S4E05 Moderated Nonlinear Factor Analysis: A Conversation with Dan Bauer

S4E05_graphic

In this week’s episode Greg and Patrick spend a wonderful, if not at times awkward, hour talking with Dan Bauer about the genesis, application, and future directions of the what may be the world’s worst acronym: MNLFA, or moderated nonlinear factor analysis. Along the way they also discuss unsolicited help from teenagers, gold stars, acronyms, words that start with “ci”, aggressive mice, manipulating your advisors, 2nd spouses, MoNoLiFa, Quantitube, rewiring your brain, $1m dollar calculators, and mod mod.

Lightly Edited Transcript

We provide a lightly-edited and obviously imperfect audio transcript of the episode available here. This is not an exact representation of the audio, but does provide a searchable document with identified speakers and associated time stamps.

Related Episodes

S1E12 Measurement (Non)Invariance — Can We Ever Fail to Not Incorrectly Reject It?

S1E13: How Do I Get Scale Scores? Weight, Weight… Don’t Tell Me…

S1E22: Factor Analysis — The Good, The Bad, & The Ugly

S2E07: Moderation — Well, It Depends

Suggested Readings

Bauer, D. J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22, 507–526.

Bauer, D. J., & Hussong, A. M. (2009). Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models. Psychological Methods, 14, 101-125.

Belzak, W., & Bauer, D. J. (2020). Improving the assessment of measurement invariance: Using regularization to select anchor items and identify differential item functioning. Psychological Methods25, 673-690.

Chen, S. M., Bauer, D. J., Belzak, W. M., & Brandt, H. (2022). Advantages of Spike and Slab Priors for Detecting Differential Item Functioning Relative to Other Bayesian Regularizing Priors and Frequentist Lasso. Structural Equation Modeling: A Multidisciplinary Journal29, 122-139.

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, A.W., & 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., 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(3), 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 Behaviors94, 65-73.

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