SEM 結構方程模型

SEM 結構方程模型

Overview

Structural Equation Modeling (SEM) simultaneously estimates measurement models (how observed indicators map to latent constructs) and structural models (directional paths among constructs). It integrates confirmatory factor analysis with path analysis to test whether empirical data are consistent with a hypothesized theoretical structure.

When to Use

• Testing a full theoretical model with latent constructs and directional paths
• Evaluating mediation chains (X → M → Y) with multiple mediators
• Assessing whether survey items adequately reflect their intended constructs (CFA)
• Comparing alternative theoretical models on the same data

When NOT to Use

• Sample size below 200 (or below 10 cases per estimated parameter)
• Exploratory research with no a priori theoretical model
• All variables are observed and model is a simple regression
• Data are severely non-normal and you lack robust estimators

Assumptions

IRON LAW: SEM does NOT prove causation — it tests whether data is CONSISTENT
with a hypothesized causal structure. Good fit does NOT mean the model is
correct; it means the model cannot be rejected.

Key assumptions:

1. Correct model specification — omitted paths or constructs bias estimates
2. Multivariate normality for ML estimation (or use robust estimators)
3. Sufficiently large sample size (N ≥ 200 as rule of thumb)
4. No excessive multicollinearity among indicators

Methodology

Step 1 — Specify the Measurement Model

Define latent constructs and their observed indicators. Run CFA to confirm factor loadings, assess convergent validity (AVE ≥ 0.50), and discriminant validity.