components and subtasks determine that ABSA is heavily domain- and context-dependent (Nazir et al 2022b ; Chebolu et al 2023 ; Howard et al 2022 ). Domain refers to the ABSA task (training or application) topic domains, and context can be either the “global” context of the document or the “local” context from the text surrounding a target word token or word chunks. At least in English, the same word or phrase could mean different things or bear different sentiments depending on the context and topic domains. For example, “a big fan” could be an electric appliance or a person, depending on the sentence and the domain; “cold” could be positive for ice cream but negative for customer service; and “DPS” (damage per second) could be either a gaming aspect or non-aspect in other domains. Thus, the ability to incorporate relevant context is essential for ABSA solutions; and those with zero or very small context windows, such as n-gram and Markov models, are rare in ABSA literature and can only tackle a limited range of subtasks (e.g. Presannakumar and Mohamed 2021 ).

Moreover, although many language models (e.g. Bidirectional Encoder Representations from Transformers (BERT, Devlin et al 2019 ), Generative pre-trained transformers (GPT, Brown et al 2020 ), recurrent neural network (RNN)-based models) already incorporated local context from the input-sequence and/or general context through pre-trained embeddings, they still performed unsatisfactorily on some ABSA domains and subtasks, especially Implicit AE (IAE), AE with multi-word aspects, AE and ACD on mixed-domain corpora, and context-dependent ASC (Phan and Ogunbona 2020 ; You et al 2022 ; Liang et al 2022 ; Howard et al 2022 ). Many studies showed that ABSA task performance benefits from expanding the feature space beyond the generic and input textual context. This includes incorporating domain-specific dataset/representations and additional input features such as Part-of-Speech (POS) tags, syntactic dependency relations, lexical databases, and domain knowledge graphs or ontologies (Howard et al 2022 ; You et al 2022 ; Liang et al 2022 ). Nonetheless, annotated datasets and domain-specific resources are costly to produce and limited in availability, and domain adaptation, as one solution to this, has been an ongoing challenge for ABSA (Chen and Qian 2022 ; Zhang et al 2022b ; Nazir et al 2022b ; Howard et al 2022 ; Satyarthi and Sharma 2023 ).

The above highlights the critical role of domain-specific datasets and resources in ABSA solution quality, especially for supervised approaches. On the other hand, it suggests the possibility that the prevalence of dataset-reliant solutions in the field, and a skewed ABSA dataset domain distribution, could systemically hinder ABSA solution performance and generalisability (Chen and Qian 2022 ; Fei et al 2023a ), thus confining ABSA research and solutions close to the resource-rich domains and languages. This idea underpins this literature review’s motivation and research questions.

2.4 Review rationale

This review is motivated by the following rationales:

First, the shift towards ML, especially supervised and/or DL solutions for ABSA, highlights the importance of dataset resources. In particular, annotated large benchmark datasets are crucial for the quality and development of ABSA research. Meanwhile, the finer granularity of ABSA also brings the persistent challenge of domain dependency described in Sect.  2.3 . The diversity of ABSA datasets and their domains can have a direct and systematic impact on research and applications.

The early seminal works in ABSA were motivated by commercial applications and focused on product and service reviews (Liu et al 2020 ; Rana and Cheah 2016 ; Do et al 2019 ), such as Ganu et al ( 2009 ), Hu and Liu ( 2004b ), and Pontiki et al ( 2014 , 2015 , 2016 ) that laid influential foundations with widely-used product and service review ABSA benchmark datasets (Rana and Cheah 2016 ; Do et al 2019 ). Nevertheless, the need for mining insights from opinions far exceeds this single domain. Many other areas, especially the public sector, also have an abundance of opinionated text data and can benefit from ABSA, such as helping policy-makers understand public attitudes and reactions towards events or changes (Sharma and Shekhar 2020 ), improving healthcare services and treatments via patient experience and concerns in clinical visits, symptoms, drug efficacy and side-effects (Cavalcanti and Prudêncio 2017 ; Gui and He 2021 ), and guiding educators in meeting teacher and learner needs and improving their experience (Wankhade et al 2022 ; Tubishat et al 2021 ; García-Pablos et al 2018 ; Poria et al 2016 ). While the more general SA research has been applied to “nearly every domain” (Nazir et al 2022b , p. 1), this does not seem to be the case for ABSA. Chebolu et al ( 2023 ) reviewed 62 public ABSA datasets released between 2004 and 2020 covering “over 25 domains” (Chebolu et al 2023 , p. 1). However, 53 out of these 62 datasets were reviews of restaurants, hotels, and digital products; only five were not related to commercial products or services, and merely one was on the public sector domain (university reviews).

The above-mentioned evidence raises questions: Will this dataset domain homogeneity be found with a larger sample of primary studies? Does this domain skewness reflect the concentration of ABSA research focus or merely the lack of dataset diversity? This motivated our RQ1 (“ To what extent is ABSA research and its dataset resources dominated by the commercial (especially the product and service review) domain? What proportion of ABSA research focuses on other domains and dataset resources? ”) Answers to these questions could inform and shape future ABSA research through individual research decisions and community resource collaboration.

Second, there are many good survey papers on ABSA, most focused on introducing the methodological details of common ABSA subtasks and solutions (e.g. Maitama et al 2020 ; Sabeeh and Dewang 2019 ; Rana and Cheah 2016 ; Soni and Rambola 2022 ; Ganganwar and Rajalakshmi 2019 ; Zhou et al 2019 ) or specific approaches such as DL methods for ABSA (e.g. Liu et al 2020 ; Do et al 2019 ; Wang et al 2021a ; Chen and Fnu 2022 ; Mughal et al 2024 ; Zhang et al 2022c ; Satyarthi and Sharma 2023 ). We list these surveys in Appendix A.3 as additional resources for the reader. Nonetheless, many of these reviews only explored each subtask and/or technique individually and often by iterating through reviewed studies, and few examined their combinations or changes over time and with quantitative evidence. For example, although the above-listed reviews (Liu et al 2020 ; Do et al 2019 ; Wang et al 2021a ; Chen and Fnu 2022 ) reported the rise of DL approaches in ABSA similar to that of NLP as a whole, it is unclear whether ABSA research was also increasingly dominated by the attention mechanism from the Transformer architecture (Vaswani et al 2017 ) and pre-trained large language models since 2018 (Manning 2022 ), and if linguistic and traditional ML approaches were still active. In addition, most of these surveys used a smaller and selected sample that could not support conclusions on trends. As the field matures, we believe it is necessary and important to examine trends and matters outside the problem solution itself, so as to inform research decisions, identify issues, and call for necessary community awareness and actions. We thus proposed RQ2 (“ What are the most common ABSA problem formulations via subtask combinations, and what proportion of ABSA studies only focus on a specific sub-task? ”) and RQ3 (“ What is the trend in the ABSA solution approaches over time? Are linguistic and traditional machine-learning approaches still in use? ”).

In order to identify patterns and trends for our RQs, a sufficiently sized representative sample and systematic approach are required. We chose to conduct an SLR, as this type of review aims to answer specific research questions from all available primary research evidence following well-defined review protocols (Kitchenham and Charters 2007 ). Moreover, none of the existing SLRs on ABSA share the same focus and RQs as ours: Among the 192 survey/review papers obtained from four major digital database searches detailed in Sect.  3 , only eight were SLRs on ABSA, within which four focused on non-English language(s) (Alyami et al 2022 ; Obiedat et al 2021 ; Hoti et al 2022 ; Rani and Kumar 2019 ), two on specific domains (software development, social media) (Cortis and Davis 2021 ; Lin et al 2022 ), one on a single subtask (Maitama et al 2020 ), and one mentioned ABSA subtasks as a side-note under the main topic of SA (Ligthart et al 2021 ).

In summary, this review aims to address gaps in the ABSA literature. The high-level nature of our research questions is best answered through a large-scale SLR to provide solid evidence. The next section presents our SLR approach and sample.

