User Guide | Factor Structure Interpreter

Welcome to the Factor Structure Interpreter. This tool helps you transition from SPSS to AMOS for Chapter 7, evaluating your Confirmatory Factor Analysis (CFA) measurement model.

1. CFA vs. EFA

While Exploratory Factor Analysis (EFA) is used to discover how items group together naturally, Confirmatory Factor Analysis (CFA) is used to test a predefined theory. In AMOS, you draw the exact relationships (e.g., "These 5 items belong to Motivation"), and the software tells you how well your data "fits" that theory.

2. Model Fit "Cheat Sheet"

When you run AMOS, it generates dozens of fit indices. In educational research, we typically report the following four to prove our model is valid:

CMIN/DF (χ²/df): Ratio of Chi-square to degrees of freedom. We want a value < 3.0 (Good) or < 5.0 (Acceptable).
CFI (Comparative Fit Index): Compares your model to a null model. We want > 0.95 (Good) or > 0.90 (Acceptable).
TLI (Tucker-Lewis Index): Similar to CFI but penalizes overly complex models. Target: > 0.95 (Good) or > 0.90 (Acceptable).
RMSEA (Root Mean Square Error of Approximation): Measures discrepancy. We want < 0.06 (Good) or < 0.08 (Acceptable).

3. Latent vs. Observed Variables

4. Standardized Regression Weights (Loadings)

This table shows how well each Observed item represents its Latent parent. Loadings range from 0 to 1.

The Rule of 0.50: If a standardized loading is below 0.50, the item is weak. It means the item has more error variance than explained variance. Removing items with loadings < 0.50 is the most common way to fix a "Poor Fit" model in AMOS.

User Guide: Factor Structure Interpreter

1. CFA vs. EFA

2. Model Fit "Cheat Sheet"

3. Latent vs. Observed Variables

4. Standardized Regression Weights (Loadings)