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Part IV - Understanding What Your Data Are Telling You About Psychological Processes

Published online by Cambridge University Press:  12 December 2024

Edited by
Harry T. Reis ,
Tessa West  and
Charles M. Judd
Harry T. Reis
Affiliation:
University of Rochester, New York
Tessa West
Affiliation:
New York University
Charles M. Judd
Affiliation:
University of Colorado Boulder
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Handbook of Research Methods in Social and Personality Psychology , pp. 469 - 704
Publisher: Cambridge University Press
Print publication year: 2024

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References

References

Allport, G. W., and Odbert, H. S. (1936). Trait-names: A psycho-lexical study. Psychological Monographs, 47(211), DOI: 10.1037/h0093360.CrossRefGoogle Scholar
Allport, G. W., and Vernon, P. E. (1933). Studies in Expressive Movement. Macmillan.CrossRefGoogle Scholar
Arias, V. B., Garrido, L. E., Jenaro, C., Martinez-Molina, A., and Arias, B. (2020). A little garbage in, lots of garbage out: Assessing the impact of careless responding in personality survey data. Behavior Research Methods, 52(6), 2489–2505.CrossRefGoogle ScholarPubMed
Athenstaedt, U. (2003). On the content and structure of the gender role self-concept: Including gender-stereotypical behaviors in addition to traits. Psychology of Women Quarterly, 27(4), 309–318.CrossRefGoogle Scholar
Bernaards, C., and Jennrich, R. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65(5), 676–696.CrossRefGoogle Scholar
Bernreuter, R. (1931). Bernreuter Personality Inventory. Stanford University Press.Google Scholar
Binet, A., and Simon, T. (1905). New methods for the diagnosis of the intellectual level of subnormals. L’annee psychologique, 12, 191–244 (translated in 1916 by E. S. Kite in The Development of Intelligence in Children. Publications of the Training School at Vineland).Google Scholar
Binet, A., and Simon, T. (1916). The Development of Intelligence in Children, translated by Kite, Elizabeth S. (ed. Goddard, H. H.). William and Wilkens Company.Google Scholar
Borsboom, D., Mellenbergh, G. J., and van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061–1071.CrossRefGoogle ScholarPubMed
Brown, W. (1910). Some experimental results in the correlation of mental abilities. British Journal of Psychology, 3(3), 296–322.Google Scholar
Campbell, D. P., and Borgen, F. H. (1999). Holland’s theory and the development of interest inventories. Journal of Vocational Behavior, 55(1), 86–101.CrossRefGoogle Scholar
Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait–multimethod matrix. Psychological Bulletin, 56(8), 81–105.CrossRefGoogle ScholarPubMed
Clark, L. A., and Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.CrossRefGoogle Scholar
Clark, L. A., and Watson, D. (2019). Constructing validity: New developments in creating objective measuring instruments. Psychological Assessment, 31(12), 1412–1427.CrossRefGoogle ScholarPubMed
Condon, D. M. (2018). The SAPA Personality Inventory: An empirically-derived, hierarchically-organized self-report personality assessment model. PsyArXiv, /sc4p9/, DOI: 10.31234/osf.io/sc4p9.CrossRefGoogle Scholar
Condon, D. M. (2019). Database of individual differences survey tools. Harvard Dataverse, DOI: 10.7910/DVN/T1NQ4V.CrossRefGoogle Scholar
Condon, D. M. (2022, June). Retest reliability = f (stability, memory, personality)+ɛ. (presented at symposium in honor of Sarah Dubrow).Google Scholar
Condon, D. M., and Revelle, W. (2014). The international cognitive ability resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52–64.CrossRefGoogle Scholar
Condon, D. M., and Revelle, W. (2015). Selected personality data from the SAPA-Project: 08dec2013 to 26jul2014. Harvard Dataverse, DOI: 10.7910/DVN/SD7SVE.CrossRefGoogle Scholar
Condon, D. M., Roney, E., and Revelle, W. (2017a). Selected personality data from the sapa-project: 22dec2015 to 07feb2017 (48,350 participant data file and codebook). Harvard Dataverse, DOI: 10.7910/DVN/TZJGAT.CrossRefGoogle Scholar
Condon, D. M., Roney, E., and Revelle, W. (2017b). Selected personality data from the sapa-project: 26jul2014 to 22dec2015 (54,855 participant data file and codebook). Harvard Dataverse, DOI: 10.7910/DVN/GU70EV.CrossRefGoogle Scholar
Condon, D. M., Wood, D., Mõttus, R., Booth, T., Costantini, G., Greiff, S., Johnson, W., Lukaszewski, A., Murray, A., Revelle, W., Wright, A. G. C., Ziegler, M., and Zimmermann, J. (2020). Bottom up construction of a personality taxonomy. European Journal of Psychological Assessment, 36, 923–934.CrossRefGoogle Scholar
Cronbach, L. J., and Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.CrossRefGoogle ScholarPubMed
Cureton, E. E. (1950). Validity, reliability, and baloney. Educational and Psychological Measurement, 10(1), 94–96.CrossRefGoogle Scholar
Dawis, R. V. (1992). The individual differences tradition in counseling psychology. Journal of Counseling Psychology, 39(1), 7–19.CrossRefGoogle Scholar
Del Giudice, M. (2021). Individual and group differences in multivariate domains: What happens with the number of traits increases? PsyArXiv, DOI: 10.31234/osf.io/rgzd2.CrossRefGoogle Scholar
Eagly, A. H., and Revelle, W. (2022). Understanding the magnitude of psychological differences between women and men requires seeing the forest and the trees. Perspectives on Psychological Science, 17(5), DOI: 10.1177/17456916211046006.CrossRefGoogle ScholarPubMed
Elleman, L. G., McDougald, S., Revelle, W., and Condon, D. (2020). That takes the BISCUIT: A comparative study of predictive accuracy and parsimony of four statistical learning techniques in personality data, with data missingness conditions. European Journal of Psychological Assessment, 36(6), 948–958.CrossRefGoogle Scholar
Embretson, S. (2007). Construct validity: A universal validity system or just another test evaluation procedure? Educational Researcher, 36(8), 449–455.CrossRefGoogle Scholar
Eysenck, H. J., and Eysenck, S. B. G. (1964). Eysenck Personality Inventory. Educational and Industrial Testing Service.Google Scholar
Fyffe, S., Lee, P., and Kaplan, S. (2023). “transforming” personality scale development: Illustrating the potential of state-of-the-art natural language processing. Organizational Research Methods, DOI: 10.1177/10944281231155771.CrossRefGoogle Scholar
Galton, F. (1865). Hereditary talent and character. Macmillan’s Magazine, 12, 157–166.Google Scholar
Galton, F. (1884). Measurement of character. Fortnightly Review, 36, 179–185.Google Scholar
Goldberg, L. R. (1972). Parameters of personality inventory construction and utilization: A comparison of prediction strategies and tactics. Multivariate Behavioral Research Monographs. No 72-2, 7.Google Scholar
Goldberg, L. R. (1990). An alternative “description of personality”: The Big-Five factor structure. Journal of Personality and Social Psychology, 59(6), 1216–1229.CrossRefGoogle ScholarPubMed
Goldberg, L. R. (1992). The development of markers for the Big-Five factor structure. Psychological Assessment, 4(1), 26–42.CrossRefGoogle Scholar
Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. In Mervielde, I., Deary, I., De Fruyt, F., and Ostendorf, F. (eds.) Personality Psychology in Europe, vol. 7. Tilburg University Press.Google Scholar
Goldberg, L. R. (2008). The Eugene-Springfield Community Sample: Information Available from the Research Participants (Technical Report No. 48-1). Oregon Research Institute.Google Scholar
Goldberg, L. R. (2010). Personality, demographics and self reported acts: The development of avocational interest scales from estimates of the amount time spent in interest-related activities. In Agnew, C., Carlston, D., Graziano, W., and Kelly, J. (eds.) Then a Miracle Occurs: Focusing on the Behavior in Social Psychological Theory and Research. Oxford University Press.Google Scholar
Goldberg, L. R., and Kilkowski, J. M. (1985). The prediction of semantic consistency in self-descriptions: Characteristics of persons and of terms that affect the consistency of responses to synonym and antonym pairs. Journal of Personality and Social Psychology, 48(1), 82–98.CrossRefGoogle ScholarPubMed
Goldberg, L. R., and Saucier, G. (2016). The Eugene-Springfield Community Sample: Information Available from the Research Participants (Technical Report No. 56-1). Oregon Research Institute.Google Scholar
Gough, H. G. (1965) Conceptual analysis of psychological test scores and other diagnostic variables. Journal of Abnormal Psychology, 70, 294–302.CrossRefGoogle ScholarPubMed
Graziano, W. G., Jensen-Campbell, L. A., Steele, R. G., and Hair, E. C. (1998). Unknown words in self-reported personality: Lethargic and provincial in Texas. Personality and Social Psychology Bulletin, 24(8), 893–905.CrossRefGoogle Scholar
Gruber, F. M., Distlberger, E., Scherndl, T., Ortner, T. M., and Pletzer, B. (2020). Psychometric properties of the multifaceted gender-related attributes survey (GERAS). European Journal of Psychological Assessment, 36(4), 612–623.CrossRefGoogle ScholarPubMed
Guttman, L. (1945). A basis for analyzing test–retest reliability. Psychometrika, 10(4), 255–282.CrossRefGoogle ScholarPubMed
Hathaway, S., and McKinley, J. (1943). Manual for Administering and Scoring the MMPI. University of Minnesota Press.Google Scholar
Hogan, R., and Nicholson, R. A. (1988). The meaning of personality test scores. American Psychologist, 43(8), 621–626.CrossRefGoogle Scholar
Holzinger, K., and Swineford, F. (1937). The bi-factor method. Psychometrika, 2(1), 41–54.CrossRefGoogle Scholar
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185.CrossRefGoogle ScholarPubMed
Johnson, J. A. (2005). Ascertaining the validity of individual protocols from web-based personality inventories. Journal of Research in Personality, 39(1), 103–129.CrossRefGoogle Scholar
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology (140), 1–53.Google Scholar
Likert, R., Roslow, S., and Murphy, G. (1934). A simple and reliable method of scoring the Thurstone attitude scales. Journal of Social Psychology, 5(2), 228–238.CrossRefGoogle Scholar
Loevinger, J. (1957). Objective tests as instruments of psychological theory. Psychological Reports Monograph Supplement 9, 3, 635–694.Google Scholar
Lord, F. M., and Novick, M. R. (1968) Statistical Theories of Mental Test Scores. Addison-Wesley.Google Scholar
McDonald, R. P. (1999). Test Theory: A Unified Treatment. L. Erlbaum Associates.Google Scholar
McNemar, Q. (1946). Opinion–attitude methodology. Psychological Bulletin, 43(4), 289–374.CrossRefGoogle ScholarPubMed
Meade, A. W., and Craig, S. B. (2012). Identifying careless responses in survey data. Psychological methods, 17(3), 437–455.CrossRefGoogle ScholarPubMed
Mõttus, R., Wood, D., Condon, D. M., Back, M. D., Baumert, A., Costantini, G., Epskamp, S., Greiff, S., Johnson, W., Lukaszewski, A., Murray, A., Revelle, W., Wright, A. G. C., Yarkoni, T., Ziegler, M., and Zimmermann, J. (2020). Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the big few traits. European Journal of Personality, 34(6), 1175–1201.CrossRefGoogle Scholar
Nájera, P., Abad, F. J. and Sorrel, M. A. (in press). Is EFA always to be preferred? A systematic comparison of factor analytic techniques throughout the confirmatory-exploratory continuum. Psychological Methods.Google Scholar
Nichols, D. S., and Greene, R. L. (1997). Dimensions of deception in personality assessment: The example of the MMPI-2. Journal of Personality Assessment, 68(2), 251–266.CrossRefGoogle ScholarPubMed
Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factors structure in peer nomination personality ratings. Journal of Abnormal and Social Psychology, 66, 574–583.CrossRefGoogle ScholarPubMed
Core Team, R. (2023). R: A Language and Environment for Statistical Computing (computer software manual), www.R-project.org.Google Scholar
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696.CrossRefGoogle ScholarPubMed
Reise, S. P., Morizot, J., and Hays, R. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16(0), 19–31.CrossRefGoogle ScholarPubMed
Revelle, W. (1979). Hierarchical cluster-analysis and the internal structure of tests. Multivariate Behavioral Research, 14(1), 57–74.CrossRefGoogle ScholarPubMed
Revelle, W. (2023a). psych: Procedures for Psychological, Psychometric, and Personality Research, ed. 2.3.3 (computer software manual). psych.Google Scholar
Revelle, W. (2023b). psychTools Tools to Accompany the psych Package for Psychological Research, R package version 2.3.3 (computer software manual). psychTools.Google Scholar
Revelle, W., and Anderson, K. J. (1998). Personality, Motivation and Cognitive Performance: Final Report to the Army Research Institute on Contract MDA 903-93-K-0008. Northwestern University.Google Scholar
Revelle, W., and Condon, D. M. (2019). Reliability from α to ω: A tutorial. Psychological Assessment., 31(12), 1395–1411.CrossRefGoogle Scholar
Revelle, W., Condon, D. M., Wilt, J., French, J. A., Brown, A., and Elleman, L. G. (2017). Web- and phone-based data collection using planned missing designs. In Fielding, N. G., Lee, R. M., and Blank, G. (eds.) Sage Handbook of Online Research Methods, 2nd ed. Sage Publications, Inc.Google Scholar
Revelle, W., Dworak, E. M., and Condon, D. M. (2021). Exploring the persome: The power of the item in understanding personality structure. Personality and Individual Differences, 169, DOI: 10.1016/j.paid.2020.109905.CrossRefGoogle Scholar
Reyes, D. L. (2020). Combatting carelessness: Can placement of quality check items help reduce careless responses? Current Psychology, 41(2), DOI: 10.1007/s12144-020-01183-4.Google Scholar
Robins, R. W., Hendin, H. M., and Trzesniewski, K. H. (2001). Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg self-esteem scale. Personality and Social Psychology Bulletin, 27(2), 151–161.CrossRefGoogle Scholar
Rodgers, J. L., and Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. American Statistician, 42(1), 59–66.CrossRefGoogle Scholar
Sartori, R., and Pasini, M. (2007). Quality and quantity in test validity: How can we be sure that psychological tests measure what they have to? Quality & Quantity, 41(3), 359–374.CrossRefGoogle Scholar
Schmid, J. J., and Leiman, J. M. (1957). The development of hierarchical factor solutions. Psychometrika, 22(1), 83–90.CrossRefGoogle Scholar
Schwaba, T., Rhemtulla, M., Hopwood, C. J., and Bleidorn, W. (2020). A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure. PLOS ONE, 15(7), 1–21.CrossRefGoogle ScholarPubMed
Simms, L. J., Zelazny, K., Williams, T. F., and Bernstein, L. (2019). Does the number of response options matter? Psychometric perspectives using personality questionnaire data. Psychological Assessment, 31(4), 557–566.CrossRefGoogle ScholarPubMed
Spearman, C. (1904a). “General intelligence,” objectively determined and measured. American Journal of Psychology, 15(2), 201–292.CrossRefGoogle Scholar
Spearman, C. (1904b). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72–101.CrossRefGoogle Scholar
Spearman, C. (1910). Correlation calculated from faulty data. British Journal of Psychology, 3(3), 271–295.Google Scholar
Strong, E. K., Jr. (1927). Vocational interest test. Educational Record, 8(2), 107–121.Google Scholar
Thayer, R. E. (1989). The Biopsychology of Mood and Arousal. Oxford University Press.Google Scholar
Ward, M., and Meade, A. W. (2018). Applying social psychology to prevent careless responding during online surveys. Applied Psychology, 67(2), 231–263.CrossRefGoogle Scholar
Widaman, K. F., and Revelle, W. (2022). Thinking thrice about sum scores, and then some more about measurement and analysis. Behavior Research Methods, 55(3), DOI: 10.3758/s13428-022-01849-w.CrossRefGoogle Scholar
Woods, S. A., and Hampson, S. E. (2005). Measuring the Big Five with single items using a bipolar response scale. European Journal of Personality, 19(5), 373–390.CrossRefGoogle Scholar
Yarkoni, T., and Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.CrossRefGoogle ScholarPubMed
Zhang, X., and Savalei, V. (2016). Improving the factor structure of psychological scales: The expanded format as an alternative to the likert scale format. Educational and Psychological Measurement, 76(3), 357–386.CrossRefGoogle Scholar
Zimmerman, J. (2020). Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the big few traits. European Journal of Personality, 34(6), DOI: 10.1002/per.2311.Google Scholar
Zinbarg, R. E., Revelle, W., Yovel, I., and Li, W. (2005). Cronbach’s α, Revelle’s β, and McDonald’s ωH: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 123–133.CrossRefGoogle Scholar
Zola, A., Condon, D. M., and Revelle, W. (2021, 08). The convergence of self and informant reports in a large online sample. Collabra: Psychology, 7(1), 25983, DOI: 10.1525/collabra.25983.CrossRefGoogle Scholar