Following the guidance of Kitchenham and Charters ( 2007 ), we conducted this SLR with pre-planned scope, criteria, and procedures highlighted below. The complete SLR methods and process are detailed in Appendix B .

3.1 Main procedures

For the main SLR sample, we sourced the primary studies in October 2022 from four major peer-reviewed digital databases: ACM Digital Library, IEEE Xplore, Science Direct, and SpringerLink. First, we manually searched and extracted 4191 database results without publication-year constraints. Appendix B.1 provides more details of the search strategies and results. Next, we applied the inclusion and exclusion criteria listed in Table  1 via automatic Footnote 1 and manual screening steps and identified 519 in-scope peer-reviewed research publications for the review. The complete screening process, including that of the automatic screening, is described in Appendix B.2 . We then manually reviewed the in-scope primary studies and recorded data following a planned scheme. Lastly, we checked, cleaned, and processed the extracted data and performed quantitative analysis against our RQs.

3.2 Main SLR sample summary

Figure  1 shows the number of total reviewed vs. included studies across all publication years for the 4191 SLR search results. The search results include studies published between 1995 and 2023 ( \(\textrm{N}=1\) ), although all of the pre-2008 ones (2 from the 90s, 8 from 2003–2006, 17 from 2007) were not ABSA-focused and were excluded during automatic screening. The earliest in-scope ABSA study in the sample was published in 2008, followed by a very sparse period until 2013. The numbers of extracted and in-scope publications have both grown noticeably since 2014, a likely result of the emergence of deep learning approaches, especially sequence models such as RNNs (Manning 2022 ; Sutskever et al 2014 ). We also present a breakdown of the included studies by publication year and type in Figure  9 in Appendix C .

Number of studies by publication year: total reviewed ( \(\textrm{N}=4191\) ) vs. included ( \(\textrm{N}=519\) )

3.3 Note on “domain” mapping

In order to answer RQ1, we made the distinction between “research application domain” (“research domain” in short) and “dataset domain”, and manually examined and classified each study and its datasets into domain categories.

We considered each study’s research domain to be “non-specific” unless the study mentioned a specific application domain or use case as its motivation. For the dataset domain, we examined each dataset used by our sample, standardised its name, and recorded the domain from which it was drawn/selected based on the description provided by the author or the dataset source webpage. Datasets without a specific domain (e.g. Twitter tweets crawled without a specific domain filter) were labelled as “non-specific”.

We then manually grouped the research and dataset domains into 19 common categories used for analysis. More details and examples on domain mapping are available in Appendix  B.3 .

3.4 Phase-2 targeted review on in-context learning

Additionally, generative “foundation models” (Bommasani et al 2022 ), defined as models with billions of parameters pre-trained on enormous general-purpose data and adaptable to diverse downstream NLP tasks, have become ubiquitous after our SLR data collection (e.g. ChatGPT OpenAI 2023 , released in November 2022). We use the term “foundation models” to distinguish them from the earlier pre-trained Large Language Models (LLMs) such as BERT (Devlin et al 2019 ), BART (Lewis et al 2020 ), and T5 (Raffel et al 2020 ), which have relatively fewer parameters and typically require fine-tuning for task adaptation (Zhang et al 2022c ). These generative foundation models brought a new paradigm of “In-context Learning” (ICL) (Brown et al 2020 , p. 4), where task adaptation can occur solely via conditioning the model on the text input instructions (“prompts”) with zero (“zero-shot ICL”) or few (“few-shot ICL”) examples and no model parameter changes (Brown et al 2020 ; Dong et al 2024 ). To capture and analyse this new development while balancing feasibility and currency, we conducted a Phase-2 targeted review in July 2024.

This Phase-2 targeted review focuses solely on the ICL implementations of pre-trained generative models for ABSA tasks, excluding those involving fine-tuning to draw a distinction from other non-ICL deep-learning approaches covered in the SLR. To extend the SLR sample beyond the original extraction time, we conducted a new database search Footnote 2 in July 2024 for studies published from 2022 onwards and removed the ones already included in the SLR sample. The new search results were screened using the SLR criteria described in Table  1 and then combined with the 519 SLR final samples. We then applied an additional filtering condition “Gen-LLM” to all the in-scope ABSA primary studies, which further selected publications with at least one occurrence of any of the following keywords outside the Reference section: “generative”, “in-context”, “in context learning”, “genai”, “bart”, “t5”, “flan-t5”, “gpt”, “chatgpt”, “llama”, and “mistral”. With the help of our automatic screening pipeline detailed in Appendix  B.2 , we were able to efficiently auto-screen the new search results and re-screen the previous SLR sample for the ”Gen-LLM” keywords in less than one hour.

In total, the new search yielded 271 additional in-scope ABSA primary studies from 4359 search results. After applying the “Gen-LLM” filtering condition to the combined 790 in-scope ABSA primary studies, we obtained 208 Phase-2 samples for manual review, which comprised 91 studies from the new search and 117 from the earlier SLR sample, ranging from 2008 to 2024. The Phase-2 targeted review results are presented in Sect.  4.5 . Unless specified otherwise, the results below only refer to those of the SLR.

This section presents the SLR results corresponding to each of the RQs:

4.1 Results for RQ1

To answer RQ1, we examined the distribution of reviewed studies by their research (application) domains, dataset domains, and the relationship between the two. From the 519 reviewed studies, we recorded 218 datasets, 19 domain categories (15 research domains and 17 dataset domains), and obtained 1179 distinct “study-dataset” pairs and 630 unique “study & dataset-domain” combinations. The key results are summarised below and presented in Table  2 and Fig.  2 . We also list the datasets used by more than one reviewed study in the Appendix Table  15 .

Distribution of unique “study–dataset” pairs ( \(\textrm{N}=1179\) , with 519 studies and 218 datasets) by research (application) domains (left) and dataset domains (right). Note (1) The top flow visualises a mismatch between the two domains: the majority of studies without a specific research domain used datasets from the product/service review domain. (2) The disproportionately small number of samples in both domains that were neither “non-specific” nor “product/service review”

In summary, our results answer RQ1 by showing that: (1) The majority (65.32%) of the reviewed studies were not for any specific application domain and only 24.28% targeted “product/service review”. (2) The dataset resources used in the sample were mostly domain-specific (84.44%) and dominated by the “product/service review” datasets (70.95%). (3) Both the research effort and dataset resources were scant in the non-commercial domains, especially the main public sector areas, with fewer than 13 studies across 14 years in each of the healthcare, policy, and education domains, where about half of the used datasets were created from scratch for the study.

Beyond RQ1, (1) and (2) above also suggest a significant mismatch between the research and dataset domains as visualised in Fig.  2 . Further, when filtering out datasets used by less than 10 studies, we discovered an alarming lack of dataset diversity as only 12 datasets remained, of which 10 were product/service reviews. When examining the three-way relationship among research domain, dataset domain, and dataset name, we further identified an over-representation (78.20%) of the four SemEval restaurant and laptop review benchmark datasets. This is illustrated in Fig.  4 .

4.1.1 Detailed results for RQ1

For research (application) domains indicated by the stated research use case or motivation, the majority (65.32%, \(\textrm{N}=339\) ) of the 519 reviewed studies have a “non-specific” research domain, followed by just a quarter (24.28%, \(\textrm{N}=126\) ) in the “product/service review” category. However, the number of studies in the rest of the research domains is magnitudes smaller in comparison, with only 12 studies (2.31%) in the third largest category “student feedback/education review” since 2008, followed by 8 in Politics/policy-reaction (1.54%), and only 7 in Healthcare/medicine (1.35%). Figure  3 revealed further insights from the trend of research domain categories with five or more reviewed studies. Interestingly, “product/service review” has been a persistently major category over time, and has only been consistently taken over by “non-specific” since 2015. The sharp increase of domain-“non-specific” studies since 2018 could be partly driven by the rise of pre-trained language models such as BERT and the greater sequence processing power from the Transformer architecture and the attention mechanism (Manning 2022 ), as more researchers explore the technicalities of ABSA solutions.

