Evaluation of Six Effect Size Measures of Measurement Non-Invariance for Continuous Outcomes

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Measurement invariance is assessed in the factor analytic framework by testing differences in model fit of a sequential series of models; however, the statistical significance of these differences is influenced by many factors, including sample size. Effect sizes are independent of sample size and can be used to determine the magnitude and practical importance of an effect. We developed four new effect size measures of measurement non-invariance for continuous outcomes. To test the properties of these effect sizes and of two existing effect sizes of non-invariance, we conducted a simulation study. We varied group sample sizes, location of the latent distributions, magnitude of non-invariance and type of non-invariance (e.g., metric invariance). Three of the effect sizes were unbiased in all conditions and all six were consistent. Recommendations for their use and future directions are discussed.

Original languageEnglish (US)
Pages (from-to)503-514
Number of pages12
JournalStructural Equation Modeling
Issue number4
StatePublished - Jul 3 2020


  • DIF
  • Measurement invariance
  • effect size
  • factor analysis

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


Dive into the research topics of 'Evaluation of Six Effect Size Measures of Measurement Non-Invariance for Continuous Outcomes'. Together they form a unique fingerprint.

Cite this