Robust test statistics with semTests

Introduction

semTests computes robust p-values for structural equation models fit with lavaan. The standard chi-square test of model fit is anti-conservative under non-normality, and the usual corrections (Satorra–Bentler and friends) only partly fix it. The eigenvalue-based tests in this package target the limiting null law of the test statistic directly: that law is a weighted sum of independent chi-squares, \(\sum_j \lambda_j \chi^2_1\), whose weights \(\lambda_j\) are the eigenvalues of a matrix built from the model and the asymptotic covariance of the sample moments. Because this holds for any minimum-discrepancy estimator, the tests are not specific to normal-theory maximum likelihood.

The penalized eigenvalue block-averaging (pEBA) and penalized regression (pOLS) methods were introduced by Foldnes, Moss, & Grønneberg (2024) and extended to nested model comparison by Foldnes, Grønneberg, & Moss (2026).

library("semTests")
library("lavaan")

Basic usage: goodness of fit

Fit a model with lavaan, then call pvalues(). Fit with a robust test (e.g. estimator = "MLM" or "MLR") so that lavaan exposes the asymptotic moment covariance the correction needs.

model <- "visual  =~ x1 + x2 + x3
          textual =~ x4 + x5 + x6
          speed   =~ x7 + x8 + x9"
fit <- cfa(model, HolzingerSwineford1939, estimator = "MLM")
pvalues(fit)
#>    peba4_rls 
#> 5.165737e-07 
#> estimator: ML | data: continuous | information: expected | df: 24

The result is a named vector that also prints a one-line provenance footer recording the estimator, data type, information matrix, and degrees of freedom actually used. The full option record is available with attr(x, "semtests").

pvalues(fit, c("SB_ML", "pEBA4_ML", "pOLS2_ML"))
#>        sb_ml     peba4_ml     pols2_ml 
#> 4.416190e-08 1.529685e-07 1.519741e-07 
#> estimator: ML | data: continuous | information: expected | df: 24

Nested model comparison

To compare two nested models, fit both and pass them to pvalues_nested() with the constrained model first. Here we test weak invariance of the textual loadings:

constrained <- "visual  =~ x1 + x2 + x3
                textual =~ a*x4 + a*x5 + a*x6
                speed   =~ x7 + x8 + x9"
m1 <- cfa(model, HolzingerSwineford1939, estimator = "MLM")
m0 <- cfa(constrained, HolzingerSwineford1939, estimator = "MLM")
pvalues_nested(m0, m1)
#> pall_ug_ml 
#>  0.0166158 
#> estimator: ML | data: continuous | information: expected | df: 2 | nested (method 2000)

pall is the recommended default for nested comparison (Foldnes, Grønneberg, & Moss, 2026). The reduction method defaults to "2000" (Satorra 2000); "2001" is also available for complete-data fits.

Other estimators and missing data (experimental)

The same machinery applies to other estimators. The non-ML paths below are experimental as of this release; the classical ML path is stable. See ?semTests-support for the authoritative matrix of what is supported.

GLS and ULS work directly (ULS needs a robust test for the asymptotic covariance):

gls <- cfa(model, HolzingerSwineford1939, estimator = "GLS")
pvalues(gls, "pEBA4")
#>     peba4_ml 
#> 8.854857e-07 
#> estimator: GLS | data: continuous | information: expected | df: 24

uls <- cfa(model, HolzingerSwineford1939, estimator = "ULS",
           test = "satorra.bentler")
pvalues(uls, "pEBA4")
#>     peba4_ml 
#> 2.527109e-08 
#> estimator: ULS | data: continuous | information: expected | df: 24

Missing data is handled through full-information maximum likelihood. Fit with missing = "fiml" and pass the fit to pvalues() as usual. FIML uses the biased gamma and the standard statistic, so the UG/RLS suffixes do not apply; the default suffix-free tests are the right choice.

HS <- HolzingerSwineford1939
set.seed(1)
HS$x1[sample(nrow(HS), 60)] <- NA
fit_fiml <- cfa(model, HS, missing = "fiml", estimator = "MLR")
pvalues(fit_fiml)
#>     peba4_ml 
#> 2.845788e-08 
#> estimator: ML (FIML) | data: continuous | information: observed | df: 24

FIML inference rests on the observed information under MAR (lavaan’s default for missing = "fiml"); a fit using expected information triggers a warning, since that is valid only under the stronger MCAR assumption.

References

Du, H., & Bentler, P. M. (2022). 40-Year Old Unbiased Distribution Free Estimator Reliably Improves SEM Statistics for Nonnormal Data. Structural Equation Modeling, 29(6), 872–887. https://doi.org/10.1080/10705511.2022.2063870

Foldnes, N., Moss, J., & Grønneberg, S. (2024). Improved goodness of fit procedures for structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 1–13. https://doi.org/10.1080/10705511.2024.2372028

Foldnes, N., Grønneberg, S., & Moss, J. (2026). Penalized eigenvalue block averaging: Extension to nested model comparison and Monte Carlo evaluations. Behavior Research Methods. https://doi.org/10.3758/s13428-026-02968-4