Number of in-scope studies by research (application) domain and publication year ( \(\textrm{N}=518\) ). This graph excludes the one 2023 study (extracted in October 2022) to avoid trend confusion

As to the dataset domains, Table  2 suggests that among the 630 unique “study & dataset-domain” pairs, the majority (70.95%, \(\textrm{N}=447\) ) are in the “product/service review” category, followed by 15.56% ( \(\textrm{N}=98\) ) in “Non-specific”. The third place is shared by two magnitude-smaller categories: “student feedback/ education review” (3.02%, \(\textrm{N}=19\) ) and “video/movie review” (3.02%, \(\textrm{N}=19\) ). The numbers of studies with datasets from the Healthcare/medicine (1.43%, \(\textrm{N}=9\) ) and Politics/policy-reaction (0.79%, \(\textrm{N}=5\) ) domains were again single-digit. Moreover, nearly half of the unique datasets in the public domains were created by the authors for the first time: 5/9 in Healthcare/medicine, 2/4 in Politics/policy-reaction, and 8/12 in Student feedback/ Education review.

Furthermore, to understand the dataset diversity across samples and domains, we grouped the 1179 unique “study-dataset” pairs by “research-domain, dataset-domain, dataset-name” combinations and zoomed into the 757 entries with ten or more study counts each. As shown in Table  3 and illustrated in Fig.  4 , among these 757 unique combinations, 95.77% ( \(\textrm{N}=725\) ) are in the “non-specific” research domain, of which 90.48% ( \(\textrm{N}=656\) ) used “product/service review” datasets. Most interestingly, these 757 entries only involve 12 distinct datasets of which 10 were product and service reviews, and 78.20% ( \(\textrm{N}=592\) ) are taken up by the four SemEval datasets from the early pioneer work (Pontiki et al 2014 , 2015 , 2016 ) mentioned in Sect.  2.4 : SemEval 2014 Restaurant, SemEval 2014 Laptop (these two alone account for 50.33% of all 757 entries), SemEval 2016 Restaurant, and SemEval 2015 Restaurant. This finding echos (Xing et al 2020 ; Chebolu et al 2023 ): “The SemEval challenge datasets... are the most extensively used corpora for aspect-based sentiment analysis” (Chebolu et al 2023 , p.4). Meanwhile, the top dataset used under “product/service review” research and dataset domains is the original product review dataset created by the researchers. Chebolu et al ( 2023 ) and Wikipedia ( 2023 ) provides a detailed introduction to the SemEval datasets.

Number of studies per each research (application) domain (left), dataset domain (middle), and dataset (right) combination, filtered by datasets used by 10 or more in-scope studies ( \(\textrm{N}=757\) ). The three-way relationship highlights that not only did the majority of the sample studies with “non-specific” research domain use datasets from the ‘product/service review‘ domain, but their datasets were also dominated by only four SemEval datasets on two types of product and service reviews

It is noteworthy that among the 519 reviewed studies, 20 focused on cross-domain or domain-agnostic ABSA, and 19 of them did not have a specific research application domain. However, while all 20 studies used multiple datasets, 17 solely involved the “product/service review” domain category by using reviews of restaurants and different products, and 14 used at least one SemEval dataset. The only three studies that went beyond the “product/service review” dataset domain added in movie reviews, singer reviews, and generic tweets.

4.2 Results for RQ2

RQ2. What are the most common ABSA problem formulations via subtask combinations, and what proportion of ABSA studies only focus on a specific sub-task?

For RQ2, we examined the 13 recorded subtasks and 805 unique “study-subtask” pairs to identify the most explored ABSA subtasks and subtask combinations across the 519 reviewed studies. As shown in Fig.  5 a, 32.37% ( \(\textrm{N}=168\) ) of the studies developed full-ABSA solutions through the combination of AE and ASC, and a similar proportion (30.83%, \(\textrm{N}=160\) ) focused on ASC alone, usually formulating the research problem as contextualised sentiment analysis with given aspects and the full input text. Only 15.22% ( \(\textrm{N}=79\) ) of the studies solely explored the AE problem. This is consistent with the number of studies by individual subtasks shown in Fig.  5 b, where ASC is the most explored subtask, followed by AE and ACD.

Moreover, Fig.  6 reveals a small but noticeable rise in composite subtask ASTE since 2020 ( \(\textrm{N}=1\) , 5 and 10 in 2017, 2021, 2022) and a decline in ASC and AE around the same period. This could signify a problem formulation shift driven by deep-learning, especially multi-task learning methods for E2E ABSA. Our Phase-2 targeted review findings in Sect.  4.5 add more insights into this.

Number of studies by ABSA subtask

Distribution of unique “Study–ABSA subtask” pairs by publication year ( \(\textrm{N}=805\) ). This graph excludes the one 2023 study (extracted in October 2022) to avoid trend confusion

4.3 Results for RQ3

To answer RQ3, we examined the 519 in-scope studies along two dimensions, which we call “paradigm” and “approach”. We use “ paradigm ” to indicate whether a study employed techniques along the supervised-unsupervised dimension and other types, such as reinforcement learning. We classify non-machine-learning approaches under the “unsupervised” paradigm, as our focus is on dataset and resource dependency. By “ approach ”, we refer to the more specific type of techniques, such as deep learning (DL), traditional machine learning (traditional ML), linguistic rules (“rules” for short), syntactic features and relations (“syntactics” for short), lexicon lists or databases (“lexicon” for short), and ontology or knowledge-driven approaches (“ontology” for short).

Overall, the results suggest that our samples are dominated by fully- (60.89%) and partially-supervised (5.40%) ML methods that are more reliant on annotated datasets and prone to their impact. As to ABSA solution approaches, the sample shows that DL methods have rapidly overtaken traditional ML methods since 2017, particularly with the prevalent RNN family (55.91%) and its combination with the fast-surging attention mechanism (26.52%). Meanwhile, traditional ML and linguistic approaches have remained a small but steady force even in the most recent years. Context engineering through introducing linguistic and knowledge features to DL and traditional ML approaches was very common. More detailed results and richer findings are presented below.

4.3.1 Paradigms

Table  4 lists the number of studies per each of the main paradigms. Among the 519 reviewed studies, 66.28% ( \(\textrm{N}=344\) ) is taken up by those using somewhat- (i.e. fully-, semi- and weakly-) supervised paradigms that have varied levels of dependency on labelled datasets, where the fully-supervised ones alone account for 60.89% ( \(\textrm{N}=316\) ). Only 19.65% ( \(\textrm{N}=102\) ) of the studies do not require labelled data, which are mostly unsupervised (18.69%, \(\textrm{N}=97\) ). In addition, hybrid studies are the third largest group (14.07%, \(\textrm{N}=73\) ).

We further analysed the approaches under each paradigm and focused on three for more details: deep learning (DL), traditional machine learning (ML), and Linguistic and Statistical Approaches. The results are detailed below and presented in Fig.  7 and Tables  5 ,  6 .

Number of studies using DL and traditional ML approaches

4.4 Approaches

As shown in Fig.  7 a and Table  5 , among the 519 reviewed studies, 60.31% ( \(\textrm{N}=313\) ) employed DL approaches, and 30.83% ( \(\textrm{N}=160\) ) are DL-only. The DL-only approach is particularly prominent among fully-supervised (47.15%, \(\textrm{N}=149\) ) and semi-supervised (31.82%, \(\textrm{N}=7\) ) studies. Supplementing DL with syntactical features is also the second most popular approach in fully-supervised studies (16.77%, \(\textrm{N}=53\) ).

