what are quantitative research methods in psychology

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1 Introduction

Todd D. Little, Texas Tech University, Lubbock, Texas

• Published: 01 October 2013
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In this introductory chapter to The Oxford Handbook of Quantitative Methods , I provide an overview of the two volumes. More specifically, I describe the rationale and motivation for the selected topics that are presented in volumes. I also list out my instructions to the chapter authors and then describe how the chapters fit together into thematic groupings. I also extend my sincerest gratitude to the persons who assisted me along the way, as no work this comprehensive can be done without the considerable help and assistance of many persons. I conclude with how pleased I am with the quality and comprehensiveness of the chapters that are included.

Oxford Introduction

Handbooks provide a crucial venue to communicate the current state of the field. They also provide a one-stop source for learning and reviewing current best practices in a field. The Oxford Handbook of Quantitative Methods serves both of these functions. The field of quantitative methods is quite broad, as you can probably imagine. I have tried to be thorough in my selection of topics to be covered. As with any handbook of this magnitude, some topics were all set to have a contribution submitted, only to have some unforeseen hindrance preclude its inclusion at the last minute (e.g., graphical representations of data, ecological inference, history of quantitative methods). Some topics overlap with others and may not have found their way to become a separate chapter, but their fundamental elements are found in parts of other chapters.

This handbook is one of many that Oxford University Press (OUP) is assembling but will be the capstone methodology handbook. As many of you know, OUP is building a comprehensive and synthetic Library of Handbooks covering the field of psychology (the Editor-in-Chief of the library is Peter Nathan, University of Iowa Foundation Distinguished Professor of Psychology and Public Health). The library comprises handbooks in the truest sense of the word: books that summarize and synthesize a topic, define the current scholarship, and set the agenda for future research. Each handbook is published as a bound book, and it will also be developed for electronic delivery. In this format, the content will be integrated across topics and available as a fully integrated electronic library. I think the idea of a comprehensive electronic library is very forward-thinking. This format is a very attractive opportunity to have a fully comprehensive and up-to-date handbook of methods in our field. Hence, I agreed to take on the role of editor of The Oxford Handbook of Quantitative Methods .

I am very pleased with the quality of the work that each author provided. As per my request to the contributing authors, each chapter is meant to be both accessible and comprehensive; nearly all the authors were very responsive to my requests. The guidelines I asked authors to consider were:

Handbook chapters should be comprehensive and authoritative; readers will rely heavily on these chapters, particularly when they move to the online format.

Handbook chapters should present not only the strengths of the topic covered but also any limitations.

Handbook chapters should make all assumptions underlying the topic explicit.

Regarding citations, handbook chapters should cover the historical origins as well as the recent renditions of a given key topic.

Handbook chapters should not present one-sided views on any debate; rather, they should report the issues and present the arguments—both pro and con. Authors can direct readers to other platforms where a position piece is presented.

To facilitate the online linkages, handbook chapters should point to other online resources related to the topic presented.

Every element of every formula presented must be explicitly explained; assume no knowledge of how to read formulae.

Examples, examples, examples, and, when in doubt, provide an example! Concrete examples are absolutely critical to communicate quantitative content.

Avoid jargon and acronyms. Please spell out acronyms, and if you use jargon, please remind the reader of the meaning or definition of the jargon every three to four times it is used; similarly, if you use an acronym, then remind the reader of what it means every three to four times it is used.

Use active voice, and do not shy away from the use of I/me or we/us. Channel how you lecture on the topic. It will create a crisp and enjoyable read.

Do not start a sentence with “This” followed by a verb. The referent to “this” must be restated because of the ambiguity this creates. This general guideline should be followed as a rule!

Authors, like editors, have preferences and habits, so you will find places, chapters, and so on where some of my admonitions were not followed. But the quality of the product that each chapter provides is nonetheless uncompromised. We have established a Wiki-based resource page for the handbook, which can be found at crmda.KU.edu/oxford. Each author has been asked to maintain and upload materials to support his or her chapter contribution. At the top of that page is a link that encourages you to offer comments and suggestions on the topics and coverage of the handbook. These comments will be reviewed and integrated into future editions of this handbook. I encourage you, therefore, to take advantage of this opportunity to help shape the directions and content coverage of this handbook.

Statistical software has blossomed with the advent of hardware that provides the necessary speed and memory and programming languages coupled with numerical algorithms that are more efficient and optimized than yesteryear. These software advances have allowed many of the advances in modern statistics to become accessible to the typical end-user. Modern missing data algorithms and Bayesian estimation procedures, for example, have been the beneficiaries of these advances. Of course, some of the software developments have included simplified interfaces with slick graphic user interfaces. The critical options are usually prefilled with default settings. These latter two aspects of advancing software are unfortunate because they lead to mindless applications of the statistical techniques. I would prefer that options not be set as default but, rather, have the software prompt the user to make a choice (and give good help for what each choice means). I would prefer that a complete script of the GUI choices and the order in which steps were taken be automatically saved and displayed.

I have organized the handbook by starting with some basics. It begins with the philosophical underpinnings associated with science and quantitative methods (Haig, Chapter 2 , Volume 1) followed by a discussion of how to construct theories and models so that they can be tested empirically and the best model selected (Jaccard, Chapter 5 , Volume 1). I then turn to an enlightened discussion of ethics in the conduct of quantitative research (Rosnow & Rosenbloom, Chapter 3 , Volume 1) and related issues when quantitative methods are applied in special populations (Widaman, Early, & Conger, Chapter 4 , Volume 1). Harlow (Chapter 6 , Volume 1) follows with an encompassing and impassioned discussion of teaching quantitative methods.

The theme in the next grouping of chapters centers on measurement issues. First, the late McDonald (Chapter 17 , Volume 1) provides a thorough overview of Modern Test Theory. 1 De Ayala (Chapter 8 , Volume 1) adds a detailed discussion of Item Response Theory as an essential measurement and analysis tool. After these principles of measurement are discussed, the principles and practices surrounding survey design and measure development are presented (Spector, Chapter 9 , Volume 1). Kingston and Kramer (Chapter 10 , Volume 1) further this discussion in the context of high-stakes testing.

A next grouping of chapters covers various design issues. Kelley (Chapter 11 , Volume 1) begins this section by covering issues of power, effect size, and sample size planning. Hallberg, Wing, Wong, and Cook (Chapter 12 , Volume 1) then address key experimental designs for causal inference: the gold standard randomized clinical trials (RCT) design and the underutilized regression discontinuity design. Some key quasi-experimental procedures for comparing groups are discussed in Steiner and Cooks’ (Chapter 13 , Volume 1) chapter on using matching and propensity scores. Finally, Van Zandt and Townsend (Chapter 14 , Volume 1) provide a detailed discussion of the designs for and analyses of response time experiments. I put observational methods (Ostrov & Hart, Chapter 15 , Volume 1), epidemiological methods (Bard, Rodgers, & Mueller, Chapter 16 , Volume 1), and program evaluation (Figueredo, Olderbak, & Schlomer, Chapter 17 , Volume 1) in with these chapters because they address more collection and design issues, although the discussion of program evaluation also addresses the unique analysis and presentation issues.

I have a stellar group of chapters related to estimation issues. Yuan and Schuster (Chapter 18 , Volume 1) provide an overview of statistical estimation method; Erceg-Hurn, Wilcox, and Keselman (Chapter 19 , Volume 1) provide a nice complement with a focus on robust estimation techniques. Bayesian statistical estimation methods are thoroughly reviewed in the Kaplan and Depaoli (Chapter 20 , Volume 1) contribution. The details of mathematical modeling are synthesized in this section by Cavagnaro, Myung, and Pitt (Chapter 21 , Volume 1). This section is completed by Johnson (Chapter 22 , Volume 1), who discusses the many issues and nuances involved in conducting Monte Carlo simulations to address the what-would-happen-if questions that we often need to answer.

The foundational techniques for the statistical analysis of quantitative data start with a detailed overview of the traditional methods that have marked social and behavioral sciences (i.e., the General Linear Model; Thompson, Chapter 2 , Volume 2). Coxe, West, and Aiken (Chapter 3 , Volume 2) then extend the General Linear Model to discuss the Generalized Linear Model. This discussion is easily followed by Woods (Chapter 4 , Volume 2), who synthesizes the various techniques of analyzing categorical data. After the chapter on configural frequency analysis by Von Eye, Mun, Mair and von Weber (Chapter 5 , Volume 5), I then segway into nonparametric techniques (Buskirk, Tomazic, & Willoughby, Chapter 6 , Volume 2) and the more specialized techniques of correspondence analysis (Greenacre, Chapter 7 , Volume 2) and spatial analysis (Anselin, Murry, & Rey, Chapter 8 , Volume 2). This section is capped with chapters dedicated to special areas of research—namely, techniques and issues related to the analysis of imaging data (e.g., fMRI; Price, Chapter 9 , Volume 2). The closely aligned worlds of behavior genetics (i.e., twin studies; Blokland, Mosing, Verweij, & Medland, Chapter 10 , Volume 2) and genes (Medland, Chapter 11 , Volume 2) follows.

