Confirmatory factor analysis
Multivariate analysis · Method and input reference
Numeric complete observations. PCA standardizes variables; EFA uses maximum likelihood without rotation. CFA accepts measurement lines only. Discriminant analysis uses first column as class; reports leave-one-out accuracy. Canonical correlation splits columns into two sets. Synthetic example: 180 respondents, four continuous indicators of one latent construct with measurement error.
Open calculator · Compare methods in the decision matrix
When to consider it
- Evaluate a prespecified latent measurement model.
Assumptions and scope
- Observations must be independent between subjects; repeated rows from one subject are not independent samples.
- Use the outcome and predictor coding required by the calculator; review missing data and model diagnostics.
Required inputs
- Data matrix (one observation per line; comma-separated columns) — Numeric observations; preserve the row and column structure described above.
- Measurement model (columns named x1, x2, …) — Numeric observations; preserve the row and column structure described above.
Review missing values and data coding before running. Sample inputs are synthetic teaching examples.
Implementation
Lifescio validates inputs and formats results; listed functions are the principal calculation routines, not an exhaustive transitive dependency list.
- semopy 2.3.11: semopy.Model, semopy.calc_stats
These are pinned library versions; saved results record the versions actually used at runtime.