DL Approaches

Figure  7 a suggests that the 313 studies involving DL approaches are dominated by Recurrent Neural Network (RNN)-based solutions (55.91%, \(\textrm{N}=175\) ), of which nearly half used a combination of RNN and the attention mechanism (26.52%, \(\textrm{N}=83\) ), followed by attention-only (19.17%, \(\textrm{N}=60\) ) and RNN-only (9.90%, \(\textrm{N}=31\) ) models. The RNN family mainly consists of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU). These neural-networks are featured by sequential processing that captures temporal dependencies of text tokens, and can thus incorporate surrounding text as context for prediction (Liu et al 2020 ; Satyarthi and Sharma 2023 ). On the other hand, the sequential nature poses challenges with parallelisation and the exploding and vanishing gradient problems associated with long sequences (Vaswani et al 2017 ; Liu et al 2020 ). Although LSTM and GRU can mitigate these issues somewhat through cell state and memory controls, efficiency and long-dependency challenges still hinder their performance (Vaswani et al 2017 ; Liu et al 2020 ; Satyarthi and Sharma 2023 ). The attention mechanism complements RNNs by dynamically updating weights across the input sequence based on each element’s relevance to the current task, and thus guides the model to focus on the most relevant elements (Vaswani et al 2017 ).

In addition, convolutional and graph-neural approaches [e.g. convolutional neural networks (CNN), graph neural networks (GNN), graph convolutional networks (GCN)] also play smaller but noticeable roles in DL-based ABSA studies. While CNN was commonly used as an alternative to the sequence models such as RNNs (Liu et al 2020 ; J et al 2021 ; Zhang et al 2020 ), the graph-based networks (GNN, GCN) were mainly used to model the non-linear relationships such as external conceptual knowledge (e.g. Liang et al 2022 ) and syntactic dependency structures (e.g. Fei et al 2023a , b ; Li et al 2022c ) that are not well captured by the sequential networks like RNNs and the flat structure of the attention modules. As a result, they inject richer context into the overall learning process (Du et al 2021 ; Xu et al 2020a ; Wang et al 2022 ).

Figure  8 depicts the trend of the main approaches across the publication years. We excluded the one study pre-published for 2023 to avoid confusing trends. It is clear that DL approaches have risen sharply and taken dominance since 2017, mainly driven by the rapid growth in RNN- and attention-based studies. This coincides with the appearance of the Transformer architecture in 2017 (Vaswani et al 2017 ) and the resulting pre-trained models such as BERT (Devlin et al 2019 ) that were a popular embedding choice to be used alongside RNNs in DL and hybrid approaches (e.g., Li et al 2021 ; Zhang et al 2022a ). GNN/GCN-based approaches remain small in number but have noticeable growth since 2020 ( \(\textrm{N}=2\) , 2, 16, 24 in each of 2019–2022, respectively), suggesting an increased effort to dynamically integrate relational context into the learning process within the DL framework.

Distribution of studies per method by publication year ( \(\textrm{N}=1017\) with 519 unique studies). This graph excludes the one 2023 study (extracted in October 2022) to avoid trend confusion

Traditional ML approaches

Interestingly, as shown in Fig.  8 traditional ML approaches remain a steady force over the decades despite the rapid rise of DL methods. Table  5 and Fig.  7 b provide some insight into this: Among the 519 reviewed studies, while 60.31% employed DL approaches as mentioned in the previous sub-section, over half (54.53%, \(\textrm{N}=283\) ) also included traditional ML approaches, with the top 3 being Support Vector Machine (SVM; 20.14%, \(\textrm{N}=57\) ), Conditional Random Field (CRF; 14.49%, \(\textrm{N}=41\) ), and Latent Dirichlet allocation (LDA; 12.72%, \(\textrm{N}=36\) ). Table  5 suggests that among the major paradigms, traditional ML were often used in combination with DL approaches for fully-supervised studies (7.28%, \(\textrm{N}=23\) ), and along with linguistic rules, syntactic features, and/or lexicons and ontology in hybrid studies (27.40%, \(\textrm{N}=20\) ). Across all paradigms, traditional ML-only approaches are relatively rare (max \(\textrm{N}=7\) ).

Linguistic and statistical approaches

While Table  5 illustrates the prevalence of fusing ML approaches with linguistic and statistical features or modules, there were 67 studies (12.91% out of the total 519) on pure linguistic or statistical approaches. As shown in Table  6 , although small in number, these non-ML approaches have persisted over time. The most popular combination (34.33%, \(\textrm{N}=23\) ) was rules built on syntactic features (e.g. POS tags and dependency parse trees) and used along lexicon resources (e.g. domain-specific aspect lists, SentiWordNet, Footnote 3 MPQA Footnote 4 ). A typical example is using POS tags and/or lexicon resources to narrow the scope of aspect or opinion term candidates, applying further rules based on POS tags or dependency relations for AE or OE, and/or using lexicon resources for candidate pruning, categorisation, or sentiment labelling (e.g. Asghar et al 2019 ; Dragoni et al 2019 ; Nawaz et al 2020 ). The second top combination (23.88%, \(\textrm{N}=16\) ) is the above-mentioned one plus ontology (e.g. domain-specific ontology, ConceptNet, Footnote 5 WordNet Footnote 6 ) to bring in external knowledge of concepts and relations (e.g. Federici and Dragoni 2016 ; Marstawi et al 2017 ). Pure statistical methods were relatively rare (5.97%, \(\textrm{N}=4\) ), and mainly included frequency-based methods such as N-gram and TF-IDF, and other statistical modelling methods that were not commonly seen in the ML field.

4.5 ICL and generative approach in ABSA - Phase-2 targeted review results

ICL is a subgenre of the DL approach. However, we discuss the relevant results in this separate subsection due to the Phase-2 review’s more focused sample and finer granularity. Despite the trending popularity of the ICL approach in NLP research and applications since 2022 (Dong et al 2024 ), our results suggest that the ABSA research community is just beginning to explore it with caution. Among the 208 ABSA studies from 2008 to 2024 containing at least one occurrence of the “Gen-LLM” keywords, only five (all published in 2024) applied ICL to both composite and traditional ABSA tasks. All of these studies were exploring the performance of foundation models via ICL against other approaches, rather than focusing on an ICL ABSA solution. Table  7 summarises the models, ABSA tasks, and key findings from these studies. Overall, four of the five studies found that zero-shot and even 5-shot ICL on foundation models (mainly GPTs) could not reach the performance of fine-tuned or fully trained DL models, especially those leveraging pre-trained LLMs to fine-tune a contextual-embedding.

In addition, we identified an emerging trend by examining the Phase-2 review non-ICL samples: Those employing fine-tuned generative LLMs mostly formulated the ABSA tasks as Sequence-to-Sequence (Seq2Seq) text generation problems, with a particular focus on composite tasks such as ASTE and ASQE. As shown in Table  8 , within the 208 samples, a total of 18 studies (all from the new search) published in 2022–2024 applied pre-trained generative LLMs with fine-tuning. The majority of these studies used models based on T5 ( \(\textrm{N}=9\) ) and BART ( \(\textrm{N}=5\) ) with the full Transformer (Vaswani et al 2017 ) encoder-decoder architecture, followed by encoder-only ( \(\textrm{N}=3\) , BERT and RoBERTa Liu et al 2019 ) and decoder-only ( \(\textrm{N}=1\) , GPT-2 Radford et al 2019 ) models. All but two of these 18 studies were on composite ABSA tasks, mainly ASTE and ASQE. Moreover, two studies (Yu et al 2023b ; Zhang et al 2024b ) also leveraged the generative capability of these LLMs to augment training data to enrich the fine-tuned embedding.

Compared with this Seq2Seq generation approach, the common applications of pre-trained LLMs in earlier studies from the main SLR sample often formulate the ABSA task as a classification problem (Zhang et al 2022c ). These studies mostly use encoder-only LLMs for their pre-trained representations to fine-tune a contextual embedding (Zhang et al 2022c ), which is then connected to other context-injection or relationship-learning modules and a classifier output layer. For instance, Zhang et al ( 2022a ) employed pre-trained BERT with BiLSTM, a feed-forward neural network (FFNN), and CRF. Li et al ( 2021 ) used pre-trained BERT as an encoder and a decoder featuring a GRU. In contrast, the Seq2Seq generative approach can be illustrated by the signature “Generative Aspect-based Sentiment analysis (GAS)” proposed by Zhang et al ( 2021b ), which leveraged the LLM’s pre-trained and fine-tuned encoder module for context-aware embedding and used the fine-tuned decoder module to generate text representations of the label sets (e.g., triplets) or as annotations next to the original input text (Zhang et al 2021b , 2022c ).

