Observed-variable path analysis
Purpose
Estimate a system of theoretical relationships and separate direct and indirect pathways.
Data and assumptions
This release uses single-group, recursive covariance MLW models for continuous variables. Directed cycles are disallowed. Specify directions from theory and use complete cases. Identification or nonpositive-definite failures require revisiting the model.
Configure this method
Select observed variables, then add directed source-to-target paths. Covariances between exogenous variables are optional. Indirect effects aggregate the corresponding directed paths; cycles are disallowed.
Variables
| Control | Input |
|---|---|
| Analysis variables | Select variables |
| Structural paths | Add source and target |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Allow exogenous factors to correlate | Set as needed | Enabled |
| Show effect decomposition | Set as needed | Enabled |
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
Read unstandardized and standardized paths alongside fit and effect decomposition. Effect intervals use the full parameter covariance delta method, not bootstrap. CFI/TLI are not clipped; inapplicable fit indices are not interpreted when df is zero.
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.