Structural Equations With Latent Variables š„
A complete structural equation model with latent variables typically consists of two distinct sub-models:
Structural Equation Modeling (SEM) with latent variables is a powerful multivariate statistical technique used to test complex relationships between observed data and underlying, unobservable constructs. By combining factor analysis and path analysis, SEM allows researchers to account for measurement error while simultaneously testing multiple causal pathways. 1. Conceptual Framework: Latent vs. Manifest Variables Structural Equations with Latent Variables
The core of this methodology lies in the distinction between what we can measure and what we want to understand: A complete structural equation model with latent variables
: These are the actual data points collected (e.g., test scores, survey responses) that serve as imperfect indicators of the latent variables. 2. The Two-Step Model Structure Conceptual Framework: Latent vs
: These are "hidden" or abstract constructs that cannot be observed directly, such as intelligence, job satisfaction, or self-esteem.