5 Discussion

This review was motivated by the literature gap in capturing trends in ABSA research to answer higher-level questions beyond technical details, and the concern that the domain-dependent nature could predispose ABSA research to systemic hindrance from a combination of resource-reliant approaches and skewed resource domain distribution. By systematically reviewing the two waves of 727 in-scope primary studies published between 2008 and 2024, our quantitative analysis results identified trends in ABSA solution approaches, confirmed the above-mentioned concern, and provided detailed insights into the relevant issues. In this section, we examine the primary findings, share ideas for future research, and reflect on the limitations of this review.

5.1 Significant findings and trends

5.1.1 the out-of-sync research and dataset domains.

Under RQ1, we examined the distributions of and relationships between our sample’s research (application) domains and dataset domains. The results showed strong skewness in both types of domains and a significant mismatch between them: While the majority (65.32%, \(\textrm{N}=339\) ) of the 519 studies did not aim for a specific research domain, a greater proportion (70.95%, \(\textrm{N}=447\) ) used datasets from the “product/service review” domain. A closer inspection of the link between the two domains revealed a clear mismatch: Among the 757 unique “research-domain, dataset-domain, dataset-name” combinations with ten or more studies: 90.48% ( \(\textrm{N}=656\) ) of the studies in the “non-specific” research domain (95.77%, \(\textrm{N}=725\) ) used datasets from the “product/service review” domain. This suggests that the lack of non-commercial-domain datasets could have forced generic technical studies to use benchmark datasets from a single popular domain. Given ABSA problem’s domain-dependent nature, this could have indirectly hindered the solution development and evaluation across domains.

The results also showed that the other important and prevalent ABSA application domains such as education, medicine/healthcare, and public policy, were clearly under-researched and under-resourced. Among the reviewed samples from these three public-sector domains, about half of their datasets were created for the studies by their authors, indicating a lack of public dataset resources, hence the cost and challenge of developing ABSA research in these areas. As a likely consequence, even the most researched domain among these three had only 12 studies (2.31% out of 519) since 2008. The dataset resource scarcity in these public sector domains deserves more research community attention and support, especially given these domains’ overall low research resources vs. the high cost and domain knowledge required for quality data annotation. In particular, for domains such as “Student feedback/education review” that often face strict data privacy and consent restrictions, it is crucial that the ABSA research community focus on creating ethical and open-access datasets to leverage community resources.

5.1.2 The dominance and limitations of the SemEval datasets

The results under RQ1 also revealed further issues with dataset diversity, even within the dominant “product/service review” domain. Out of the 757 unique “research-domain, dataset-domain, dataset-name” combinations with ten or more studies, 78.20% ( \(\textrm{N}=592\) ) are taken up by the four popular SemEval datasets: The SemEval 2014 Restaurant and Laptop datasets alone account for 50.33% of all 757 entries, and the other two (SemEval 2015 and 2016 Restaurant).

The level of dominance of the SemEval datasets is alerting, not only because of their narrow domain range, but also for the inheritance and impact of the SemEval datasets’ limitations. Several studies (e.g. Chebolu et al 2023 ; Xing et al 2020 ; Jiang et al 2019 ; Fei et al 2023a ) suggest that these datasets fail to capture sufficient complexity and granularity of the real-world ABSA scenarios, as they primarily only include single-aspect or multi-aspect-but-same-polarity sentences, and thus mainly reflect sentence-level ABSA tasks and ignored subtasks such as multi-aspect multi-sentiment ABSA. The experiment results from Xing et al ( 2020 ), Jiang et al ( 2019 ) and Fei et al ( 2023a ) consistently showed that all 35 ABSA models (including those that were state-of-the-art at the time) (9 in Xing et al 2020 , 16 in Jiang et al 2019 , 10 in Fei et al 2023a ) that were trained and performed well on the SemEval 2014 ABSA datasets showed various extents of performance drop (by up to 69.73% in Xing et al 2020 ) when tested on same-source datasets created for more complex ABSA subtasks and robustness challenges. Given that the SemEval datasets are heavily used as both training data and “benchmark” to measure ABSA solution performance, their limitations and prevalence are likely to form a self-reinforcing loop that confines ABSA research. To break free from this dataset-performance self-reinforcing loop, it is critical that the ABSA research community be aware of this issue, and develop and adopt datasets and practices that are robustness-oriented, such as the automatic data-generation framework and the resulting Aspect Robustness Test Set (ARTS) developed by Xing et al ( 2020 ) for probing model robustness in distinguishing target and non-target aspects, and the Multi-Aspect Multi-Sentiment (MAMS) dataset created by Jiang et al ( 2019 ) to reflect more realistic challenges and complexities in aspect-term sentiment analysis (ATSA) and aspect-category sentiment analysis (ACSA) tasks.

5.1.3 The reliance on labelled-datasets

The domain and dataset issues discussed above would not be as problematic if most ABSA studies employed methods that are dataset-agnostic. However, our results under RQ3 show the opposite. Only 19.65% ( \(\textrm{N}=102\) , with 97 being unsupervised) of the 519 reviewed studies do not require labelled data, whereas 66.28% ( \(\textrm{N}=344\) ) are somewhat-supervised, and fully-supervised studies alone account for 60.89% ( \(\textrm{N}=316\) ).

As demonstrated in Sect.  2.3 , the domain can directly affect whether a chunk of text is considered an aspect or the relevant sentiment term, and plays a crucial role in contextual inferences such as implicit aspect extraction and multi-aspect multi-sentiment pairing. The domain knowledge reflected via ABSA labelled datasets can further shape the linguistic rules, lexicons, and knowledge graphs for non-machine-learning approaches; and define the underpinning feature space, representations, and acquired relationships and inferences for trained machine-learning models. When applying a solution built on datasets from a domain that is very remote from or much narrower than the intended application domain, it is predictable that the solution performance would be capped at subpar and even fail at more context-heavy tasks (Phan and Ogunbona 2020 ; You et al 2022 ; Liang et al 2022 ; Howard et al 2022 ; Chen and Qian 2022 ; Zhang et al 2022b ; Nazir et al 2022b ). Thus, domain transfer is crucially necessary for balancing the uneven ABSA research and resource distributions across domains. However, our finding that 17 out of the 20 reviewed cross-domain or domain-agnostic ABSA studies solely used datasets from the “product/service review” domain raised questions about these approaches’ generalisability and robustness in other domains, as well as whether such dataset choices became another reinforcement of concentrating research effort and benchmarks within this one dominant domain.

The rapid rise of deep learning (DL) in ABSA research could further add to the challenge of overcoming the negative impact of this domain mismatch and dataset limitations via the non-linear multi-layer dissemination of bias in the representation and learned relations, thus making problem-tracking and solution-targeting difficult. In reality, of the 519 reviewed studies, 60.31% ( \(\textrm{N}=313\) ) employed DL approaches, and nearly half (47.15%, \(\textrm{N}=149\) ) of the fully-supervised studies and 30.83% ( \(\textrm{N}=160\) ) of all reviewed studies were DL-only.

Moreover, RNN-based solutions dominate the DL approaches (55.91%, \(\textrm{N}=175\) ), mainly with the RNN-attention combination (26.52%, \(\textrm{N}=83\) ) and RNN-only (9.90%, \(\textrm{N}=31\) ) models. RNN and its variants such as LSTM, BiLSTM, and GRU are known for their limitations in capturing long-distance relations due to their sequential nature and the subsequent memory constraints (Vaswani et al 2017 ; Liu et al 2020 ). Although the addition of the attention mechanism enhances the model’s focus on more important features such as aspect terms (Vaswani et al 2017 ; Liu et al 2020 ), traditional attention weights calculation struggles with multi-word aspects or multi-aspect sentences (Liu et al 2020 ; Fan et al 2018 ). In addition, whilst 16.77% ( \(N=53\) ) of the fully-supervised studies introduced syntactical features to their DL solutions, additional features also increased the input size. According to Prather et al ( 2020 ), sequential models, even the state-of-the-art LLMs, showed impaired performance as the input grew longer and could not always benefit from additional features.