References

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.CrossRefGoogle Scholar
Bollen, K. A., and Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265–284.CrossRefGoogle ScholarPubMed
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis, Multivariate Behavioral Research, 36(1), 111–150.CrossRefGoogle Scholar
Byrne, B. M., Shavelson, R. J., and Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105(3), 456–466.CrossRefGoogle Scholar
Chen, F. F., Sousa, K. H. and West, S. G. (2005) Teacher’s corner: Testing measurement invariance of second-order factor models. Structural Equation Modeling, 12(3), 471–492.CrossRefGoogle Scholar
Chen, F. F., West, S. G. and Sousa, K. H. (2006) A comparison of bifactor and second-order models of quality of life, Multivariate Behavioral Research, 41(2), 189–225.CrossRefGoogle ScholarPubMed
Cohen, J., Cohen, P., West, S. G., Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis, 3d ed. Routledge.Google Scholar
Cronbach, L. J., and Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.CrossRefGoogle ScholarPubMed
Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Medical care, 44(11, Suppl. 3), S78–S94.CrossRefGoogle ScholarPubMed
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling, 4th ed. Guilford Publications.Google Scholar
Lim, S., and Jahng, S. (2019). Determining the number of factors using parallel analysis and its recent variants. Psychological Methods, 24(4), 452–469.CrossRefGoogle ScholarPubMed
Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543.CrossRefGoogle Scholar
Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.Google Scholar
Muthén, B., and Muthén, L. (2017). Mplus. In van der Linden, W. J. (ed.) Handbook of Item Response Theory. Chapman and Hall/CRC.Google Scholar
Neuberg, S. L., West, S. G., Judice, T. N., and Thompson, M. M. (1997). On dimensionality, discriminant validity, and the role of psychometric analyses in personality theory and measurement: Reply to Kruglanski et al.’s (1997) defense of the need for closure scale. Journal of Personality and Social Psychology, 73(5), 1017–1029.CrossRefGoogle Scholar
Pacewicz, C. E., Hill, C. R., Lee, S., Myers, N. D., Prilleltensky, I., McMahon, A., … Brincks, A. M. (2022). Testing measurement invariance in physical education and exercise science: A tutorial using the well-being self-efficacy scale. Measurement in Physical Education and Exercise Science, 26(2), 165–177.CrossRefGoogle Scholar
Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.CrossRefGoogle Scholar
Revelle, W. (2021). psych: Procedures for personality and psychological research (Version 2.1.9) (computer software), https://CRAN.R-project.org/package=psych.Google Scholar
Roets, A., and van Hiel, A. (2011). Item selection and validation of a brief, 15-item version of the need for closure scale. Personality and Individual Differences, 50(1), 90–94.CrossRefGoogle Scholar
Rosenberg, M. (1965). Society and the Adolescent Self-Image. Princeton University Press.CrossRefGoogle Scholar
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.CrossRefGoogle Scholar
Saucier, G., and Ostendorf, F. (1999). Hierarchical subcomponents of the Big Five personality factors: A cross-language replication. Journal of Personality and Social Psychology, 76(4), 613–627.CrossRefGoogle ScholarPubMed
Shrout, P. E., Mogami, M., Xu, Q., Ghodse-Elahi, Y., Mutter, E., Riccio, M. T., … Goudarzi, S. (2022). Measuring openness to political pluralism. Journal of Social and Political Psychology, 10(2), 624–642.CrossRefGoogle Scholar
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173–180.CrossRefGoogle ScholarPubMed
Thissen, D., Steinberg, L., and Kuang, D. (2002). Quick and easy implementation of the Benjamini–Hochberg procedure for controlling the false positive rate in multiple comparisons. Journal of Educational and Behavioral Statistics, 27(1), 77–83.CrossRefGoogle Scholar
Vandenberg, R. J., and Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.CrossRefGoogle Scholar
Bolger, N., and Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press.Google Scholar
Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2012). Applied Longitudinal Analysis. Wiley.Google Scholar
Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.Google Scholar
Mehl, M. R., and Conner, T. S. (eds.) (2012). Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
O’Connell, A. A., McCoach, D. B., and Bell, B. A. (eds.). (2022). Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing.Google Scholar

