Longitudinal Structural Equation Modeling

Our motivating goal is to provide an interactive and engaging instructional experience that focuses on a large number of both introductory and advanced topics in longitudinal structural equation modeling. We strive to strike an equal balance between core concepts of the underlying statistical models along with the practical application and interpretation of longitudinal SEMs fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of the longitudinal SEM and to be able to thoughtfully apply a variety of basic and advanced models to their own data.

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences.

Although not essential, prior exposure to structural equation modeling (SEM) is helpful to enhance understanding in the class. Participants with no prior exposure to SEM may wish to consider also taking our Introduction to Structural Equation Modeling workshop, which can be obtained along with the current workshop at a significant discount via our Structural Equation Modeling Workshop Bundle.

For those who simply need a refresher or want a quick overview, the current workshop includes a brief, optional review of SEM. To clarify, a prior class in SEM is helpful but not required to successfully participate in the current class.

Chapter 1. Introduction and Summary of Traditional Models of Change
1.1 Introduction and Organization of the Workshop
1.2 Why Longitudinal Data are Needed
1.3 Raw Score Change versus Residualized Change

Chapter 2. Longitudinal Measurement Models and Factorial Invariance
2.1 The Measurement of Change
2.2 Assumptions Invoked When Modeling Change
2.3 Evaluating Measurement Invariance Over Time (Longitudinal Invariance)
2.4 The Longitudinal Confirmatory Factor Analysis Model

Chapter 3. The Autoregressive Cross-lagged (ARCL) Panel Model
3.1 Residualized Change as a Two Time-point Autoregressive Panel Model
3.2 Bivariate Autoregressive Cross-Lagged Models
3.3 Strengths and Limitations of the ARCL

Chapter 4. The Unconditional Linear Latent Curve Model (LCM)
4.1 Defining a Latent Growth Curve
4.2 Latent Growth Curves as a Confirmatory Factor Model
4.3 Intercept-only vs. Intercept and Linear Slope LCMs
4.4 Alternative Metrics of Time

Chapter 5. Nonlinear and Conditional Latent Curve Models
5.1 Modeling Nonlinear Trajectories: Quadratic, Piecewise, and Freed-Loading Models
5.2 Time-Invariant Covariates (TICs): Defining Main Effects and Interactions
5.3 Probing and Graphing Effects Between TICs and Growth Factors

Chapter 6. Multivariate Latent Curve Models
6.1 Time-Varying Covariates: Defining Contemporaneous and Lagged Effects
6.2 The Fully Multivariate Latent Curve Model
6.3 Separating Within- and Between-Person Effects: The RI-CLPM & LCM-SR

Chapter 7. Modeling Population Heterogeneity: The Multiple Groups Model
7.1 Population Heterogeneity in the Conventional LCM
7.2 The Multiple Groups Growth Model
7.3 Extensions of Multiple Groups LCM
7.4 Where to Go from Here

Pre-recorded software demonstrations are provided for self-paced learning using R (focused on the lavaan package), Mplus and Stata. Note that R can be downloaded for free. While it is helpful to have some familiarity with one or more of these software programs, this is not necessary. The lectures which constitute the majority of the workshop are software-independent

Participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates.

All material is also evergreen content, meaning that any course updates or added material will be immediately available to you no matter when you registered for the workshop.