The foundations of multivariate techniques are grouped beginning with Ding’s (Chapter 12 , Volume 2) presentation of multidimensional scaling and Brown’s (Chapter 13 , Volume 2) summary of the foundations of latent variable measurement models. Hox layers in the multilevel issues as handled in both the manifest regression framework and the latent variable work of structural equation modeling. McArdle and Kadlec (Chapter 15 , Volume 2) detail, in broad terms, different structural equation models and their utility. MacKinnon, Kisbu-Sakarya, and Gottschall (Chapter 16 , Volume 2) address the many new developments in mediation analysis, while Marsh, Hau, Wen, and Nagengast (Chapter 17 , Volume 2) do the same for analyses of moderation.

The next group of chapters focuses on repeated measures and longitudinal designs. It begins with a chapter I co-wrote with Wu and Selig and provides a general overview of longitudinal models (Wu, Selig, & Little, Chapter 18 , Volume 2). Deboeck (Chapter 19 , Volume 2) takes things further into the burgeoning world of dynamical systems and continuous-time models for longitudinal data. Relatedly, Walls (Chapter 20 , Volume 2) provides an overview of designs for doing intensive longitudinal collection and analysis designs. The wonderful world of dynamic-factor models (a multivariate model for single-subject data) is presented by Ram, Brose, and Molenaar (Chapter 21 , Volume 2). Wei (Chapter 22 , Volume 2) covers all the issues of traditional time-series models and Peterson (Chapter 23 , Volume 2) rounds out this section with a thorough coverage of event history models.

The volume finishes with two small sections. The first focuses on techniques dedicated to finding heterogeneous subgroups in one’s data. Rupp (Chapter 24 , Volume 2) covers tradition clustering and classification procedures. Masyn and Nylund-Gibson (Chapter 25 , Volume 2) cover the model-based approaches encompassed under the umbrella of mixture modeling. Beauchaine (Chapter 26 , Volume 2) completes this first group with his coverage of the nuances of taxometrics. The second of the final group of chapters covers issues related to secondary analyses of extant data. I put the chapter on missing data in here because it generally is applied after data collection occurs, but it is also a little out of order here because of the terrific and powerful features of planned missing data designs. In this regard, Baraldi and Enders (Chapter 27 , Volume 2) could have gone into the design section. Donnellan and Lucas (Chapter 28 , Volume 2) cover the issues associated with analyzing the large-scale archival data sets that are available via federal funding agencies such as NCES, NIH, NSF, and the like. Data mining can also be classified as a set of secondary modeling procedures, and Strobl’s (Chapter 29 , Volume 2) chapter covers the techniques and issues in this emerging field of methodology. Card and Casper (Chapter 30 , Volume 2) covers the still advancing world of meta-analysis and current best practices in quantitative synthesis of published studies. The final chapter of The Oxford Handbook of Quantitative Methods is one I co-authored with Wang, Watts, and Anderson (Wang, Watts, Anderson, & Little, Chapter 31 , Volume 2). In this capstone chapter, we address the many pervasive fallacies that still permeate the world of quantitative methodology.

A venture such as this does involve the generous and essential contributions of expert reviewers. Many of the chapter authors also served as reviewers for other chapters, and I won’t mention them by name here. I do want to express gratitude to a number of ad hoc reviewers who assisted me along the way (in arbitrary order): Steve Lee, Kris Preacher, Mijke Rhemtulla, Chantelle Dowsett, Jason Lee, Michael Edwards, David Johnson (I apologize now if I have forgotten that you reviewed a chapter for me!). I also owe a debt of gratitude to Chad Zimmerman at OUP, who was relentless in guiding us through the incremental steps needed to herd us all to a final and pride-worthy end product and to Anne Dellinger who was instrumental in bringing closure to this mammoth project.

Author Note

Partial support for this project was provided by grant NSF 1053160 (Todd D. Little & Wei Wu, co-PIs) and by the Center for Research Methods and Data Analysis at the University of Kansas (Todd D. Little, director). Correspondence concerning this work should be addressed to Todd D. Little, Center for Research Methods and Data Analysis, University of Kansas, 1425 Jayhawk Blvd. Watson Library, 470. Lawrence, KS 66045. E-mail: [email protected] . Web: crmda.ku.edu .

This chapter was completed shortly before Rod’s unexpected passing. His legacy and commitment to quantitative methods was uncompromising and we will miss his voice of wisdom and his piercing intellect; R.I.P. , Rod McDonald and, as you once said, pervixi… .

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Donnellan, M. B. & Lucas, R. E. ( 2012 ). Secondary data analysis. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 665–677). New York: Oxford University Press.

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Figueredo, A. J. , Olderbak, S. G. , & Schlomer, G. L. ( 2012 ) Program evaluation: Principles, procedures, and practices. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 332–360). New York: Oxford University Press.

Greenacre, M. J. ( 2012 ). Correspondence analysis. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 142–153). New York: Oxford University Press.

Haig, B. D. ( 2012 ). The philosophy of quantitative methods. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 7–31). New York: Oxford University Press.

Hallberg, K. , Wing, C. , Wong, V. , & Cook, T. D. ( 2012 ). Experimental design for causal inference: Clinical trials and regression discontinuity designs. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 223–236). New York: Oxford University Press.

Harlow, L. ( 2012 ). Teaching quantitative psychology. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 105–117). New York: Oxford University Press.

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Jaccard, J. ( 2012 ). Theory construction, model building, and model selection. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 82–104). New York: Oxford University Press.

Johnson, P. E. ( 2012 ). Monte Carlo analysis in academic research. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 454–479). New York: Oxford University Press.

Kaplan, D. & Depaoli, S. ( 2012 ). Bayesian statistical methods. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 407–437). New York: Oxford University Press.

Kelley, K. ( 2012 ). Effect size and sample size planning. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 206–222). New York: Oxford University Press.

Kingston, N. M. & Kramer, L. B. ( 2012 ). High stakes test construction and test use. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 189–205). New York: Oxford University Press.

MacKinnon, D. P. , Kisbu-Sakarya, Y. , & Gottschall, A. C. ( 2012 ). Developments in mediation analysis. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 338–360). New York: Oxford University Press.

Marsh, H. W. , Hau, K-T. , Wen, Z. , & Nagengast, B. ( 2012 ). Moderation. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 361–386). New York: Oxford University Press.

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McArdle, J. J. & Kadlec, K. M. ( 2012 ). Structural equation models. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 295–337). New York: Oxford University Press.

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Peterson, T. ( 2012 ) Analyzing event history data. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 486–516. New York: Oxford University Press: Peterson, T.O.

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Ram, N. , Brose, A. , & Molenaar, P. C. M. ( 2012 ). Dynamic factor analysis: Modeling person-specific process. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 441–457). New York: Oxford University Press.

Rosnow, R. L. & Rosenthal, R. ( 2012 ). Quantitative methods and ethics. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 32–54). New York: Oxford University Press.

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Spector, P. E. ( 2012 ). Survey design and measure development. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 170–188). New York: Oxford University Press.

Steiner, P. M. & Cook, D. ( 2012 ). Matching and Propensity Scores. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 1, pp. 237–259). New York: Oxford University Press.

Strobl, C. ( 2012 ). Data mining. In T. D. Little (Ed.), The Oxford Handbook of Quantitative Methods (Vol. 2, pp. 678–700). New York: Oxford University Press.

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Quantitative and Qualitative Research in Psychological Science

• Thematic Issue Article: Historical Perspective
• Published: 29 July 2015
• Volume 10 , pages 263–272, ( 2015 )

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• Katherine Nelson 1  

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The field of psychology has emphasized quantitative laboratory research as a defining character of its role as a science, and has generally de-emphasized qualitative research and theorizing throughout its history. This article reviews some of the effects of this emphasis in two areas, intelligence testing, and learning and memory. On one side, quantitative measurement produced the widely used IQ test but shed little light on the construct of intelligence and its role in human cognition. On the other side, reductive quantification and experimental constraints limited the investigation and understanding of human memory systems and complex learning throughout the first century of the field’s history. Recent research under fewer constraints has made greater progress in these areas.

