The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models

Daniel McNeish, Ji An, Gregory R. Hancock

Research output: Contribution to journalArticlepeer-review

183 Scopus citations


Latent variable modeling is a popular and flexible statistical framework. Concomitant with fitting latent variable models is assessment of how well the theoretical model fits the observed data. Although firm cutoffs for these fit indexes are often cited, recent statistical proofs and simulations have shown that these fit indexes are highly susceptible to measurement quality. For instance, a root mean square error of approximation (RMSEA) value of 0.06 (conventionally thought to indicate good fit) can actually indicate poor fit with poor measurement quality (e.g., standardized factors loadings of around 0.40). Conversely, an RMSEA value of 0.20 (conventionally thought to indicate very poor fit) can indicate acceptable fit with very high measurement quality (standardized factor loadings around 0.90). Despite the wide-ranging effect on applications of latent variable models, the high level of technical detail involved with this phenomenon has curtailed the exposure of these important findings to empirical researchers who are employing these methods. This article briefly reviews these methodological studies in minimal technical detail and provides a demonstration to easily quantify the large influence measurement quality has on fit index values and how greatly the cutoffs would change if they were derived under an alternative level of measurement quality. Recommendations for best practice are also discussed.

Original languageEnglish (US)
Pages (from-to)43-52
Number of pages10
JournalJournal of Personality Assessment
Issue number1
StatePublished - Jan 2 2018
Externally publishedYes

ASJC Scopus subject areas

  • Clinical Psychology
  • Psychiatry and Mental health
  • Health, Toxicology and Mutagenesis


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