5.1.4 The potential of generative LLMs and foundation models

Lastly, the Phase-2 targeted review highlights the ABSA community’s caution toward the direct adoption of generative foundation models, with only five out of 208 recent studies testing the ICL approach and most yielding subpar results compared to other methods. However, most of these studies only tested zero-shot instructions with simple model settings. It is worth further exploring the potential of foundational models and ICL in ABSA by focusing more on instruction and example engineering, model parameter optimisation, and task re-formulation (Dong et al 2024 ).

On the other hand, the fine-tuning of smaller generative LLMs has seen increasing adoption through the “ABSA as Seq2Seq text generation” approach, demonstrating promising task performance. Although this generative approach can incorporate data augmentation and self-training to reduce reliance on labelled datasets, the cost of fine-tuning, the need for labelled base data, and the domain-transfer problem remain significant challenges (Zhang et al 2022c ). In this context, the task adaptability and multi-domain pre-trained knowledge of foundation models could provide potential solutions.

As Zhang et al ( 2022c ) noted, progress in applying pre-trained LLMs and foundation models to ABSA could be impeded by dataset resources constraints. To match the parameter size of these models, more diverse, complex, and larger datasets are required for effective fine-tuning or comprehensive testing. In low-resource domains where dataset resources are already limited, this requirement could further complicate the adoption of these technologies (Satyarthi and Sharma 2023 ).

5.2 Ideas for future research

Overall, by adopting a “systematic perspective, i.e., model, data, and training” (Fei et al 2023a ,  p.28) combined with a quantitative approach, we identified high-level trends unveiling the development and direction of ABSA research, and found clear evidence of large-scale issues that affect the majority of the existing ABSA research. The skewed domain distributions of resources and benchmarks could also restrict the choice of new studies. On the other hand, this evidence also highlights areas that need more attention and exploration, including: ABSA solutions and resource development for the less-studied domains (e.g. education and public health), low-resource and/or data-agnostic ABSA, domain adaptation, alternative training schemes such as adversarial (e.g. Fei et al 2023a ; Chen et al 2021 ) and reinforcement learning (e.g. Vasanthi et al 2022 ; Wang et al 2021b ), and more effective feature and knowledge injection. Future research could contribute to addressing these issues by focusing on ethically producing and sharing more diverse and challenging datasets in minority domains such as education and public health, improving data synthesis and augmentation techniques, exploring methods that are less data-dependent and resource-intensive, and leveraging the rapid advancements in pre-trained LLMs and foundation models.

In addition, our results also revealed emerging trends and new ideas. The relatively recent growth of end-to-end models and composite ABSA subtasks provide opportunities for further exploration and evaluation. The fact that hybrid approaches with non-machine-learning techniques and non-textual features remain steady forces in the field after nearly three decades suggests valuable characteristics that are worth re-examining under the light of new paradigms and techniques. Moreover, the small number of Phase-2 samples using ICL and fine-tuning generative LLM approaches may suggest that we have only captured early adopters. More thorough exploration of these approaches and continued tracking of their development alongside other methods are necessary to understand how the ABSA community can leverage the resources and capabilities embedded within LLMs and foundation models.

Lastly, it is crucial that the community invest in solution robustness, especially for machine-learning approaches (Xing et al 2020 ; Jiang et al 2019 ; Fei et al 2023a ). This could mean critical examination of the choice of evaluation metrics, tasks, and benchmarks, and being conscious of their limitations vs. the real-world challenges. The “State-Of-The-Art” (SOTA) performance based on certain benchmark datasets should never become the motivation and holy grail of research, especially in fields like ABSA where the real use cases are often complex and even SOTA models do not generalise far beyond the training datasets. More attention and effort should be paid to analysing the limitations and mistakes of ABSA solutions, and drawing from the ideas of other disciplines and areas to fill the gaps.

5.3 Limitations

We acknowledge the following limitations of this review: First, our sample scope is by no means exhaustive, as it only includes primary studies from four peer-reviewed digital databases and only those published in the English language. Although this can be representative of a core proportion of ABSA research, it does not generalise beyond this without assumptions. The “peer-reviewed” criteria also meant that we overlooked preprint servers such as arXiv.org that more closely track the latest development of ML and NLP research. Second, no search string is perfect. Our database search syntax and auto-screening keywords represent our best effort in capturing ABSA primary studies, but may have missed some relevant ones, especially with the artificial “total pages \(< 3\) ” and “total keyword (except SA, OM) outside Reference \(< 5\) ” exclusion criteria. Moreover, our search completeness might have been affected by the performance of the database search engines. This is evidenced by the significant number of extracted search results that were entirely irrelevant to the search keywords, as well as our abandonment of the 2024 SpringerLink search due to interface issues. Enhancements in digital database search capabilities could significantly improve the effectiveness and reliability of future literature review studies, particularly SLRs. Third, we may have missed datasets, paradigms, and approaches that are not clearly described in the primary studies, and our categorisation of them is also subject to the limitations of our knowledge and decisions. Future review studies could consider a more innovative approach to enhance analytical precision and efficiency, such as applying ABSA and text summarisation alongside the screening and reviewing process. Fourth, we did not compare solution performance across studies due to the review focus, sample size, and the variability in experimental settings across studies. Evaluating the effectiveness of comparable methods and the suitability of evaluation metrics would enhance our findings and offer more valuable insights.

6 Conclusion

ABSA research is riding the wave of the explosion of online digital opinionated text data and the rapid development of NLP resources and ideas. However, its context- and domain-dependent nature and the complexity and inter-relations among its subtasks pose challenges to improving ABSA solutions and applying them to a wider range of domains. In this review, we systematically examined existing ABSA literature in terms of their research application domain, dataset domain, and research methodologies. The results suggest a number of potential systemic issues in the ABSA research literature, including the predominance of the “product/service review” dataset domain among the majority of studies that did not have a specific research application domain, coupled with the prevalence of dataset-reliant methods such as supervised machine learning. We discussed the implication of these issues to ABSA research and applications, as well as their implicit effect in shaping the future of this research field through the mutual reinforcement between resources and methodologies. We suggested areas that need future research attention and proposed ideas for exploration.

Our PDF mining for automatic review screening code is available at https://doi.org/10.5281/zenodo.12872948 .

This new database search followed the same procedures and criteria as the SLR, except that we aborted the SpringerLink search due to persistent database interface search result navigation issues during our data collection period.

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Appendix A: Aspect-based sentiment analysis (ABSA)

1.1 appendix a.1: definition and examples.

Aspect-based sentiment analysis (ABSA) is a sub-domain of fine-grained SA (Nazir et al 2022a ). ABSA focuses on identifying the sentiments towards specific entities or their attributes/ features called aspects (Nazir et al 2022a ; Akhtar et al 2020 ). An aspect can be explicitly expressed in the text ( explicit aspect ) or absent from the text but implied from the context ( implicit aspects ) (Maitama et al 2020 ; Xu et al 2020b ). Moreover, the aspect-level sentiment could differ across aspects and be different from the overall sentiment of the sentence or the document (e.g. Akhtar et al 2020 , 2017 ; Li et al 2022a ). Some studies further distinguish aspect into aspect term and aspect category , with the former referring to the aspect expression in the input text (e.g. “pizza”), and the latter a latent construct that is usually a high-level category across aspect terms (e.g. “food”) that are either identified or given (Chauhan et al 2019 ; Akhtar et al 2018 ).

