A compact CFA

The local continuous_items data are generated from one common factor. It is simulated and exists only for reproducible examples.

continuous_items <- lavaan::simulateData(
  "general =~ 0.7*item1 + 0.7*item2 + 0.7*item3 + 0.7*item4 + 0.7*item5 + 0.7*item6",
  sample.nobs = 200, seed = 2401
)
model <- "general =~ item1 + item2 + item3 + item4 + item5 + item6"

cfa <- PsychoMatic::cfa_auto(
  continuous_items,
  model = model,
  estimator = "ML",
  language = "eng"
)
summary(cfa)$fit
summary(cfa)$loadings

The fit is not evaluated while building this vignette because cfa_auto() may also run optional multivariate-normality and reliability diagnostics. The installed script provides a deterministic smoke run.

When estimator = NULL, automatic selection is a documented heuristic. Inspect the requested estimator, effective estimator reported by lavaan, analytic sample, convergence, post-check, finite parameters, Heywood diagnostics, and the actual base/scaled/robust variant used for each fit index. A good-fitting model does not establish construct validity.

Compare models only when comparable

models <- list(
  one_factor = model,
  two_factor = paste(
    "factor1 =~ item1 + item2 + item3",
    "factor2 =~ item4 + item5 + item6",
    sep = "\n"
  )
)
comparison <- PsychoMatic::compare_models_auto(
  continuous_items,
  models = models,
  estimator = "ML",
  language = "eng"
)
comparison$fit
comparison$recommendation

compare_models_auto() ranks only a unique set with compatible observed variables, cases, estimator/estimand, and a common finite fit-index variant. AIC and BIC are not exposed as ranking evidence for candidates outside that set. Its recommendation is descriptive and should be combined with theory and model diagnostics.

Reliability and model form

A fitted lavaan model may be supplied to reliability_auto() for model-based coefficients when the optional semTools package is available. Composite, second-order, and bifactor coefficients are different estimands. For bifactor models, verify the general/specific structure and whether orthogonality is substantively intended; do not interpret coefficient omega as proof of a unidimensional scale.

reliability <- PsychoMatic::reliability_auto(
  continuous_items,
  level = "continuous",
  model = cfa$fit,
  language = "eng"
)
summary(reliability)$coefficients

Conditions, precision, and migration

Analytical values retain full precision; printed and exported tables may use a chosen number of digits. From 1.0.0, cfa$warnings is a structured condition table. Code written for 0.3.x that expected a character vector can temporarily use cfa$warnings_list; new code should consume the structured form. Engine errors are preserved and can produce a not_evaluable result rather than a favorable interpretation.

export_cfa() and psychomatic_report() refuse existing output paths unless overwrite is explicitly enabled.