Abstract
Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this ‘typical CFA’, leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan software platforms. Supplementary materials such as videos, files, and scripts are freely available.
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References
AERA, APA, & NCME. (2014). Standards for Educational and Psychological Testing. American Educational Research Association, American Psychological Association & National Council on Measurement in Education.
Agawin , M. (2020). Perceived Ease of Use, Perceived Usefulness, and Attitude towards Jeffrey's Amazing Statistics Program (JASP) among Students of a Private Higher Educational Institution in Region IV-A. Laguna Journal of Multidisciplinary Research, 4 (1), 1–14. Center for Research, Publication, and Intellectual Property. Retrieved from: https://lpulaguna.edu.ph/journal-disciplinary-research/. Accessed 4 Mar 2024.
Bandalos, D. L. (2014). Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation. Structural Equation Modeling, 21(1), 102–116. https://doi.org/10.1080/10705511.2014.859510
Bandalos, D. L. (2018). Measurement theory and applications for the social sciences. Guilford Press.
Beauducel, A., & Herzberg, P. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling, 13(2), 186–203. https://doi.org/10.1207/s15328007sem1302_2
Beaujean, A. A. (2014). Latent Variable Modeling Using R: A Step-by-Step Guide. Routledge.
Bell, S.M., Chalmers, R.P., & Flora, D.B. (2023). The Impact of Measurement Model Misspecification on Coefficient Omega Estimates of Composite Reliability. Educational and Psychological Measurement, 1–36. https://doi.org/10.1177/00131644231155804
Bido, D. S., Mantovani, D. M. N., & Cohen, E. D. (2018). Destruction of measurement scale through exploratory factor analysis in production and operations research. Management & Production, 25(2), 384–397. https://doi.org/10.1590/0104-530X3391-16
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press.
Brown, T. A. (2023). Confirmatory Factor Analysis. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (2nd ed.). UK: The Guilford Press.
Byrne, B. M. (2016). Structural Equation Modeling with AMOS: Basic Concepts, Application, and Programming (3rd ed.). Routledge Taylor & Francis Group.
Campayo, F. S., Sancho-Esper, F., Rodríguez-Sánchez, C., Ostrovskaya, L., Martínez, N. J., Romero-Ortiz, A., Fernández-Díaz, F. M., Mora, C. F., Bruno, J. M., & Casado, L. A. A. (2022). Approaching it classroom technology : implementation and use of free software (JASP) in quantitative market research. In R. C. Satorre (Ed.), Memories of the Research Networks Program en teaching university (pp. 1141–1169). Universitat d’Alacant http://rua.ua.es/dspace/handle/10045/130544. Accessed 4 Mar 2024.
Chinchu, C. (2022). Free and (Mostly) Open source data analysis software for academic research. OSF Preprint. https://doi.org/10.31234/osf.io/9pnkw
Cho, E. (2022). Reliability and Omega Hierarchical in Multidimensional Data: A Comparison of Various Estimators. Psychological Methods. https://doi.org/10.1037/met0000525
Collier, J. E. (2020). Applied Structural Equation Modeling Using AMOS: Basic to Advanced Techniques. Routledge.
Crede, M., & Harms, P. (2019). Questionable research practices when using confirmatory factor analysis. Journal of Managerial Psychology, 34(1), 18–30. https://doi.org/10.1108/JMP-06-2018-0272
Davvetas, V., Diamantopoulos, A., Zaefarian, G., & Sichtmann, C. (2020). Ten basic questions about structural equations modeling you should know the answers to – But perhaps you don’t. Industrial Marketing Management, 90, 252–263. https://doi.org/10.1016/j.indmarman.2020.07.016
DiStefano, C., & Morgan, G. B. (2014). A Comparison of Diagonal Weighted Least Squares Robust Estimation Techniques for Ordinal Data. Structural Equation Modeling, 21(3), 425–438. https://doi.org/10.1080/10705511.2014.915373
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
Feng, Y., & Hancock, G. R. (2023). Power Analysis within a Structural Equation Modeling Framework. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (2nd ed.). The Guilford Press.