References

Arend, M. G., and Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological Methods, 24, 1–19.CrossRefGoogle ScholarPubMed
Arriaga, X. B. (2001). The ups and downs of dating: Fluctuations in satisfaction in newly formed romantic relationships. Journal of Personality and Social Psychology, 80(5), 754–765.CrossRefGoogle ScholarPubMed
Asparouhov, T., Hamaker, E. L., and Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388.CrossRefGoogle Scholar
Barr, D. J., Levy, R., Scheepers, C., and Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278.CrossRefGoogle ScholarPubMed
Bauer, D. J., Preacher, K. J., and Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11(2), 142–163.CrossRefGoogle ScholarPubMed
Boker, S. M. (2012). Dynamical systems and differential equation models of change. In APA Handbook of Research Methods in Psychology, vol. 3, Data Analysis and Research PublicationAmerican Psychological Association.Google Scholar
Bolger, N., and Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press.Google Scholar
Bolger, N., and Shrout, P. E. (2007). Accounting for statistical dependency in longitudinal data on dyads. In Little, T. D., Bovaird, J. A., and Card, N. A. (eds.) Modeling Contextual Effects in Longitudinal Studies. Lawrence Erlbaum Associates Publishers.Google Scholar
Bolger, N., Stadler, G., and Laurenceau, J.-P. (2012). Power analysis for intensive longitudinal studies. In Mehl, M. R. and Conner, T. S. (eds.) Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
Bolger, N., and Zee, K. S. (2019). Heterogeneity in temporal processes: Implications for theories in health psychology. Applied Psychology: Health and Well-Being, 11(2), 198–201.Google ScholarPubMed
Bolger, N., Zuckerman, A., and Kessler, R. C. (2000). Invisible support and adjustment to stress. Journal of Personality and Social Psychology, 79(6), 953–961.CrossRefGoogle ScholarPubMed
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.Google Scholar
Brock, R. L., and Lawrence, E. (2008). A longitudinal investigation of stress spillover in marriage: Does spousal support adequacy buffer the effects? Journal of Family Psychology, 22(1), 11–20.CrossRefGoogle ScholarPubMed
Butler, E. A., and Barnard, K. J. (2019). Quantifying interpersonal dynamics for studying socio-emotional processes and adverse health behaviors. Psychosomatic Medicine, 81(8), 749–758.CrossRefGoogle ScholarPubMed
Chun, C. A. (2016). The expression of posttraumatic stress symptoms in daily life: A review of experience sampling methodology and daily diary studies. Journal of Psychopathology and Behavioral Assessment, 38(3), 406–420.CrossRefGoogle Scholar
DiGiovanni, A. M., Fagle, T., Vannucci, A., Ohannessian, C. M., and Bolger, N. (2022). Within-person changes in co-rumination and rumination in adolescence: Examining heterogeneity and the moderating roles of gender and time. Journal of Youth and Adolescence, 51(10), 1958–1969.CrossRefGoogle ScholarPubMed
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., and Schabenberger, O. (2008). An R2 statistic for fixed effects in the linear mixed model. Statistics in Medicine, 27(29), 6137–6157.CrossRefGoogle ScholarPubMed
Enders, C. K., and Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138.CrossRefGoogle Scholar
Foster, K. T., and Beltz, A. M. (2022). Heterogeneity in affective complexity among men and women. Emotion, 22(8), 1815–1827.CrossRefGoogle ScholarPubMed
Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models. Sage.Google Scholar
Frost, D. M., and Forrester, C. (2013). Closeness discrepancies in romantic relationships: Implications for relational well-being, stability, and mental health. Personality and Social Psychology Bulletin, 39(4), 456–469.CrossRefGoogle ScholarPubMed
Gable, S. L., and Reis, H. T. (1999). Now and then, them and us, this and that: Studying relationships across time, partner, context, and person. Personal Relationships, 6(4), 415–432.CrossRefGoogle Scholar
Garson, G. D. (2019). Multilevel Modeling. SAGE Publications, Inc.Google Scholar
Gates, K. M., and Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), 310–319.CrossRefGoogle ScholarPubMed
Girme, Y. U. (2020). Step out of line: Modeling nonlinear effects and dynamics in close-relationships research. Current Directions in Psychological Science, 29(4), 351–357.CrossRefGoogle Scholar
Goldring, M. R., and Bolger, N. (2021). Physical effects of daily stressors are psychologically mediated, heterogeneous, and bidirectional. Journal of Personality and Social Psychology, 121, 722–746.CrossRefGoogle ScholarPubMed
Gordon, A. M. (2023). Within-person variance in daily conflict and relationship satisfaction. Unpublished data.Google Scholar
Gordon, A. M., and Chen, S. (2014). The role of sleep in interpersonal conflict: Do sleepless nights mean worse fights? Social Psychological and Personality Science, 5, 168–175.CrossRefGoogle Scholar
Gordon, A. M., Cross, E., Ascigil, E., Balzarini, R., Luerssen, A., and Muise, A. (2022). Feeling appreciated buffers against the negative effects of unequal division of household labor on relationship satisfaction. Psychological Science, 33(8), 1313–1327.CrossRefGoogle ScholarPubMed
Greene, W. H. (2008). Econometric Analysis, 6th ed. Pearson/Prentice Hall.Google Scholar
Hamaker, E. L., and Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25, 365–379.CrossRefGoogle ScholarPubMed
Harris, P. E., Gordon, A. M., Dover, T. L., Small, P. A., Collins, N. L., and Major, B. (2022). Sleep, emotions, and sense of belonging: A daily experience study. Affective Science, 3(3), DOI:10.1007/s42761-021-00088-0.CrossRefGoogle ScholarPubMed
Hayes, A. M., Laurenceau, J.-P., Feldman, G., Strauss, J. L., and Cardaciotto, L. (2007). Change is not always linear: The study of nonlinear and discontinuous patterns of change in psychotherapy. Clinical Psychology Review, 27(6), 715–723.CrossRefGoogle ScholarPubMed
Hox, J., Moerbeek, M., and van de Schoot, R. (2018). Multilevel Analysis: Techniques and Applications, 3rd ed. Routledge.Google Scholar
Iida, M., Shrout, P. E., Laurenceau, J.-P., and Bolger, N. (2012). Using diary methods in psychological research. In Cooper, H. et al. (eds.) APA Handbook of Research Methods In Psychology, vol. 1, Foundations, Planning, Measures, and Psychometrics. American Psychological Association.Google Scholar
Karney, B. R., and Bradbury, T. N. (1997). Neuroticism, marital interaction, and the trajectory of marital satisfaction. Journal of Personality and Social Psychology, 72(5), 1075–1092.CrossRefGoogle ScholarPubMed
Kashdan, T., and Steger, M. F. (2006). Expanding the topography of social anxiety: An experience-sampling assessment of positive emotions, positive events, and emotion suppression. Psychological Science, 17(2), 120–128.CrossRefGoogle ScholarPubMed
Kashy, D. A., and Donnellan, M. B. (2008). Comparing MLM and SEM approaches to analyzing developmental dyadic data: Growth curve models of hostility in families. In Card, N. A., Selig, J. P., and Little, T. D. (eds.) Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences. Routledge.Google Scholar
Kenny, D. A., Kashy, D. A., and Bolger, N. (1998). Data analysis in social psychology. In Gilbert, D. T., Fiske, S. T., and Lindzey, G. (eds.) The Handbook of Social Psychology, vol. 1. Oxford University Press.Google Scholar
Kenny, D. A., Kashy, D. A., and Cook, W. L. (2006). Dyadic Data Analysis. Guilford Press.Google Scholar
Killip, S., Mahfoud, Z., and Pearce, K. (2004). What is an intracluster correlation coefficient? Crucial concepts for primary care researchers. Annals of Family Medicine, 2(3), 204–208.CrossRefGoogle ScholarPubMed
Lafit, G., Adolf, J. K., Dejonckheere, E., Myin-Germeys, I., Viechtbauer, W., and Ceulemans, E. (2021). Selection of the number of participants in intensive longitudinal studies: A user-friendly shiny app and tutorial for performing power analysis in multilevel regression models that account for temporal dependencies. Advances in Methods and Practices in Psychological Science, 4(1), https://doi.org/10.1177/2515245920978738.CrossRefGoogle Scholar
Lane, S. P., and Hennes, E. P. (2018). Power struggles: Estimating sample size for multilevel relationships research. Journal of Social and Personal Relationships, 35(1), 7–31.CrossRefGoogle Scholar
Lavner, J. A., and Bradbury, T. N. (2010). Patterns of change in marital satisfaction over the newlywed years. Journal of Marriage and Family, 72(5), 1171–1187.CrossRefGoogle ScholarPubMed
Ledermann, T., and Kenny, D. A. (2017). Analyzing dyadic data with multilevel modeling versus structural equation modeling: A tale of two methods. Journal of Family Psychology, 31, 442–452.CrossRefGoogle ScholarPubMed
Liang, M., Koslovsky, M. D., Hébert, E. T., Kendzor, D. E., Businelle, M. S., and Vannucci, M. (2021). Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error. Psychological Methods, 28(4), 880–894.CrossRefGoogle ScholarPubMed
McArdle, J. J., and Nesselroade, J. R. (2014). Longitudinal Data Analysis Using Structural Equation Models. American Psychological Association.CrossRefGoogle Scholar
McClelland, G. H. (2000). Nasty data: Unruly, ill-mannered observations can ruin your analysis. In Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology, 1st ed. Cambridge University Press.Google Scholar
McNeish, D. (2017). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward–Roger correction. Multivariate Behavioral Research, 52(5), 661–670.CrossRefGoogle ScholarPubMed
McNeish, D., and Hamaker, E. L. (2020). A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychological Methods, 25, 610–635.CrossRefGoogle ScholarPubMed
McNeish, D., and Matta, T. (2018). Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavior Research Methods, 50(4), 1398–1414.CrossRefGoogle ScholarPubMed
McNeish, D., Stapleton, L. M., and Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22(1), 114–140.CrossRefGoogle ScholarPubMed
Matthews, R. A., Wayne, J. H., and Ford, M. T. (2014). A work–family conflict/subjective well-being process model: A test of competing theories of longitudinal effects. Journal of Applied Psychology, 99(6), 1173–1187.CrossRefGoogle Scholar
Moller, A. C., Deci, E. L., and Elliot, A. J. (2010). Person-level relatedness and the incremental value of relating. Personality and Social Psychology Bulletin, 36(6), 754–767.CrossRefGoogle ScholarPubMed
Nezlek, J. B. (2012). Multilevel modeling analyses of diary-style data. In Mehl, M. R. and Conner, T. S. (eds.) Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
Orth, U., Clark, D. A., Donnellan, M. B., and Robins, R. W. (2021). Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. Journal of Personality and Social Psychology, 120(4), 1013–1034.CrossRefGoogle ScholarPubMed
Raudenbush, S. W., and Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. Sage Publications.Google Scholar
Reis, H. T., and Wheeler, L. (1991). Studying social interaction with the Rochester interaction record. In Zanna, M. P. (ed.) Advances in Experimental Social Psychology, vol. 24. Academic Press.Google Scholar
Reynolds, B. M., Robles, T. F., and Repetti, R. L. (2016). Measurement reactivity and fatigue effects in daily diary research with families. Developmental Psychology, 52, 442–456.CrossRefGoogle ScholarPubMed
Rights, J. D., and Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24, 309–338.CrossRefGoogle ScholarPubMed
Schrader, S. M., Turner, T. W., Breitenstein, M. J., and Simon, S. D. (1988). Longitudinal study of semen quality of unexposed workers: I. Study overview. Reproductive Toxicology, 2(3), 183–190.CrossRefGoogle Scholar
Shrout, P. E., Stadler, G., Lane, S. P., McClure, M. J., Jackson, G. L., Clavél, F. D., Iida, M., Gleason, M. E. J., Xu, J. H., and Bolger, N. (2018). Initial elevation bias in subjective reports. Proceedings of the National Academy of Sciences of the United States of America, 115(1), E15–E23.Google ScholarPubMed
Simonsohn, U. (2018). Two lines: A valid alternative to the invalid testing of U-shaped relationships with quadratic regressions. Advances in Methods and Practices in Psychological Science, 1(4), 538–555.CrossRefGoogle Scholar
Sin, N. L., Graham-Engeland, J. E., Ong, A. D., and Almeida, D. M. (2015). Affective reactivity to daily stressors is associated with elevated inflammation. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 34(12), 1154–1165.CrossRefGoogle ScholarPubMed
Singer, J. D., and Willett, J. B. (2003). Survival analysis. In Schinka, J. A. and Velicer, W. F. (eds.) Handbook of Psychology, vol. 2,Research Methods in Psychology. John Wiley & Sons Inc.Google Scholar
Snijders, T. A. B., and Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed. Sage.Google Scholar
Stadler, G., Snyder, K. A., Horn, A. B., Shrout, P. E., and Bolger, N. P. (2012). Close relationships and health in daily life: A review and empirical data on intimacy and somatic symptoms. Psychosomatic Medicine, 74(4), 398–409.CrossRefGoogle ScholarPubMed
Teague, S., Youssef, G. J., Macdonald, J. A., Sciberras, E., Shatte, A., Fuller-Tyszkiewicz, M., Greenwood, C., McIntosh, J., Olsson, C. A., Hutchinson, D., Bant, S., Barker, S., Booth, A., Capic, T., Di Manno, L., Gulenc, A., Le Bas, G., Letcher, P., Lubotzky, C. A., and the SEED Lifecourse Sciences Theme. (2018). Retention strategies in longitudinal cohort studies: A systematic review and meta-analysis. BMC Medical Research Methodology, 18(1), 151, https://doi.org/10.1186/s12874-018-0586-7.CrossRefGoogle ScholarPubMed
Thorson, K. R., West, T. V., and Mendes, W. B. (2018). Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies. Psychological Methods, 23(4), 595–616.CrossRefGoogle ScholarPubMed
Torre, J. B., and Lieberman, M. D. (2018). Putting feelings into words: Affect labeling as implicit emotion regulation. Emotion Review, 10(2), 116–124.CrossRefGoogle Scholar
Uhlig, S., Meylan, A., and Rudolph, U. (2020). Reliability of short-term measurements of heart rate variability: Findings from a longitudinal study. Biological Psychology, 154, 107905, https://doi.org/10.1016/j.biopsycho.2020.107905.CrossRefGoogle ScholarPubMed
Vajargah, K. F., and Masoomehnikbakht. (2015). Application REML model and determining cut off of ICC by multi-level model based on Markov chains simulation in health. Indian Journal of Fundamental and Applied Life Sciences, 5(S2), 1432–1448.Google Scholar
van Breukelen, G. J. P. (2013). ANCOVA versus CHANGE from baseline in nonrandomized studies: The difference. Multivariate Behavioral Research, 48(6), 895–922.CrossRefGoogle ScholarPubMed
Williamson, H. C., and Lavner, J. A. (2020). Trajectories of marital satisfaction in diverse newlywed couples. Social Psychological and Personality Science, 11(5), 597–604.CrossRefGoogle ScholarPubMed
Wu, W., Selig, J. P, and Little, T. D. (2013). Longitudinal data analysis. In Little, T. D. (ed.) The Oxford Handbook of Quantitative Methods, vol. 2, Statistical Analysis. Oxford University Press.Google ScholarPubMed
Zee, K. S., and Bolger, N. (2022). Physiological coregulation during social support discussions. Emotion, 23(3), 825–843.CrossRefGoogle ScholarPubMed

