Latent Curve Modeling

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 taking our free pre-recorded workshop Introduction to Structural Equation Modeling. For those who simply need a refresher or want a quick overview, we also provide a brief pre-recorded review of SEM that can be viewed in advance of the class (available upon enrollment, through My Workshops on your profile menu). To clarify, a prior class in SEM is helpful but not required to successfully participate in the current class.

Chapter 1. Introduction to the Analysis of Change
1.1 Overview and Structure of the Workshop
1.2 Two Time Point Designs & Raw Change Scores

Chapter 2. The Unconditional Linear Latent Curve Model
2.1 Defining a Latent Growth Curve
2.2 Latent Growth Curves as a Confirmatory Factor Model
2.3 Thinking More Closely About Time: Zero Point & Spacing

Chapter 3. Nonlinear Latent Curve Models
3.1 Implications of Linearity
3.2 Higher-Order Polynomial Growth
3.3 Piecewise Splines and Freed Factor Loadings

Chapter 4. Time-Invariant Covariates & Conditional Latent Curve Models
4.1 Time-Invariant Covariates: Predictors of Growth
4.2 Main Effects and Interactions with Time
4.3 Probing and Plotting Predictors of Growth

Chapter 5. Time-Varying Covariates & Multivariate Latent Curve Models
5.1 Time-Varying Covariates: Contemporaneous and Lagged Effects
5.2 The Fully Multivariate Latent Curve Model
5.3 Survey of Advanced Multivariate Model Structures

Chapter 6. Model Extensions and Next Steps
6.1 Non-Normality and Robust Methods of Estimation
6.2 Observed Population Heterogeneity and Moderated LCMs
6.3 Survey of Model Extensions

Recorded software demonstrations are available in Mplus, R, and Stata. Note that R can be downloaded for free.

While it is helpful to have some familiarity with Mplus, R, or Stata, 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. Pretty awesome, huh?

Content for this class is evergreen, meaning participants will always have access to the most current version of the course materials. Thus, if we replace a lecture recording or software demonstration, or update a syntax file, you will have immediate access to the updated content without needing to re-enroll in the class. Even more awesome.