Fiates, G. G. S., Serra, F. A. R., & Martins, C. (2014). The aptitude of Brazilian researchers belonging to stricto sensu postgraduate programs in Administration for quantitative research. Revista de Administração, 49(2), 384–398. https://doi.org/10.5700/rausp1153
Finch, W. H., & French, B. F. (2015). Latent Variable Modeling with R. Routledge.
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
Flake, J. K., Pek, J., & Hehman, E. (2017). Construct validation in social and personality research: Current practices and recommendations. Social Psychological and Personality Science, 8(4), 370–378. https://doi.org/10.1177/1948550617693063
Flake, J. K., Davidson, I. J., Wong, O., & Pek, J. (2022). Construct validity and the validity of replication studies: A systematic review. American Psychologist, 77(4), 576–588. https://doi.org/10.1037/amp0001006
Flora, D. B. (2020). Your coefficient alpha Is probably wrong, but which coefficient omega Is right? A tutorial on using R to obtain better reliability estimates. Advances in Methods and Practices in Psychological Science, 3(4), 484–501. https://doi.org/10.1177/2515245920951747
Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. https://doi.org/10.1037/1082-989X.9.4.466
Flora, D. B., & Flake, J. K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: Decisions for scale development and validation. Canadian Journal of Behavioral Science, 49(2), 78–88. https://doi.org/10.1037/cbs0000069
Forero, C. G., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation. Structural Equation Modeling: A Multidisciplinary Journal, 16(4), 625–641. https://doi.org/10.1080/10705510903203573
Furr, M. R. (2021). Psychometrics: An Introduction (4th ed.). SAGE Publications.
Gana, K., & Broc, G. (2019). Structural Equation Modeling with lavaan. John Wiley & Sons Inc.
Goodboy, A. K., & Martin, M. M. (2020). Omega over alpha for reliability estimation of unidimensional communication measures. Annals of the International Communication Association, 44(4), 422–439. https://doi.org/10.1080/23808985.2020.1846135
Goss-Sampson, M. (2022). Statistical Analysis in JASP: A Guide for Students (5th ed.). University of Greenwich. Retrieved from: https://jasp-stats.org/jasp-materials/. Accessed 4 Mar 2024.
Green, S. B., & Yang, Y. (2015). Evaluation of Dimensionality in the Assessment of Internal Consistency Reliability: Coefficient Alpha and Omega Coefficients. Educational Measurement: Issues and Practice, 34(4), 14–20. https://doi.org/10.1111/emip.12100
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.https://doi.org/10.3758/s13428-023-02193-3
Hair, J. F., Sarstedt, M., Ringle, C., & Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling (2nd ed.). SAGE Publications Inc.
Hair, J., Black, W., Babin, B., & Anderson, R. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
Hair, J. F., Hult, T. M. G., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.
Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press.
Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than Cronbach’s alpha for estimating reliability. But... Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629
Henseler, J. (2021). Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables. The Guilford Press.
Holgado-Tello, F., Morata-Ramirez, M., & García, M. (2016). Robust Estimation Methods in Confirmatory Factor Analysis of Likert Scales: A Simulation Study. International Review of Social Sciences and Humanities, 11(2), 80–96.
Holgado-Tello, F. P., Morata-Ramírez, M. Á., & BarberoGarcía, M. I. (2018). Confirmatory factor analysis of ordinal variables: A simulation study comparing the main estimation methods. Avances En Psicologia Latinoamericana, 36(3), 601–618. https://doi.org/10.12804/revistas.urosario.edu.co/apl/a.4932
Hoyle, R. H. (2023). Handbook of structural equation modeling (2nd ed.). The Guilford Press.
Hughes, D. J. (2018). Psychometric Validity: Establishing the Accuracy and Appropriateness of Psychometric Measures. In P. Irwing, T. Booth, & D. J. Hughes (Eds.), The Wiley Handbook of Psychometric Testing: A Multidisciplinary Reference on Survey, Scale and Test Development (1st ed.). John Wiley & Sons Ltd.
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). https://doi.org/10.1037/a0014694
Jak, S., Jorgensen, T. D., Verdam, M. G. E., Oort, F. J., & Elffers, L. (2021). Analytical power calculations for structural equation modeling: A tutorial and Shiny app. Behavior Research Methods, 53, 1385–1406. https://doi.org/10.3758/s13428-020-01479-0/Published
JASP Team. (2023). JASP (Version 0.17.1). [Computer Software]. Available at: https://jasp-stats.org/. Accessed 4 Mar 2024.
