Identifying Latent Data Structures: Structural Equation Modelling I
AI en Data Science
AI en Data Science
Structural equation modeling (SEM) is a general statistical modeling technique to study the relationships among 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 had 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.
This course is part of a larger course series in Data Analysis consisting of 19 individual modules. Find more information and enroll for this module via www.ipvw-ices.ugent.be
The first day of the course provides an introduction to the theory and application of structural equation modeling. On the second day, we discuss several special topics that are often needed by applied users (handling missing data, nonnormal data, categorical data, longitudinal data, etc.). 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).