References


AERA, APA, & NCME. (1999). Standards for Educational and Psychological Testing. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education.
AERA, APA, & NCME. (2014). Standards for Educational and Pshychological Testing. American Educational Research Association, American Psychological Association & National Council on Measurement in Education.
Arbuckle, J. L. (2019). Amos. IBM Corp.
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.
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
Bentler, P. M., & Wu, E. (2020). EQS 6.4 for Windows. Multivariate Software, Inc. https://mvsoft.com
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research. The Guilford Press.
Brown, T. A. (2023). Confirmatory Factor Analysis. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. The Guilford Press.
Cho, E. (2022). Reliability and Omega Hierarchical in Multidimensional Data: A Comparison of Various Estimators. Psychological Methods. https://doi.org/10.1037/met0000525
Cohen, R. J., Schneider, J. W., & Tobin, R. M. (2022). Psychological Testing and Assessment: An Introduction to Test and Measurement. McGraw Hill LLC.
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
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
Enders, C. (2023). Fitting structural Equation Models with Missing data. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. The Guilford Press.
Feng, Y., & Hancock, G. R. (2023). Power Analysis within a Structural Equation Modeling Framework. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. The Guilford Press.
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
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
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., & 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 Behavioural Science, 49(2), 78–88. https://doi.org/10.1037/cbs0000069
Fox, J. (2022). Sem: Structural Equation Modeling. R package. https://cran.r-project.org/web/packages/sem/
Furr, M. R. (2021). Psychometrics: An Introduction. SAGE Publications.
Gilroy, S. P., & Kaplan, B. A. (2019). Furthering Open Science in Behavior Analysis: An Introduction and Tutorial for Using GitHub in Research. Perspectives on Behavior Science, 42(3), 565–581. https://doi.org/10.1007/s40614-019-00202-5
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
Green, S. B., Akey, T. M., Fleming, K. K., Hershberger, S. L., & Marquis, J. G. (1997). Effect of the number of scale points on chi-square fit indices in confirmatory factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 4(2), 108–120. https://doi.org/10.1080/10705519709540064
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., Hult, T. M. G., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications.
Hair, J. F., Sarstedt, M., Ringle, C., & Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. SAGE Publications, Inc.
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.
Hoyle, R. H. (2023). Structural Equation Modeling: An Overview. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. Guilford Press.
Huang, P.-H. (2017). Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling. Psychometrika, 82(2), 407–426. https://doi.org/10.1007/s11336-017-9572-y
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. 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 Mehods, 53, 1385–1406. https://doi.org/10.3758/s13428-020-01479-0/Published
JASP Team. (2023). JASP. [Computer Software]. https://jasp-stats.org/
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
Jöreskog, K. G., & Sörbom, D. (2022). LISREL 12 for Windows. Scientific Software International, Inc. https://ssicentral.com/index.php/products/lisrel/
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
Kathawalla, U.-K., Silverstein, P., & Syed, M. (2021). Easing Into Open Science: A Guide for Graduate Students and Their Advisors. Collabra: Psychology, 7(1), 18684. https://doi.org/10.1525/collabra.18684
Klein, O., Hardwicke, T. E., Aust, F., Breuer, J., Danielsson, H., Mohr, A. H., IJzerman, H., Nilsonne, G., Vanpaemel, W., & Frank, M. C. (2018). A Practical Guide for Transparency in Psychological Science. Collabra: Psychology, 4(1), 20. https://doi.org/10.1525/collabra.158
Kline, R. B. (2016). Principles and Pratice of Structural Equation Modeling. The Guilford Press.
Kline, R. B. (2023). Principles and Pratice of Structural Equation Modeling (Fifth Edition). The Guilford Press.
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
Lai, K. (2020). Confidence Interval for RMSEA or CFI Difference Between Nonnested Models. Structural Equation Modeling: A Multidisciplinary Journal, 27(1), 16–32. https://doi.org/10.1080/10705511.2019.1631704
Lai, K. (2021). Fit Difference Between Nonnested Models Given Categorical Data: Measures and Estimation. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 99–120. https://doi.org/10.1080/10705511.2020.1763802
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. The Guilford Press.