References

Acitelli, L. K., Veroff, J., and Douvan, E. (2013). Couples and Well-Being Project, 1993–1995, Detroit Metropolitan Area. Inter-university Consortium for Political and Social Research (distributor), https://doi.org/10.3886/ICPSR22081.v1.CrossRefGoogle Scholar
Ackerman, R. A., Kashy, D. A., and Corretti, C. A. (2015). A tutorial on analyzing data from speed‐dating studies with heterosexual dyads. Personal Relationships, 22(1), 92–110.CrossRefGoogle Scholar
Ackerman, R. A., Kashy, D. A., Donnellan, M. B., and Conger, R. D. (2011). Positive engagement in family interactions: A social relations perspective. Journal of Family Psychology, 25, 719–730.CrossRefGoogle ScholarPubMed
Ackerman, R. A., and Kenny, D. A. (2016, December). APIMPower: An interactive tool for Actor–Partner Interdependence Model power analysis (computer software), available from https://robert-a-ackerman.shinyapps.io/apimpower.Google Scholar
Anderson, C. A., Buckley, K. E., and Carnagey, N. L. (2008). Creating your own hostile environment: A laboratory examination of trait aggressiveness and the violence escalation cycle. Personality and Social Psychology Bulletin, 34, 462–474.CrossRefGoogle ScholarPubMed
Back, M. D., and Kenny, D. A. (2010). The social relations model: How to understand dyadic processes. Social and Personality Psychology Compass, 4, 855–870.CrossRefGoogle Scholar
Biesanz, J. C. (2010). The social accuracy model of interpersonal perception: Assessing individual differences in perceptive and expressive accuracy. Multivariate Behavioral Research, 45, 853–885.CrossRefGoogle ScholarPubMed
Bond, C. F., Jr., and Cross, D. (2008). Beyond the dyad: Prospects for social development. In Card, N. A., Selig, J. P., and Little, T. D. (eds.) Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences. Routledge/Taylor & Francis Group.Google Scholar
Bond, C. F., Jr., and Kenny, D. A. (2002). The triangle of interpersonal models. Journal of Personality and Social Psychology, 83, 355–366.CrossRefGoogle ScholarPubMed
Bonito, J. A. (2022). Extending the group actor–partner interdependence model: MSEM applications. Small Group Research, 53, 207–243.CrossRefGoogle Scholar
Bonito, J. A., Ervin, J. N., and Staggs, S. M. (2016). Estimation and application of the latent group model. Group Dynamics: Theory, Research, and Practice, 20, 126–143.CrossRefGoogle Scholar
Brinberg, M., Ram, N., Conroy, D. E., Pincus, A. L., and Gerstorf, D. (2022). Dyadic analysis and the reciprocal one-with-many model: Extending the study of interpersonal processes with intensive longitudinal data. Psychological Methods, 27, 65–81.CrossRefGoogle Scholar
Campbell, L., Simpson, J. A., Boldry, J. G., and Kashy, D. A. (2005). Perceptions of conflict and support in romantic relationships: The role of attachment anxiety. Journal of Personality and Social Psychology, 88, 510–531.CrossRefGoogle ScholarPubMed
Claxton, S. E., Deluca, H. K., and van Dulmen, M. H. M. (2015). Testing psychometric properties in dyadic data using confirmatory factor analysis: Current practices and recommendations. TPM-Testing, Psychometrics, Methodology in Applied Psychology, 22, 181–198.Google Scholar
Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528.CrossRefGoogle ScholarPubMed
Cook, W. L., and Kenny, D. A. (2005). The actor–partner interdependence model: A model of bidirectional effects in developmental studies. International Journal of Behavioral Development, 29, 101–109.CrossRefGoogle Scholar
Correll, J., Park, B., Judd, C. M., and Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83, 1314–1329.CrossRefGoogle ScholarPubMed
Corretti, C. A. (2019). An investigation into the effects of cross-sex friendships on heterosexual romantic relationship dynamics, retrieved from http://utd.ir.tdl.org/handle/10735.1/7370.Google Scholar
Dabbs, J. M., Jr., and Ruback, R. B. (1987). Dimensions of group process: Amount and structure of vocal interaction. In Berkowitz, L. (ed.) Advances in Experimental Social Psychology, vol. 20. Academic Press.Google Scholar
Duncan, O. D., Haller, A. O., and Portes, A. (1968). Peer influences on aspirations: A reinterpretation. American Journal of Sociology, 74, 119–137.CrossRefGoogle ScholarPubMed
Dwyer, L. A., Bolger, N., Laurenceau, J. P., Patrick, H., Oh, A. Y., Nebeling, L. C., and Hennessy, E. (2017). Autonomous motivation and fruit/vegetable intake in parent–adolescent dyads. American Journal of Preventative Medicine, 52, 63–871.CrossRefGoogle ScholarPubMed
Finkel, E. J., and Eastwick, P. W. (2008). Speed-dating. Current Directions in Psychological Science, 17, 193–197.CrossRefGoogle Scholar
Galovan, A. M., Holmes, E. K., and Proulx, C. M. (2017). Theoretical and methodological issues in relationship research: Considering the common fate model. Journal of Social and Personal Relationships, 34, 44–68.CrossRefGoogle Scholar
Garcia, R., Kenny, D. A., and Ledermann, T. (2015). Moderation in the actor–partner interdependence model. Personal Relationships, 22, 8–29.CrossRefGoogle Scholar
Garcia, R. L., Meagher, B. R., Kenny, D. A. (2015). Analyzing the effects of group members’ characteristics: A guide to the group actor–partner interdependence model. Group Processes & Intergroup Relations, 18, 315–328.CrossRefGoogle Scholar
Gistelinck, F., and Loeys, T. (2019). The actor–partner interdependence model for longitudinal dyadic data: An implementation in the SEM framework. Structural Equation Modeling, 26, 329–347.CrossRefGoogle Scholar
Gistelinck, F., Loeys, T., Decuyper, M., and Dewitte, M. (2018). Indistinguishability tests in the actor–partner interdependence model. British Journal of Mathematical and Statistical Psychology, 71, 472–498.CrossRefGoogle ScholarPubMed
Gistelinck, F., Loeys, T., and Flamant, N. (2021). Multilevel autoregressive models when the number of time points is small. Structural Equation Modeling, 28, 15–27.CrossRefGoogle Scholar
Goldberg, A. E., Smith, J. Z., and Kashy, D. A. (2010). Pre-adoptive factors predicting lesbian, gay, and heterosexual couples’ relationship quality across the transition to adoptive parenthood. Journal of Family Psychology, 24, 221–232.CrossRefGoogle Scholar
Grimm, K. J., Ram, N., and Estabrook, R. (2016). Growth Modeling: Structural Equation and Multilevel Modeling Approaches. Guilford Press.Google Scholar
Grimm, K. J., Ram, N., and Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82, 1357–1371.CrossRefGoogle ScholarPubMed
Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., and Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO Study. Multivariate Behavioral Research, 53, 820–841.CrossRefGoogle ScholarPubMed
Hamaker, E. L., and Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25, 365–379.CrossRefGoogle ScholarPubMed
Herzberg, P. Y. (2013). Coping in relationships: The interplay between individual and dyadic coping and their effects on relationship satisfaction. Anxiety, Stress & Coping, 26, 136–153.CrossRefGoogle ScholarPubMed
Hibel, L. C., Mercado, E., and Valentino, K. (2019). Child maltreatment and mother–child transmission of stress physiology. Child Maltreatment, 24, 340–352.CrossRefGoogle ScholarPubMed
Iida, M., Seidman, G., and Shrout, P. E. (2018). Models of interdependent individuals versus dyadic processes in relationship research. Journal of Social and Personal Relationships, 35(1), 59–88.CrossRefGoogle Scholar
Jans, L., Leach, C. W., Garcia, R. L., and Postmes, T. (2015). The development of group influence on in-group identification: A multilevel approach. Group Processes and Intergroup Relations, 18, 190–209.CrossRefGoogle Scholar
Joel, S., Eastwick, P. W., and Finkel, E. J. (2017). Is romantic desire predictable? Machine learning applied to initial romantic attraction. Psychological Science, 28, 1478–1489.CrossRefGoogle ScholarPubMed
Jongerling, J., Laurenceau, J. P., and Hamaker, E. L. (2015). A multilevel AR(1) model: Allowing for inter-individual differences in trait-scores, inertia, and innovation variance. Multivariate Behavioral Research, 50, 334–349.CrossRefGoogle ScholarPubMed
Kashy, D. A. and Donnellan, M. B. (2012). Conceptual and methodological issues in the analysis of data from dyads and groups. In Deaux, K. and Snyder, M. (eds.) The Oxford Handbook of Personality and Social Psychology. Oxford University Press.Google Scholar
Kashy, D. A., Donnellan, M. B., Burt, S. A., and McGue, M. (2008). Growth curve models for indistinguishable dyads using multilevel modeling and structural equation modeling: The case of adolescent twins’ conflict with their mothers. Developmental Psychology, 44, 316–329.CrossRefGoogle ScholarPubMed
Kashy, D. A., and Kenny, D. A. (2000). The analysis of data from dyads and groups. In Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology. Cambridge University Press.Google Scholar
Kenny, D. A. (1996). Models of nonindependence in dyadic research. Journal of Social and Personal Relationships, 13, 279–294.CrossRefGoogle Scholar
Kenny, D. A. (2015, February). An interactive tool for the estimation and testing the actor–partner interdependence model using multilevel modeling (computer software), available from https://davidakenny.shinyapps.io/APIM_MM.Google Scholar
Kenny, D. A. (2019). Interpersonal Perception: The Foundation of Social Relationships, 2nd ed. Guilford Press.Google Scholar
Kenny, D. A. (2022, January). Dyadic_CFA: An interactive tool for estimating a single-factor model for indistinguishable and distinguishable dyads (computer software), available from https://davidakenny.shinyapps.io/Dyadic_CFA.Google Scholar
Kenny, D. A., and Ackerman, R. A. (2019). Power Computations for the Cross-sectional Actor–Partner Interdependence Model, retrieved from https://osf.io/2pcfb.Google Scholar
Kenny, D. A., and Ackerman, R. A. (2023). Estimation of Random Effects with Over-Time Dyadic Data Using Multilevel Modeling: The Sum and Difference Method, retrieved from https://osf.io/9pw7d.Google Scholar
Kenny, D. A., and Garcia, R. L. (2012). Using the actor–partner interdependence model to study the effects of group composition. Small Group Research, 43, 468–496.CrossRefGoogle Scholar
Kenny, D. A., Goldring, M. R., and Jung, T. (2023). The extended social relations model: Understanding dissimilation and dissensus in the judgment of others. European Journal of Personality, 37, 57–71.CrossRefGoogle Scholar
Kenny, D. A., and Kashy, D. A. (2014). The Design and Analysis of Data from Dyads and Groups. In Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology, 2nd ed. Cambridge University Press.Google Scholar
Kenny, D. A., Kashy, D. A., and Cook, W. L. (2006). Dyadic Data Analysis. Guilford Press.Google Scholar
Kenny, D. A., and La Voie, L. (1985). Separating individual and group effects. Journal of Personality and Social Psychology, 48, 339–348.CrossRefGoogle Scholar
Kenny, D. A., and Ledermann, T. (2010). Detecting, measuring, and testing dyadic patterns in the actor–partner interdependence model. Journal of Family Psychology, 24, 359–366.CrossRefGoogle ScholarPubMed
Kenny, D. A., and Livi, S. (2009). A componential analysis of leadership using the social relations model. In Yammarino, F. J. and Dansereau, F. (eds.) Research in Multi-level Issues, vol. 8, Multi-level Issues in Organizational Behavior and Leadership. Emerald.Google Scholar
Kenny, D. A., Mannetti, L., Pierro, A., Livi, S., and Kashy, D. A. (2002). The statistical analysis of data from small groups. Journal of Personality and Social Psychology, 83, 126–137.CrossRefGoogle Scholar
Kenny, D. A., West, T. V., Cillessen, A. H. N., Coie, J. D., Dodge, K. A., Hubbard, J. A., and Schwartz, D. (2007). Accuracy in judgments of aggressiveness. Personality and Social Psychology Bulletin, 33, 1225–1236.CrossRefGoogle ScholarPubMed
Kaufman, T. M. L., Laninga-Wijnen, L, and Lodder, G. M. A. (2022) Are victims of bullying primarily social outcasts? Person–group dissimilarities in relational, socio-behavioral, and physical characteristics as predictors of victimization. Child Development, 93, 1458–1474.CrossRefGoogle ScholarPubMed
Lam, C., van der Vegt, G. S., Walter, F., and Huang, X. (2011). Harming high performers: Social comparison and interpersonal harming in work teams. Journal of Applied Psychology, 96, 588–601.CrossRefGoogle ScholarPubMed
Lane, S. P., and Hennes, E. P. (2018). Power struggles: Estimating sample size for multilevel relationships research. Journal of Social and Personal Relationships, 35, 7–31.CrossRefGoogle Scholar
Laurenceau, J. P., and Bolger, N. (2005). Using diary methods to study marital and family processes. Journal of Family Psychology, 19, 86–97.CrossRefGoogle ScholarPubMed
Ledermann, T., and Kenny, D. A. (2012). The common fate model for dyadic data: Variations of a theoretically important but underutilized model. Journal of Family Psychology, 26, 140–148.CrossRefGoogle ScholarPubMed
Ledermann, T., and Kenny, D. A. (2017). Analyzing dyadic data with multilevel modeling versus structural equation modeling: A tale of two methods. Journal of Family Psychology, 31, 442–452.CrossRefGoogle ScholarPubMed
Ledermann, T., and Macho, S. (2014). Analyzing change at the dyadic level: The common fate growth model. Journal of Family Psychology, 28, 204–213.CrossRefGoogle ScholarPubMed
Ledermann, T., Macho, S., and Kenny, D. A. (2011). Assessing mediation in dyadic data using the actor–partner interdependence model. Structural Equation Modeling, 18, 595–612.CrossRefGoogle Scholar
Lee, T. K., Wickrama, K. A. S., and O’Neal, C. W. (2021). Modeling longitudinal dyadic processes in family research. Journal of Family Psychology, 35, 994–1006.CrossRefGoogle ScholarPubMed
Lemay, E. P., Jr., and Clark, M. S. (2008). How the head liberates the heart: Projection of communal responsiveness guides relationship promotion. Journal of Personality and Social Psychology, 94, 647–671.CrossRefGoogle ScholarPubMed
McNeish, D. and Hamaker, E. L. (2020). A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychological Methods, 25, 610–635.CrossRefGoogle ScholarPubMed
Marcus, D. K., Robinson, S. L., and Eichenbaum, A. E. (2017). Externalizing behavior and psychopathy: A social relations analysis. Journal of Personality Disorders, 31, 1–16.Google Scholar
Moreland, R. L. (2010). Are dyads really groups? Small Group Research, 41, 251–267.CrossRefGoogle Scholar
Nestler, S., Geukes, K., Hutteman, R., and Back, M. D. (2017). Tackling longitudinal round-robin data: A social relations growth model. Psychometrika, 82, 1162–1181.CrossRefGoogle Scholar
Ogolsky, B. G. (2009). Deconstructing the association between relationship maintenance and commitment: Testing two competing models. Personal Relationships, 16, 99–115.CrossRefGoogle Scholar
Olsen, J. A., and Kenny, D. A. (2006). Structural equation modeling with interchangeable dyads. Psychological Methods, 11, 127–141.CrossRefGoogle ScholarPubMed
Planalp, E. M., Du, H., Braungart-Rieker, J. M., and Wang, L. (2017). Growth curve modeling to studying change: A comparison of approaches using longitudinal dyadic data with distinguishable dyads. Structural Equation Modeling, 24, 129–147.CrossRefGoogle Scholar
Raudenbush, S. W., Brennan, R. T., and Barnett, R. C. (1995). A multivariate hierarchical model for studying psychological change within married couples. Journal of Family Psychology, 9, 161–174.CrossRefGoogle Scholar
Sakaluk, J. K. (2022). GITHUB site, at https://jsakaluk.github.io/dySEM.Google Scholar
Sakaluk, J. K., Kilshaw, R. E., and Fisher, A. N. (2021) Dyadic measurement invariance and its importance for replicability in romantic relationship science. Personal Relationships, 28, 190–226.CrossRefGoogle Scholar
Savord, A., McNeish, D., Iida, M., Quiroz, S., and Ha, T. (2023). Fitting the longitudinal actor–partner interdependence model as a dynamic structural equation model in Mplus. Structural Equation Modeling, 30, 296–314.CrossRefGoogle Scholar
Schoemann, A. M., and Sakaluk, J. L. (May 2016). Missing data in dyadic modeling: issues and opportunities. Modern Modeling Methods Conference, Storrs, CT.Google Scholar
Schönbrodt, F. D. (2016). RSA: An R package for response surface analysis (version 0.9.10), retrieved from https://cran.r-project.org/package=RSA.Google Scholar
Schönbrodt, F. D., Back, M. D., and Schmukle, SC. (2012). TripleR: An R package for social relations analyses based on round-robin designs. Behavioral Research Methods, 44, 455–470.CrossRefGoogle Scholar
Schönbrodt, F. D., Humberg, S., and Nestler, S. (2018). Testing similarity effects with dyadic response surface analysis. European Journal of Personality, 32, 627–641.CrossRefGoogle Scholar
Stas, L., Kenny, D. A., Mayer, A. and Loeys, T. (2018). Giving dyadic data analysis away: A user-friendly app for actor–partner interdependence models. Personal Relationships, 25, 103–119.CrossRefGoogle Scholar
Tagliabue, S., and Lanz, M. (2014). Exploring social and personal relationships: The issue of measurement invariance of non‐independent observations. European Journal of Social Psychology, 44, 683–690.CrossRefGoogle Scholar
Thorson, K. R., West, T. V., and Mendes, W. B. (2018). Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies. Psychological Methods, 23, 595–616.CrossRefGoogle ScholarPubMed
Webster, G. D., Brunell, A. B., and Pilkington, C. J. (2009). Individual differences in men’s and women’s warmth and disclosure differentially moderate couples’ reciprocity in conversational disclosure. Personality and Individual Differences, 46, 292–297.CrossRefGoogle Scholar
Weingart, L. R., Brett, J. M., Olekalns, M., and Smith, P. L. (2007). Conflicting social motives in negotiating groups. Journal of Personality & Social Psychology, 93, 994–1010.CrossRefGoogle ScholarPubMed
West, T. V., and Kenny, D. A. (2011). The truth and bias model of judgment. Psychological Review, 118, 357–378.CrossRefGoogle ScholarPubMed
Whittaker, T. A., Beretvas, S. N., and Falbo, T. (2014). Dyadic curve-of-factors model: An introduction and illustration of a model for longitudinal non-exchangeable dyadic data. Structural Equation Modeling, 21, 303–317.CrossRefGoogle Scholar
Wickham, R. E., and Macia, K. S. (2019). Examining cross-level effects in dyadic analysis: A structural equation modeling perspective. Behavior Research Methods, 51, 2629–2645.CrossRefGoogle ScholarPubMed
Wong, M-N. , Kenny, D. A., and Knight, A. (2024). SRM_R: A web-based shiny app for social relations analyses. Organizational Research Methods, 27, 114–139.CrossRefGoogle Scholar
Wood, A., Templeton, E., Morrel, J., Schubert, F., and Wheatley, T. (2022). The tendency to laugh is a stable trait: Findings from a round-robin conversation study. Philosophical Transactions of the Royal Society, B377: 20210187.Google Scholar
Wood, W., Oldham, C. R., Reifman, A., and Niehuis, S. (2017). Accuracy and bias in newlywed spouses’ perceptions of each other’s personalities. Personal Relationships, 24, 886–901.CrossRefGoogle Scholar
Xie, S. Y., Flake, J. K., Stolier, R. M., Freeman, J. B., and Hehman, E. (2021). Facial impressions are predicted by the structure of group stereotypes. Psychological Science, 32, 1979–1993.CrossRefGoogle ScholarPubMed
Xu, R., DeShon, R. P., and Dishop, C. R. (2020). Challenges and opportunities in the estimation of dynamic models. Organizational Research Methods, 23, 595–619.CrossRefGoogle Scholar
Yu, S., Kilduff, G. J., and West, T. (2023). Status acuity: The ability to accurately perceive status hierarchies reduces status conflict and benefits group performance. Journal of Applied Psychology, 108, 114–137.CrossRefGoogle Scholar
Zhou, L., Wang, M., and Zhang, Z. (2019). Intensive longitudinal data analyses with dynamic structural equation modeling. Organizational Research Methods, 24, 219–250.CrossRefGoogle Scholar

