Structural Equation Modelling (SEM)

About

This 6-day course will familiarize participants with Structural Equation Modelling (SEM) and how to use SEM in testing theories that contain multiple equations involving dependence relationships. Structural equation modelling (SEM) is a statistical methodology that takes a hypothesis-testing (i.e., confirmatory) approach to the multivariate analysis of a structural theory bearing on some phenomenon. Structural equation models are used to assess unobservable “latent” constructs. They often invoke a measurement model that defines latent variables using one or more observed variables, and a structural model that imputes relationships between latent variables. SEM can examine a series of dependence relationships simultaneously. SEM is widely used in the social sciences.

Course content
Structural Equation Modeling: An Introduction
Confirmatory Factor Analysis
Testing a Structural Model
Formative constructs, higher-order CFA, and multi-group models
Testing for mediation and moderation using SEM procedures and assessing measurement bias
Reflective activities – use SEM procedures to address contemporary research problems

Learning Objectives
By the end of this course participants will be able to:
Understand the distinguishing characteristics of Structural Equation Modeling
Explain the type of relationships involved in SEM
Understand the role of theory in developing structural equation models
Understand the difference between a measurement model and a structural model
Understand the differences between formative and reflective constructs
Know how to represent a measurement and a structural model using a path diagram
Test confirmatory factor analysis (CFA) and structural models
Assess the goodness-of-fit of measurement and structural models
Assess construct validity and reliability using SEM procedures
Test structural models that involve mediating and moderating effects
Perform multi-group analysis

Who is the program designed for:
Participants should be familiar with basic statistics and should know how to run a correlation analysis, linear regression and t-test. No prior knowledge of Structural Equation Modelling software is necessary. The course is appropriate for doctoral students and academic researchers that use quantitative research methodologies.

Target group

Who is the program designed for:
Participants should be familiar with basic statistics and should know how to run a correlation analysis, linear regression and t-test. No prior knowledge of Structural Equation Modelling software is necessary. The course is appropriate for doctoral students and academic researchers that use quantitative research methodologies.

Fee info