Li, C. H. (2016). 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
Mai, R., Niemand, T., & Kraus, S. (2021). A tailored-fit model evaluation strategy for better decisions about structural equation models. Technological Forecasting and Social Change, 173, 121142. https://doi.org/10.1016/j.techfore.2021.121142
Martins, H. C. (2021). Tutorial-Articles: The Importance of Data and Code Sharing. Revista de Administração Contemporânea, 25(1), e200212. https://doi.org/10.1590/1982-7849rac2021200212
Maydeu-Olivares, A., Fairchild, A. J., & Hall, A. G. (2017). Goodness of Fit in Item Factor Analysis: Effect of the Number of Response Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 24(4), 495–505. https://doi.org/10.1080/10705511.2017.1289816
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. (2023a). Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/4r9fq
McNeish, D., & Wolf, M. G. (2023b). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61–88. https://doi.org/10.1037/met0000425
Mendes-Da-Silva, W. (2023). What Lectures and Research in Business Management Need to Know About Open Science. Revista de Administração de Empresas, 63(4), e0000–0033. https://doi.org/10.1590/s0034-759020230408x
Moshagen, M., & Bader, M. (2023). semPower: General power analysis for structural equation models. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02254-7
Muthén, L. K., & Muthén, B. O. (2023). Mplus version 8.9 user’s guide.
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
Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H. H., & Boker, S. M. (2016). OpenMx 2.0: Extended Structural Equation and Statistical Modeling. Psychometrika, 81(2), 535–549. https://doi.org/10.1007/s11336-014-9435-8
Niemand, T., & Mai, R. (2018). Flexible cutoff values for fit indices in the evaluation of structural equation models. Journal of the Academy of Marketing Science, 46(6), 1148–1172. https://doi.org/10.1007/s11747-018-0602-9
Nye, C. D. (2022). Reviewer Resources: Confirmatory Factor Analysis. Organizational Research Methods, 109442812211205. https://doi.org/10.1177/10944281221120541
Pilcher, N., & Cortazzi, M. (2023). ’Qualitative’ and ’quantitative’ methods and approaches across subject fields: Implications for research values, assumptions, and practices. Quality & Quantity. https://doi.org/10.1007/s11135-023-01734-4
Pornprasertmanit, S., Miller, P., Jorgensen, T. D., & Corbin, Q. (2022). Simsem: SIMulated Structural Equation Modeling. R package. www.simsem.org
Preacher, K. J., & Yaremych, H. E. (2023). Model Selection in Structural Equation Modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. The Guilford Press.
Price, L. R. (2017). Psychometric Methods: Theory into Practice (1st Edition). The Guilford Press.
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
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4. SmartPLS. https://www.smartpls.com
Rios, J., & Wells, C. (2014). Validity evidence based on internal structure. Psicothema, 26(1), 108–116. https://doi.org/10.7334/psicothema2013.260
Robitzsch, A. (2020). Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods. Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.589965
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. (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
Rogers, P. (2024). Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial. Behavior Research Methods. 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
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
Sireci, S. G., & Sukin, T. (2013). Test validity. In APA handbook of testing and assessment in psychology, Vol. 1: Test theory and testing and assessment in industrial and organizational psychology. (pp. 61–84). American Psychological Association. https://doi.org/10.1037/14047-004
The jamovi project. (2023). Jamovi. [Computer Software]. https://www.jamovi.org
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
Urbina, S. (2014). Essentials of Psychological Testing. John Wiley & Sons.
Wang, Y. A. (2023). How to Conduct Power Analysis for Structural Equation Models: A Practical Primer [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/4n3uk
Wang, Y. A., & Rhemtulla, M. (2021). Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial. Advances in Methods and Practices in Psychological Science, 4(1), 1–17. https://doi.org/10.1177/2515245920918253
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. The Guilford Press.
Westland, C. J. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6). https://doi.org/10.1016/j.elerap.2010.07.003
Whittaker, T. A., & Schumacker, R. E. (2022). A Beginner’s Guide to Structural Equation Modeling (Fifth Edition). Routledge Taylor & Francis Group.
Widodo, E. (2018). Some Notes on the Contemporary Views of Validity in Psychological and Educational Assessment. Advances in Social Science, Education and Humanities Research, 231, 732–734. https://doi.org/10.3968/8877
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., Da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18
Xia, Y., & Yang, Y. (2018). The Influence of Number of Categories and Threshold Values on Fit Indices in Structural Equation Modeling with Ordered Categorical Data. Multivariate Behavioral Research, 53(5), 731–755. https://doi.org/10.1080/00273171.2018.1480346
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, Y., & Liang, X. (2013). Confirmatory factor analysis under violations of distributional and structural assumptions. Int. J. Quantitative Research in Education, 1(1), 61–84.
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