First-order multifactor CFA
Purpose
Evaluate a prespecified relationship between observed items and latent constructs.
Data and assumptions
This release uses single-group, first-order covariance MLW estimation for continuous indicators. Assign at least three distinct items to each factor. Complete cases are used; ordinal WLSMV and measurement invariance are outside this release.
Configure this method
Add named dimensions and nonoverlapping item assignments. Factors may correlate by default; disable the option for an orthogonal model. The discriminant-validity option compares square roots of AVE with factor correlations.
Variables
| Control | Input |
|---|---|
| Dimensions and items | Add named dimensions and their items |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Allow exogenous factors to correlate | Set as needed | Enabled |
| Include discriminant validity matrix | Set as needed | Disabled |
Analysis parameters remain visible in the main configuration area. Advanced formatting contains only the table title and numeric decimals. Methods that calculate intervals also show the significance level; 0.05 corresponds to 95% intervals.
Read the results
Review loadings, CR, AVE and overall fit together. The first loading is fixed to one for identification, so its significance is not tested. Zero-degree-of-freedom fit cannot establish model support.
Workflow and exports
- Upload the dataset and choose the method from the sidebar or search.
- Select the variables above, set the design-specific options, then run the analysis.
- Inspect the paper-style result. Reconfigure from the result area, or open raw output for diagnostics and sample details.
- Rename and keep result tabs as needed. Export Word, Excel, CSV or TXT; batch export can include the selected AI interpretation. Word retains figures. AI text should be checked against the estimates.