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Intelligence: The Psychological View

Meta-intelligence: understanding, control, and coordination of higher cognitive processes.

• Intelligence

Neisser appears later here for his work on memory and intelligence.

This assumption holds today in many arenas. A more moderate assumption is that all levels are appropriate for investigation, and that feedback from a higher level to a lower may be as informative—and sometimes more informative—as the discovery of an important unit and function at the lower level can be for understanding the higher (see Gottlieb 1992 ).

See Nelson ( 2012 ) for a version of the history and influence of this theory.

My own experience bridged the two orientations: My PhD from UCLA in 1968 was in experimental psychology, essentially behaviorist, specialized in child psychology. My first two publications were in the Journal of Experimental Child Psychology .

This is not the place for reviewing the controversies about this research and its assumed theoretical basis (see Allen and Bickhard 2013 for review and discussion from several angles).

It was assumed that adults do not develop further intelligence, although with increasing age they acquire more knowledge, on the one hand, but presumably lose more mental speed and memory on the other. This assumption is now in question.

If we consider 1920 as the starting point of widespread IQ testing of populations, this implies a 30 point rise in IQs by the present day, or the difference between average and “genius” on some scales.

Publishers of these tests generally deny that they are the same as IQ tests; rather they are said to measure “academic aptitude.”

For those who may have forgotten or never learned these terms, classical conditioning as designed by Pavlov takes place through the association of a stimulus (e.g., a bell) with a desired outcome (e.g., food); instrumental conditioning is the establishment of a habitual action when followed by a reward (e.g., a rat learning to press a lever to receive food. Rats learning to run through a maze to be rewarded with food or water is another example).

The quotation was part of a paper presented at a memory conference in 1978 and published as a chapter in the 1982 book.

“H.M.” is now famous in memory work, his disabilities (the inadvertent result of surgery to relieve epilepsy) having been studied continuously over decades. A good brief account of his case and its impact on understanding memory in terms of systems may be found in Squire and Wixted ( 2015 ).

See Moscovitch ( 1984 ) for early discussion of these distinctions relevant to infant and child memory.

The revival of interest in Vygotsky’s contributions to social, cultural–historical thinking is especially notable and quite widespread. A return to Piaget’s thinking is less visible, although research in its framework continues in European contexts, but it is always in the background as a model of developmental theory and research program. In the U.S. it was the target of strong criticism during the computational era as cognitive development from a sensorimotor beginning in infancy was deemed inconceivable. Presently interest in terms of a merger of biological and social–cultural–experiential contributions to development has become more prominent.

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Nelson, K. Quantitative and Qualitative Research in Psychological Science. Biol Theory 10 , 263–272 (2015). https://doi.org/10.1007/s13752-015-0216-0

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Received : 04 January 2015

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Published : 29 July 2015

Issue Date : September 2015

DOI : https://doi.org/10.1007/s13752-015-0216-0

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Critically Thinking About Qualitative Versus Quantitative Research

What should we do regarding our research questions and methodology.

Posted January 26, 2022 | Reviewed by Davia Sills

• Neither a quantitative nor a qualitative methodology is the right way to approach every scientific question.
• Rather, the nature of the question determines which methodology is best suited to address it.
• Often, researchers benefit from a mixed approach that incorporates both quantitative and qualitative methodologies.

As a researcher who has used a wide variety of methodologies, I understand the importance of acknowledging that we, as researchers, do not pick the methodology; rather, the research question dictates it. So, you can only imagine how annoyed I get when I hear of undergraduates designing their research projects based on preconceived notions, like "quantitative is more straightforward," or "qualitative is easier." Apart from the fact that neither of these assertions is actually the case, these young researchers are blatantly missing one of the foundational steps of good research: If you are interested in researching a particular area, you must get to know the area (i.e., through reading) and then develop a question based on that reading.

The nature of the question will dictate the most appropriate methodological approach.

I’ve debated with researchers in the past who are "exclusively" qualitative or "exclusively" quantitative. Depending on the rationale for their exclusivity, I might question a little deeper, learn something, and move on, or I might debate further. Sometimes, I throw some contentious statements out to see what the responses are like. For example, "Qualitative research, in isolation, is nothing but glorified journalism . " This one might not be new to you. Yes, qualitative is flawed, but so, too, is quantitative.

Let's try this one: "Numbers don’t lie, just the researchers who interpret them." If researchers are going to have a pop at qual for subjectivity, why don’t they recognize the same issues in quant? The numbers in a results section may be objectively correct, but their meaningfulness is only made clear through the interpretation of the human reporting them. This is not a criticism but is an important observation for those who believe in the absolute objectivity of quantitative reporting. The subjectivity associated with this interpretation may miss something crucial in the interpretation of the numbers because, hey, we’re only human.

With that, I love quantitative research, but I’m not unreasonable about it. Let’s say we’ve evaluated a three-arm RCT—the new therapeutic intervention is significantly efficacious, with a large effect, for enhancing "x" in people living with "y." One might conclude that this intervention works and that we must conduct further research on it to further support its efficacy—this is, of course, a fine suggestion, consistent with good research practice and epistemological understanding.

However, blindly recommending the intervention based on the interpretation of numbers alone might be suspect—think of all the variables that could be involved in a 4-, 8-, 12-, or 52-week intervention with human participants. It would be foolish to believe that all variables were considered—so, here is a fantastic example of where a qualitative methodology might be useful. At the end of the intervention, a researcher might decide to interview a random 20 percent of the cohort who participated in the intervention group about their experience and the program’s strengths and weaknesses. The findings from this qualitative element might help further explain the effects, aid the initial interpretation, and bring to life new ideas and concepts that had been missing from the initial interpretation. In this respect, infusing a qualitative approach at the end of quantitative analysis has shown its benefits—a mixed approach to intervention evaluation is very useful.

What about before that? Well, let’s say I want to develop another intervention to enhance "z," but there’s little research on it, and that which has been conducted isn’t of the highest quality; furthermore, we don’t know about people’s experiences with "z" or even other variables associated with it.

To design an intervention around "z" would be ‘jumping the gun’ at best (and a waste of funds). It seems that an exploration of some sort is necessary. This is where qualitative again shines—giving us an opportunity to explore what "z" is from the perspective of a relevant cohort(s).

Of course, we cannot generalize the findings; we cannot draw a definitive conclusion as to what "z" is. But what the findings facilitate is providing a foundation from which to work; for example, we still cannot say that "z" is this, that, or the other, but it appears that it might be associated with "a," "b" and "c." Thus, future research should investigate the nature of "z" as a particular concept, in relation to "a," "b" and "c." Again, a qualitative methodology shows its worth. In the previous examples, a qualitative method was used because the research questions warranted it.

Through considering the potentially controversial statements about qual and quant above, we are pushed into examining the strengths and weaknesses of research methodologies (regardless of our exclusivity with a particular approach). This is useful if we’re going to think critically about finding answers to our research questions. But simply considering these does not let poor research practice off the hook.

For example, credible qualitative researchers acknowledge that generalizability is not the point of their research; however, that doesn’t stop some less-than-credible researchers from presenting their "findings" as generalizable as possible, without actually using the word. Such practices should be frowned upon—so should making a career out of strictly using qualitative methodology in an attempt to find answers core to the human condition. All these researchers are really doing is spending a career exploring, yet never really finding anything (despite arguing to the contrary, albeit avoiding the word "generalize").

The solution to this problem, again, is to truly listen to what your research question is telling you. Eventually, it’s going to recommend a quantitative approach. Likewise, a "numbers person" will be recommended a qualitative approach from time to time—flip around the example above, and there’s a similar criticism. Again, embrace a mixed approach.

What's the point of this argument?

I conduct both research methodologies. Which do I prefer? Simple—whichever one helps me most appropriately answer my research question.

Do I have problems with qualitative methodologies? Absolutely—but I have issues with quantitative methods as well. Having these issues is good—it means that you recognize the limitations of your tools, which increases the chances of you "fixing," "sharpening" or "changing out" your tools when necessary.

