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Competing Confirmatory Factor Analysis Models in Management Research: Bifactor Modeling of the Employee Work Assessment Tool


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Arbuckle, J. L. (2016). AMOS 24.0 User’s Guide. IBM SPSS. Search in Google Scholar

Block, J. H., Fisch, C., Kanwal, N., Lorenzen, S., & Schulze, A. (2023). Replication studies in top management journals: An empirical investigation of prevalence, types, outcomes, and impact. Management Review Quarterly, 73, 1109–1134. https://doi.org/10.1007/s11301-022-00269-6 Search in Google Scholar

Bonifay, W., Lane, S. P., & Reise, S. P. (2017). Three concerns with applying a bifactor model as a structure of psychopathology. Clinical Psychological Science, 5(1), 184-186. https://doi.org/10.1177/2167702616657069 Search in Google Scholar

Brunner, M., Nagy, G., & Wilhelm, O. (2012). A tutorial on hierarchically structured constructs. Journal of Personality, 80(4), 796-846. https://psycnet.apa.org/doi/10.1111/j.1467-6494.2011.00749.x Search in Google Scholar

Byllesby, B. M., & Palmieri, P. A. (2023). A bifactor model of general and specific PTSD symptom change during treatment. Assessment. 30(8), 2595-2604. https://doi.org/10.1177/10731911231156646. Search in Google Scholar

Canivez, G. L. (2014). Construct validity of the WISC-IV with a referred sample: Direct versus indirect hierarchical structures. School Psychology Quarterly, 29(1), 38-51. http://doi.org/10.1037/spq0000032 Search in Google Scholar

Canivez, G. L. (2016). Bifactor modeling in construct validation of multifactored tests: Implications for multidimensionality and test interpretation. In K. Schweizer & C. DiStefano (Eds.), Principles and methods of test construction: Standards and recent advancements (pp. 247–271). Hogrefe. Search in Google Scholar

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464-504. http://doi.org/10.1080/10705510701301834 Search in Google Scholar

Chen, F. F., Hayes, A., Carver, C. S., Laurenceau, J.‐P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80(1), 219–251. https://doi.org/10.1111/j.1467-6494.2011.00739.x Search in Google Scholar

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233-255. http://doi.org/10.1207IS15328007SEM0902_5 Search in Google Scholar

Cole, D. A., Ciesla, J. A., & Steiger, J. H. (2007). The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis. Psychological Methods, 12(4), 381–398. https://psycnet.apa.org/doi/10.1037/1082-989X.12.4.381 Search in Google Scholar

Collier, J. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques. Routledge. Search in Google Scholar

Dau, L. A., Santangelo, G. D., & van Witteloostuijn, A. (2022). Replication studies in international business. Journal of International Business Studies, 53(2), 215-230. https://doi.org/10.1057/s41267-021-00471-w Search in Google Scholar

Dueber, D. M. (2017, April 10). Bifactor indices calculator: a Microsoft excel-based tool to calculate various indices relevant to bifactor CFA models. Digital Commons. https://doi.org/10.13023/edp.tool.01 Search in Google Scholar

Dunn, K. J., & McCray, G. (2020). The place of the bifactor model in confirmatory factor analysis investigations into construct dimensionality in language testing. Frontiers in Psychology, 11, 1357. https://doi.org/10.3389/fpsyg.2020.01357 Search in Google Scholar

Gegenfurtner, A. (2022). Bifactor exploratory structural equation modeling: A meta-analytic review of model fit. Frontiers in Psychology, 13, 1037111. https://doi.org/10.3389/fpsyg.2022.1037111 Search in Google Scholar

Gignac, G. E. (2008). Higher-order models versus direct hierarchical models: G as superordinate or breadth factor? Psychology Science Quarterly, 50, 21-43. Search in Google Scholar

Gorsuch, R. L. (1983). Two-and three-mode factor analysis. In R.L. Gorsuch (Ed.), Factor Analysis (2nd Ed.). Erlbaum. Search in Google Scholar

Grice, J. W. (2001). Computing and evaluating factor scores. Psychological Methods, 6(4), 430-450. https://doi.org/10.1037/1082-989x.6.4.430 Search in Google Scholar

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. 2017. A primer on partial least squares structural equation modelling. Sage. Search in Google Scholar

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and report the results of PLS-SEM. European Business Review, 31(1), 2-24. Search in Google Scholar

Hammer, J. H., & Toland, M. D. (2017). Internal structure and reliability of the Internalized stigma of mental illness scale (ISMI-29) and brief versions (ISMI-10, ISMI-9) among Americans with depression. Stigma and Health, 2(3), 159-174. https://doi.org/10.1037/sah0000049. Search in Google Scholar

Hammer, J. H., McDermott, R. C., Levant, R. F., & McKelvey, D. K. (2018). Dimensionality, reliability, and validity of the Gender-Role Conflict Scale-Short Form (GRCS-SF). Psychology of Men & Masculinity, 19(4), 570-583. Search in Google Scholar

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. Search in Google Scholar

Hu, L.-T., & Bentler, P. M. (1999). Cut-off 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 Search in Google Scholar

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th Ed). Guilford Press. Search in Google Scholar

Landis, R. S., Edwards, B. D., & Cortina, J. M. (2010). On the practice of allowing correlated residuals among indicators in structural equation models. In C.E. Lance & R.J. Vandenberg (Eds.), Statistical and methodological myths and urban legend (pp. 213-236). Routledge. Search in Google Scholar

