Asparouhov, T., & Muthén, B. (2009). Exploratory Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397–438. https://doi.org/10.1080/10705510903008204 Asparouhov, T., Muthén, B., & Morin, A. J. S. (2015). Bayesian Structural Equation Modeling With Cross-Loadings and Residual Covariances: Comments on Stromeyer et al. Journal of Management, 41(6), 1561–1577. https://doi.org/10.1177/0149206315591075 Achim, A. (2017). Testing the number of required dimensions in exploratory factor analysis. The Quantitative Methods for Psychology, 13(1), 64–74. https://doi.org/10.20982/tqmp.13.1.p064 Allaire, J., Xie, Y., Dervieux, C., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2014). rmarkdown: Dynamic Documents for {R} (Version 2.30) [R package]. https://doi.org/10.32614/CRAN.package.rmarkdown Amiot, C. E., Gaudreau, P., & Blanchard, C. M. (2004). Self-determination, coping, and goal attainment in sport. Journal of Sport & Exercise Psychology, 26(3), 396–411. https://doi.org/10.1123/jsep.26.3.396 Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238 Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate software. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588 Bollen, K. A. (1989). Structural Equations with Latent Variables. Chapel Hill, NC: Wiley. https://doi.org/10.1002/9781118619179 Brown, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 445–455). New York: Sage Publications. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY: The Guilford Press. Brown, T. A., & Moore, M. T. (2012). Confirmatory factor analysis. In R. H. Hoyle (Ed.), Handbook of structural equation modeling. (pp. 361–379). New York: The Guilford Press. Byrne, B. M. (2005). Factor Analytic Models: Viewing the Structure of an Assessment Instrument From Three Perspectives. Journal of Personality Assessment, 85(1), 17–32. https://doi.org/10.1207/s15327752jpa8501_02 Byrne, B. M. (2013). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. New York, NY: Routledge. https://doi.org/10.4324/9780203807644 Brauer, K., Ranger, J., & Ziegler, M. (2023). Confirmatory Factor Analyses in Psychological Test Adaptation and Development: A Nontechnical Discussion of the WLSMV Estimator. Psychological Test Adaptation and Development, 4(1), 4–12. https://doi.org/10.1027/2698-1866/a000034 Caron, P.-O. (2018). La modélisation par équations structurelles avec Mplus. Montréal: Presses de l’Université du Québec. https://doi.org/10.2307/j.ctvt1sh9g Caron, P.-O. (2023). Méthodes Quantitatives avec {R}. Montréal: TÉLUQ. https://mqr.teluq.ca Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models. Sociological Methods & Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720 Depaoli, S. (2021). Bayesian structural equation modeling. New York, NY: The Guilford Press. Dion, K., Bodnaruc, A. M., Trudel, G., Lamarche, J., Ranger, V., Fobert, S., Church, K. A., Ntumba Mukunzi, J., & René, J.-L. (2021). La modélisation par équations structurelles—Un guide d’accompagnement pour l’interface {R}. The Quantitative Methods for Psychology, 17(3), 198–271. https://doi.org/10.20982/tqmp.17.3.p198 Elliot, A. J., & McGregor, H. A. (2001). A 2 × 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501–519. https://doi.org/10.1037/0022-3514.80.3.501 Epskamp, S. (2013). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages’ Output (Version 1.1.7) [{R} package]. https://doi.org/10.32614/CRAN.package.semPlot Flake, J. K., & Fried, E. I. (2020). Measurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them. Advances in Methods and Practices in Psychological Science, 3(4), 456–465. https://doi.org/10.1177/2515245920952393 Gaudreau, P. (2015). Self-assessment of the four subtypes of perfectionism in the 2 x 2 model of perfectionism. Personality and Individual Differences, 84, 52-62. https://doi.org/10.1016/j.paid.2014.10.039 Gaudreau, P., & Blondin, J.-P. (2004). Differential Associations of Dispositional Optimism and Pessimism With Coping, Goal Attainment, and Emotional Adjustment During Sport Competition. International Journal of Stress Management, 11(3), 245–269. https://doi.org/10.1037/1072-5245.11.3.245 Greiff, S., & Heene, M. (2017). Why Psychological Assessment Needs to Start Worrying About Model Fit. European Journal of Psychological Assessment, 33(5), 313–317. https://doi.org/10.1027/1015-5759/a000450 Groskurth, K., Bluemke, M., & Lechner, C. M. (2023). Why we need to abandon fixed cutoffs for goodness-of-fit indices: An extensive simulation and possible solutions. Behavior Research Methods, 56(4), 3891–3914. https://doi.org/10.3758/s13428-023-02193-3 Hayduk, L., Cummings, G., Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! Testing! One, two, three – Testing the theory in structural equation models! Personality and Individual Differences, 42(5), 841–850. https://doi.org/10.1016/j.paid.2006.10.001 Horton, N. J., & Kleinman, K. (2015). Using R and RStudio for Data Management, Statistical Analysis, and Graphics (2nd ed.). New York: CRC Press. https://doi.org/10.1201/b18151 Howard, M. C. (2016). A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve? International Journal of Human-Computer Interaction, 32(1), 51–62. https://doi.org/10.1080/10447318.2015.1087664 Hu, L.