In this week’s episode, the first of Season 6, Greg and Patrick visit with Dr. Ethan McCormick, an Assistant Professor of Educational Statistics and Data Science in the School of Education at the University of Delaware. After catching up on Ethan’s international adventures they discuss his recent work on using growth factors as predictors of distal outcomes and how pretty much everything he expected to find came out exactly the opposite. Along the way they also discuss chain sawing family memories, the 31st 1st day of school, Irish goodbyes, barn doors, ridiculous footnotes, blatant plagiarism, done and dusted, throwing R code at your advisor, landing a triple axel, umlauts, being proudly uneducated, hiding in the bathroom, and in fairness to us.
Related Episodes
- S5E10: Nonlinear Latent Growth Curve Models (Taylor’s Version)
- S4E14: Growth Trajectory Estimates: What’s My Line?
- S4E09: Intensive Longitudinal Data: Be Careful What You Wish For
- S2E26: MLM vs. SEM: Opportunities for Growth
Suggested Readings
Biesanz, J. C., Deeb-Sossa, N., Papadakis, A. A., Bollen, K. A., & Curran, P. J. (2004). The role of coding time in estimating and interpreting growth curve models. Psychological Methods, 9, 30–52. https://doi.org/10 .1037/1082-989X.9.1.30
Feng, Y., & Hancock, G. R. (2021). Model-based incremental validity. Psychological Methods, 27, 1039–1060. https://doi.org/10.1037/met0000342
Hancock, G. R., & Choi, J. (2006). A vernacular for linear latent growth models. Structural Equation Modeling: A Multidisciplinary Journal, 13, 352–377. https://doi.org/10.1207/s15328007sem1303_2
Liu, X., & Wang, L. (2024). Causal Mediation Analysis with the Parallel Process Latent Growth Curve Mediation Model. Structural Equation Modeling: A Multidisciplinary Journal, 1-22.
McCormick, E. M., Curran, P. J., & Hancock, G. R. (2024). Latent growth factors as predictors of distal outcomes. Psychological Methods, doi:10.1037/met0000642
Muthén, B. O., & Curran, P. J. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371–402. https://doi.org/10.1037/1082-989X.2.4.371