Test Statistics for Confirmatory Factor Analysis under Non-Normality and Large P/n Ratios: A Comment on Muda and Yangxuan (2026)

Muda and Yangxuan recommended a ridge-calibrated test statistic for controlling Type I error inflation in confirmatory factor analysis under non-normality. Although improving finite-sample inference is an important goal, their empirical evaluation is undermined by incorrectly implemented benchmark statistics, misattributed comparison methods, and a simulation design restricted to asymptotically robust conditions. Using corrected code, we reanalyze the original conditions and replace the misattributed benchmarks with the penalized eigenvalue procedures pEBA4RLS and pOLS2RLS. Under the original conditions, the best-performing procedures are the ridge-calibrated TCsFCr statistic and the penalized eigenvalue methods. However, under additional non-asymptotically robust conditions, including high-dimensional models, pEBA4RLS provides the most reliable Type I error control and outperforms the ridge-calibrated competitors.