Jobst, L. J., Bader, M., & Moshagen, M. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods, 28(1), 207–221. https://doi.org/10.1037/met0000423
Kalkbrenner, M. T. (2023). Alpha, Omega, and H Internal Consistency Reliability Estimates: Reviewing These Options and When to Use Them. Counseling Outcome Research and Evaluation, 14(1), 77–88. https://doi.org/10.1080/21501378.2021.1940118
Kiliç, A., Uysal, İ, & Atar, B. (2020). Comparison of confirmatory factor analysis estimation methods on binary data. International Journal of Assessment Tools in Education, 2020(3), 451–487. https://doi.org/10.21449/ijate.660353
Kiliç, A.F., & Dogan, N. (2021). Comparison of confirmatory factor analysis estimation methods on mixed-format data. International Journal of Assessment Tools in Education, 21–37. https://doi.org/10.21449/ijate.782351
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.
Kluthcovsky , A.C.G.C., & Kluthcovsky , F.A. (2009). The WHOQOL- bref, an instrument to assess quality of life: a systematic review. Revista de Psiquiatria Do Rio Grande Do Sul, 31 (3 suppl). https://doi.org/10.1590/S0101-81082009000400007
Kyriazos, T. A. (2018). Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General. Psychology, 09(08), 2207–2230. https://doi.org/10.4236/psych.2018.98126
Lei, P. W. (2009). Evaluating estimation methods for ordinal data in structural equation modeling. Quality and Quantity, 43(3), 495–507. https://doi.org/10.1007/s11135-007-9133-z
Lei, P. W., & Shiverdecker, L. K. (2020). Performance of Estimators for Confirmatory Factor Analysis of Ordinal Variables with Missing Data. Structural Equation Modeling: A Multidisciplinary Journal, 27(4), 584–601. https://doi.org/10.1080/10705511.2019.1680292
Leite, W. L., Bandalos, D. L., & Shen, Z. (2023). Simulation Methods in Structural Equation Modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (2nd ed.). The Guilford Press.
Li, C. H. (2016a). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949. https://doi.org/10.3758/s13428-015-0619-7
Li, C. H. (2016b). The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables. Psychological Methods, 21(3), 369–387. https://doi.org/10.1037/met0000093
Lin, L. C., & Yao, G. (2022). Validation of the factor structure of the WHOQOL-BREF using meta-analysis of exploration factor analysis and social network analysis. Psychological Assessment, 34(7), 660–670. https://doi.org/10.1037/pas0001122
Love, J., Selker, R., Marsman, M., Jamil, T., Dropmann , D., Verhagen, J., Ly, A., Gronau, Q. F., Šmíra , M., Epskamp , S., Matzke , D., Wild, A., Knight, P., Rouder , J.N., Morey, RD, & Wagenmakers , E.J. (2019). JASP: Graphical statistical software for common statistical designs. Journal of Statistical Software, 88 (1). https://doi.org/10.18637/jss.v088.i02
Marcoulides, K. M., & Yuan, K. H. (2017). New Ways to Evaluate Goodness of Fit: A Note on Using Equivalence Testing to Assess Structural Equation Models. Structural Equation Modeling, 24(1), 148–153. https://doi.org/10.1080/10705511.2016.1225260
Maydeu-Olivares, A. (2017). Maximum Likelihood Estimation of Structural Equation Models for Continuous Data: Standard Errors and Goodness of Fit. Structural Equation Modeling, 24(3), 383–394. https://doi.org/10.1080/10705511.2016.1269606
McNeish, D. (2018). Thanks coefficient alpha, We’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144
McNeish, D. (2023a). Dynamic Fit Index Cutoffs for Factor Analysis with Likert, Ordinal, or Binary Responses. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/tp35s
McNeish, D. (2023b). Generalizability of Dynamic Fit Index, Equivalence Testing, and Hu & Bentler Cutoffs for Evaluating Fit in Factor Analysis. Multivariate Behavioral Research, 58(1), 195–219. https://doi.org/10.1080/00273171.2022.2163477
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
McNeish, D., & Manapat, P. D. (2023). Dynamic fit index cutoffs for hierarchical and second-order factor models. PsyArXiv Preprints. https://doi.org/10.31234/osf.io/sm6az
Mîndrilă, D. (2010). Maximum Likelihood (ML) and Diagonally Weighted Least Squares (DWLS) Estimation Procedures: A Comparison of Estimation Bias with Ordinal and Multivariate Non-Normal Data. International Journal of Digital Society (IJDS), 1(1), 60–66.
