Structural Equation Modeling (SEM) Training

Course Category : Strategy

An advanced training programme focused on building and analysing causal models using SEM to support evidence-based decision-making.
Duration: 5 Days | Level: Advanced

Introduction

Introduction:
As quantitative analysis becomes central to understanding complex variable relationships, Structural Equation Modeling (SEM) has emerged as a critical methodology in research and organisational analytics. SEM integrates factor analysis and regression into a unified framework for examining direct and indirect causal relationships. This course focuses on model development, hypothesis testing, and model fit evaluation, enabling participants to construct robust analytical models that support strategic decision-making across business and research environments.

Targeted Audience

  • Social and business researchers
  • Data and statistical analysts
  • Business intelligence professionals
  • Strategic planning managers
  • Postgraduate students
  • Marketing and consumer behaviour analysts

Targeted Skills

  • Understanding SEM fundamentals
  • Developing conceptual and measurement models
  • Analysing direct and indirect causal relationships
  • Evaluating model fit indices
  • Using advanced statistical software
  • Interpreting results for decision-making

Expected Outcomes

  • Develop a comprehensive understanding of SEM concepts.
  • Build and estimate SEM models effectively.
  • Analyse complex causal relationships accurately.
  • Evaluate model fit using standard indices.
  • Apply SEM in research and practical studies.

Training Topics Index

  • Concept and importance of SEM
  • SEM vs traditional regression
  • Model components (latent and observed variables)
  • Types of SEM models
  • Modeling assumptions

  • Developing research hypotheses
  • Designing conceptual models
  • Exploratory and confirmatory factor analysis
  • Reliability and validity
  • Model specification

  • Estimation methods (ML, GLS)
  • Path analysis
  • Direct and indirect effects
  • Hypothesis testing
  • Statistical interpretation

  • Fit indices (CFI, RMSEA, Chi-square)
  • Model refinement
  • Multicollinearity issues
  • Model validation
  • Model comparison

  • Multi-group analysis
  • Mediation and moderation
  • SEM in marketing research
  • Longitudinal modeling
  • Applied case studies

Course Features

  • Updated and Interactive Content
  • Hypothetical Examples and Case Studies
  • Pre- and Post-assessments to Measure Impact
  • Verified Certificate with a QR Verification Code