References

Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Akaike, H. (1992[1973]). Information theory and an extension of the maximum likelihood principle. In S. Kotz & K. L. Johnson (Eds.), Breakthroughs in statistics. Vol 1 (pp. 610–624). London: Springer-Verlag.
Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., & Kievit, R. A. (2019). Raincloud plots: A multi-platform tool for robust data visualization [version 1; peer review: 2 approved]. Wellcome Open Research, 4. https://doi.org/10.12688/wellcomeopenres.15191.1
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed). Washington, DC: American Psychological Association.
Baron, R. M., & 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, 1173.
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255–278.
Bartholomew, D. J., Deary, I. J., & Lawn, M. (2009). The origin of factor scores: Spearman, thomson and bartlett. British Journal of Mathematical and Statistical Psychology, 62, 569–582.
Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious mixed models. arXiv:1506.04967 [Stat].
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Berger, J. O., & Wolpert, R. L. (1988). The likelihood principle. Hayward, CA: Institute of Mathematical Statistics.
Bollen, K. A., & Noble, M. D. (2011). Structural equation models and the quantification of behavior. Proceedings of the National Academy of Sciences, 108, 15639–15646.
Box, G. E. (1954). Some theorems on quadratic forms applied in the study of analysis of variance problems, i. Effect of inequality of variance in the one-way classification. The Annals of Mathematical Statistics, 290–302.
Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces. New York, NY: John Wiley & Sons.
Carragher, D. J., Thomas, N. A., Gwinn, O. S., & Nicholls, M. E. (2019). Limited evidence of hierarchical encoding in the cheerleader effect. Scientific Reports, 9, 1–13.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
Cohen, J. (1994). The earth is round (p<. 05). American Psychologist, 49, 997–1003.
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25, 7–29.
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge university press.
Devlieger, I., & Rosseel, Y. (2023). Using factor scores in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modelling (pp. 316–328). Guilford Press.
Dobson, A. J., & Barnett, A. G. (2018). An introduction to generalized linear models. Chapman; Hall/CRC.
Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer.
Fisman, R., Iyengar, S. S., Kamenica, E., & Simonson, I. (2006). Gender differences in mate selection: Evidence from a speed dating experiment. The Quarterly Journal of Economics, 121, 673–697.
Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.
Galton, F. (1907). Vox populi. Nature, 75, 450--451.
Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no "fishing expedition" or "p-hacking" and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University.
Gilder, T. S. E., & Heerey, E. A. (2018). The role of experimenter belief in social priming. Psychological Science, 29, 403–417.
Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.
Greenhouse, S. W., & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24, 95–112.
Guennouni, I., & Speekenbrink, M. (2022). Transfer of learned opponent models in zero sum games. Computational Brain & Behavior, 5, 326--342.
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55–67.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.
Howell, D. C. (2012). Statistical methods for psychology. Cengage Learning.
Hřebı́čková, M., Jelı́nek, M., Květon, P., Benkovič, A., Botek, M., Sudzina, F., … John, O. P. (2020). Big five inventory 2 (BFI-2): Hierarchick model s 15 subškálami. Ceskoslovenska Psychologie, 64.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.
Huynh, H., & Feldt, L. S. (1976). Estimation of the Box correction for degrees of freedom from sample data in randomized block and split-plot designs. Journal of Educational Statistics, 1, 69–82.
James, E. L., Bonsall, M. B., Hoppitt, L., Tunbridge, E. M., Geddes, J. R., Milton, A. L., & Holmes, E. A. (2015). Computer game play reduces intrusive memories of experimental trauma via reconsolidation-update mechanisms. Psychological Science. https://doi.org/10.1177/0956797615583071
Jeffreys, H. (1939). The theory of probability. Oxford: Oxford University Press.
John, O. P., Donahue, E. M., & Kentle, R. L. (1991). Big five inventory. Journal of Personality and Social Psychology.
Judd, Charles M., McClelland, G. H., & Ryan, C. S. (2011). Data analysis: A model comparison approach. Routledge.
Judd, Charles M., Westfall, J., & 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.
Kaplan, D. (2001). Structural equation modeling. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social & behavioral sciences (pp. 15215–15222). Oxford: Pergamon.
Kenny, D. A., & Judd, C. M. (1986). Consequences of violating the independence assumption in analysis of variance. Psychological Bulletin, 99, 422–431.
Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997.
Klein, R. A., Ratliff, K. A., Vianello, M., Adams Jr, R. B., Bahnı́k, Š., Bernstein, M. J., et al.others. (2014). Investigating variation in replicability: A "many labs"" replication project. Social Psychology, 142–152.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th edition). Guilford Press.
Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification? Sociological Methods & Research, 41, 124–167.
Kruschke, J. (2014). Doing bayesian data analysis: A tutorial with r, JAGS, and stan. Academic Press.
Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.), Contributions to probability and statistics. Essays in honor of Harold Hotelling (pp. 279–292). Palo Alto, CA: Stanford University Press.
Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. O. (2008). Mixtures of g priors for Bayesian variable selection. Journal of the American Statistical Association, 103, 410–423.
Lix, L. M., Keselman, J. C., & Keselman, H. J. (1996). Consequences of assumption violations revisited: A quantitative review of alternatives to the one-way analysis of variance F test. Review of Educational Research, 66, 579–619.
Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in r. Behavior Research Methods, 49, 1494–1502.
Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. Annual Review of Public Health, 23, 151–169.
MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490.
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614.
MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1, 173–181.
Maraun, M. D. (1996). Metaphor taken as math: Indeterminancy in the factor analysis model. Multivariate Behavioral Research, 31, 517–538.
Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing type i error and power in linear mixed models. Journal of Memory and Language, 94, 305–315.
Mauchly, J. W. (1940). Significance test for sphericity of a normal n-variate distribution. The Annals of Mathematical Statistics, 11, 204–209.
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective. Routledge.
McCullagh, P., & Nelder, J. A. (2019). Generalized linear models. Routledge.
McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior.
Morey, Richard D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E.-J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, 23, 103–123.
Morey, Richard D., & Rouder, J. N. (2018). BayesFactor: Computation of bayes factors for common designs. Retrieved from https://CRAN.R-project.org/package=BayesFactor
Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241.
Pearl, J. (2012). The causal foundations of structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modelling (pp. 68–91). New York: California Univ Los Angeles Dept of Computer Science; Guilford Press.
Pinheiro, J., & Bates, D. (2006). Mixed-effects models in s and s-PLUS. Springer Science & Business Media.
Popper, K. (1959). The logic of scientific discovery. Routledge.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.
Rausch, M., & Zehetleitner, M. (2016). Visibility is not equivalent to confidence in a low contrast orientation discrimination task. Frontiers in Psychology, 7, 591.
Raykov, T., & Penev, S. (1999). On structural equation model equivalence. Multivariate Behavioral Research, 34, 199–244.
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, 27–42.
Rosenthal, R., & Gaito, J. (1963). The interpretation of levels of significance by psychological researchers. The Journal of Psychology, 55, 33–38.
Rouder, J. N., & Morey, R. D. (2012). Default bayes factors for model selection in regression. Multivariate Behavioral Research, 47, 877–903.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56, 356–374.
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237.
Sakia, R. M. (1992). The box-cox transformation technique: A review. Journal of the Royal Statistical Society: Series D (The Statistician), 41, 169–178.
Satterthwaite, F. E. (1941). Synthesis of variance. Psychometrika, 6, 309–316.
Schaffner, B. F., Macwilliams, M., & Nteta, T. (2018). Understanding white polarization in the 2016 vote for president: The sobering role of racism and sexism. Political Science Quarterly, 133, 9–34.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359–1366.
Simonsohn, U. (2014). Posterior-hacking: Selective reporting invalidates Bayesian results also. Retrieved from https://dx.doi.org/10.2139/ssrn.2374040
Singmann, H., & Kellen, D. (2019). An introduction to linear mixed modeling in experimental psychology. In New methods in cognitive psychology (pp. 4–31). Psychology Press.
Soto, C. J., & John, O. P. (2017). The next big five inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113, 117.
Spearman, C. (1904). General intelligence,objectively determined and measured. The American Journal of Psychology, 15, 201–292.
Student. (1908). The probable error of a mean. Biometrika, 6, 1–25.
Thompson, B. (1992). Two and one-half decades of leadership in measurement and evaluation. Journal of Counseling & Development, 70, 434–438.
Tomarken, A. J., & Serlin, R. C. (1986). Comparison of ANOVA alternatives under variance heterogeneity and specific noncentrality structures. Psychological Bulletin, 99, 90.
Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley,.
Venzon, D., & Moolgavkar, S. (1988). A method for computing profile-likelihood-based confidence intervals. Journal of the Royal Statistical Society: Series C (Applied Statistics), 37, 87–94.
Wagenmakers, E.-J. (2007). A practical solution to the pervasive problems of p values. Psychonomic Bulletin & Review, 14, 779–804.
Walker, D., & Vul, E. (2014). Hierarchical encoding makes individuals in a group seem more attractive. Psychological Science, 25, 230–235.
Wickens, T. D. (2014). Multiway contingency tables analysis for the social sciences. Psychology Press.
Wilks, S. S. (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9, 60–62.
Winter, B., & Bürkner, P.-C. (2021). Poisson regression for linguists: A tutorial introduction to modelling count data with brms. Language and Linguistics Compass, 15, e12439.
Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences, 6, 320–332.
Zabell, S. L. (2008). On student’s 1908 article "the probable error of a mean"". Journal of the American Statistical Association, 103, 1–7.
Zaval, L., Markowitz, E. M., & Weber, E. U. (2015). How will I be remembered? Conserving the environment for the sake of one’s legacy. Psychological Science, 26, 231–236.