Morata-Ramírez, M. A., & Holgado-Tello , F.P. (2013). Construct Validity of Likert Scales through Confirmatory Factor Analysis: A Simulation Study Comparing Different Methods of Estimation Based on Pearson and Polychoric Correlations. International Journal of Social Science Studies, 1(1). https://doi.org/10.11114/ijsss.v1i1.27
Muthén, L. K., & Muthén, B. O. (2002). How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
Nalbantoğlu-Yılmaz, F. (2019). Comparison of Different Estimation Methods Used in Confirmatory Factor Analyses in Non-Normal Data: A Monte Carlo Study. International Online Journal of Educational Sciences, 11(4). https://doi.org/10.15345/iojes.2019.04.010
Navarro, D. J., Foxcroft, D. R., & Faulkenberry, T. J. (2019). Learning statistics with JASP: A tutorial for psychology students and other beginners (0.7). University of New South Wales https://learnstatswithjasp.com/. Accessed 4 Mar 2024.
Niu, G., Segall, R. S., Zhao, Z., & Wu, Z. (2021). A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities. International Journal of Open Source Software and Processes, 12(1), 1–20. https://doi.org/10.4018/IJOSSP.2021010101
Nye, C. D. (2022). Reviewer Resources: Confirmatory Factor Analysis. Organizational Research Methods, 109442812211205. https://doi.org/10.1177/10944281221120541
Perera, H. N., Izadikhah, Z., O’Connor, P., & McIlveen, P. (2018). Resolving Dimensionality Problems With WHOQOL-BREF Item Responses. Assessment, 25(8), 1014–1025. https://doi.org/10.1177/1073191116678925
Pornprasertmanit, S., Miller, P., Jorgensen, T. D., & Corbin, Q. (2022). simsem: SIMulated Structural Equation Modeling (0.5-16). [Computer software]. R package. Available at: www.simsem.org. Accessed 4 Mar 2024.
R Core Team. (2022). A: A language and environment for statistical computing (Version 4.2.2). R Foundation for Statistical Computing. https://www.R-project.org/. Accessed 4 Mar 2024.
Reeves, T. D., & Marbach-Ad, G. (2016). Contemporary test validity in theory and practice: A primer for discipline-based education researchers. CBE Life Sciences Education, 15(1). https://doi.org/10.1187/cbe.15-08-0183
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315
Rios, J., & Wells, C. (2014). Validity evidence based on internal structure. Psychothema, 26(1), 108–116. https://doi.org/10.7334/psicothema2013.260
Robitzsch, A. (2022). On the Bias in Confirmatory Factor Analysis When Treating Discrete Variables as Ordinal Instead of Continuous. Axioms, 11 (4). https://doi.org/10.3390/axioms11040162
Rogers, P. (2021b). RAC-Revista de Administração Contemporânea. Harvard Dataverse. https://doi.org/10.7910/DVN/RCX8FF
Rogers, P. (2021a). Data for “Best Practices for Your Exploratory Factor Analysis: a Factor Tutorial” published by RAC-Revista de Administração Contemporânea, Mendeley Data, V2. https://doi.org/10.17632/rdky78bk8r.2
Rogers, P. (2022). Best Practices for Your Exploratory Factor Analysis: A Factor Tutorial. Revista de Administração Contemporânea, 26 (6). https://doi.org/10.1590/1982-7849rac2022210085.en
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.
Rosseel, Y. (2023). The lavaan tutorial. Retrieved from: https://lavaan.ugent.be/. Accessed 4 Mar 2024.
Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66(2), 201–223. https://doi.org/10.1111/j.2044-8317.2012.02049.x
Schmitt, T. A. (2011). Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis. Journal of Psychoeducational Assessment, 29(4), 304–321. https://doi.org/10.1177/0734282911406653
Schumacker, R. E., Wind, S. A., & Holmes, L. F. (2021). Resources for Identifying Measurement Instruments for Social Science Research. Measurement: Interdisciplinary Research and Perspectives, 19(4), 250–257. https://doi.org/10.1080/15366367.2021.1950486
Shek, D. T. L., & Yu, L. (2014). Use of structural equation modeling in human development research. International Journal on Disability and Human Development, 13(2), 157–167. https://doi.org/10.1515/ijdhd-2014-0302. Freund Publishing House Ltd.
Shi, D., & Maydeu-Olivares, A. (2020). The Effect of Estimation Methods on SEM Fit Indices. Educational and Psychological Measurement, 80(3), 421–445. https://doi.org/10.1177/0013164419885164
Shi, D., Maydeu-Olivares, A., & Rosseel, Y. (2020). Assessing Fit in Ordinal Factor Analysis Models: SRMR vs RMSEA. Structural Equation Modeling: A Multidisciplinary Journal, 27(1), 1–15. https://doi.org/10.1080/10705511.2019.1611434
Sireci, S. G., & Sukin, T. (2013). Test validity. APA handbook of testing and assessment in psychology: Test theory and testing and assessment in industrial and organizational psychology (pp. 61–84). American Psychological Association. https://doi.org/10.1037/14047-004
Skevington, S. M., & Epton, T. (2018). How will the sustainable development goals deliver changes in well-being? A systematic review and meta-analysis to investigate whether WHOQOL-BREF scores respond to change. BMJ Global Health, 3. https://doi.org/10.1136/bmjgh-2017-000609
Trizano-Hermosilla, I., & Alvarado, J.M. (2016). Best alternatives to Cronbach's alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology, 7 (MAY). https://doi.org/10.3389/fpsyg.2016.00769
Valentini, F., & Damásio, B.F. (2016). Extracted Mean Variance and Composite Reliability: Precision Indicators. Psychology: Theory and Research, 32 (2). https://doi.org/10.1590/0102-3772e322225
Wagenmakers , E.J., Love, J., Marsman , M., Jamil, T., Ly , A., Verhagen , J., Selker , R., Gronau , Q.F., Dropmann , D., Boutin , B ., Meerhoff , F., Knight, P., Raj , A., van Kesteren , E.-J., van Doorn , J., Šmíra , M., Epskamp , S., Etz, A., Matzke , D. ., …, Morey, R. D. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58–76. https://doi.org/10.3758/s13423-017-1323-7
Walker, R., Moraine, A., Osborn, H., Black, K. J., Palmer, A., Scott, K., & Humphrey, K. (2022). Exploring diversity with statistics: Step-by-step JASP guides. University of Tennessee at Chattanooga. https://scholar.utc.edu/open-textbooks/1/. Accessed 4 Mar 2024.
Whittaker, T. A., & Schumacker, R. E. (2022). A Beginner’s Guide to Structural Equation Modeling (5th ed.). Routledge Taylor & Francis Group.
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
Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51(1), 409–428. https://doi.org/10.3758/s13428-018-1055-2
Yang-Wallentin, F., Jöreskog, K. G., & Luo, H. (2010). Confirmatory factor analysis of ordinal variables with misspecified models. Structural Equation Modeling, 17(3), 392–423. https://doi.org/10.1080/10705511.2010.489003
Yuan, K. H., Chan, W., Marcoulides, G. A., & Bentler, P. M. (2016). Assessing Structural Equation Models by Equivalence Testing With Adjusted Fit Indexes. Structural Equation Modeling, 23(3), 319–330. https://doi.org/10.1080/10705511.2015.1065414
Zhang, Z., & Yuan, K. H. (2018). Practical statistical power analysis using Webpower and R. ISDSA Press. https://doi.org/10.35566/power
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Appendix: Best practices in a typical CFA in applied social sciences under appendix section
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Rogers, P. Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial. Behav Res (2024). https://doi.org/10.3758/s13428-024-02375-7
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DOI: https://doi.org/10.3758/s13428-024-02375-7