[
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