Structural Equation Modeling (SEM) with R

This course deals with intermediate and advanced aspects of Structural Equation Modeling (SEM), a method that is becoming more and more popular in the behavioral and social sciences. The course comprises five three-hour sessions; the time for each session is divided equally into a theoretical and a practical part. In this context, various examples from educational research, psychology, and the social sciences are discussed. The course starts with a conceptual overview of the different classes of structural equation models and introduces the basic terminology that is used throughout the course. Furthermore, key concepts such as model identification or the distinction between covariance and mean structure are covered. After this overview, the participants are introduced to the fundamentals, the logic, and the syntax of the R package lavaan that is subsequently used for all structural equation modeling.

Participants learn to specify Confirmatory Factor Analyses (CFA) and interpret the lavaan output. In comparison to other latent variable approaches such as IRT, SEM has the advantage of providing good omnibus tests for model fit evaluation. Accordingly, participants calculate different fit statistics (e.g., CFI, RMSEA, SRMR), learn their adequate use and their limitations. As empirical research is often accompanied by some limitations of the data, most prominently, missingness or skewed distributions of variables, the course training also includes handling of missing data, usage of different estimators (e.g., for categorical/dichotomous and continuous variables), and correcting of parameter estimation for nested data structure. Moreover, strengths and weaknesses of item parceling are also addressed.

In many cases an initial, theory-driven CFA model will not fit the data perfectly. We will pay close attention to sources of model misspecification and ways how to improve model fit by data-drive adjustments. On the other hand, we will address some basic philosophical and statistical issues that are often neglected, but have to be taken into account before sound SEM analyses are conducted. Participants will also become acquainted with different modeling approaches (higher-order models, nested factor models, CTCM, CTCU, etc.) and understand their statistical and interpretational differences. As SEM is a powerful method to test competing theories, we will also discuss how to conduct model comparisons of nested and non-nested models. Furthermore, both structural regression models which postulate regressions between latent variables and models that include manifest covariates (MIMIC approach) are examined.

Testing for measurement invariance is a necessary prerequisite to making valid statements about group differences. For instance, in order to ascertain that boys outperform girls in mathematics it is necessary to demonstrate that the assumptions of strong measurement invariance holds (i.e., that we are measuring the same construct in both groups identically). A common, straightforward procedure of testing for measurement invariance is Multi-Group Confirmatory Factor Analysis (MGCFA). Moreover, we extend the procedure to ordinal data and also discuss the concept of partial measurement invariance.

Because of its flexibility SEM is often employed to analyze longitudinal data, besides cross-sectional data. In the last part of the course, we expand our exercises to longitudinal data. One class of models deals with modeling variability of scores around time-stable traits; the other class of models we will get to know is concerned with change in latent variables over time (i.e., latent growth curve modeling). The focus is on (latent) cross-lagged panel analysis and latent change score models. In the last session there will also be time to answer further questions concerning topics discussed in previous sessions or specific issues of the participants.

Prospective participants should possess a solid knowledge of basic statistical concepts (e.g., variance, covariance, correlation) and of manifest regression analyses (e.g., prediction, standard error). They should also be familiar with the terms often used in test theory (e.g., items, scale, reliability, validity). With regard to software, potential participants should have worked with R on a productive level. More specifically, participants should be able to read in data from different sources, to select and to reference specific variables and observations within a given data set, and to calculate basic descriptive statistics (mean, correlation, etc.). They should be familiar with RStudio or any another R programming environment. In the course a combination of Notepad++ and NppToR will be used to write and execute R syntax; but previous experience with this simple software solution is not necessary.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.