Best Practices in the Use of Bifactor Models: Conceptual Grounds, Fit Indices and Complementary Indicators

Autores/as

  • Pablo Ezequiel Flores-Kanter Universidad Siglo 21, Córdoba. Facultad de Psicología, Universidad Nacional de Córdoba.
  • Sergio Dominguez-Lara Instituto de Investigación de Psicología, Universidad de San Martín de Porres, Lima.
  • Mario Alberto Trógolo Universidad Siglo 21. Facultad de Psicología, Universidad Nacional de Córdoba.
  • Leonardo Adrián Medrano Universidad Siglo 21. Facultad de Psicología, Universidad Nacional de Córdoba.

DOI:

https://doi.org/10.35670/1667-4545.v18.n3.22221

Palabras clave:

confirmatory factor analyses, bifactor models, PANAS, complementary statistical fit indices

Resumen

Bifactor models have gained increasing popularity in the literature concerned with personality, psychopathology and assessment. Empirical studies using bifactor analysis generally judge the estimated model using SEM model fit indices, which may lead to erroneous interpretations and conclusions. To address this problem, several researchers have proposed multiple criteria to assess bifactor models, such as a) conceptual grounds, b) overall model fit indices, and c) specific bifactor model indicators. In this article, we provide a brief summary of these criteria. An example using data gathered from a recently published research article is also provided to show how taking into account all criteria, rather than solely SEM model fit indices, may prevent researchers from drawing wrong conclusions.

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Biografía del autor/a

Pablo Ezequiel Flores-Kanter, Universidad Siglo 21, Córdoba. Facultad de Psicología, Universidad Nacional de Córdoba.

Doctorando. Docente e Investigador.

Sergio Dominguez-Lara, Instituto de Investigación de Psicología, Universidad de San Martín de Porres, Lima.

Doctor en Psicología. Docente e Investigador.

Mario Alberto Trógolo, Universidad Siglo 21. Facultad de Psicología, Universidad Nacional de Córdoba.

Doctorando. Docente e Investigador.

Leonardo Adrián Medrano, Universidad Siglo 21. Facultad de Psicología, Universidad Nacional de Córdoba.

Doctor en Psicología. Secretario de Investigación.

Citas

Arias, V. B., Jenaro, C., & Ponce, F. P. (2018). Testing the generality of the general factor of personality: An exploratory bifactor approach. Personality and Individual Differences, 129, 17-23. doi: 10.1016/j.paid.2018.02.042

Bäckström, M., & Björklund, F. (2016). Is the general factor of personality based on evaluative responding? Experimental manipulation of item-popularity in personality inventories. Personality and Individual Differences, 96, 31-35. doi: 10.1016/j.paid.2016.02.058

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. doi: 10.1177/2167702616657069

Davies, S. E., Connelly, B. S., Ones, D. S., & Birkland, A. S. (2015). The general factor of personality: The “Big One,” a self-evaluative trait, or a methodological gnat that won’t go away? Personality and Individual Differences, 81, 13-22. doi: 10.1016/j.paid.2015.01.006

Gignac, 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. doi: 10.1016/j.intell.2016.01.006

Hancock, G. R., & Mueller, R. O. (2001). Rethinking construct reliability within latent variable systems. In R. Cudeck, S. du Toit & D. Sorbom (Eds.), Structural equation modeling: Present and future—A Festschrift in honor of Karl Joreskog (pp. 195-216). Lincolnwood, IL: Scientific Software International.

Morgan, G. B., Hodge, K. J., Wells, K. E., & Watkins, M. W. (2015). Are fit indices biased in favor of bi-factor models in cognitive ability research?: A comparison of fit in correlated factors, higher-order, and bi-factor models via Monte Carlo Simulations. Journal of Intelligence, 3(1), 2-20. doi: 10.3390/jintelligence3010002

Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98(3), 223-237. doi: 10.1080/00223891.2015.1089249

Seib-Pfeifer, L.-E., Pugnaghi, G., Beauducel, A., & Leue, A. (2017). On the replication of factor structures of the Positive and Negative Affect Schedule (PANAS). Personality and Individual Differences, 107, 201-207. doi: 10.1016/j.paid.2016.11.053

Stucky, B. D., Thissen, D., & Edelen, M. O. (2013). Using logistic approximations of marginal trace lines to develop short assessments. Applied Psychological Measurement, 37(1), 41-57. doi: 10.1177/0146621612462759

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Publicado

2018-12-04

Cómo citar

Flores-Kanter, P. E., Dominguez-Lara, S., Trógolo, M. A., & Medrano, L. A. (2018). Best Practices in the Use of Bifactor Models: Conceptual Grounds, Fit Indices and Complementary Indicators. Revista Evaluar, 18(3). https://doi.org/10.35670/1667-4545.v18.n3.22221

Número

Sección

Artículos metodológicos