Single-factor 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
Select all indicators of one construct. A single factor explains their covariance without additional correlated errors. Provide at least three indicators and check model degrees of freedom.
Variables
| Control | Input |
|---|---|
| Analysis variables | Select variables |
Analysis settings
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.