References

Ackerman, R. A., Kashy, D. A., and Corretti, C. A. (2015). A tutorial on analyzing data from speed-dating studies with heterosexual dyads. Personal Relationships, 22, 92–110.CrossRefGoogle Scholar
Barr, D. J., Levy, R., Scheepers, C., and Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278.CrossRefGoogle ScholarPubMed
Bates, D., Kliegl, R., Vasishth, S., and Baayen, H. (2015). Parsimonious mixed models. arXiv, 1506.04967v1 [stat.ME].Google Scholar
Beck, A. T., Steer, R. A., and Carbin, M. G. (1988). Psychometric properties of the Beck depression inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8, 77–100.CrossRefGoogle Scholar
Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., and Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 561–571.CrossRefGoogle ScholarPubMed
Belmi, P., and Schroeder, J. (2021). Human “resources”? Objectification at work. Journal of Personality and Social Psychology, 120, 384–417.CrossRefGoogle ScholarPubMed
Brauer, M., and Curtin, J. J. (2018). Linear mixed-effects models and the analysis of nonindependent data: A unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychological Methods, 23(3), 389–411.CrossRefGoogle ScholarPubMed
Brysbaert, M., and Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1(1), 9, http://doi.org/10.5334/joc.10.CrossRefGoogle ScholarPubMed
Cronbach, L. J., Gleser, G. C., Nanda, H., and Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. John Wiley.Google Scholar
Eastwick, P. W., Finkel, E. J., Mochon, D., and Ariely, D. (2007). Selective versus unselective romantic D\desire: Not all reciprocity is created equal. Psychological Science, 18, 317–319.CrossRefGoogle Scholar
Fazio, R. H., Jackson, J. R., Dunton, B. C., and Williams, C. J. (1995). Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline? Journal of Personality and Social Psychology, 69(6), 1013–1027.CrossRefGoogle ScholarPubMed
Goenka, S., and Thomas, M. (2020). The malleable morality of conspicuous consumption. Journal of Personality and Social Psychology, 118, 562–583.CrossRefGoogle ScholarPubMed
Greenwald, A. G., McGhee, D. E., and Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480.CrossRefGoogle ScholarPubMed
Higgins, E. T., Rholes, W. S., and Jones, C. R. (1977). Category accessibility and impression formation. Journal of Experimental Social Psychology, 13, 141–154.CrossRefGoogle Scholar
Judd, C. M., Westfall, J., and Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103, 54–69.CrossRefGoogle ScholarPubMed
Judd, C. M., Westfall, J., and Kenny, D. A. (2017). Experiments with more than one random factor: Designs, analytic models, and statistical power. Annual Review of Psychology, 68, 601–625.CrossRefGoogle ScholarPubMed
Kenny, D. A., and Judd, C. M. (2019). The unappreciated heterogeneity of effect sizes: Implications for power, precision, planning of research, and replication. Psychological Methods, 24, 578–589.CrossRefGoogle ScholarPubMed
Klein, R. A., Vianello, M., Hasselman, F., et al. (2018). Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science, 1, 443–490.CrossRefGoogle Scholar
Kogan, N., and Wallach, M. A. (1967). Risky-shift phenomenon in small decision-making groups: A test of the information-exchange hypothesis. Journal of Experimental Social Psychology, 3, 75–84.CrossRefGoogle Scholar
Linden, A. H., and Hönekopp, J. (2021). Heterogeneity of research results: A new perspective from which to assess and promote progress in psychological science. Perspectives on Psychological Science, 16, 358–376.CrossRefGoogle ScholarPubMed
McArthur, L. A. (1972). The how and what of why: Some determinants and consequences of causal attribution. Journal of Personality and Social Psychology, 22, 171–193.CrossRefGoogle Scholar
McShane, B. B., and Böckenholt, U. (2014). You cannot step into the same river twice: When power analyses are optimistic. Perspectives on Psychological Science, 9, 612–625.CrossRefGoogle ScholarPubMed
Meagher, B. R. (2015). The effects of interpersonal differences within religious communities: A group actor–partner interdependence model of U.S. congregations. International Journal for the Psychology of Religion, 25, 74–90.CrossRefGoogle Scholar
Myers, D. G., and Lamm, H. (1976). The group polarization phenomenon. Psychological Bulletin, 83(4), 602–627.CrossRefGoogle Scholar
Simons, D. J., Holcombe, A. O., and Spellman, B. A. (2014). An introduction to registered replication reports at Perspectives on Psychological Science. Perspectives on Psychological Science, 9, 552–555.CrossRefGoogle ScholarPubMed
Stephenson, B., and Wicklund, R. A. (1984). The contagion of self-focus within a dyad. Journal of Personality and Social Psychology, 46, 163–168.CrossRefGoogle Scholar
Thorson, K. R., Mendes, W. B., and West, T. V. (2021). Controlling the uncontrolled: Are there incidental experimenter effects on physiologic responding? Psychophysiology, 57(3), e13500.CrossRefGoogle Scholar
Vesely, S., & Klöckner, C. A. (2020). Social desirability in environmental psychology research: Three meta-analyses. Frontiers in Psychology, 11, 1395, at https://doi.org/10.3389/fpsyg.2020.01395.CrossRefGoogle ScholarPubMed
Wallach, M. A., Kogan, N., and Bem, D. (1962). Group influence on individual risk taking. Journal of Abnormal and Social Psychology, 2, 72–86.Google Scholar
Westfall, J. (2017). A shiny app for power analysis with random targets and participants, https://jakewestfall.shinyapps.io/two_factor_power.Google Scholar
Westfall, J., Kenny, D. A., and Judd, C. M. (2014). Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli. Journal of Experimental Psychology: General, 143, 2020–2045.CrossRefGoogle ScholarPubMed
Wolsiefer, K., Westfall, J., and Judd, C. M. (2017). Modeling stimulus variation in three common implicit attitude tasks. Behavior Research Methods, 49, 1193–1209.CrossRefGoogle ScholarPubMed
Word, C. O., Zanna, M. P., and Cooper, J. (1974). The nonverbal mediation of self-fulfilling prophecies in interracial interaction. Journal of Experimental Social Psychology, 10(2), 109–120.CrossRefGoogle Scholar
Xie, S. Y., Flake, J. K., and Hehman, E. (2019). Perceiver and target characteristics contribute to impression formation differently across race and gender. Journal of Personality and Social Psychology, 117, 364–385.CrossRefGoogle ScholarPubMed