-T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications. (pp. 76–99). New York: Sage Publications. Hu, L.-T., & 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), 1–55. https://doi.org/10.1080/10705519909540118 Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23. https://doi.org/10.1037/a0014694 Jöreskog, K. G. (1969). A General Approach to Confirmatory Maximum Likelihood Factor Analysis. Psychometrika, 34(2), 183–202. https://doi.org/10.1007/BF02289343 Jöreskog, K. G. (1993). Testing Structural Equation Models. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 294–316). New York: Sage Publications. Jöreskog, K. G. (2007). Factor Analysis and Its Extensions. In R. Cudeck & R. C. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 47–77). Mahwah: Lawrence Erlbaum Associates Publishers. Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The Performance of RMSEA in Models With Small Degrees of Freedom. Sociological Methods & Research, 44(3), 486–507. https://doi.org/10.1177/0049124114543236 Kenny, D. A., & McCoach, D. B. (2003). Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 10(3), 333–351. https://doi.org/10.1207/S15328007SEM1003_1 Kljajic, K., Gaudreau, P., & Franche, V. (2017). An investigation of the 2 × 2 model of perfectionism with burnout, engagement, self-regulation, and academic achievement. Learning and Individual Differences, 57, 103-113. https://doi.org/10.1016/j.lindif.2017.06.004 Knekta, E., Runyon, C., & Eddy, S. (2019). One Size Doesn’t Fit All: Using Factor Analysis to Gather Validity Evidence When Using Surveys in Your Research. CBE—Life Sciences Education, 18(1), 1-17. https://doi.org/10.1187/cbe.18-04-0064 MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130 MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111(3), 490–504. https://doi.org/10.1037/0033-2909.111.3.490 Marsh, H. W., & Balla, J. (1994). Goodness of fit in confirmatory factor analysis: The effects of sample size and model parsimony. Quality & Quantity, 28(2), 185–217. https://doi.org/10.1007/bf01102761 Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103(3), 391–410. https://doi.org/10.1037/0033-2909.103.3.391 Marsh, H. W., Hau, K.-T., & Grayson, D. (2005). Goodness of Fit in Structural Equation Models. In A. Maydey-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics: A festschrift for Roderick P. McDonald. (pp. 275–340). Mahwah: Lawrence Erlbaum Associates Publishers. Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler’s (1999) Findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2 Marsh, H. W., Morin, A. J. S., Parker, P. D., & Kaur, G. (2014). Exploratory Structural Equation Modeling: An Integration of the Best Features of Exploratory and Confirmatory Factor Analysis. Annual Review of Clinical Psychology, 10(1), 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700 Martinent, G., Nicolas, M., Gaudreau, P., & Campo, M. (2013). A Cluster Analysis of Affective States Before and During Competition. Journal of Sport and Exercise Psychology, 35(6), 600–611. https://doi.org/10.1123/jsep.35.6.600 McDonald, R. P. (1985). Factor Analysis and Related Methods. Hillsdale, NJ: Erlbaum. McNeish, D. (2023). Dynamic fit index cutoffs for categorical factor analysis with Likert-type, ordinal, or binary responses. American Psychologist, 78(9), 1061–1075. https://doi.org/10.1037/amp0001213 McNeish, D. (2024). Dynamic fit index cutoffs for treating likert items as continuous. Psychological Methods, Advance online publication. https://doi.org/10.1037/met0000683 McNeish, D. (2025). How Do Psychologists Determine Whether a Measurement Scale Is Good? A Quarter-Century of Scale Validation with Hu & Bentler (1999). Annual Review of Psychology, Advance online publication. https://doi.org/10.1146/annurev-psych-121924-104021 McNeish, D. (2026). How Do Psychologists Determine Whether a Measurement Scale Is Good? A Quarter-Century of Scale Validation with Hu & Bentler (1999). Annual Review of Psychology, 77(1), 