SEM is significant in epidemiology because it allows researchers to:
1. Model Complex Relationships: SEM can handle multiple dependent and independent variables simultaneously, making it ideal for studying multifaceted health phenomena.
2. Incorporate Measurement Error: Unlike traditional regression models, SEM accounts for measurement errors in the observed variables, providing more accurate estimates.
3. Test Causal Hypotheses: SEM enables the testing of complex causal models, which is crucial for understanding the underlying mechanisms of diseases.
4. Assess Indirect Effects: It helps in evaluating indirect effects where one variable affects another through one or more intervening variables.