So, the next time someone speaks with you about labeling researchers as one type or another, ask them why they think that way, ask them which they think you are, and then reflect on the responses alongside your own views of methodology and epistemology. It might just help you become a better researcher.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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Quantitative Research in Psychology

• Jeremy Miles - RAND Corporation, USA
• Brian Stucky - RAND Corporation, USA
• Description

Quantitative psychology is a branch of psychology developed using certain methods and approaches which are designed to answer empirical questions, such as the development of measurement models and factor analysis. While quantitative psychology is often associated with the use of statistical models and psychological measurement research methods, this five volume set draws together the key conceptual and methodological techniques and addresses each research question at length. Each volume is accompanied by an introduction which contextualises the subject area, giving an understanding of established theories and how they are continuing to develop in one of the most fundamental and broadly researched psychological fields.

These volumes are an excellent resource for academics and scholars who will benefit from the framing provided by the editorial introduction and overview, and will also appeal to advanced students and professionals studying or using quantitative psychological methods in their research.

Volume One: Statistical hypothesis testing and power

Volume Two: Measurement

Volume Three: Research Design and sampling

Volume Four: Statistical Tests

Volume Five: Complex Models

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Quantitative Methods

The Psychology Department has a strategic focus on training in advanced methodology, especially quantitative methods.  Advanced quantitative skills are increasingly important in conducting state-of-the–art research, and in post-doctoral and academic placements.  Recent and ongoing faculty searches have emphasized expertise in quantitative methods.  In addition to a standard two-semester sequence in statistics (PSY 507 and PSY 508), students may choose courses in topics such as multi-level modeling, test theory, structural equation modeling, computational modeling, and other topics.  A course in neuroscience methods (PSY 511) is offered annually.  Other resources for students seeking training in advanced methodology include courses in other departments (Statistics, Human Development and Family Studies, Educational Psychology, Information Sciences and Technology) and workshops on specific methodological topics. 

The Psychology Department has a faculty committee focused on quantitative methods in research and teacheing (see below), and it participates in the  Consortium for the Advancement of Research Methods and Analysis (CARMA) .

Quantitative Methods Faculty

Quantitative methods labs, measurement, applied psychology, and statistics lab, program areas:, gene environment interplay across the lifespan, associated centers:, related resources, courses in psychology.

PSY 535 Research Methods in I/O Psychology (Spring 2022)

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Quantitative and Qualitative Approaches to Generalization and Replication–A Representationalist View

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se , but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

Introduction

Qualitative and quantitative research strategies have long been treated as opposing paradigms. In recent years, there have been attempts to integrate both strategies. These “mixed methods” approaches treat qualitative and quantitative methodologies as complementary, rather than opposing, strategies (Creswell, 2015 ). However, whilst acknowledging that both strategies have their benefits, this “integration” remains purely pragmatic. Hence, mixed methods methodology does not provide a conceptual unification of the two approaches.

Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007 ). These different standards affect the way researchers handle contradictory empirical findings. For example, many empirical findings in psychology have failed to replicate in recent years (Klein et al., 2014 ; Open Science, Collaboration, 2015 ). This “replication crisis” has been discussed on statistical, theoretical and social grounds and continues to have a wide impact on quantitative research practices like, for example, open science initiatives, pre-registered studies and a re-evaluation of statistical significance testing (Everett and Earp, 2015 ; Maxwell et al., 2015 ; Shrout and Rodgers, 2018 ; Trafimow, 2018 ; Wiggins and Chrisopherson, 2019 ).

However, qualitative research seems to be hardly affected by this discussion. In this paper, we argue that the latter is a direct consequence of how the concept of generalizability is conceived in the two approaches. Whereas most of quantitative psychology is committed to a top-down strategy of generalization based on the idea of random sampling from an abstract population, qualitative studies usually rely on a bottom-up strategy of generalization that is grounded in the successive exploration of the field by means of theoretically sampled cases.

Here, we show that a common methodological framework for qualitative and quantitative research methodologies is possible. We accomplish this by introducing a formal description of quantitative and qualitative models from a representationalist perspective: both approaches can be reconstructed as special kinds of representations for empirical relational structures. We then use this framework to analyze the generalization strategies used in the two approaches. These turn out to be logically independent of the type of model. This has wide implications for psychological research. First, a top-down generalization strategy is compatible with a qualitative modeling approach. This implies that mainstream psychology may benefit from qualitative methods when a numerical representation turns out to be difficult or impossible, without the need to commit to a “qualitative” philosophy of science. Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory.

The Quantitative Strategy–Numbers and Functions

Quantitative science is about finding valid mathematical representations for empirical phenomena. In most cases, these mathematical representations have the form of functional relations between a set of variables. One major challenge of quantitative modeling consists in constructing valid measures for these variables. Formally, to measure a variable means to construct a numerical representation of the underlying empirical relational structure (Krantz et al., 1971 ). For example, take the behaviors of a group of students in a classroom: “to listen,” “to take notes,” and “to ask critical questions.” One may now ask whether is possible to assign numbers to the students, such that the relations between the assigned numbers are of the same kind as the relations between the values of an underlying variable, like e.g., “engagement.” The observed behaviors in the classroom constitute an empirical relational structure, in the sense that for every student-behavior tuple, one can observe whether it is true or not. These observations can be represented in a person × behavior matrix 1 (compare Figure 1 ). Given this relational structure satisfies certain conditions (i.e., the axioms of a measurement model), one can assign numbers to the students and the behaviors, such that the relations between the numbers resemble the corresponding numerical relations. For example, if there is a unique ordering in the empirical observations with regard to which person shows which behavior, the assigned numbers have to constitute a corresponding unique ordering, as well. Such an ordering coincides with the person × behavior matrix forming a triangle shaped relation and is formally represented by a Guttman scale (Guttman, 1944 ). There are various measurement models available for different empirical structures (Suppes et al., 1971 ). In the case of probabilistic relations, Item-Response models may be considered as a special kind of measurement model (Borsboom, 2005 ).

Constructing a numerical representation from an empirical relational structure; Due to the unique ordering of persons with regard to behaviors (indicated by the triangular shape of the relation), it is possible to construct a Guttman scale by assigning a number to each of the individuals, representing the number of relevant behaviors shown by the individual. The resulting variable (“engagement”) can then be described by means of statistical analyses, like, e.g., plotting the frequency distribution.

Although essential, measurement is only the first step of quantitative modeling. Consider a slightly richer empirical structure, where we observe three additional behaviors: “to doodle,” “to chat,” and “to play.” Like above, one may ask, whether there is a unique ordering of the students with regard to these behaviors that can be represented by an underlying variable (i.e., whether the matrix forms a Guttman scale). If this is the case, we may assign corresponding numbers to the students and call this variable “distraction.” In our example, such a representation is possible. We can thus assign two numbers to each student, one representing his or her “engagement” and one representing his or her “distraction” (compare Figure 2 ). These measurements can now be used to construct a quantitative model by relating the two variables by a mathematical function. In the simplest case, this may be a linear function. This functional relation constitutes a quantitative model of the empirical relational structure under study (like, e.g., linear regression). Given the model equation and the rules for assigning the numbers (i.e., the instrumentations of the two variables), the set of admissible empirical structures is limited from all possible structures to a rather small subset. This constitutes the empirical content of the model 2 (Popper, 1935 ).

Constructing a numerical model from an empirical relational structure; Since there are two distinct classes of behaviors that each form a Guttman scale, it is possible to assign two numbers to each individual, correspondingly. The resulting variables (“engagement” and “distraction”) can then be related by a mathematical function, which is indicated by the scatterplot and red line on the right hand side.

The Qualitative Strategy–Categories and Typologies

The predominant type of analysis in qualitative research consists in category formation. By constructing descriptive systems for empirical phenomena, it is possible to analyze the underlying empirical structure at a higher level of abstraction. The resulting categories (or types) constitute a conceptual frame for the interpretation of the observations. Qualitative researchers differ considerably in the way they collect and analyze data (Miles et al., 2014 ). However, despite the diverse research strategies followed by different qualitative methodologies, from a formal perspective, most approaches build on some kind of categorization of cases that share some common features. The process of category formation is essential in many qualitative methodologies, like, for example, qualitative content analysis, thematic analysis, grounded theory (see Flick, 2014 for an overview). Sometimes these features are directly observable (like in our classroom example), sometimes they are themselves the result of an interpretative process (e.g., Scheunpflug et al., 2016 ).

In contrast to quantitative methodologies, there have been little attempts to formalize qualitative research strategies (compare, however, Rihoux and Ragin, 2009 ). However, there are several statistical approaches to non-numerical data that deal with constructing abstract categories and establishing relations between these categories (Agresti, 2013 ). Some of these methods are very similar to qualitative category formation on a conceptual level. For example, cluster analysis groups cases into homogenous categories (clusters) based on their similarity on a distance metric.

