Course 6: Structural Equation Modeling with Panel Data

Lecturers: Dr. Daniel Seddig, Georg Kessler

August 14-18 August, 2017

Time: 11:00-13:00, 14:00-18:00


Course starts Monday morning at 11:00

Short Course Description:

The course will expand on the course “Introduction to Structural Equation Modeling: Confirmatory Factor Analysis with Mplus” and show how to apply the SEM approach to longitudinal data using the Mplus computer
program. In the first part of the course, we will introduce the autoregressive model (ARM) and the cross-lagged panel model (CLPM) to study the stability of a single variable over time as well as reciprocal effects and ‘causal’ predominance between two variables over time. Each type of model will be discussed as a single-indicator as well as a multiple-indicator model. All models will be applied to data from a longitudinal study on authoritarianism and anomia in Germany. In the second part of the course we will focus on modeling development and change over time with the latent growth model (LGM) applying the same dataset. We will begin with the univariate case to analyze growth of a single variable and extend the basic model to the multivariate case to analyze parallel processes of two variables over time. Finally, two ‘hybrid-models’ will be discussed: the autoregressive latent trajectory model (ALT) and the more recently developed random-intercept cross-lagged panel model (RI-CLPM). Topics in both parts include parameterization of autocorrelations, Socratic effects, latent means, and MIMIC models. Furthermore, we will extend our discussion to multiple group comparisons as well as the issue of (longitudinal) measurement invariance as a prerequisite for comparisons across groups and over time.

Course Prerequisites:

Basic knowledge of and basic experience with confirmatory factor analysis and structural equation modeling. This could be acquired in the course “Introduction to Structural Equation Modeling: Confirmatory Factor Analysis with Mplus“ in week 1.

We will use the software package Mplus. We will briefly introduce Mplus during the first exercise. Mplus will also be used in the course “Introduction to Structural Equation Modeling“ in week 1. The short course “Introduction to Data Analysis Using Mplus” in week 0 will explicitly focus on how to use Mplus effectively.

Target Group:

Participants will find the course useful if:

  • they are interested in assessing and explaining change over time
  • they are interested in the relationship between processes of change in different variables
  • they are interested in assessing the degree of reciprocity in the relationship between variables over time
  • they are interested in causal predominance of one variable over another
  • they are interested in conducting meaningful comparisons of (latent) variables over time (or across groups)

Course and Learning Objectives:

By the end of the course participants will:

  • know how to specify autoregressive and cross-lagged structural equation models to test for stability and reciprocity in manifest and latent variables over time as predicted by a social scientific theory
  • know how to test for measurement invariance of latent variables across time (and groups)
  • know how to examine various forms of change with different specifications of the latent growth model
  • be able to specify all models with the software package Mplus;
  • (if time allows), learn to separate interpersonal from intrapersonal variance in the variables of interest and run a random-intercept cross-lagged panel model test causal predominance as predicted by a social scientific theory

Organizational Structure of the Course:

First four days:

  • about 180 minutes – lecture
  • about 180 minutes – exercises on data prepared by the instructors

Note: In some of the days we may divide the theory and exercises time differently, depending on our progress.

Last day:

  • expansion of topics and exercises due to participants’ preferences
  • it will be possible to discuss participants’ projects in small groups focusing on conceptual and analytical problems; groups will rotate so that each group spends time on each project; possible solutions and difficulties will be discussed in class
  • general summary and discussion; open questions

Free study time and what we expect from participants:

  • Participants are encouraged to discuss the topics of the course every day with each other.
  • Participants are expected to repeat the exercises conducted in class on their own.
  • Participants should read the course literature.
  • Participants are encouraged to work on their own projects and analyze their own data individually or in groups.

Software and Hardware Requirements:

SPSS/Stata and Mplus Version 7.4. Course participants will not need to bring a laptop computer for this course. This course will take place in a computer lab.