Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: A Monte Carlo Study

Hsien Yuan Hsu*, Oi man Kwok, Jr Huang Lin, Sandra Acosta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

Abstract

This study investigated the sensitivity of common fit indices (i.e., RMSEA, CFI, TLI, SRMR-W, and SRMR-B) for detecting misspecified multilevel SEMs. The design factors for the Monte Carlo study were numbers of groups in between-group models (100, 150, and 300), group size (10, 20, 30, and 60), intra-class correlation (low, medium, and high), and the types of model misspecification (Simple and Complex). The simulation results showed that CFI, TLI, and RMSEA could only identify the misspecification in the within-group model. Additionally, CFI, TLI, and RMSEA were more sensitive to misspecification in pattern coefficients while SRMR-W was more sensitive to misspecification in factor covariance. Moreover, TLI outperformed both CFI and RMSEA in terms of the hit rates of detecting the within-group misspecification in factor covariance. On the other hand, SRMR-B was the only fit index sensitive to misspecification in the between-group model and more sensitive to misspecification in factor covariance than misspecification in pattern coefficients. Finally, we found that the influence of ICC on the performance of targeted fit indices was trivial.

Original languageEnglish
Pages (from-to)197-215
Number of pages19
JournalMultivariate Behavioral Research
Volume50
Issue number2
DOIs
Publication statusPublished - 2015 Mar 4
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

Fingerprint

Dive into the research topics of 'Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: A Monte Carlo Study'. Together they form a unique fingerprint.

Cite this