Although category formation can be formalized in a mathematically rigorous way (Ganter and Wille, 1999 ), qualitative research hardly acknowledges these approaches. 3 However, in order to find a common ground with quantitative science, it is certainly helpful to provide a formal interpretation of category systems.

Let us reconsider the above example of students in a classroom. The quantitative strategy was to assign numbers to the students with regard to variables and to relate these variables via a mathematical function. We can analyze the same empirical structure by grouping the behaviors to form abstract categories. If the aim is to construct an empirically valid category system, this grouping is subject to constraints, analogous to those used to specify a measurement model. The first and most important constraint is that the behaviors must form equivalence classes, i.e., within categories, behaviors need to be equivalent, and across categories, they need to be distinct (formally, the relational structure must obey the axioms of an equivalence relation). When objects are grouped into equivalence classes, it is essential to specify the criterion for empirical equivalence. In qualitative methodology, this is sometimes referred to as the tertium comparationis (Flick, 2014 ). One possible criterion is to group behaviors such that they constitute a set of specific common attributes of a group of people. In our example, we might group the behaviors “to listen,” “to take notes,” and “to doodle,” because these behaviors are common to the cases B, C, and D, and they are also specific for these cases, because no other person shows this particular combination of behaviors. The set of common behaviors then forms an abstract concept (e.g., “moderate distraction”), while the set of persons that show this configuration form a type (e.g., “the silent dreamer”). Formally, this means to identify the maximal rectangles in the underlying empirical relational structure (see Figure 3 ). This procedure is very similar to the way we constructed a Guttman scale, the only difference being that we now use different aspects of the empirical relational structure. 4 In fact, the set of maximal rectangles can be determined by an automated algorithm (Ganter, 2010 ), just like the dimensionality of an empirical structure can be explored by psychometric scaling methods. Consequently, we can identify the empirical content of a category system or a typology as the set of empirical structures that conforms to it. 5 Whereas the quantitative strategy was to search for scalable sub-matrices and then relate the constructed variables by a mathematical function, the qualitative strategy is to construct an empirical typology by grouping cases based on their specific similarities. These types can then be related to one another by a conceptual model that describes their semantic and empirical overlap (see Figure 3 , right hand side).

Constructing a conceptual model from an empirical relational structure; Individual behaviors are grouped to form abstract types based on them being shared among a specific subset of the cases. Each type constitutes a set of specific commonalities of a class of individuals (this is indicated by the rectangles on the left hand side). The resulting types (“active learner,” “silent dreamer,” “distracted listener,” and “troublemaker”) can then be related to one another to explicate their semantic and empirical overlap, as indicated by the Venn-diagram on the right hand side.

Variable-Based Models and Case-Based Models

In the previous section, we have argued that qualitative category formation and quantitative measurement can both be characterized as methods to construct abstract representations of empirical relational structures. Instead of focusing on different philosophical approaches to empirical science, we tried to stress the formal similarities between both approaches. However, it is worth also exploring the dissimilarities from a formal perspective.

Following the above analysis, the quantitative approach can be characterized by the use of variable-based models, whereas the qualitative approach is characterized by case-based models (Ragin, 1987 ). Formally, we can identify the rows of an empirical person × behavior matrix with a person-space, and the columns with a corresponding behavior-space. A variable-based model abstracts from the single individuals in a person-space to describe the structure of behaviors on a population level. A case-based model, on the contrary, abstracts from the single behaviors in a behavior-space to describe individual case configurations on the level of abstract categories (see Table 1 ).

Variable-based models and case-based models.

From a representational perspective, there is no a priori reason to favor one type of model over the other. Both approaches provide different analytical tools to construct an abstract representation of an empirical relational structure. However, since the two modeling approaches make use of different information (person-space vs. behavior-space), this comes with some important implications for the researcher employing one of the two strategies. These are concerned with the role of deductive and inductive reasoning.

In variable-based models, empirical structures are represented by functional relations between variables. These are usually stated as scientific laws (Carnap, 1928 ). Formally, these laws correspond to logical expressions of the form

In plain text, this means that y is a function of x for all objects i in the relational structure under consideration. For example, in the above example, one may formulate the following law: for all students in the classroom it holds that “distraction” is a monotone decreasing function of “engagement.” Such a law can be used to derive predictions for single individuals by means of logical deduction: if the above law applies to all students in the classroom, it is possible to calculate the expected distraction from a student's engagement. An empirical observation can now be evaluated against this prediction. If the prediction turns out to be false, the law can be refuted based on the principle of falsification (Popper, 1935 ). If a scientific law repeatedly withstands such empirical tests, it may be considered to be valid with regard to the relational structure under consideration.

In case-based models, there are no laws about a population, because the model does not abstract from the cases but from the observed behaviors. A case-based model describes the underlying structure in terms of existential sentences. Formally, this corresponds to a logical expression of the form

In plain text, this means that there is at least one case i for which the condition XYZ holds. For example, the above category system implies that there is at least one active learner. This is a statement about a singular observation. It is impossible to deduce a statement about another person from an existential sentence like this. Therefore, the strategy of falsification cannot be applied to test the model's validity in a specific context. If one wishes to generalize to other cases, this is accomplished by inductive reasoning, instead. If we observed one person that fulfills the criteria of calling him or her an active learner, we can hypothesize that there may be other persons that are identical to the observed case in this respect. However, we do not arrive at this conclusion by logical deduction, but by induction.

Despite this important distinction, it would be wrong to conclude that variable-based models are intrinsically deductive and case-based models are intrinsically inductive. 6 Both types of reasoning apply to both types of models, but on different levels. Based on a person-space, in a variable-based model one can use deduction to derive statements about individual persons from abstract population laws. There is an analogous way of reasoning for case-based models: because they are based on a behavior space, it is possible to deduce statements about singular behaviors. For example, if we know that Peter is an active learner, we can deduce that he takes notes in the classroom. This kind of deductive reasoning can also be applied on a higher level of abstraction to deduce thematic categories from theoretical assumptions (Braun and Clarke, 2006 ). Similarly, there is an analog for inductive generalization from the perspective of variable-based modeling: since the laws are only quantified over the person-space, generalizations to other behaviors rely on inductive reasoning. For example, it is plausible to assume that highly engaged students tend to do their homework properly–however, in our example this behavior has never been observed. Hence, in variable-based models we usually generalize to other behaviors by means of induction. This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains.

Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases. A variable-based approach implies to draw conclusions about cases by means of logical deduction; a case-based approach implies to draw conclusions about cases by means of inductive reasoning. In the following, we build on this distinction to differentiate between qualitative (bottom-up) and quantitative (top-down) strategies of generalization.

Generalization and the Problem of Replication

We will now extend the formal analysis of quantitative and qualitative approaches to the question of generalization and replicability of empirical findings. For this sake, we have to introduce some concepts of formal logic. Formal logic is concerned with the validity of arguments. It provides conditions to evaluate whether certain sentences (conclusions) can be derived from other sentences (premises). In this context, a theory is nothing but a set of sentences (also called axioms). Formal logic provides tools to derive new sentences that must be true, given the axioms are true (Smith, 2020 ). These derived sentences are called theorems or, in the context of empirical science, predictions or hypotheses . On the syntactic level, the rules of logic only state how to evaluate the truth of a sentence relative to its premises. Whether or not sentences are actually true, is formally specified by logical semantics.

On the semantic level, formal logic is intrinsically linked to set-theory. For example, a logical statement like “all dogs are mammals,” is true if and only if the set of dogs is a subset of the set of mammals. Similarly, the sentence “all chatting students doodle” is true if and only if the set of chatting students is a subset of the set of doodling students (compare Figure 3 ). Whereas, the first sentence is analytically true due to the way we define the words “dog” and “mammal,” the latter can be either true or false, depending on the relational structure we actually observe. We can thus interpret an empirical relational structure as the truth criterion of a scientific theory. From a logical point of view, this corresponds to the semantics of a theory. As shown above, variable-based and case-based models both give a formal representation of the same kinds of empirical structures. Accordingly, both types of models can be stated as formal theories. In the variable-based approach, this corresponds to a set of scientific laws that are quantified over the members of an abstract population (these are the axioms of the theory). In the case-based approach, this corresponds to a set of abstract existential statements about a specific class of individuals.

