Research in the social sciences has been getting more and more complicated with the realization that the complexity of human behavior cannot be captured with simple theories that were current. This understanding has led to increasingly sophisticated theories of human behavior. These theories and their assumptions cannot be investigated using the simple statistical analyses that were applied some decades ago. Structural Equation Modeling (SEM) is a widely applied approach to analyzing complex sets of data. The workshop provides an introduction to the basics of the SEM and gives the participants hands-on experience with some of the most current SEM analyses.
Outline of the course:
Overview of basic descriptive statistics (e.g., variance and covariance matrices)
Principles of statistical hypothesis testing
Covariance and correlation matrices
Simple and multiple regression analysis
Formative and reflective measurement
Exploratory Factor Analysis (EFA) vs. Principal Components Analysis (PCA)