References

Aberson, C. L. (2019). Applied Power Analysis for the Behavioral Sciences, 2nd ed. Routledge.CrossRefGoogle Scholar
Aguinis, H., Beaty, J. C., Boik, R. J., and Pierce, C. A. (2005). Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review. Journal of Applied Psychology, 90(1), 94–107.CrossRefGoogle ScholarPubMed
American Psychological Association. (2020). Publication Manual of the American Psychological Association 2020: The Official Guide to APA Style, 7th ed. American Psychological Association.Google Scholar
Arbuckle, J. L. (2019). Amos (Version 26.0) [computer program]. IBM SPSS.Google Scholar
Baron, R. M., and Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.CrossRefGoogle ScholarPubMed
Bauer, D. J., Preacher, K. J., and Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendation. Psychological Methods, 11(2), 142–163.CrossRefGoogle Scholar
Biesanz, J. C., Falk, C. F., and Savalei, V. (2010). Assessing mediational models: Testing and interval estimation for indirect effects. Multivariate Behavioral Research, 45, 661–701.CrossRefGoogle ScholarPubMed
Bind, M.- VanderWeele, A., Coull, T., B. A., and Schwartz, J. D. (2016). Causal mediation analysis for longitudinal data with exogenous exposure. Biostatistics, 17(1), 122–134.CrossRefGoogle ScholarPubMed
Bullock, J. G., and Green, G. P. (2021). The failings of conventional mediation analysis and a design-based alternative. Advances in Methods and Practices in Psychological Science, 4(4), 1–18.CrossRefGoogle Scholar
Bullock, J. G., Green, D. P., and Ha, S. E. (2010). Yes, but what’s the mechanism? (Don’t expect an easy answer). Journal of Personality and Social Psychology, 98, 550–558.CrossRefGoogle ScholarPubMed
Card, N. A. (2012). Multilevel mediational analysis in the study of daily lives. In Mehl, M. R. and Conner, T. S. (eds.) Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
Charlton, A., Montoya, A. K., Price, J., and Hilgard, J. (2021). Noise in the process: An assessment of the evidential value of mediation effects in marketing journals. DOI: 10.31234/osf.io/ck2r5.CrossRefGoogle Scholar
Chen, D., and Fritz, M. S. (2021). Comparing alternative corrections for bias in the bias-corrected bootstrap test of mediation. Evaluation & the Health Professions, 44(4), 416–427.CrossRefGoogle ScholarPubMed
Cheung, G. W., and Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296–325.CrossRefGoogle Scholar
Cheung, M. W. L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 14(2), 227–446.CrossRefGoogle Scholar
Cole, D. A., and Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577.CrossRefGoogle ScholarPubMed
Cole, D. A., and Preacher, K. J. (2014). Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19(2), 300–315.CrossRefGoogle ScholarPubMed
Daniel, R. M., Stavola, B. L. D., Cousens, S. N., and Vansteelandt, S. (2015). Causal mediation with multiple mediators. Biometrics, 71(1), 1–15.CrossRefGoogle ScholarPubMed
Earp, B. D., and Trafimow, D. (2015). Replication, falsification, and the crisis of confidence in social psychology. Frontiers in Psychology, 6, 1–11.CrossRefGoogle ScholarPubMed
Falk, C. F., and Biesanz, J. C. (2015). Inference and interval estimation methods for indirect effects with latent variable models. Structural Equation Modeling: A Multidisciplinary Journal, 22, 24–38.CrossRefGoogle Scholar
Fiedler, K., Harris, C., and Schott, M. (2018). Unwarranted inferences from statistical mediation tests: An analysis of articles published in 2015. Journal of Experimental Psychology, 75, 95–102.Google Scholar
Flake, J. K., Pek, J., and Hehman, E. (2017). Construct validation in social and personality research: Current practice and recommendations. Social Psychological and Personality Science, 8(4), 370–378.CrossRefGoogle Scholar
Fritz, M. S., and MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239.CrossRefGoogle ScholarPubMed
Funk, M. J., Westreich, D., Wiesen, C., Sturmer, T., Brookhart, M. A., and Davidson, M. (2011). Doubly robust estimation of causal effects. American Journal of Epidemiology, 173(7), 761–767.CrossRefGoogle ScholarPubMed
Gonzalez, O., and MacKinnon, D. P. (2021). The measurement of the mediator and its influence on statistical mediation conclusions. Psychological Methods, 26(1), 1–17.CrossRefGoogle ScholarPubMed
Gonzalez, O., and Valente, M. J. (2022). Accommodating a latent XM interaction in statistical mediation analysis. Multivariate Behavioral Research, 58(4), 659–674.CrossRefGoogle Scholar
Gorsuch, R. L. (1983). Factor Analysis. Lawrence Erlbaum Associates.Google Scholar
Götz, M., O’Boyle, E. H., Gonzalez-Mulé, E., Banks, G. C., and Bollmann, S. S. (2021). The “Goldilocks” zone: (Too) many confidence intervals in tests of mediation just exclude zero. Psychological Bulletin, 147(1), 95–114.CrossRefGoogle Scholar
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420.CrossRefGoogle Scholar
Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50(1), 1–22.CrossRefGoogle ScholarPubMed
Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis, 3rd ed. Guilford Press.Google Scholar
Hayes, A. F., and Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24, 1918–1927.CrossRefGoogle ScholarPubMed
Hoyle, R. H., and Kenny, D. A. (1999). Sample size, reliability, and tests of statistical mediation. In Hoyle, R. H. (ed.) Statistical Strategies for Small Sample Research. Sage.Google Scholar
Iacobucci, D. (2012). Mediation analysis and categorical variables: The final frontier. Journal of Consumer Psychology, 22(4), 582–594.CrossRefGoogle Scholar
Iacobucci, D., Saldanha, N., and Deng, J. X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 140–154.CrossRefGoogle Scholar
Imai, K., Keele, K., and Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71.CrossRefGoogle Scholar
Imai, K., Tingley, D., and Yamamoto, T. (2013). Experimental designs for identifying causal mechanisms. Journal of the Royal Statistical Society, 176(1), 5–51.CrossRefGoogle Scholar
Imbens, G. W., and Rubin, D. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.CrossRefGoogle Scholar
Kenny, D. A. (2017). An interactive tool for the estimation of power in tests of mediation (computer software), https://davidakenny.shinyapps.io/MedPower.Google Scholar
Kenny, D. A., and Judd, C. M. (2014). Power anomalies in testing mediation. Psychological Science, 25(2), 334–339.CrossRefGoogle ScholarPubMed
Kenny, D. A., Korchmaros, J. D., and Bolger, N. (2003). Lower-level mediation in multilevel models. Psychological Methods, 8(2), 115–128.CrossRefGoogle ScholarPubMed
Kozlov, M. (2022). NIH issues a seismis mandate: Share data publicly. Nature, 602, 558–559.CrossRefGoogle ScholarPubMed
Krull, J. L., and MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research, 36, 249–277.CrossRefGoogle ScholarPubMed
Ledgerwood, A., and Shrout, P. E. (2011). The trade-off between accuracy and precision in latent variable models of mediation processes. Journal of Personality and Social Psychology, 101, 1174–1188.CrossRefGoogle ScholarPubMed
Lee, H., Cashin, A. G., Lamb, S. E., Hopewell, S., Vansteelandt, S., VanderWeele, T. J., and McAuley, J. H. (2021). A guideline for reporting mediation analyses of randomized trials and observational studies: The agrema statement. JAMA, 326, 1045–1056.CrossRefGoogle ScholarPubMed
Lindenberger, U., von Oertzen, T., Ghisletta, P., and Hertzog, C. (2011). Cross-sectional age variance extraction: What’s change got to do with it? Psychology and Aging, 26(1), 34–47.CrossRefGoogle Scholar
Liu, S. H., Ulbricht, C. M., Chrysanthopoulou, S. A., and Lapane, K. L. (2016). Implementation and reporting of causal mediation analysis in 2015: A systematic review in epidemiological studies. BMC Research Notes, 9(354), DOI: 10.1186/s13104-016-2163-7.CrossRefGoogle ScholarPubMed
Liu, X., and Wang, L. (2019). Sample size planning for detecting mediation effects: A power analysis procedure considering uncertainty in effect size estimates. Multivariate Behavioral Research, 54(6), 822–839.CrossRefGoogle Scholar
MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In Rose, J., Chassin, L., Presson, C. C., and Sherman, S. J. (eds.) Multivariate Applications in Substance Use Research: New Methods for New Questions. Erlbaum.Google Scholar
MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. Lawrence Erlbaum Associates.Google Scholar
MacKinnon, D. P., Fritz, M. S., Williams, J., and Lockwood, C. M. (2007). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39(3), 384–389.CrossRefGoogle ScholarPubMed
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., and Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83–104.CrossRefGoogle ScholarPubMed
MacKinnon, D. P., Lockwood, C. M., and Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128.CrossRefGoogle ScholarPubMed
MacKinnon, D. P., Valente, M. J., and Gonzalez, O. (2020). The correspondence between causal and traditional mediation analysis: The link is the mediator by treatment interaction. Prevention Science, 21, 147–157.CrossRefGoogle ScholarPubMed
Martínez, C. A., van Prooijen, J., and Lange, P. A. M. V. (2022). A threat-based hate model: How symbolic and realistic threats underlie hate and aggression. Journal of Experimental Social Psychology, 103, 1–13.CrossRefGoogle Scholar
Maxwell, S. E., and Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44.CrossRefGoogle ScholarPubMed
Maxwell, S. E., Cole, D. A., and Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Psychological Methods, 12, 23–44.CrossRefGoogle Scholar
Meule, A. (2019). Contemporary understanding of mediation testing. Meta-Psychology, 3, DOI: 10.15626/MP.2018.870.CrossRefGoogle Scholar
Montoya, A. K., and Hayes, A. F. (2017). Two-condition within-participant statistical mediation analysis: A path-analytic framework. Psychological Methods, 22(1), 6–27.CrossRefGoogle ScholarPubMed
Muthèn, B., and Asprouhov, T. (2015). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modeling, 22(1), 12–23.CrossRefGoogle Scholar
Muthèn, L. K., and Muthèn, B. O. (1998–2011). Mplus User’s Guide, 6th ed. Muthèn and Muthèn.Google Scholar
Nezlek, J. B. (2011). Multilevel Modeling for Social and Personality Psychology. Sage.CrossRefGoogle Scholar
Nuijten, M. B., Borghuis, J., Veldkamp, C. L. S., Dominguez-Alvarez, L., van Assen, M. A. L. M., and Wicherts, J. M. (2017). Journal data sharing policies and statistical reporting inconsistencies in psychology. Collabra: Psychology, 3(1), 31, DOI: 10.1525/collabra.102.CrossRefGoogle Scholar
O’Laughlin, K. D., Martin, M. J., and Ferrer, E. (2018). Cross-sectional analysis of longitudinal mediation processes. Multivariate Behavioral Research, 53(3), 375–402.CrossRefGoogle ScholarPubMed
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–710.CrossRefGoogle Scholar
Pearl, J. (2001). Direct and Indirect Effects. Morgan Kaufman.Google Scholar
Pituch, K. A., Whittaker, T. A., and Stapleton, L. M. (2005). A comparison of methods to test for mediation in multisite experiments. Multivariate Behavioral Research, 40, 1–23.CrossRefGoogle ScholarPubMed
Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825–852.CrossRefGoogle ScholarPubMed
Preacher, K. J., and Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, 36, 717–731.CrossRefGoogle ScholarPubMed
Qin, X. (2023) Sample size and power calculations for causal mediation analysis: A tutorial and shiny app. Behavior Research Methods, https://doi.org/10.3758/s13428-023-02118-0.CrossRefGoogle Scholar
Raudenbush, S. W., and Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Sage.Google Scholar
Revelle, W. (2022). psych: Procedures for psychological, psychometric, and personality research (computer software manual), https://CRAN.R-project.org/package=psych (R package version 2.2.5).Google Scholar
Robins, J. M., and Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155.CrossRefGoogle ScholarPubMed
Rockwood, N. J., and Hayes, A. F. (2022). Multilevel mediation analysis. In O’Connell, A. A., McCoach, D. B., and Bell, B. (eds.) Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing.Google Scholar
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42.CrossRefGoogle Scholar
Rohrer, J. M., and Aslan, R. C. (2021). Precise answers to vague questions: Issues with interactions. Advances in Methods and Practices in Psychology, 4(2), 1–19.Google Scholar
Rosenbaum, P. R., and Rubin, D. B. (1984). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.CrossRefGoogle Scholar
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.CrossRefGoogle Scholar
Roth, D. L., and MacKinnon, D. P. (2012). Mediation analysis with longitudinal data. In Newsom, J. T., Jones, R. N., and Hofer, S. M. (eds.) Longitudinal Data Analysis: A Practical Guide for Researchers in Aging, Health, and Social Sciences. Routledge.Google Scholar
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.CrossRefGoogle Scholar
Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of American Statistical Association, 100(469), 322–331.CrossRefGoogle Scholar
Rucker, D. D., Preacher, K. J., Tormala, Z. L., and Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5, 359–371.CrossRefGoogle Scholar
Schoemann, A. M., Boulton, A. J., and Short, S. D. (2017). Determining power and sample size for simple and complex mediation models. Social Psychological and Personality Science, 8(4), 379–386.CrossRefGoogle Scholar
Schroder, H. S., Fisher, M. E., Yanli, L., Lo, S. L., Danovitch, J. H., and Moser, J. S. (2017). Neural evidence for enhanced attention to mistakes among school-aged children with a growth mindset. Developmental Cognitive Neuroscience, 24(1), 42–50.CrossRefGoogle ScholarPubMed
Selig, J. P., and Preacher, K. J. (2008). Monte Carlo method for assessing mediation: An interactive tool for creating confidence intervals for indirect effects (computer software), available from http://quantpsy.org.Google Scholar
Selig, J. P., and Preacher, K. J. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6(2–3), 144–164.CrossRefGoogle Scholar
Shrout, P. E. (2011). Commentary: Mediation analysis, causal process, and cross-sectional data. Multivariate Behavioral Research, 46(5), 852–860.CrossRefGoogle ScholarPubMed
Shrout, P. E., and Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422–445.CrossRefGoogle ScholarPubMed
Siy, J. O., Germano, A., Vianna, L., Azpeitia, J., Yan, S., Montoya, A. K., and Cheryan, S. (2023). Does the follow-your-passions ideology cause greater academic and occupational gender disparities than other cultural ideologies? Journal of Personality and Social Psychology, 125(3) 548–570.CrossRefGoogle ScholarPubMed
Smith, L. H., and VanderWeele, T. J. (2019). Mediational E-values: Approximate sensitivity analysis for unmeasured mediator-outcome confounding. Epidemiology, 30(6), 835–837.CrossRefGoogle ScholarPubMed
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.CrossRefGoogle Scholar
Stone, C. A., and Sobel, M. E. (1990). The robustness of estimates of total indirect effects in covariance structure models estimated by maximum likelihood. Psychometrika, 55(2), 337–352.CrossRefGoogle Scholar
Syropoulos, S., Lifshin, U., Greenberg, J., Horner, D. E., and Leidner, B. (2022). Bigotry and the human–animal divide: (Dis)belief in human evolution and bigoted attitudes across different cultures. Journal of Personality and Social Psychology, 123(6), 1264–1292.CrossRefGoogle ScholarPubMed
Thoemmes, F. J., and Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 1, 90–118.CrossRefGoogle Scholar
Thoemmes, F. J., and Ong, A. D. (2015). A primer on inverse probability of treatment weighting and marginal structural models. Emerging Adulthood, 4(1), 40–59.CrossRefGoogle Scholar
Tibbe, T. D., and Montoya, A. K. (2022). Correcting the bias correction for the bootstrap confidence interval in mediation analysis. Frontiers in Psychology, 13, DOI: 10.3389/fpsyg.2022.810258.CrossRefGoogle ScholarPubMed
Tingley, D., Yamamoto, T., Hirose, K., Keele, L., and Imai, K. (2014). Mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38.CrossRefGoogle Scholar
Tofighi, D., Hsiao, Y.- Y., Kruger, E. S., MacKinnon, D. P., Horn, M. L. V., and Witkiewitz, K. (2018). Sensitivity analysis of the no-omitted confounder assumption in latent growth curve mediation models. Structural Equation Modeling: A Multidisciplinary Journal, 26(1), 94–109.CrossRefGoogle Scholar
Tofighi, D., and MacKinnon, D. P. (2011). Rmediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700.CrossRefGoogle Scholar
Valente, M. J., Pelham, W. E., Smyth, H., and MacKinnon, D. P. (2017). Confounding in statistical mediation analysis: What it is and how to address it. Journal of Counseling Psychology, 64(6), 659–671.CrossRefGoogle Scholar
VanderWeele, T. J. (2010). Direct and indirect effect for neighborhood-based clustered and longitudinal data. Sociological Methods & Research, 38(4), 515–544.CrossRefGoogle ScholarPubMed
VanderWeele, T. J., and Chiba, Y. (2014). Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders. Epidemiology, Biostatistics and Public Health, 11(2), e9027, DOI: 10.2427/9027.Google ScholarPubMed
Vanderweele, T. J., and Robins, J. M. (2007). Four types of effect modification: A classification based on directed acyclic graphs. Epidemiology, 18(5), 561–568.CrossRefGoogle ScholarPubMed
Vo, T., Superchi, C., Boutron, I., and Vansteelandt, S. (2020). The conduct and reporting of mediation analysis in recently published randomized controlled trials: Results from a methodological systematic review. Journal of Clinical Epidemiology, 117, 78–88.CrossRefGoogle ScholarPubMed
Williams, J., and MacKinnon, D. P. (2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling, 15, 23–51.CrossRefGoogle ScholarPubMed
Wysocki, A. C., Lawson, K. M., and Rhemtulla, M. (2022). Statistical control requires causal justification. Advances in Methods and Practice in Psychological Science, 5(2), DOI: 10.1177/25152459221095823.CrossRefGoogle Scholar
Yzerbyt, V. Y., Muller, D., Batailler, C., and Judd, C. M. (2018). New recommendations for testing indirect effects in mediational models: The need to report and test component paths. Journal of Personality and Social Psychology: Attitudes and Social Cognition, 115(6), 929–943.CrossRefGoogle ScholarPubMed
Yzerbyt, V. Y., Muller, D., and Judd, C. M. (2004). Adjusting researchers’ approach to adjustment: On the use of covariates when testing interactions. Journal of experimental social psychology, 40, 424–431.CrossRefGoogle Scholar
Zhang, Z., and Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154–184.CrossRefGoogle ScholarPubMed
Zhang, Z., and Yuan, K. H. (2018). Practical Statistical Power Analysis Using Webpower and R. ISDSA Press.CrossRefGoogle Scholar
Zhang, Z., Zyphur, M. J., and Preacher, K. J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12, 695–719.CrossRefGoogle Scholar