In contrast to mathematical axiom systems, empirical theories are usually not considered to be necessarily true. This means that even if we find no evidence against a theory, it is still possible that it is actually wrong. We may know that a theory is valid in some contexts, yet it may fail when applied to a new set of behaviors (e.g., if we use a different instrumentation to measure a variable) or a new population (e.g., if we draw a new sample).

From a logical perspective, the possibility that a theory may turn out to be false stems from the problem of contingency . A statement is contingent, if it is both, possibly true and possibly false. Formally, we introduce two modal operators: □ to designate logical necessity, and ◇ to designate logical possibility. Semantically, these operators are very similar to the existential quantifier, ∃, and the universal quantifier, ∀. Whereas ∃ and ∀ refer to the individual objects within one relational structure, the modal operators □ and ◇ range over so-called possible worlds : a statement is possibly true, if and only if it is true in at least one accessible possible world, and a statement is necessarily true if and only if it is true in every accessible possible world (Hughes and Cresswell, 1996 ). Logically, possible worlds are mathematical abstractions, each consisting of a relational structure. Taken together, the relational structures of all accessible possible worlds constitute the formal semantics of necessity, possibility and contingency. 7

In the context of an empirical theory, each possible world may be identified with an empirical relational structure like the above classroom example. Given the set of intended applications of a theory (the scope of the theory, one may say), we can now construct possible world semantics for an empirical theory: each intended application of the theory corresponds to a possible world. For example, a quantified sentence like “all chatting students doodle” may be true in one classroom and false in another one. In terms of possible worlds, this would correspond to a statement of contingency: “it is possible that all chatting students doodle in one classroom, and it is possible that they don't in another classroom.” Note that in the above expression, “all students” refers to the students in only one possible world, whereas “it is possible” refers to the fact that there is at least one possible world for each of the specified cases.

To apply these possible world semantics to quantitative research, let us reconsider how generalization to other cases works in variable-based models. Due to the syntactic structure of quantitative laws, we can deduce predictions for singular observations from an expression of the form ∀ i : y i = f ( x i ). Formally, the logical quantifier ∀ ranges only over the objects of the corresponding empirical relational structure (in our example this would refer to the students in the observed classroom). But what if we want to generalize beyond the empirical structure we actually observed? The standard procedure is to assume an infinitely large, abstract population from which a random sample is drawn. Given the truth of the theory, we can deduce predictions about what we may observe in the sample. Since usually we deal with probabilistic models, we can evaluate our theory by means of the conditional probability of the observations, given the theory holds. This concept of conditional probability is the foundation of statistical significance tests (Hogg et al., 2013 ), as well as Bayesian estimation (Watanabe, 2018 ). In terms of possible world semantics, the random sampling model implies that all possible worlds (i.e., all intended applications) can be conceived as empirical sub-structures from a greater population structure. For example, the empirical relational structure constituted by the observed behaviors in a classroom would be conceived as a sub-matrix of the population person × behavior matrix. It follows that, if a scientific law is true in the population, it will be true in all possible worlds, i.e., it will be necessarily true. Formally, this corresponds to an expression of the form

The statistical generalization model thus constitutes a top-down strategy for dealing with individual contexts that is analogous to the way variable-based models are applied to individual cases (compare Table 1 ). Consequently, if we apply a variable-based model to a new context and find out that it does not fit the data (i.e., there is a statistically significant deviation from the model predictions), we have reason to doubt the validity of the theory. This is what makes the problem of low replicability so important: we observe that the predictions are wrong in a new study; and because we apply a top-down strategy of generalization to contexts beyond the ones we observed, we see our whole theory at stake.

Qualitative research, on the contrary, follows a different strategy of generalization. Since case-based models are formulated by a set of context-specific existential sentences, there is no need for universal truth or necessity. In contrast to statistical generalization to other cases by means of random sampling from an abstract population, the usual strategy in case-based modeling is to employ a bottom-up strategy of generalization that is analogous to the way case-based models are applied to individual cases. Formally, this may be expressed by stating that the observed qualia exist in at least one possible world, i.e., the theory is possibly true:

This statement is analogous to the way we apply case-based models to individual cases (compare Table 1 ). Consequently, the set of intended applications of the theory does not follow from a sampling model, but from theoretical assumptions about which cases may be similar to the observed cases with respect to certain relevant characteristics. For example, if we observe that certain behaviors occur together in one classroom, following a bottom-up strategy of generalization, we will hypothesize why this might be the case. If we do not replicate this finding in another context, this does not question the model itself, since it was a context-specific theory all along. Instead, we will revise our hypothetical assumptions about why the new context is apparently less similar to the first one than we originally thought. Therefore, if an empirical finding does not replicate, we are more concerned about our understanding of the cases than about the validity of our theory.

Whereas statistical generalization provides us with a formal (and thus somehow more objective) apparatus to evaluate the universal validity of our theories, the bottom-up strategy forces us to think about the class of intended applications on theoretical grounds. This means that we have to ask: what are the boundary conditions of our theory? In the above classroom example, following a bottom-up strategy, we would build on our preliminary understanding of the cases in one context (e.g., a public school) to search for similar and contrasting cases in other contexts (e.g., a private school). We would then re-evaluate our theoretical description of the data and explore what makes cases similar or dissimilar with regard to our theory. This enables us to expand the class of intended applications alongside with the theory.

Of course, none of these strategies is superior per se . Nevertheless, they rely on different assumptions and may thus be more or less adequate in different contexts. The statistical strategy relies on the assumption of a universal population and invariant measurements. This means, we assume that (a) all samples are drawn from the same population and (b) all variables refer to the same behavioral classes. If these assumptions are true, statistical generalization is valid and therefore provides a valuable tool for the testing of empirical theories. The bottom-up strategy of generalization relies on the idea that contexts may be classified as being more or less similar based on characteristics that are not part of the model being evaluated. If such a similarity relation across contexts is feasible, the bottom-up strategy is valid, as well. Depending on the strategy of generalization, replication of empirical research serves two very different purposes. Following the (top-down) principle of generalization by deduction from scientific laws, replications are empirical tests of the theory itself, and failed replications question the theory on a fundamental level. Following the (bottom-up) principle of generalization by induction to similar contexts, replications are a means to explore the boundary conditions of a theory. Consequently, failed replications question the scope of the theory and help to shape the set of intended applications.

We have argued that quantitative and qualitative research are best understood by means of the structure of the employed models. Quantitative science mainly relies on variable-based models and usually employs a top-down strategy of generalization from an abstract population to individual cases. Qualitative science prefers case-based models and usually employs a bottom-up strategy of generalization. We further showed that failed replications have very different implications depending on the underlying strategy of generalization. Whereas in the top-down strategy, replications are used to test the universal validity of a model, in the bottom-up strategy, replications are used to explore the scope of a model. We will now address the implications of this analysis for psychological research with regard to the problem of replicability.

Modern day psychology almost exclusively follows a top-down strategy of generalization. Given the quantitative background of most psychological theories, this is hardly surprising. Following the general structure of variable-based models, the individual case is not the focus of the analysis. Instead, scientific laws are stated on the level of an abstract population. Therefore, when applying the theory to a new context, a statistical sampling model seems to be the natural consequence. However, this is not the only possible strategy. From a logical point of view, there is no reason to assume that a quantitative law like ∀ i : y i = f ( x i ) implies that the law is necessarily true, i.e.,: □(∀ i : y i = f ( x i )). Instead, one might just as well define the scope of the theory following an inductive strategy. 8 Formally, this would correspond to the assumption that the observed law is possibly true, i.e.,: ◇(∀ i : y i = f ( x i )). For example, we may discover a functional relation between “engagement” and “distraction” without referring to an abstract universal population of students. Instead, we may hypothesize under which conditions this functional relation may be valid and use these assumptions to inductively generalize to other cases.

If we take this seriously, this would require us to specify the intended applications of the theory: in which contexts do we expect the theory to hold? Or, equivalently, what are the boundary conditions of the theory? These boundary conditions may be specified either intensionally, i.e., by giving external criteria for contexts being similar enough to the ones already studied to expect a successful application of the theory. Or they may be specified extensionally, by enumerating the contexts where the theory has already been shown to be valid. These boundary conditions need not be restricted to the population we refer to, but include all kinds of contextual factors. Therefore, adopting a bottom-up strategy, we are forced to think about these factors and make them an integral part of our theories.

