Name: Grad Sem
Author: asgard-ai-platform

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

SEM 結構方程模型

Overview

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

Grad Sem

SEM 結構方程模型

Overview

When to Use

When NOT to Use

Grad Sem

SEM 結構方程模型

Overview

When to Use

When NOT to Use

Assumptions

Methodology

Step 1 — Specify the Measurement Model

Step 2 — Assess Measurement Model Fit

Step 3 — Specify and Estimate the Structural Model

Step 4 — Report and Interpret

Output Format

Gotchas

References

Deep Research

Data Analyst

Academic Researcher

Data Scientist

Biopython

Binary Analysis Patterns