References

Anderson, J. R. (1983). The Architecture of Cognition. Harvard University Press.Google Scholar
Batchelder, W. H., and Riefer, D. M. (1990). Multinomial processing models of source monitoring. Psychological Review, 97, 548–564.CrossRefGoogle Scholar
Bayen, U. J., and Kuhlmann, B. G. (2011). Influences of source–item contingency and schematic knowledge on source monitoring: Tests of the probability-matching account. Journal of Memory and Language, 64, 1–17.CrossRefGoogle ScholarPubMed
Bayen, U. J., Murnane, K., and Erdfelder, E. (1996). Source discrimination, item detection, and multinomial models of source monitoring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 197–215.Google Scholar
Bott, F. M., Kellen, D., and Klauer, K. C. (2021). Normative accounts of illusory correlations. Psychological Review, 128, 856–878.CrossRefGoogle ScholarPubMed
Correll, J., Park, B., Judd, C. M., and Wittenbrink, B. (2002). The police officer’s dilemma: Using ethnicity to disambiguate potentially threatening individuals. Journal of Personality and Social Psychology, 83(6), 1314–1329.CrossRefGoogle ScholarPubMed
Costello, F., and Watts, P. (2019). The rationality of illusory correlation. Psychological Review, 126, 437–450.CrossRefGoogle ScholarPubMed
Fiedler, K., Freytag, P., and Unkelbach, C. (2007). Pseudocontingencies in a simulated classroom. Journal of Personality and Social Psychology, 92, 665–677.CrossRefGoogle Scholar
Fiedler, K., and Unkelbach, C. (2014). Regressive judgment: Implications of a universal property of the empirical world. Current Directions in Psychological Science, 23, 361–367.CrossRefGoogle Scholar
Freeman, J. B., and Ambady, N. (2011). A dynamic interactive theory of person construal. Psychological Review, 118, 247–279.CrossRefGoogle ScholarPubMed
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis. Chapman and Hall/CRC.Google Scholar
Glanzer, M., Hilford, A., and Maloney, L. T. (2009). Likelihood ratio decisions in memory: Three implied regularities. Psychonomic Bulletin & Review, 16, 431–455.CrossRefGoogle ScholarPubMed
Greenwald, A. G., McGhee, D. E., and Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464–1480.CrossRefGoogle ScholarPubMed
Hamilton, D. L., and Gifford, R. K. (1976). Illusory correlation in interpersonal perception: A cognitive basis of stereotypic judgments. Journal of Experimental Social Psychology, 12, 392–407.CrossRefGoogle Scholar
Hugenberg, K., and Bodenhausen, G. V. (2004). Ambiguity in social categorization: The role of prejudice and facial affect in race categorization. Psychological Science, 15, 342–345.CrossRefGoogle ScholarPubMed
Hütter, M., and Klauer, K. C. (2016). Applying processing trees in social psychology. European Review of Social Psychology, 27, 116–159.CrossRefGoogle Scholar
Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30, 513–541.CrossRefGoogle Scholar
Kellen, D., and Klauer, K. C. (2018). Elementary signal detection and threshold theory. In E.-J. Wagenmakers (ed.) Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 4th ed., vol 5. Wiley. https://doi.org/10.1002/9781119170174.epcn505.Google Scholar
Kemp, C., and Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116(1), 20–58.CrossRefGoogle ScholarPubMed
Klauer, K. C. (2015). Model testing and selection, theory of. In Wright, J. D. (ed.) International Encyclopedia of the Social and Behavioral Sciences, 2nd ed., vol. 15. Elsevier.Google Scholar
Klauer, K. C. (in press). Methods. In Carlston, D., Johnson, K., and Hugenberg, K. (eds.) The Oxford Handbook of Social Cognition, 2nd ed. Oxford University Press.Google Scholar
Klauer, K. C., Dittrich, K., Scholtes, C., and Voss, A. (2015). The invariance assumption in process-dissociation models: An evaluation across three domains. Journal of Experimental Psychology: General, 144, 198–221.CrossRefGoogle ScholarPubMed
Klauer, K. C., and Meiser, T. (2000). A source-monitoring analysis of illusory correlations. Personality and Social Psychology Bulletin, 26, 1074–1093.CrossRefGoogle Scholar
Klauer, K. C., Stahl, C., and Voss, A. (2012). Multinomial models and diffusion models. In Klauer, K. C., Voss, A., and Stahl, C. (eds.) Cognitive Methods in Social Psychology, abridged ed. Guilford Press.Google Scholar
Klauer, K. C., and Wegener, I. (1998). Unraveling social categorization in the “Who said what?” paradigm. Journal of Personality and Social Psychology, 75, 1155–1178.CrossRefGoogle Scholar
Kuhlmann, B. G., Symeonidou, N., Tanyas, H., and Wulff, L. (2021). Remembering and reconstructing episodic context: An overview of source monitoring methods and behavioral findings. In Federmeier, K. D. and Sahakyan, L. (eds.) Psychology of Learning and Motivation, vol. 75. Academic Press.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman.Google Scholar
Matzke, D., and Wagenmakers, E.-J. (2009). Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, 16, 798–817.CrossRefGoogle ScholarPubMed
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108.CrossRefGoogle Scholar
Read, S. J., and Monroe, B. M. (2008). Computational models in personality and social psychology. In Sun, R. (ed.) The Cambridge Handbook of Computational Psychology. Cambridge University Press.Google Scholar
Rogers, T. T., and McClelland, J. L. (2014). Parallel distributed processing at 25: Further explorations in the microstructure of cognition. Cognitive Science, 38, 1024–1077.CrossRefGoogle ScholarPubMed
Sui, J., and Humphreys, G. W. (2013). The boundaries of self face perception: Response time distributions, perceptual categories, and decision weighting. Visual Cognition, 21, 415–445.CrossRefGoogle Scholar
Sun, R., Slusarz, P., and Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112, 159–192.CrossRefGoogle ScholarPubMed
Tarka, P. (2018). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52, 313–354.CrossRefGoogle ScholarPubMed
Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27, 1413–1432.CrossRefGoogle Scholar

