Structural equation modeling with lavaan

 

Lecturers Prof. Dr. Yves Rosseel, Department of Data Analysis, Ghent University (Belgium)
Certificate Certificate of attendance
Target audience Users from all professional groups who have used R before. (For introductory R courses please revisit the course list.) In addition, participants should have a solid understanding of regression analysis and basic statistics (hypothesis testing, p-values, etc.). Some knowledge of exploratory factor analysis (or PCA) is recommended, but not required.
Costs
  • CHF 600.- for members of UZH/ETH and associated institutes
  • CHF 800.- for alumni of UZH/ETH, members of other universities, the public sector and non-profit organizations
  • CHF 1200.- for companies
Persons without current employment can register for the UZH/ETH fee upon request.
Course language Englisch
Course description Structural equation modeling (SEM) is a general statistical modeling technique to study the relationships among a set of observed variables. It spans a wide range of multivariate methods including path analysis, mediation analysis, confirmatory factor analysis, growth curve modeling, and many more. Many applications of SEM can be found in the social, economic, behavioral and health sciences, but the technology is increasingly used in disciplines like biology, neuroscience and operation research. SEM is often used to test theories or hypotheses that can be represented by a path diagram. In a path diagram, observed variables are depicted by boxes, while latent variables (hypothetical constructs measured by multiple indicators) are depicted by circles. Hypothesized (possibly causal) effects among these variables are represented by single-headed arrows. If you have ever found yourself drawing a path diagram in order to get a better overview of the complex interrelations among some key variables in your data, this course is for you.

The two-day course will be based on lecture-style presentations interchanged with practical sessions. The first day provides an introduction to the theory and application of structural equation modeling. The second day focuses on the use of SEM with categorical and longitudinal data respectively.
Objectives The aim of this workshop is twofold. First, we will present a concise overview of the theory of structural equation modeling (SEM), including many special topics (e.g. handling missing data, nonnormal data, categorical data, longitudinal data, etc.). Second, hands-on sessions are included in order to ensure that all participants are able to perform the analyses using SEM software. The software used in this course is the open-source R package ‘lavaan’ (see http://lavaan.org).
Course outline Day 1: introduction to structural equation modeling and lavaan
  • SEM basics
  • model estimation, model evaluation, and model respecification
  • introduction to lavaan
  • meanstructures, multiple groups, and measurement invariance
  • missing data
  • non-normal continuous data and alternative estimators
Day 2: SEM for categorical data, longitudinal data and multilevel data
  • SEM with categorical data:
    • tetrachoric, polychoric and polyserial correlations
    • the limited-information (three-stage) approach (ULS, WLS and robust variants)
    • the relationship with item response theory (IRT)
  • longitudinal SEM:
    • repeated measures ANOVA in a SEM framework
    • the longitudinal CFA model, establishing time invariance
    • autoregressive models, cross-lagged effects
    • growth curve models, and the relationship with linear mixed effects models
  • multilevel SEM:
    • overview and different frameworks
    • two-level SEM with random intercepts
    • alternative ways to analyze multilevel data with SEM
Background reading Kline, R. B. (2011). Principles and practice of structural equation modeling (Third Edition). New York: Guilford Press.
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual review of psychology, 51(1), 201-226.
Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling: Strengths, limitations, and misconceptions. Annu. Rev. Clin. Psychol., 1, 31-65.
Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115-132.
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modeling. Psychometrika, 69(2), 167-190.
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338.
McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual review of psychology, 60, 577-605.
Project After the course, all participants are encouraged to analyze their own data using SEM, and write up the results in a short project paper. The paper should contain a brief description of the context and the research questions, and a full description of the SEM analysis. The appendix should include the full R script that has been used to produce the results as they are reported in the paper. Projects papers are corrected, and participants receive feedback.
Dates

March 28-29, 2019

Registration

  After registering you will receive a short automatic confirmation by email. If you received this email you are successfully and bindingly registered for the course. For administrative reasons the written invoice won't be sent out until about two weeks before the course.