The unicorn, the normal curve, and other improbable creatures.

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1. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
   Curran, Patrick J., West, Stephen G., Finch, John F. · 1996 · Psychological Methods
2. Principles and practice in reporting structural equation analyses.
   McDonald, Roderick P., Ho, Moon-Ho Ringo · 2002 · Psychological Methods
3. Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares
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4. An empirical comparison of the efficacy of covariance-based and variance-based SEM
   Reinartz, Werner, Haenlein, Michael, Henseler, Jörg · 2009 · International Journal of Research in Marketing
5. Exploratory Factor Analysis: A Guide to Best Practice
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6. At what sample size do correlations stabilize?
   Schönbrodt, Felix D., Perugini, Marco · 2013 · Journal of Research in Personality
7. Determining power and sample size for simple and complex mediation models
   Schoemann, Alexander M., Boulton, Aaron J., Short, Stephen D. · 2017 · Social Psychological and Personality Science
8. Non-normal data: Is ANOVA still a valid option?
   Blanca, María, Alarcón, Rafael, Arnau, Jaume, Bono, Roser, Bendayan, Rebecca · 2017 · Psicothema
9. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: a tutorial using simulations and empirical data
   de Winter, Joost C. F., Gosling, Samuel D., Potter, Jeff · 2016 · Psychological Methods
10. Sex Differences in Mental Test Scores, Variability, and Numbers of High-Scoring Individuals
    Hedges, Larry V., Nowell, Amy · 1995 · Science
11. Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.
    Erceg-Hurn, David M., Mirosevich, Vikki M. · 2008 · American Psychologist
12. In search of underlying dimensions: The use (and abuse) of factor analysis in Personality and Social Psychology Bulletin
    Russell, Daniel W. · 2002 · Personality and Social Psychology Bulletin
13. Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.
    Bishara, Anthony J., Hittner, James B. · 2012 · Psychological Methods
14. Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox
    Pernet, Cyril R., Wilcox, Rand, Rousselet, Guillaume A. · 2013 · Frontiers in Psychology
15. The integration of continuous and discrete latent variable models: Potential problems and promising opportunities
    Bauer, Daniel J., Curran, Patrick J. · 2004 · Psychological Methods
16. The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree
    Lai, Keke, Green, Samuel B. · 2016 · Multivariate Behavioral Research
17. A Tutorial on Hierarchically Structured Constructs
    Brunner, Martin, Nagy, Gabriel, Wilhelm, Oliver · 2012 · Journal of Personality
18. Fit Indices Versus Test Statistics
    Yuan, Ke-Hai · 2005 · Multivariate Behavioral Research
19. The scientific study of dreams: Neural networks, cognitive development, and content analysis.
    Domhoff, G. William · 2003
20. How to address non-normality: a taxonomy of approaches, reviewed, and illustrated
    Pek, Jolynn, Wong, Octavia, Wong, Augustine C. M. · 2018 · Frontiers in Psychology
21. Analyzing social experiments as implemented: A reexamination of the evidence from the HighScope Perry Preschool Program
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22. To test or not to test: Preliminary assessment of normality when comparing two independent samples
    Rochon, Justine, Gondan, Matthias, Kieser, Meinhard · 2012 · BMC Medical Research Methodology
23. Evaluating SEM Model Fit with Small Degrees of Freedom
    Shi, Dexin, DiStefano, Christine, Maydeu-Olivares, Alberto, Lee, Taehun · 2022 · Multivariate Behavioral Research
24. The trustworthiness of online channels for experience- and goal-directed search tasks
    Dickinger, Astrid · 2011 · Journal of Travel Research
25. Using bootstrap estimation and the plug-in principle for clinical psychology data
    Wright, Daniel B., London, Kamala, Field, Andy P. · 2011 · Journal of Experimental Psychopathology
26. The Noncentral Chi-square Distribution in Misspecified Structural Equation Models: Finite Sample Results from a Monte Carlo Simulation
    Curran, Patrick J., Bollen, Kenneth A., Paxton, Pamela, Kirby, James, Chen, Feinian · 2002 · Multivariate Behavioral Research
27. A UK validation of a general measure of subjective well-being: the modified BBC subjective well-being scale (BBC-SWB)
    Pontin, Eleanor, Schwannauer, Matthias, Tai, Sara, Kinderman, Peter · 2013 · Health and Quality of Life Outcomes
28. Permutation test is not distribution-free: Testing H₀: ρ = 0.
    Hayes, Andrew F. · 1996 · Psychological Methods
29. Problematic standard errors and confidence intervals for skewness and kurtosis
    Wright, Daniel B., Herrington, Joshua A. · 2011 · Behavior Research Methods
30. Evaluating Intervention Programs with a Pretest-Posttest Design: A Structural Equation Modeling Approach
    Alessandri, Guido, Zuffianò, Antonio, Perinelli, Enrico · 2017 · Frontiers in Psychology
31. Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling?
    Falk, Carl F. · 2018 · Structural Equation Modeling: A Multidisciplinary Journal
32. Flow on the Internet: a longitudinal study of Internet addiction symptoms during adolescence
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33. The meaning of significant mean group differences for biomarker discovery
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34. Normality and Sample Size Do Not Matter for the Selection of an Appropriate Statistical Test for Two-Group Comparisons
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35. A randomization test wrapper for synthesizing single-case experiments using multilevel models: a Monte Carlo simulation study
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36. Student interviews as a tool for assessment and learning in a systems analysis and design course
    Dick, Martin · 2005 · Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education