References
- Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
- Bollen, K. A., & Ting, K. F. (1993). Confirmatory tetrad analysis. In P. Marsden (Ed.), Sociological methodology 1993 (pp. 147–1750). American Sociological Association.
- Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage.
- Byrne, B. M. (2012). Structural equation modeling with Mplus. Taylor & Francis.
- Cignac, G. E. (2016). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68. https://doi.org/10.1016/j.intell.2016.01.006
- Cudeck, R., & MacCallum, R. C. (2007). Factor analysis at. Erlbaum.
- Dunn, K., & McCray, D. (2020). The place of the bifactor model in confirmatory factor analysis investigations into construct dimensionality and language testing. Frontiers in Psychology, 11, 1–16. https://doi.org/10.3389/fpsyg.2020.01357
- Greene, W. H. (2018). Econometric analysis. Pearson.
- Jennrich, R. I., & Bentler, P. M. (2011). Exploratory bi-factor analysis. Psychometrika, 76(4), 537–549. https://doi.org/10.1007/s11336-011-9218-4
- Jennrich, R. I., & Bentler, P. M. (2012). Exploratory bi-factor analysis: The oblique case. Psychometrika, 77(3), 442–454. https://doi.org/10.1007/s11336-012-9269-1
- Mansolf, M., & Reise, S. P. (2017). When and why the second-order and bifactor models are distinguishable. Intelligence, 61, 120–129. https://doi.org/10.1016/j.intell.2017.01.012
- Mulaik, S. A. (2009). Foundations of factor analysis. CRC Press.
- Murray, A. L., & Johnson, W. (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence, 41(5), 407–422. https://doi.org/10.1016/j.intell.2013.06.004
- 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, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
- Muthén, L. K., & Muthén, B. O. (2023). Mplus user’s guide. Muthén &Muthén.
- Raykov, T., Marcoulides, G. A., Menold, N., & Harrison, M. (2019). Revisiting the bi-factor model: Can mixture modeling help assess its applicability? Structural Equation Modeling, 26(1), 110–118. https://doi.org/10.1080/10705511.2018.1436441
- Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. https://doi.org/10.1080/00273171.2012.715555
- 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, 354–373.
- Rindskopf, D. (1984). Structural equation models: Empirical identification, Heywood cases, and related problems. Sociological Methods & Research, 13(1), 109–119. https://doi.org/10.1177/0049124184013001004
- Satorra, A., & Bentler, P. M. (2001). A scale difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. https://doi.org/10.1007/BF02296192
- Yang, R., Spirtes, P., Scheines, R., Reise, S. P., & Mansoff, M. (2017). Finding pure submodels for improved differentiation of bifactor and second-order models. Structural Equation Modeling, 24(3), 402–413. https://doi.org/10.1080/10705511.2016.1261351
- Yung, Y. -F., Thissen, D., & McLeod, L. D. (1999). On the relationship between the higher-order factor model and the hierarchical factor model. Psychometrika, 64(2), 113–128. https://doi.org/10.1007/BF02294531