Luo, Y., & Al-Harbi, K. (2016). The utility of the bifactor method for unidimensionality assessment when other methods disagree: an empirical illustration. Sage Open, 6(4), 1–7. https://doi.org/10.1177/2158244016674513 Search in Google Scholar

Oamen, T. E. (2021). The analysis of factors influencing pharmaceutical sales workforce engagement in pharmaceutical marketing in Nigeria: A structural equation modeling approach. Global Journal of Pure and Applied Sciences, 27(4), 45-51. https://doi.org/10.4314/gjpas.v27i4.7. Search in Google Scholar

Oamen, T. E. (2023). The moderating effect of contextual factors on the impact of competitive behavior on community pharmacists’ performance in Nigeria. International Journal of Economic Behavior, 13(1), 93-108. https://doi.org/10.14276/2285-0430.3743. Search in Google Scholar

Oamen, T. E., Idiake, J., & Omorenuwa, O. S. (2022b). Assessment of measurement invariance of psychometric tool for pharmaceutical sales executives: implications for social and behavioral pharmacy research. Journal of Pharmaceutical Health Services Research, 13(4), 262-268. https://doi.org/10.1093/jphsr/rmac041. Search in Google Scholar

Oamen, T. E., Omorenuwa, O. S., & Moshood, L. B. (2022a). A structural equation analysis of employment work assessment tool for pharmaceutical executives. Journal of Social and Educational Research, 1(1), 14-20. Search in Google Scholar

Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667-696. https://doi.org/10.1080/00273171.2012.7 Search in Google Scholar

Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95, 129-140. http://doi.org/10.1080/00223891.2012.725437 Search in Google Scholar

Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92(6), 544–559, https://doi.org/10.1080/00223891.2010.496477 Search in Google Scholar

Reise, S. P., Morizot, J., & Hays, R. D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16(1), 19-31. https://doi.org/10.1007/s11136-007-9183-7 Search in Google Scholar

Rijmen, F. (2010). Formal relations and an empirical comparison among the bifactor, the testlet, and a second-order multidimensional IRT model. Journal of Educational Measurement, 47(3), 361–372. https://doi.org/10.1111/j.1745-3984.2010.0011 Search in Google Scholar

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137-150. https://doi.org/10.1037/met0000045 Search in Google Scholar

Sarstedt, M., Adler, S. J., Ringle, C. M., Cho, G., Diamantopoulos, A., Hwang, H., & Liengaard, B. D. (2024). Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modelling. Journal of Product Innovation management, 1-17. https://doi.org/10.1111/jpim.12738 Search in Google Scholar

Savahl, S., Casa, F., & Adams, S. (2023). Considering a bifactor model of children’s subjective well‐being using a multinational sample. Child Indicators Research, 16, 2253–2278. https://doi.org/10.1007/s12187-023-10058-6 Search in Google Scholar

Schuberth, F. (2021). Confirmatory composite analysis using partial least squares: Setting the record straight. Review of Managerial Science, 15, 1311–1345. Search in Google Scholar

Stone, B. M. (2021). The ethical use of fit indices in structural equation modeling: Recommendations for psychologists. Frontiers in Psychology, 12, 783226. https://doi.org/10.3389/fpsyg.2021.783226 Search in Google Scholar

Stucky, B. D., & Edelen, M. O. (2015). Using hierarchical IRT models to create unidimensional measures from multidimensional data. In S.P. Reise & D.A. Revicki (Eds.), Handbook of item response theory modeling: Applications to typical performance assessment (pp. 183-206). Routledge. Search in Google Scholar

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education. Search in Google Scholar

Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association. https://doi.org/10.1037-0694-000 Search in Google Scholar

Tomarken, A. J., & Waller, N. G. (2003). Potential problems with "well-fitting" models. Journal of Abnormal Psychology, 112(4), 578–598. https://doi.org/10.1037/0021-843X.112.4.578 https://doi.org/10.1037/0021-843X.112.4.578 Search in Google Scholar

Torres-Vallejos, J., Juarros-Basterretxea, J., Oyanedel, J. C., & Sato, M. (2021). A bifactor model of subjective well-being at personal, community, and country levels: a case with three Latin-American countries. Frontiers in Psychology, 12, 641641. https://doi.org/10.3389/fpsyg.2021.641641 Search in Google Scholar

Vandenberg, R. J. (2006). Statistical and Methodological Myths and Urban Legends. Organisational Research Methods, 9(2), 194-201. Search in Google Scholar

Ventura-León, J., Quiroz-Burga, L., Caycho-Rodríguez, T., & Valencia, P. D. (2021). BifactorCalc: An online calculator for auxiliary measures of bifactor models. Revista Evaluar, 21(3), 1-14. Search in Google Scholar

Zhang, B., Sun, T., Cao, M., & Drasgow, F. (2021). Using bifactor models to examine the predictive validity of hierarchical constructs: Pros, cons, and solutions. Organizational Research Methods, 24(3), 530–571. https://doi.org/10.1177/1094428120915522 Search in Google Scholar

Zinbarg, R. E., Yovel, I., Revelle, W., & McDonald, R. P. (2006). Estimating generalizability to a latent variable common to all of a scale's indicators: A comparison of estimators for wh. Applied Psychological Measurement, 30(2), 121-144. http://doi.org/10.1177/0146621605278814 Search in Google Scholar