Course Information
This ATI is designed to highlight recent methodological advances in the analysis of longitudinal psychological data using structural equation modeling (SEM). The training is intended for faculty, postdocs and advanced graduate students who are familiar with SEM (e.g., took an introductory SEM course). The workshop covers a range of topics, including growth models, factorial invariance, dealing with incomplete data, growth mixture models, ordinal outcomes, and latent change score models.
Course materials include basic readings on the fundamental theoretical issues in contemporary longitudinal data analysis, lecture notes and computer scripts for commonly used SEM programs. The workshop alternates between lectures on theory and specification of models using Mplus, and lab sections, which review the specification of the same models in lavaan (available through R) and AMOS, and include time to fit models to your data. Participants are strongly encouraged to bring their own data and research problems, and a notebook computer equipped with SEM and general statistical (R, SAS, SPSS) software.
Course instructors will include Craig Enders of the Department of Psychology at The University of California, Los Angeles, Kevin J. Grimm of the Department of Psychology at Arizona State University, and Nilam Ram of the Departments of Human Development and Family Studies and Psychology at The Pennsylvania State University.
Applications are invited from investigators at the faculty/professional, postdoctoral and advanced graduate student levels. The ATI is open to investigators from both within and outside of the United States.
The language of the course is English; no translation services will be provided.
COVID-19 Update
The Advanced Training Institute on Structural Equation Modeling in Longitudinal Research will be held remotely. To provide maximum flexibility for participants, lecture presentations will be pre-recorded and made available prior to the workshop. For each lecture, the instructors will host, via video conference, a half-hour online question and answer (Q&A) session and a one-hour lab session that covers code and implementation details.
We encourage participants to watch the lectures prior to the live sessions and be ready with their questions. During the lab sessions, we encourage participants to apply the longitudinal models reviewed in the lectures to analyze their own data or exemplar longitudinal data provided by instructors, and to ask questions about the programming and interpretation of the modeling results.
Course Schedule
Day 1: Introduction to Structural Equation Modeling & Longitudinal Data
- A: Introduction to Structural Equation Modeling (59 min)
- B: Introduction to Multilevel Modeling (34 min)
- Question & Answer Live Session
- C: Linear Growth Model (35 min)
- D1: Linear Growth Models in Mplus (29 min)
- D2: Linear Growth Models in R (lavaan) (34 min)
- D3: Linear Growth Models in R (nlme)(30 min)
- Question & Answer Live Session
Day 2: Covariates and Missing Data
- E: Time-Invariant Covariates (30 min)
- F1: Time-Invariant Covariates in Mplus (15 min)
- F2: Time-Invariant Covariates in R (26 min)
- Question & Answer Live Session
- G: Missing Data in Growth Models
- H1: Missing Data Handling in Mplus
- H2: Missing Data Handling in lavaan
- Question & Answer Live Session
Day 3: Group-based Growth Models
- I: Multiple Groups in Latent Growth Models (26 min)
- J1: Multiple Group Growth Models in Mplus (15 min)
- J2: Multiple Group Growth Models in R (lavaan & nlme) (27 min)
- Question & Answer Live Session
- K: Growth Mixture Models (43 min)
- L1: Growth Mixture Models in Mplus (29 min)
- L2: Growth Mixture Models in lcmm (15 min)
- Question & Answer Live Session
Day 4: Modeling Change in Latent Variables
- M: Longitudinal Factor Analysis, Measurement Invariance, & Second-Order Growth Models (TBD min)
- N1: Longitudinal Factor Analysis in Mplus (30 min)
- N2: Longitudinal Factor Analysis in lavaan (TBD min)
- Question & Answer Live Session
- O:Longitudinal Item Factor Models & Growth Models (33 min)
- P1:Longitudinal Item Factor Models in Mplus (23 min)
- P2:Longitudinal Item Factor Models in lavaan (TBD min)
- Question & Answer Live Session
Day 5: Latent Change Scores & Multivariate Dynamics
- Q: Univariate Latent Change Score (LCS) Models (42 min)
- R1: Univariate Latent Change Score Models in Mplus (18 min)
- R2: Univariate Latent Change Score Models in lavaan (TBD min)
- Question & Answer Live Session
- S: Bivariate Latent Change Score Models (47 min)
- T1: Bivariate Latent Change Score Models in Mplus (20 min)
- T2: Bivariate Latent Change Score Models in lavaan (TBD min)
- Question & Answer Live Session