Four New Corrected Statistics for SEM With Small Samples and Nonnormally Distributed Data

Ge Jiang, Ke-hai Yuan

Research output: Contribution to journalArticle

Abstract

When the assumption of multivariate normality is violated and the sample sizes are relatively small, existing test statistics such as the likelihood ratio statistic and Satorra–Bentler’s rescaled and adjusted statistics often fail to provide reliable assessment of overall model fit. This article proposes four new corrected statistics, aiming for better model evaluation with nonnormally distributed data at small sample sizes. A Monte Carlo study is conducted to compare the performances of the four corrected statistics against those of existing statistics regarding Type I error rate. Results show that the performances of the four new statistics are relatively stable compared with those of existing statistics. In particular, Type I error rates of a new statistic are close to the nominal level across all sample sizes under a condition of asymptotic robustness. Other new statistics also exhibit improved Type I error control, especially with nonnormally distributed data at small sample sizes.
Original languageEnglish (US)
Pages (from-to)479-494
JournalStructural Equation Modeling
Volume24
Issue number4
DOIs
StatePublished - Jul 4 2017
Externally publishedYes

Keywords

  • nonnormality
  • Satorra-Bentler's corrected statistics
  • small sample size
  • test statistic

Fingerprint Dive into the research topics of 'Four New Corrected Statistics for SEM With Small Samples and Nonnormally Distributed Data'. Together they form a unique fingerprint.

  • Cite this