The following examples illustrate the ABSA terminologies:

(From a restaurant review Footnote 7 ): “ The restaurant was expensive, but the menu was great. ” This sentence has one explicit aspect “menu” (sentiment term: “great”, sentiment polarity: positive), one implicit aspect “price” (sentiment term: “expensive”, sentiment polarity: negative). Depending on the target/given categories, the aspects can be further classified into categories, such as “menu” into “general” and “price” into “price”.

(From a laptop review Footnote 8 ): “ It is extremely portable and easily connects to WIFI at the library and elsewhere. ” This sentence has two implicit aspects: “portability” (sentiment term: “portable”, sentiment polarity: positive), “connectivity” (sentiment term: “easily”, sentiment polarity: positive). The aspects can be further classified into categories, such as both under “laptop” (as opposed to “software” or “support”).

(Text from a course review): “ It was too difficult and had an insane amount of work, I wouldn’t recommend it to new students even though the tutorial and the lecturer were really helpful .” The two explicit aspects in Example 3 are “tutorial” and “lecturer” (sentiment terms: “helpful”, polarities: positive). The implicit aspects are “content” (sentiment term: “too difficult”, sentiment polarity: negative), “workload” (sentiment term: “insane amount”, sentiment polarity: negative), and “course” (sentiment term: “would not recommend”, sentiment polarity: negative). An illustration of aspect categories would be assigning the aspect “lecturer” to the more general category “staff” and “tutorial” to the category “course component”.

As demonstrated above, the fine granularity makes ABSA more targetable and informative than document- or sentence-level SA. Thus, ABSA can precede downstream applications such as attribute weighting in overall review ratings (e.g. Da’u et al 2020 ), aspect-based opinion summarisation (e.g. Yauris and Khodra 2017 ; Kumar et al 2022 ; Almatrafi and Johri 2022 ), and automated personalised recommendation systems (e.g. Ma et al 2017 ; Nawaz et al 2020 ).

Compared with document- or sentence-level SA, while being the most detailed and informative, ABSA is also the most complex and challenging (Huan et al 2022 ). The most noticeable challenges include the number of ABSA subtasks, their interrelations and context dependencies, and the generalisability of solutions across topic domains.

1.2 Appendix A.2: ABSA Subtasks

A full ABSA solution has more subtasks than coarser-grained SA. The most fundamental ones (Li et al 2022a ; Huan et al 2022 ; Li et al 2020 ; Fei et al 2023b ) include:

Aspect (term) extraction/identification (AE) , which has a slight variation in meaning depending on the overall ABSA approach. Some authors (e.g. (Zhang et al 2023 ; Luo et al 2019 ; Ruskanda et al 2019 )) consider AE as identifying the attribute or entity that is the target of an opinion expressed in the text and sometimes call it “opinion target extraction” (Guo et al 2018 ). In these cases, opinion terms were often identified in order to find their target aspect terms. Others (e.g. Akhtar et al 2020 ; Gunes 2016 ; Li et al 2020 ; Ettaleb et al 2022 ; Tran et al 2020 ) define AE as identifying the key or all attributes of entities mentioned in the text. Implicit-Aspect Extraction (IAE) is often mentioned as a task by itself due to its technical challenge.

Opinion (term) Extraction/Identification (OE) , which relates to identifying the “opinion terms” or the sentiment expression of a specific entity/aspect (e.g. Li et al 2022a ; Wang et al 2018 ; Fernando et al 2019 ; Fei et al 2023b ). In Example 1 above, an OE task would extract the sentiment terms “great” (associated with the aspect term “menu”) and “expensive” (associated with the implicit aspect “price”).

Aspect-Sentiment Classification (ASC) , which refers to obtaining the sentiment polarity category (e.g. negative, neutral, positive, conflict) or sentiment score (e.g. 1 to 5 or \(-1\) to 1 along the scale from negative to positive) associated with a given aspect or aspect category (e.g. Akhtar et al 2020 ; Gojali and Khodra 2016 ; Castellanos et al 2011 ). This is often done via evaluating the associated opinion term(s), and sentiment lexicon resources such as the SentiWordNet (Baccianella et al 2010 ) and SenticNet (Cambria et al 2016 ) can be used to assign polarity scores (Gojali and Khodra 2016 ). Sentiment scores can be further aggregated across opinion terms for the same aspect, or across aspect terms to generate higher-level ratings, such as aspect-category ratings within or across documents (Gojali and Khodra 2016 ; Castellanos et al 2011 ).

As an extension of AE, some studies also involve Aspect-Category Detection (ACD) and Aspect Category Sentiment Analysis (ACSA) when the focus of sentiment analysis is on (often pre-defined) latent topics or concepts and requires classifying aspect terms into categories (Pathan and Prakash 2022 ).

Traditional full ABSA solutions often perform the subtasks in a pipeline manner (Li et al 2022b ; Nazir and Rao 2022 ) using one or more of the linguistic (e.g. lexicons, syntactic rules, dependency relations), statistical (e.g. n-gram, Hidden Markov Model (HMM)), and machine-learning approaches (Maitama et al 2020 ; Cortis and Davis 2021 ; Federici and Dragoni 2016 ). For instance, for AE and OE, some studies used linguistic rules and sentiment lexicons to first identify opinion terms and then the associated aspect terms of each opinion term, or vice versa (e.g. You et al 2022 ; Cavalcanti and Prudêncio 2017 ), and then moved on to ASC or ACD using a supervised model or unsupervised clustering and/or ontology (Nawaz et al 2020 ; Gojali and Khodra 2016 ). Hybrid approaches are common given the task combinations in a pipeline.

With the rise of multi-task learning and deep learning (Chen et al 2022 ), an increasing number of studies explore ABSA under an End-to-end (E2E) framework that performs multiple fundamental ABSA subtasks in one model to better capture the inter-task relations (Liu et al 2024 ), and some combine them into a single composite task (Huan et al 2022 ; Li et al 2022b ; Zhang et al 2022b ). These composite tasks are most commonly formulated as a sequence- or span-based tagging problem (Huan et al 2022 ; Li et al 2022b ; Nazir and Rao 2022 ). The most common composite tasks are: Aspect-Opinion Pair Extraction (AOPE) , which directly outputs {aspect, opinion} pairs from text input (Nazir and Rao 2022 ; Li et al 2022c ; Wu et al 2021 ) such as “ \(\langle \) menu, great \(\rangle \) ” from Example 1; Aspect-Polarity Co-Extraction (APCE) (Huan et al 2022 ; He et al 2019 ), which outputs {aspect, sentiment polarity} pairs such as “ \(\langle \) menu, positive \(\rangle \) ”; Aspect-Sentiment Triplet Extraction (ASTE) (Huan et al 2022 ; Li et al 2022b ; Du et al 2021 ; Fei et al 2023b ), which outputs {aspect, opinion, sentiment category} triplets, such as “ \(\langle \) menu, great, positive \(\rangle \) ”; and Aspect-Sentiment Quadruplet Extraction/Prediction (ASQE/ASQP) (Zhang et al 2022a ; Lim and Buntine 2014 ; Zhang et al 2021a , 2024a ) that outputs {aspect, opinion, aspect category, sentiment category} quadruplets, such as “ \(\langle \) menu, great, general, positive \(\rangle \) ”.

1.3 Appendix A.3: Other ABSA reviews

As this review focuses on trends instead of detailed solutions and methodologies, we refer interested readers to existing review papers that provide comprehensive and in-depth summaries of common ABSA subtask solutions and approaches, for example:

Explicit and implicit AE : Rana and Cheah ( 2016 ), Ganganwar and Rajalakshmi ( 2019 ), Soni and Rambola ( 2022 ), Maitama et al ( 2020 ).

Deep learning (DL) methods for ABSA : Do et al ( 2019 ), Liu et al ( 2020 ), Wang et al ( 2021a ), Chen and Fnu ( 2022 ), Zhang et al ( 2022c ), Mughal et al ( 2024 ). Specifically:

DL methods for ASC : Zhou et al ( 2019 ), Satyarthi and Sharma ( 2023 ).