In fact, there is good reason to believe that bottom-up generalization may be more adequate in many psychological studies. Apart from the pitfalls associated with statistical generalization that have been extensively discussed in recent years (e.g., p-hacking, underpowered studies, publication bias), it is worth reflecting on whether the underlying assumptions are met in a particular context. For example, many samples used in experimental psychology are not randomly drawn from a large population, but are convenience samples. If we use statistical models with non-random samples, we have to assume that the observations vary as if drawn from a random sample. This may indeed be the case for randomized experiments, because all variation between the experimental conditions apart from the independent variable will be random due to the randomization procedure. In this case, a classical significance test may be regarded as an approximation to a randomization test (Edgington and Onghena, 2007 ). However, if we interpret a significance test as an approximate randomization test, we test not for generalization but for internal validity. Hence, even if we use statistical significance tests when assumptions about random sampling are violated, we still have to use a different strategy of generalization. This issue has been discussed in the context of small-N studies, where variable-based models are applied to very small samples, sometimes consisting of only one individual (Dugard et al., 2012 ). The bottom-up strategy of generalization that is employed by qualitative researchers, provides such an alternative.

Another important issue in this context is the question of measurement invariance. If we construct a variable-based model in one context, the variables refer to those behaviors that constitute the underlying empirical relational structure. For example, we may construct an abstract measure of “distraction” using the observed behaviors in a certain context. We will then use the term “distraction” as a theoretical term referring to the variable we have just constructed to represent the underlying empirical relational structure. Let us now imagine we apply this theory to a new context. Even if the individuals in our new context are part of the same population, we may still get into trouble if the observed behaviors differ from those used in the original study. How do we know whether these behaviors constitute the same variable? We have to ensure that in any new context, our measures are valid for the variables in our theory. Without a proper measurement model, this will be hard to achieve (Buntins et al., 2017 ). Again, we are faced with the necessity to think of the boundary conditions of our theories. In which contexts (i.e., for which sets of individuals and behaviors) do we expect our theory to work?

If we follow the rationale of inductive generalization, we can explore the boundary conditions of a theory with every new empirical study. We thus widen the scope of our theory by comparing successful applications in different contexts and unsuccessful applications in similar contexts. This may ultimately lead to a more general theory, maybe even one of universal scope. However, unless we have such a general theory, we might be better off, if we treat unsuccessful replications not as a sign of failure, but as a chance to learn.

Author Contributions

MB conceived the original idea and wrote the first draft of the paper. MS helped to further elaborate and scrutinize the arguments. All authors contributed to the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Annette Scheunpflug for helpful comments on an earlier version of the manuscript.

1 A person × behavior matrix constitutes a very simple relational structure that is common in psychological research. This is why it is chosen here as a minimal example. However, more complex structures are possible, e.g., by relating individuals to behaviors over time, with individuals nested within groups etc. For a systematic overview, compare Coombs ( 1964 ).

2 This notion of empirical content applies only to deterministic models. The empirical content of a probabilistic model consists in the probability distribution over all possible empirical structures.

3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick ( 2014 ) nor the Oxford Handbook of Qualitative Research edited by Leavy ( 2014 ) mention formal approaches to category formation.

4 Note also that the described structure is empirically richer than a nominal scale. Therefore, a reduction of qualitative category formation to be a special (and somehow trivial) kind of measurement is not adequate.

5 It is possible to extend this notion of empirical content to the probabilistic case (this would correspond to applying a latent class analysis). But, since qualitative research usually does not rely on formal algorithms (neither deterministic nor probabilistic), there is currently little practical use of such a concept.

6 We do not elaborate on abductive reasoning here, since, given an empirical relational structure, the concept can be applied to both types of models in the same way (Schurz, 2008 ). One could argue that the underlying relational structure is not given a priori but has to be constructed by the researcher and will itself be influenced by theoretical expectations. Therefore, abductive reasoning may be necessary to establish an empirical relational structure in the first place.

7 We shall not elaborate on the metaphysical meaning of possible worlds here, since we are only concerned with empirical theories [but see Tooley ( 1999 ), for an overview].

8 Of course, this also means that it would be equally reasonable to employ a top-down strategy of generalization using a case-based model by postulating that □(∃ i : XYZ i ). The implications for case-based models are certainly worth exploring, but lie beyond the scope of this article.

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Autistic Camouflaging and its Relationship with Mental Health: Systematic Review

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Camouflaging (also known as masking) refers to strategies used by autistic individuals to mask or hide their autistic characteristics in social situations to fit in or avoid negative reactions from others. Examples include forced eye contact, mimicking others’ social behaviors, and suppressing repetitive movements. While camouflaging may help autistic people navigate social situations, it often comes at a cost to their mental health and sense of authenticity. Camouflaging is thought to be more common in autistic females and may contribute to missed or late diagnosis. Understanding camouflaging is important for improving recognition and support for autistic individuals’ needs.

This mixed methods systematic review synthesized qualitative and quantitative research on psychosocial factors associated with camouflaging and its relationship with mental well-being in autistic and non-autistic people.

Seven main themes were identified relating to psychosocial correlates and consequences of camouflaging:

• Social norms and pressures drive camouflaging
• Camouflaging is used to gain social acceptance and avoid rejection
• Self-esteem and identity influence camouflaging
• Camouflaging has some practical benefits for functioning
• Camouflaging leads to difficulties being overlooked
• Camouflaging negatively impacts relationships
• Camouflaging causes identity confusion and low self-esteem

More research with diverse participants is needed to better understand psychosocial influences on camouflaging. The findings call for a whole society approach to increase acceptance of autistic people.

Camouflaging involves autistic individuals hiding their autistic characteristics in social situations , often to fit in and avoid stigma (Hull et al., 2017).

While a rapidly growing area of research, most studies have focused on the experiences of a narrow demographic – White, female, late-diagnosed autistic adults with average to above average abilities (Cook et al., 2021; Libsack et al., 2021).

Though camouflaging enables some to achieve social and functional goals (Hull et al., 2017; Livingston et al., 2019), it has been consistently associated with poorer mental health (Beck et al., 2020; Cassidy et al., 2018).

Qualitative accounts suggest various psychosocial factors may motivate camouflaging and explain its mental health impact, such as societal stigma and the desire for belonging (Cage & Troxell-Whitman, 2019; Cook et al., 2021). However, no review has systematically examined psychosocial influences on camouflaging and well-being.

This mixed-methods systematic review aimed to critically synthesize qualitative and quantitative research on psychosocial factors associated with camouflaging and its relationship with mental well-being in autistic and non-autistic people.

Understanding psychosocial motivations for camouflaging could inform support to promote more adaptive camouflaging and authentic self-expression, contributing to better mental health for autistic people.

This review followed PRISMA guidelines. Six databases were searched, and backward citation searching and expert consultations were conducted.

A thematic synthesis was conducted, where data were categorized and pooled together based on similar meanings.

Quantitative data were first transformed into themes and textual descriptions to allow integration with qualitative data. Then, line-by-line coding of findings was completed, codes were organized into descriptive themes, and analytical themes were developed.

Codes and themes were iteratively discussed among the research team, which included academics and two autistic advisors who provided input on the data synthesis and interpretation.

The first author conducted coding using NVivo software. A sample of 30 data extracts was independently coded by the last author using the finalized thematic map, demonstrating very good (80%) inter-rater agreement.

58 studies (40 qualitative, 13 quantitative, 5 mixed methods) were included, encompassing 4808 autistic and 1780 non-autistic participants.

Participants were predominantly White (85.9%), female (61.8%), and late-diagnosed (mean age of diagnosis 30.47 years) autistic adults with likely average to above average intellectual/verbal abilities.

Seven themes relating to psychosocial correlates and consequences of camouflaging were identified:

• Social norms and pressures drive camouflaging. Participants faced expectations to conform to neurotypical norms and experienced stigma for autistic differences.
• Camouflaging is used to gain social acceptance and avoid rejection. Camouflaging was a protective response to bullying. The desire for belonging was a key motivation.
• Self-esteem and identity influence camouflaging. Internalized stigma motivated camouflaging, while self-acceptance reduced the perceived need to camouflage.
• Camouflaging has some practical benefits for functioning. It enabled everyday functioning and impression management.
• Camouflaging leads to difficulties being overlooked. Camouflaging resulted in participants’ needs being unmet, delayed diagnosis, and autistic burnout.
• Camouflaging negatively impacts relationships. While camouflaging facilitated social connections, relationships felt inauthentic.
• Camouflaging causes identity confusion and low self-esteem. Extensive camouflaging eroded participants’ sense of self.

The themes highlight the bidirectional influences between the individual and environment in camouflaging.

Camouflaging emerged as a largely socially-motivated yet self-reinforcing response that comes with serious costs to authenticity and mental well-being.