567–591. https://doi.org/10.1146/annurev-psych-121924-104021 McNeish, D., An, J., & Hancock, G. R. (2018). The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models. Journal of Personality Assessment, 100(1), 43–52. https://doi.org/10.1080/00223891.2017.1281286 McNeish, D., & Manapat, P. D. (2024). Dynamic Fit Index Cutoffs for Hierarchical and Second-Order Factor Models. Structural Equation Modeling: A Multidisciplinary Journal, 31(1), 27–47. https://doi.org/10.1080/10705511.2023.2225132 McNeish, D., & Wolf, M. G. (2022). Dynamic fit index cutoffs for one-factor models. Behavior Research Methods, 55(3), 1157–1174. https://doi.org/10.3758/s13428-022-01847-y McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61–88. https://doi.org/10.1037/met0000425 Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. https://doi.org/10.1037/a0026802 Nicholls, J. G. (1984). Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance. Psychological Review, 91(3), 328–346. https://doi.org/10.1037/0033-295X.91.3.328 Perry, J. L., Nicholls, A. R., Clough, P. J., & Crust, L. (2015). Assessing Model Fit: Caveats and Recommendations for Confirmatory Factor Analysis and Exploratory Structural Equation Modeling. Measurement in Physical Education and Exercise Science, 19(1), 12–21. https://doi.org/10.1080/1091367X.2014.952370 Pornprasertmanit, S., Miller, P., Schoemann, A., & Jorgensen, T. D. (2012). simsem: SIMulated Structural Equation Modeling (Version 0.5-17) [{R} package]. https://doi.org/10.32614/CRAN.package.simsem Pornprasertmanit, S., Wu, W., & Little, T. D. (2013). A Monte Carlo Approach for Nested Model Comparisons in Structural Equation Modeling. In R. E. Millsap, L. A. Van Der Ark, D. M. Bolt, & C. M. Woods (Eds.), New Developments in Quantitative Psychology (Vol. 66, pp. 187–197). Springer New York. https://doi.org/10.1007/978-1-4614-9348-8_12 {R Core Team}. (2025). {R}: A Language and Environment for Statistical Computing (Version 4.5.2) [Computer software]. {R} Foundation for Statistical Computing. https://www.R-project.org Rogers, P. (2024). Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial. Behavior Research Methods, 56(7), 6634–6654. https://doi.org/10.3758/s13428-024-02375-7 Rosseel, Y. (2012). lavaan: An {R} Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02 Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784 Shi, D., Lee, T., & Maydeu-Olivares, A. (2019). Understanding the Model Size Effect on SEM Fit Indices. Educational and Psychological Measurement, 79(2), 310–334. https://doi.org/10.1177/0013164418783530 Steiger, J. H., & Lind, J. C. (1980, May). Statistically Based Tests for the Number of Common Factors [Paper presentation]. Annual meeting of the Psychometric Society, Iowa City, Iowa, United States of America. Taylor, J. M. (2019). Overview and Illustration of Bayesian Confirmatory Factor Analysis with Ordinal Indicators. Practical Assessment, Research & Evaluation, 24(1), 4. https://doi.org/10.7275/vk6g-0075 Thurstone, L. L. (1947). Multiple factor analysis. Chicago, IL: University of Chicago press. Tucker, L. R., & Lewis, C. (1973). A Reliability Coefficient for Maximum Likelihood Factor Analysis. Psychometrika, 38(1), 1–10. https://doi.org/10.1007/BF02291170 West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). New York: The Guilford Press. West, S. G., Wu, W., McNeish, D., & Savord, A. (2023). Model Fit in Structural Equation Modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (2nd ed., pp. 184–205). New York: The Guilford Press. Weston, R., & Gore Jr., P. A. (2006). A Brief Guide to Structural Equation Modeling. The Counseling Psychologist, 34(5), 719–751. https://doi.org/10.1177/0011000006286345 Wickham, H., Hester, J., Chang, W., & Bryan, J. (2011). devtools: Tools to Make Developing {R} Packages Easier (Version 2.4.6) [R package]. https://doi.org/10.32614/CRAN.package.devtools Wolf, M. G., & McNeish, D. (2021). Dynamic Model Fit (Version 1.1.0) [Software]. https://www.dynamicfit.app Wolf, M. G., & McNeish, D. (2023). dynamic: An {R} Package for Deriving Dynamic Fit Index Cutoffs for Factor Analysis. Multivariate Behavioral Research, 58(1), 189–194. https://doi.org/10.1080/00273171.2022.2163476 Xiong, Z., Xia, H., Ni, J., & Hu, H. (2025). Basic assumptions, core connotations, and path methods of model modification—Using confirmatory factor analysis as an example. Frontiers in Education, 10, 1506415-1. https://doi.org/10.3389/feduc.2025.1506415 Yong, A. G., & Pearce, S. (2013). A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79–94. https://doi.org/10.20982/tqmp.09.2.p079