Unscripted E9: Moderated Nonlinear Factor Analysis

In Episode 9 of Unscripted, Patrick and Dan discuss moderated nonlinear factor analysis (also known as the world's worst acronym: MNLFA). In Episode 7 they explored methods for estimating models across two or more observed groups (e.g., biological sex, race) and in Episode 8 they extended this to groups that were unobserved (or latent; e.g., treatment responders vs. non-responders). However, both of these approaches rely on testing moderation across levels of a single grouping variable (whether observed or latent). Neither allows for inclusion of continuous moderators (e.g., age, years of education) or the possibility of modeling multiple moderators simultaneously (that might themselves interact). In contrast, MNLFA builds upon the structural equation model to permit moderation of the model parameters across a set of observed variables, continuous or categorical. MNLFA thus allows for testing a variety of hypotheses about individual differences that would not otherwise be possible. Please visit centerstat.org for additional freely-available instructional materials and other training opportunities. You can also sign up for notifications about future Unscripted episodes at centerstat.org/centerstat-unscripted/ Bauer, D. J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22, 507. Bauer, D. J., Belzak, W. C., & Cole, V. T. (2020). Simplifying the assessment of measurement invariance over multiple background variables: Using regularized moderated nonlinear factor analysis to detect differential item functioning. Structural Equation Modeling: A Multidisciplinary Journal, 27, 43-55. Bauer, D. J., & Hussong, A. M. (2009). Psychometric approaches for developing commensurate measures across independent studies: traditional and new models. Psychological Methods, 14, 101. Curran, P. J., Cole, V., Bauer, D. J., Hussong, A. M., & Gottfredson, N. (2016). Improving factor score estimation through the use of observed background characteristics. Structural Equation Modeling: A Multidisciplinary Journal, 23, 827-844. Curran, P. J., Cole, V. T., Bauer, D. J., Rothenberg, W. A., & Hussong, A. M. (2018). Recovering predictor–criterion relations using covariate-informed factor score estimates. Structural Equation Modeling: A Multidisciplinary Journal, 25, 860-875. Curran, P. J., Cole, V., Giordano, M., Georgeson, A. R., Hussong, A. M., & Bauer, D. J. (2018). Advancing the study of adolescent substance use through the use of integrative data analysis. Evaluation & The Health Professions, 41, 216-245. Curran, P. J., McGinley, J. S., Bauer, D. J., Hussong, A. M., Burns, A., Chassin, L., ... & Zucker, R. (2014). A moderated nonlinear factor model for the development of commensurate measures in integrative data analysis. Multivariate Behavioral Research, 49, 214-231. Gottfredson, N. C., Cole, V. T., Giordano, M. L., Bauer, D. J., Hussong, A. M., & Ennett, S. T. (2019). Simplifying the implementation of modern scale scoring methods with an automated R package: Automated moderated nonlinear factor analysis (aMNLFA). Addictive Behaviors, 94, 65-73.