E2E ABSA, composite tasks, and pre-trained Large Language Models (LLMs) in ABSA : Zhang et al ( 2022c ) provided a comprehensive review and shared extensive reading lists and dataset resource links via https://github.com/IsakZhang/ABSA-Survey . Mughal et al ( 2024 ) introduced common benchmark datasets, including more challenging ones for composite ABSA tasks. They also reviewed and tested the ABSA task performance of representative RNN-based models and pre-trained LLMs.

Multimodal ABSA : Zhao et al ( 2024 ).

Appendix B: Full SLR methodology

This section provides a complete, detailed description of the SLR methodology and procedures.

1.1 Appendix B.1: Research identification

To obtain the files for review, we conducted database searches between 24–25 October 2022, when we manually queried and exported a total of 4191 research papers’ PDF and BibTeX (or the equivalent) files via the web interfaces of four databases. Table  9 details the search string, search criteria, and the PDF files exported from each database.

Given the limited search parameters allowed in these digital databases, we adopted a “search broad and filter later” strategy. These database search strings were selected based on pilot trials to capture the ABSA topic name, the relatively prevalent yet unique ABSA subtask term (“extraction”), and the interchangeable use between ABSA and opinion mining; while avoiding generating false positives from the highly active, broader field of SA. The “filter later” step was carried out during the “selection of primary studies” stage introduced in the next section, which aimed at excluding cases where the keywords are only mentioned in the reference list or sparsely mentioned as a side context, and opinion mining studies that were at document or sentence levels.

1.2 Appendix B.2: Selection of primary studies

After obtaining the 4191 initial search results, we conducted a pilot manual file examination of 100 files to refine the pre-defined inclusion and exclusion criteria. We found that some search results only contained the search keywords in the reference list or Appendix, which was also reported in Prather et al ( 2020 ). In addition, there are a number of papers that only mentioned ABSA-specific keywords in their literature review or introduction sections, and the studies themselves were on coarser-grained sentiment analysis or opinion mining. Lastly, there were instances of very short research reports that provided insufficient details of the primary studies. Informed by these observations, we refined our inclusion and exclusion criteria to those in Table  1 in Sect.  3 . Note that we did not include popularity criteria such as citation numbers so we can better identify novel practices and avoid mainstream method over-dominance introduced by the citation chain (Chu and Evans 2021 ).

To implement the inclusion and exclusion criteria, we first applied PDF mining to automatically exclude files that meet the exclusion criteria, and then refined the selection with manual screening under the exclusion and inclusion criteria. Both of these processes are detailed below. Our PDF mining for automatic review screening code is also available at https://doi.org/10.5281/zenodo.12872948 .

The automatic screening consists of a pipeline with two Python packages: Pandas (Team 2023 ) and PyMuPDF. Footnote 9 We first used Pandas to extract into a dataframe (i.e. table) all exported papers’ file locations and key BibTex or equivalent information including title, year, page number, DOI, and ISBN. Next, we used PyMuPDF to iterate through each PDF file and add to the dataframe multiple data fields: whether the file was successfully decoded Footnote 10 for text extraction (if marked unsuccessful, the file was marked for manual screening), the occurrence count of each Regex keyword pattern listed below, and whether each keyword occurs after the section headings that fit into Regex patterns that represent variations of “references” and “bibliography” (referred to as “non-target sections” below). We then marked the files for exclusion by evaluating the eight criteria listed under “Auto-excluded” in Table  10 against the information recorded in the dataframe. Each of the auto-exclusion results from Steps 1–4 and 7 in Table  10 were manually checked, and those under Steps 5, 6, and 8 were spot-checked. These steps excluded 3277 out of the 4194 exported files.

Below are the regex patterns used for automatic keyword extraction and occurrence calculation:

PDF search keyword Regex list: [’absa’, ’aspect \(\backslash \) W+base \(\backslash \) w*’, ’aspect \(\backslash \) W+extrac \(\backslash \) w*’, ’aspect \(\backslash \) W+term \(\backslash \) w*’, ’aspect \(\backslash \) W+level \(\backslash \) w*’, ’term \(\backslash \) W+level’, ’sentiment \(\backslash \) W+analysis’, ’opinion \(\backslash \) W+mining’]

For the 914 files filtered through the auto-exclusion process, we manually screened them individually according to the inclusion and exclusion criteria. As shown in the second half of Table  10 , this final screening step refined the review scope to 519 papers.

1.3 Appendix B.3: Data extraction and synthesis

In the final step of the SLR, we manually reviewed each of the 519 in-scope publications and recorded information according to a pre-designed data extraction form. The key information recorded includes each study’s research focus, research application domain (“research domain” below), ABSA subtasks involved, name or description of all the datasets directly used, model name (for machine-learning solutions), architecture, whether a certain approach or paradigm is present in the study (e.g. supervised learning, deep learning, end-to-end framework, ontology, rule-based, syntactic-components), and the specific approach used (e.g. attention mechanism, Naïve Bayes classifier) under the deep learning and traditional machine learning categories.

After the data extraction, we performed data cleaning to identify and fix recording errors and inconsistencies, such as data entry typos and naming variations of the same dataset across studies. Then we created two mappings for the research and dataset domains described below.

For each reviewed study, its research domain was defaulted to “non-specific” unless the study mentioned a specific application domain or use case as its motivation, in which case that domain description was recorded instead.

The dataset domain was recorded and processed at the individual dataset level, as many reviewed studies used multiple datasets. We standardised the recorded dataset names, checked and verified the recorded dataset domain descriptions provided by the authors or the source web-pages, and then manually categorised each domain description into a domain category. For published/well-known datasets, we unified the recorded naming variations and checked the original datasets or their descriptions to verify the domain descriptions. For datasets created (e.g. web-crawled) by the authors of the reviewed studies, we named them following the “[source] [domain] (original)” format, e.g. “Yelp restaurant review (original) ”, or “Twitter (original)” if there was no distinct domain, and did not differentiate among the same-name variations. In all of the above cases, if a dataset was not created with a specific domain filter (e.g. general Twitter tweets), then it was classified as “non-specific”.

The recorded research and dataset domain descriptions were then manually grouped into 19 common domain categories. We tried to maintain consistency between the research and dataset domain categories. The following are two examples of possible mapping outcomes:

A study on a full ABSA solution without mentioning a specific application domain and using Yelp restaurant review and Amazon product review datasets would be assigned a research domain of “non-specific” and a dataset domain of “product/service review”.

A study mentioning “helping companies improve product design based on customer reviews” as the motivation would have a research domain of “product/service review”, and if they used a product review dataset and Twitter tweets crawled without filtering, the dataset domains would be “product/service review” and “non-specific”.

After applying the above-mentioned standardisation and mappings, we analysed the synthesised data quantitatively using the Pandas (Team 2023 ) library to obtain an overview of the reviewed studies and explore the answers to our RQs.

Appendix C: Additional results

See Figs.  9 ,  10 and Tables  11 ,  12 ,  13 ,  14 ,  15 .

Number of included studies by publication year and type ( \(\textrm{N}=519\) ). Note Although our original search scope included journal articles, conference papers, newsletters, and magazine articles, the final 519 in-scope studies consist of only journal articles and conference papers. Conference papers noticeably outnumbered journal articles in all years until 2022, with the gap closing since 2016. We think this trend could be due to multiple factors, such as the fact that our search was conducted in late October 2022 when some conference publications were still not available; the publication lag for journal articles due to a longer processing period; and potentially a change in publication channels that is outside the scope of this review

Number of included studies with the top 5 dataset languages by publication year

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Hua, Y.C., Denny, P., Wicker, J. et al. A systematic review of aspect-based sentiment analysis: domains, methods, and trends. Artif Intell Rev 57 , 296 (2024). https://doi.org/10.1007/s10462-024-10906-z

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Accepted : 06 August 2024

Published : 17 September 2024

DOI : https://doi.org/10.1007/s10462-024-10906-z

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