Terrence Jorgensen is an assistant professor in the Faculty of Social and Behavioural Sciences at the University of Amsterdam. His areas of expertise span structural equation modeling (SEM), multilevel modeling (MLM), social-relations modeling (SRM), and their intersections (i.e., ML-SEM and the SR-SEM), as well as Bayesian inference and modern missing data methods. His psychometric research interests involve reliability for complex data structures and ways to evaluate measurement invariance / differential item functioning. The Dutch Research Council has twice funded his research to expand SEM's applicability to "round-robin" (or "social network") data, which he accomplishes by integrating it with the social relations model (SRM) in the lavaan.srm R package. As a research software engineer, Terrence also coauthors the lavaan package itself and several critical extensions, such as lavaan.mi (for multiple imputations), semTools, simsem, and blavaan (the "Bayesian lavaan").

Suzanne Jak is an associate professor in the Faculty of Social and Behavioural Sciences at the University of Amsterdam. She is interested in structural equation modeling (SEM) in general, and specifically in combination with measurement invariance, multilevel data, or meta-analysis (MASEM). MASEM combines using the most informative data with a technique that is so versatile that many other methods can come to be viewed as a form of SEM. With the MASEM lab, she aims to make the technique better known and more user friendly for researchers, and to develop new MASEM methods where needed. This has led to several publications on new methods, as well as tutorial articles,  Shiny applications , and an instructive  book  (second edition forthcoming). Suzanne's research was funded by prestigious VENI (2016) and VIDI (2020) awards from the Dutch Research Council.

We use our own open SEM online book called 'A lavaan Compendium for Structural Equation Modeling in Educational Research' during this course:  https://tdjorgensen.github.io/SEM-in-Ed-compendium/. The 'Chapters' mentioned below refer to chapters of this book.

Day 3:

Day 4:

 

We use our own open SEM book called 'A lavaan Compendium for Structural Equation Modeling in Educational Research' during this course:  https://tdjorgensen.github.io/SEM-in-Ed-compendium/.

 

Chapters 1 to 20 are recommended to look at in advance, as they introduce the very basics of SEM and of using lavaan (e.g., importing relevant data formats, running familiar regression and ANOVA models as types of SEM).

Given the limited time for reading during the week of the course, students may consider reading the chapters (and other literature) recommended for each day in the schedule below.

 

 

 

Jak, S., Jorgensen, T.D., Verdam, M.G.E., Oort, F.J. & Elffers, L. (2020). Analytical power calculations for structural equation modeling: A tutorial and shiny app. Behavior Research Methods, 54, 1385-1406.

 

Jak, S., Cheung, M. W.-L., Acar, S., & Kindred, R.A. (preprint). Evaluating differences in latent means across studies: Extending meta-analytic confirmatory factor analysis with the analysis of means. https://doi.org/10.31234/osf.io/35gtz_v3

 

Kamata, A., & Bauer, D. J. (2008). A note on the relation between factor analytic and item response theory models. Structural Equation Modeling, 15(1), 136-153. https://doi.org/10.1080/10705510701758406

Ke, Z., Zhang, Q., & Tong, X. (2019). Bayesian meta-analytic SEM: A one-stage approach to modeling between-

studies heterogeneity in structural parameters. Structural Equation Modeling: A Multidisciplinary Journal, 26(3), 348-370.

 

Mehta, P. D., Neale, M. C., & Flay, B. R. (2004). Squeezing interval change from ordinal panel data: Latent growth curves with ordinal outcomes. Psychological Methods, 9(3), 301-333. https://doi.org/10.1037/1082-989x.9.3.301

 

Newsom, J. T., Hachem, Z. A., Granger, A. M., & Denning, E. C. (2023). Where did I go wrong with my model? Ten tips for getting results in SEM. Structural Equation Modeling, 30(3), 491-500. https://doi.org/10.1080/10705511.2022.2101113