This review provides a novel and comprehensive synthesis of psychosocial factors implicated in camouflaging.

The findings indicate that camouflaging arises from the dynamic interplay between the individual and their social environment, challenging purely individual-focused explanations.

Autistic individuals’ camouflaging efforts were driven by societal pressures, stigma, and the need for acceptance. Over time, repeated exposure to adverse social experiences led them to anticipate rejection and develop camouflaging as a learned response.

Furthermore, internalizing stigmatizing narratives motivated individuals to mask their differences.

However, camouflaging often had the unintended effect of leaving stigma unchallenged while increasing internalized stigma. It also resulted in autistic people’s needs being overlooked and unmet.

Additionally, while the desire for social connections drove camouflaging, participants felt that the relationships formed through it were inauthentic. These “double binds” made it difficult for individuals to break out of the camouflaging cycle.

The findings call for a shift from changing the individual to fostering more inclusive environments.

A whole-society approach is needed to increase understanding and acceptance of autism, thus reducing pressures on autistic people to camouflage.

Encouragingly, participants described reducing their camouflaging when experiencing self-acceptance, often facilitated by their autism diagnosis and connections with the autistic community.

This review extends previous research by providing an in-depth examination of psychosocial influences on camouflaging.

Future studies should empirically test the conceptual model presented and prioritize diverse participant representation.

Additionally, research examining the influence of everyday psychosocial experiences on camouflaging can provide insight into how autistic individuals navigate camouflaging in daily life.

This study had several methodological strengths, including:

• Rigorous mixed methods approach enabled a rich, comprehensive understanding of psychosocial factors in camouflaging
• Utilized participatory methods by involving two autistic advisors who provided input on data synthesis and interpretation
• Developed a novel conceptual model of psychosocial correlates and consequences of camouflaging that can guide future research
• Highlighted critical gaps in current research, such as the lack of diverse participant representation

Limitations

Despite its strengths, there are several limitations of this study, including:

• Overrepresentation of White, female, late-diagnosed autistic adults with average to above average cognitive abilities limits the generalizability of findings
• Restricted to English-language articles and participants mainly from Western societies introduces potential language and cultural bias
• Inadequate reporting of key demographics (e.g. race/ethnicity, education) in most included studies further limits generalizability
• As a systematic review, cannot determine causal relationships between variables

Implications

The findings have important implications for increasing awareness, acceptance, and support for autistic people:

• Highlights the need for anti-stigma interventions and a shift towards accommodating rather than pathologizing autistic differences
• Professionals and educators should have a greater understanding of camouflaging to improve recognition of autistic people’s needs
• Diagnostic processes should account for camouflaging behaviors, especially for females and late-diagnosed individuals
• Psychosocial supports focused on strengthening autistic identity and community connections may enhance authenticity and well-being

However, as most included studies involved a narrow participant demographic, more research is needed to understand the relevance of findings for underrepresented groups, including racial/ethnic minorities, males, gender diverse individuals, those with intellectual disability, and people from non-Western cultures.

Additionally, prospective studies are required to establish directional relationships.

Primary reference

Zhuang, S., Tan, D. W., Reddrop, S., Dean, L., Maybery, M., & Magiati, I. (2023). Psychosocial factors associated with camouflaging in autistic people and its relationship with mental health and well-being: A mixed methods systematic review.  Clinical Psychology Review, 105,  1–16.  https://doi.org/10.1016/j.cpr.2023.102335

Other references

Beck, J. S., Lundwall, R. A., Gabrielsen, T., Cox, J. C., & South, M. (2020). Looking good but feeling bad: “Camouflaging” behaviors and mental health in women with autistic traits. Autism, 24 (4), 809–821. https://doi.org/10.1177/1362361320912147

Cage, E., & Troxell-Whitman, Z. (2019). Understanding the reasons, contexts and costs of camouflaging for autistic adults. Journal of Autism and Developmental Disorders, 49 (5), 1899–1911. . https://doi.org/10.1007/s10803-018-03878-x

Cassidy, S., Bradley, L., Shaw, R., & Baron-Cohen, S. (2018). Risk markers for suicidality in autistic adults. Molecular Autism, 9 (1), 42. https://doi.org/10.1186/s13229-018-0226-4

Cook, J., Crane, L., Bourne, L., Hull, L., & Mandy, W. (2021). Camouflaging in an everyday social context: An interpersonal recall study. Autism, 25 (5), 1444–1456. https://doi.org/10.1177/1362361321992641

Hull, L., Petrides, K. V., Allison, C., Smith, P., Baron-Cohen, S., Lai, M. C., & Mandy, W. (2017). “Putting on my best normal”: Social camouflaging in adults with autism spectrum conditions. Journal of Autism and Developmental Disorders, 47(8), 2519–2534. https://doi.org/10.1007/s10803-017-3166-5

Libsack, E. J., Keenan, E. G., Freden, C. E., Mirmina, J., Iskhakov, N., Krishnathasan, D., & Lerner, M. D. (2021). A systematic review of passing as non-autistic in autism spectrum disorder. Clinical Child and Family Psychology Review, 24(4), 783–812. https://doi.org/10.1007/s10567-021-00365-1

Livingston, L. A., Shah, P., & Happé, F. (2019). Compensatory strategies below the behavioral surface in autism: A qualitative study. The Lancet Psychiatry, 6(9), 766–777. https://doi.org/10.1016/s2215-0366(19)30224-x

Keep Learning

Here are some reflective questions related to this study that could prompt further discussion:

• How might the social environment be changed to reduce pressures on autistic people to camouflage?
• What kinds of supports would be most helpful for autistic individuals in managing decisions around camouflaging versus authentic self-expression?
• How can mental health professionals, educators, and workplaces create environments where autistic people feel psychologically safe to unmask?
• What are respectful ways for non-autistic people to respond when an autistic person discloses their diagnosis?
• How might camouflaging present differently across cultures? What unique challenges might autistic people with multiple marginalized identities face?

ORIGINAL RESEARCH article

This article is part of the research topic.

Orthorexia Nervosa: New Insights into Clinical Management and Social Environment Aspects

Orthorexic Tendency and Its Association with Weight Control Methods and Dietary Variety in Polish Adults: A Cross-Sectional Study Provisionally Accepted

• 1 Department of Food Market and Consumer Research, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, Poland
• 2 Department of Human Nutrition, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Poland

The final, formatted version of the article will be published soon.

The methods for controlling weight play a central role in formally diagnosed eating disorders (EDs) and appear to be important in the context of other nonformally recognized disorders, such as orthorexia nervosa (ON). These methods also have an impact on eating behaviors, including dietary variety. Our study aimed to: (i) assess the intensity of ON tendency by sex and BMI groups, (ii) evaluate the associations between ON tendency, weight control methods, and dietary variety, and (iii) determine the extent to which weight control methods and dietary variety contribute to the ON tendency among both females and males. Data were gathered from a sample of 936 Polish adults (463 females and 473 males) through a cross-sectional quantitative study conducted in 2019. Participants were requested to complete the ORTO-6, the Weight Control Methods Scale, and the Food Intake Variety Questionnaire (FIVeQ). Multiple linear regression analysis was employed to evaluate associations between ON tendency, weight control methods, and dietary variety. Females exhibited a higher ON tendency than males (14.4 ± 3.4 vs. 13.5 ± 3.7, p < 0.001, d = 0.25). In the regression model, the higher ON tendency was predicted by more frequent use of weight control methods, such as restricting the amount of food consumed, using laxatives, and physical exercise among both females and males as well as following a starvation diet in females, and drinking teas to aid bowel movements among males. Moreover, the higher ON tendency was predicted by higher dietary variety, lower age in both sexes, and higher level of education among males. However, there were no differences in ON tendency across BMI groups. In conclusion, the findings showed that ON tendency was predicted by a higher frequency of weight control methods commonly used by individuals with anorexia nervosa (AN) and bulimia nervosa (BN). The resemblance to these two EDs is also suggested by the higher intensity of ON tendency among females and younger people. However, the prediction of ON tendency by dietary variety indicates that the obsessive preoccupation with healthy eating may not be advanced enough to observe a decrease in the dietary variety among these individuals.

Keywords: orthorexic tendency, Weight control methods, dietary variety, adults, Poland

Received: 14 Dec 2023; Accepted: 02 Apr 2024.

Copyright: © 2024 Plichta and Kowalkowska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Marta Plichta, Department of Food Market and Consumer Research, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, Warsaw, Poland

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