References

Albajes-Eizagirre, A., Solanes, A., and Radua, J. (2019). Meta-analysis of non-statistically significant unreported effects. Statistical Methods in Medical Research, 28(12), 3741–3754.CrossRefGoogle ScholarPubMed
American Psychological Association. (2020). Meta-analysis reporting standards (MARS), https://apastyle.apa.org/jars/quant-table-9.pdf.Google Scholar
Bartoš, F., Maier, M., Quintana, D. S., and Wagenmakers, E. J. (2022). Adjusting for publication bias in JASP and R: Selection models, PET-PEESE, and robust Bayesian meta-analysis. Advances in Methods and Practices in Psychological Science, 5(3), 25152459221109259.CrossRefGoogle Scholar
Becker, B. J. (2005). Failsafe N or file-drawer number. In Rothstein, H. R., Sutton, A. J., and Borenstein, M. (eds.) Publication Bias in Meta-analysis: Prevention, Assessment and Adjustments. John Wiley and Sons, Ltd.Google Scholar
Borenstein, M. (2019). Common Mistakes in Meta-analysis and How to Avoid Them. Biostat, Inc.Google Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P. T., and Rothstein, H. R. (2009). Introduction to Meta-analysis. John Wiley and Sons.CrossRefGoogle Scholar
Borenstein, M., Higgins, J. P. T., Hedges, L. V., and Rothstein, H. R. (2017). Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods, 8(1), 5–18.CrossRefGoogle Scholar
Bushman, B. J., and Wang, M. C. (2009). Vote-counting procedures in meta-analysis. In Cooper, H., Hedges, L. V., and Valentine, J. C. (eds.) The Handbook of Research Synthesis and Meta-analysis. Russell Sage Foundation.Google Scholar
Button, K. S., Ioannidis, J., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., and Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376.CrossRefGoogle ScholarPubMed
Carter, E. C., Schönbrodt, F. D., Gervais, W. M., and Hilgard, J. (2019). Correcting for bias in psychology: A comparison of meta-analytic methods. Advances in Methods and Practices in Psychological Science, 2(2), 115–144.CrossRefGoogle Scholar
Cheung, M. W. L. (2014). Fixed‐ and random‐effects meta‐analytic structural equation modeling: Examples and analyses in R. Behavioral Research Methods, 46(1), 29–40.CrossRefGoogle ScholarPubMed
Cinar, O., Umbanhowar, J., Hoeksema, J. D., and Viechtbauer, W. (2021). Using information‐theoretic approaches for model selection in meta‐analysis. Research Synthesis Methods, 12(4), 537–556.CrossRefGoogle ScholarPubMed
Cleophas, T. J. M., and Zwinderman, A. H. (2017). Modern Meta-analysis: Review and Update of Methodologies. Springer.CrossRefGoogle Scholar
Coburn, K. M., and Vevea, J. L. (2019). Weightr: Estimating weight-function models for publication bias. R package version 2.0.2, https://CRAN.R-project.org/package=weightr.Google Scholar
Cohen, J. (1989). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum Associates.Google Scholar
Cooper, H., Hedges, L. V., and Valentine, J. C. (eds.). (2019). The Handbook of Research Synthesis and Meta-analysis. Russell Sage Foundation.CrossRefGoogle Scholar
Cooper, H. M. (2016). Research Synthesis and Meta-analysis: A Step-by-Step Approach, 5th ed. Sage.Google Scholar
Del Re, A. C., and Flückiger, C. (2016). Meta-analysis. In Norcross, J. C., VandenBos, G. R., Freedheim, D. K., and Olatunji, B. O. (eds.) APA Handbook of Clinical Psychology: Theory and Research. American Psychological Association.Google Scholar
Dickens, L. R., and Robins, R. W. (2022). Pride: A meta-analytic project. Emotion, 22(5), 1071–1087.CrossRefGoogle Scholar
DiMatteo, M. R. (2004). Social support and patient adherence to medical treatment: A meta-analysis. Health Psychology, 23, 207–218.CrossRefGoogle ScholarPubMed
Donnelly, K., and Twenge, J. M. (2017). Masculine and feminine traits on the Bem Sex-Role Inventory, 1993–2012: A cross-temporal meta-analysis. Sex Roles, 76(9), 556–565.CrossRefGoogle Scholar
Duval, S. (2005). The trim and fill method. In Rothstein, H. R., Sutton, A. J., and Borenstein, M. (eds.) Publication Bias in Meta-analysis: Prevention, Assessment and Adjustments. John Wiley and Sons.Google Scholar
Eagly, A. H., and Steffen, V. J. (1986). Gender and aggressive behavior: A meta-analytic review of the social psychological literature. Psychological Bulletin, 100(3), 309–330.CrossRefGoogle ScholarPubMed
Eysenck, H. J. (1978). An exercise in mega-silliness. American Psychologist, 33(5), 517.CrossRefGoogle Scholar
Freudenberg, M., Albohn, D. N., Kleck, R. E., Adams, R. B., Jr., and Hess, U. (2020). Emotional stereotypes on trial: Implicit emotion associations for young and old adults. Emotion, 20(7), 1244–1254.CrossRefGoogle ScholarPubMed
Goh, J. X., Hall, J. A., and Rosenthal, R. (2016). Mini meta-analysis of your own studies: Some arguments on why and a primer on how. Social and Personality Psychology Compass, 10, 535–549.CrossRefGoogle Scholar
Hall, J. A. (1978). Gender effects in decoding nonverbal cues. Psychological Bulletin, 85, 845–857.CrossRefGoogle Scholar
Hall, J. A. (2006). How big are nonverbal sex differences? The case of smiling and nonverbal sensitivity. In Dindia, K. and Canary, D. J. (eds.) Sex Differences and Similarities in Communication. Lawrence Erlbaum Associates Publishers.Google Scholar
Hall, J. A., Coats, E. J., and Smith LeBeau, L. (2005). Nonverbal behavior and the vertical dimension of social relations: A meta-analysis. Psychological Bulletin, 131, 898–924.CrossRefGoogle ScholarPubMed
Hall, J. A., and Rosenthal, R. (2018). Choosing between random effects models in meta-analysis: Units of analysis and the generalizability of obtained results. Social and Personality Psychology Compass, 12(10), article e12414.CrossRefGoogle Scholar
Hedges, L. V. (2009). Statistical considerations. In Cooper, H., Hedges, L. V., and Valentine, J. C. (eds.) The Handbook of Research Synthesis and Meta-analysis, 2nd ed. Russell Sage Foundation.Google Scholar
Hedges, L. V., and Pigott, T. D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9(4), 426–445.CrossRefGoogle ScholarPubMed
Hedges, L. V., and Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3(4), 486–504.CrossRefGoogle Scholar
Higgins, J. P., Savović, J., Page, M. J., Elbers, R. G., and Sterne, J. A. (2019). Assessing risk of bias in a randomized trial. In Cochrane Handbook for Systematic Reviews of Interventions, 205–228, at https://training.cochrane.org/handbook.Google Scholar
Iyengar, S., and Greenhouse, J. B. (1988). Selection models and the file drawer problem. Statistical Science, 3(1), 109–135.Google Scholar
Johnson, B. T. (2021). Toward a more transparent, rigorous, and generative psychology. Psychological Bulletin, 147(1), 1–15.CrossRefGoogle Scholar
Johnson, B. T., and Eagly, A. H. (2014). Meta-analysis of research in social and personality psychology. In Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology. Cambridge University Press.Google Scholar
Kenny, D. A., and Judd, C. M. (2019). The unappreciated heterogeneity of effect sizes: Implications for power, precision, planning of research, and replication. Psychological Methods, 24(5), 578–589.CrossRefGoogle ScholarPubMed
Konrath, S. H., O’Brien, E. H., and Hsing, C. (2011). Changes in dispositional empathy in American college students over time: A meta-analysis. Personality and Social Psychology Review, 15(2), 180–198.CrossRefGoogle ScholarPubMed
Kossmeier, M., Tran, U. S., and Voracek, M. (2020). Charting the landscape of graphical displays for meta-analysis and systematic reviews: A comprehensive review, taxonomy, and feature analysis. BMC Medical Research Methodology, 20(1), 1–24.CrossRefGoogle ScholarPubMed
Kvarven, A., Strømland, E., and Johannesson, M. (2019). Comparing meta-analyses and preregistered multiple-laboratory replication projects. Nature Human Behavior, 4, 423–434.CrossRefGoogle ScholarPubMed
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4(26), 863, DOI:10.3389/fpsyg.2013.00863.CrossRefGoogle Scholar
Lakens, D., Hilgard, J., and Staaks, J. (2016). On the reproducibility of meta-analyses: Six practical recommendations. BMC Psychology, 4(1), 1–10.CrossRefGoogle ScholarPubMed
Lipsey, M. W., and Wilson, D. B. (1993). The efficacy of psychological, educational, and behavioral treatment: Confirmation from meta-analysis. American Psychologist, 48(12), 1181–1209.CrossRefGoogle ScholarPubMed
Lipsey, M. W., and Wilson, D. B. (2001). Practical Meta-analysis. Sage.Google ScholarPubMed
López‐López, J. A., Page, M. J., Lipsey, M. W., and Higgins, J. P. T. (2018). Dealing with effect size multiplicity in systematic reviews and meta‐analyses. Research Synthesis Methods, 9(3), 336–351.CrossRefGoogle Scholar
López‐López, J. A., van den Noortgate, W., Tanner‐Smith, E. E., Wilson, S. J., and Lipsey, M. W. (2017). Assessing meta‐regression methods for examining moderator relationships with dependent effect sizes: A Monte Carlo simulation. Research Synthesis Methods, 8(4), 435–450.CrossRefGoogle ScholarPubMed
Lovakov, A., and Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. European Journal of Social Psychology, 51(3), 485–504.CrossRefGoogle Scholar
McShane, B. B., Böckenholt, U., and Hansen, K. T. (2016). Adjusting for publication bias in meta-analysis: An evaluation of selection methods and some cautionary notes. Perspectives on Psychological Science, 11(5), 730–749.CrossRefGoogle ScholarPubMed
Marks‐Anglin, A., and Chen, Y. (2020). A historical review of publication bias. Research Synthesis Methods, 11(6), 725–742.CrossRefGoogle ScholarPubMed
Mathur, M. B., and VanderWeele, T. J. (2020). Sensitivity analysis for publication bias in meta‐analyses. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(5), 1091–1119.Google ScholarPubMed
Mathur, M. B., and VanderWeele, T. J. (2021). Estimating publication bias in meta‐analyses of peer‐reviewed studies: A meta‐meta‐analysis across disciplines and journal tiers. Research Synthesis Methods, 12(2), 176–191.CrossRefGoogle ScholarPubMed
Miller, D. I., Nolla, K. M., Eagly, A. H., and Uttal, D. H. (2018). The development of children’s gender-science stereotypes: A meta-analysis of five decades of U.S. Draw-A-Scientist studies. Child Development, 89(6), 1943–1955.CrossRefGoogle Scholar
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and the Group, PRISMA. (2009) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med, 6(7), e1000097.CrossRefGoogle ScholarPubMed
Mosteller, F. M., and Bush, R. R. (1954). Selected quantitative techniques. In Lindzey, G. (ed.) Handbook of Social Psychology, vol. 1, Theory and Method. Addison-Wesley.Google Scholar
Collaboration, Open Science. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.CrossRefGoogle Scholar
Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ, 372, n160.CrossRefGoogle ScholarPubMed
Page, M. J., Moher, D., and McKenzie, J. E. (2022). Introduction to PRISMA 2020 and implications for research synthesis methodologists. Research Synthesis Methods, 13(2), 156–163.CrossRefGoogle ScholarPubMed
Polanin, J. R., Hennessy, E. A., and Tanner-Smith, E. E. (2017). A review of meta-analysis packages in R. Journal of Educational and Behavioral Statistics, 42(2), 206–242.CrossRefGoogle Scholar
Polanin, J. R., Hennessy, E. A., and Tsuji, S. (2020). Transparency and reproducibility of meta-analyses in psychology: A meta-review. Perspectives on Psychological Science, 15(4), 1026–1041.CrossRefGoogle ScholarPubMed
Polanin, J. R., Pigott, T. D., Espelage, D. L., and Grotpeter, J. K. (2019). Best practice guidelines for abstract screening large‐evidence systematic reviews and meta‐analyses. Research Synthesis Methods, 10(3), 330–342.CrossRefGoogle Scholar
Pustejovsky, J. E., and Rodgers, M. A. (2019). Testing for funnel plot asymmetry of standardized mean differences. Research Synthesis Methods, 10(1), 57–71.CrossRefGoogle ScholarPubMed
Pustejovsky, J. E., and Tipton, E. (2022). Meta-analysis with robust variance estimation: Expanding the range of working models. Prevention Science, 23, 425–438.CrossRefGoogle ScholarPubMed
Rathbone, J., Hoffmann, T., and Glasziou, P. (2015). Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers. Systematic Reviews, 4(1), 1–7.CrossRefGoogle ScholarPubMed
Razpurker-Apfeld, I., and Shamoa-Nir, L. (2021). Is an outgroup welcome with open arms? Approach and avoidance motor activations and outgroup prejudice. Journal of Experimental Psychology: Applied, 27(2), 417–429.Google ScholarPubMed
Rosenthal, R. (1966). Experimenter Effects in Behavioral Research. Appleton-Century-Crofts.Google Scholar
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641.CrossRefGoogle Scholar
Rosenthal, R. (1994). Parametric measures of effect size. In Cooper, H. and Hedges, L. V. (eds.) The Handbook of Research Synthesis. Russell Sage Foundation.Google Scholar
Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118, 183–192.CrossRefGoogle Scholar
Rosenthal, R., Rosnow, R. L., and Rubin, D. B. (2000). Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press.Google Scholar
Rosenthal, R., and Rubin, D. B. (1979). Comparing significance levels of independent studies. Psychological Bulletin, 86, 1165–1168.CrossRefGoogle Scholar
Schild, A. H., and Voracek, M. (2015). Finding your way out of the forest without a trail of bread crumbs: development and evaluation of two novel displays of forest plots. Research Synthesis Methods, 6(1), 74–86.CrossRefGoogle ScholarPubMed
Schlegel, K., Boone, R. T., and Hall, J. A. (2017). Individual differences in interpersonal accuracy: A multi-level meta-analysis to assess whether judging other people is one skill or many. Journal of Nonverbal Behavior, 41, 103–137.CrossRefGoogle Scholar
Schlegel, K., Palese, T., Mast, M. S., Rammsayer, T. H., Hall, J. A., and Murphy, N. A. (2020). A meta-analysis of the relationship between emotion recognition ability and intelligence. Cognition and Emotion, 34(2), 329–351.CrossRefGoogle ScholarPubMed
Schmid, C. H., Stijnen, T., and White, I. R. (eds.) (2021). Handbook of Meta-analysis. Routledge/Taylor and Francis Group.Google Scholar
Schmidt, F. L., Le, H., and Oh, I. (2009). Correcting for the distorting effects of study artifacts in meta-analysis. In Cooper, H., Hedges, L. V., and Valentine, J. C. (eds.) The Handbook of Research Synthesis and Meta-analysis, 2nd ed. Russell Sage Foundation.Google Scholar
Schmidt, F. L., Oh, I.-S., and Hayes, T. L. (2009). Fixed- versus random-effects models in meta-analysis: Model properties and an empirical comparison of differences in results. British Journal of Mathematical and Statistical Psychology, 62(1), 97–128.CrossRefGoogle Scholar
Shuster, J. J, Guo, J. D., and Skyler, J. S. (2012). Meta‐analysis of safety for low event‐rate binomial trials. Research Synthesis Methods, 3, 30–50.CrossRefGoogle ScholarPubMed
Siddaway, A. P., Wood, A. M., and Hedges, L. V. (2019). How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual Review of Psychology, 70, 747–770.CrossRefGoogle Scholar
Siegel, M., Eder, J. S. N., Wicherts, J. M., and Pietschnig, J. (2021). Times are changing, bias isn’t: A meta-meta-analysis on publication bias detection practices, prevalence rates, and predictors in industrial/organizational psychology. Journal of Applied Psychology, 107(11), 2013–2039.CrossRefGoogle ScholarPubMed
Simonsohn, U., Nelson, L. D., and Simmons, J. P. (2014). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534–547.CrossRefGoogle Scholar
Simonsohn, U., Simmons, J., and Nelson, L. (2017, June 15). Why p-curve excludes ps > .05 (blog post), https://datacolada.org/61.+.05+(blog+post),+https://datacolada.org/61.>Google Scholar
Simonsohn, U., Simmons, J., and Nelson, L. (2018, January 8). P-curve handles heterogeneity just fine (blog post), https://datacolada.org/67.Google Scholar
Smith, M. L., and Glass, G.V. (1977). Meta-analysis of psychotherapy outcome studies. American Psychologist, 32(9), 752–760.CrossRefGoogle ScholarPubMed
Stanley, T. D., and Doucouliagos, H. (2014). Meta‐regression approximations to reduce publication selection bias. Research Synthesis Methods, 5(1), 60–78.CrossRefGoogle ScholarPubMed
Sterne, J. A. C., Becker, B. J., and Egger, M. (2005). The funnel plot. In Rothstein, H. R., Sutton, A. J., and Borenstein, M. (eds.) Publication Bias in Meta-analysis: Prevention, Assessment and Adjustments. John Wiley & Sons.Google Scholar
Tanner-Smith, E. E., and Tipton, E. (2014). Robust variance estimation with dependent effect sizes: Practical considerations including a software tutorial in Stata and SPSS. Research Synthesis Methods, 5(1), 13–30.CrossRefGoogle ScholarPubMed
Tanner-Smith, E. E., Tipton, E., and Polanin, J. R. (2016). Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R. Journal of Developmental and Life-Course Criminology, 2, 85–112.CrossRefGoogle Scholar
Taylor, J. A., Pigott, T., and Williams, R. (2022). Promoting knowledge accumulation about intervention effects: Exploring strategies for standardizing statistical approaches and effect size reporting. Educational Researcher, 51(1), 72–80.CrossRefGoogle Scholar
Thomas, J., and Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, 45, at https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-8-45.CrossRefGoogle ScholarPubMed
Tipton, E., Pustejovsky, J. E., and Ahmadi, H. (2019). A history of meta-regression: Technical, conceptual, and practical developments between 1974 and 2018. Research Synthesis Methods, 10(2), 161–179.CrossRefGoogle ScholarPubMed
Tucker-Drob, E. M., Brandmaier, A. M., and Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301.CrossRefGoogle ScholarPubMed
Valentine, J. C. (2009). Judging the quality of primary research. In Cooper, H., Hedges, L. V., and Valentine, J. C. (eds.) The Handbook of Research Synthesis and Meta-analysis, 2nd ed. Russell Sage Foundation.Google Scholar
Valentine, J. C. (2012). Meta-analysis. In Cooper, H., Camic, P. M., Long, D. L., Panter, A. T., Rindskopf, D., and Sher, K. J. (eds.). APA Handbook of Research Methods in Psychology, vol. 3. American Psychological Association.Google Scholar
Valentine, J. C., Pigott, T. D., and Rothstein, H. R. (2010). How many studies do you need? A primer on statistical power for meta-analysis. Journal of Educational and Behavioral Statistics, 35(2), 215–247.CrossRefGoogle Scholar
van Aert, R. C. M., Wicherts, J. M., and van Assen, M. A. L. M. (2016). Conducting meta-analyses based on p-values: Reservations and recommendations for applying p-uniform and p-curve. Perspectives on Psychological Science, 11(5), 713–729.CrossRefGoogle ScholarPubMed
van Lissa, C. J. (2020). Small sample meta-analyses: Exploring heterogeneity using MetaForest. In Van De Schoot, R. and Miočević, M. (eds.) Small sample size solutions (open access): A guide for applied researchers and practitioners. CRC Press, www.crcpress.com/Small-Sample-SizeSolutions-Open-Access-A-Guide-for-Applied-Researchers/Schoot-Miocevic/p/book/9780367222222.Google Scholar
Vevea, J. L. and Hedges, L. V. (1995). A general linear model for estimating effect size in the presence of publication bias. Psychometrika, 60(3), 419–435.CrossRefGoogle Scholar
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.CrossRefGoogle Scholar
Waffenschmidt, S., Knelangen, M., Sieben, W., Bühn, S., and Pieper, D. (2019). Single screening versus conventional double screening for study selection in systematic reviews: A methodological systematic review. BMC Medical Research Methodology, 19(1), 1–9.CrossRefGoogle ScholarPubMed
Wells, G., Shea, B., O’Connell, D., Peterson, J., Welch, V., Losos, M., and Tugwell, P. (2000). The Newcastle–Ottawa Scale (NOS) for assessing the quality of non-randomized studies in meta-analysis, www.ohri.ca/programs/clinical_epidemiology/oxford.asp.Google Scholar
Clearinghouse, What Works. (2022). What Works Clearinghouse Procedures and Standards Handbook, Version 5.0. National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education, https://ies.ed.gov/ncee/wwc/handbooks.Google Scholar
White, H. D. (2009). Scientific communication and literature retrieval. In Cooper, H., Hedges, L. V., and Valentine, J. C. (eds.) The Handbook of Research Synthesis and Meta-analysis, 2nd ed. Russell Sage Foundation.Google Scholar
Zuckerman, M., Li, C., and Hall, J. A. (2016). When men and women differ in self-esteem and when they don’t: A meta-analysis. Journal of Research in Personality, 64, 34–51.CrossRefGoogle Scholar
Zuckerman, M., Silberman, J., and Hall, J. A. (2013). The relation between intelligence and religiosity: A meta-analysis and some proposed explanations. Personality and Social Psychology Review, 17, 325–354.CrossRefGoogle ScholarPubMed

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