Testing the Performance of Level-Specific Fit Evaluation in MCFA Models With Different Factor Structures Across Levels

Bibliographic Data

ID20287164
AuthorsBitna Lee (0000-0001-9926-1945, Kyungpook National University, Daegu, Republic of Korea), Wonsook Sohn (0000-0001-5203-3355, Kyungpook National University, Daegu, Republic of Korea, corresponding author)
Year2022
Volume82
Issue6
Pages1153-1179
Publication date2022-12-01
Open AccessYes
TypeARTICLE
VenueEducational and Psychological Measurement (JOURNAL)
Journal identifiersISSN: 0013-1644 • E-ISSN: 1552-3888
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/00131644211066956
PMID36325122
OpenAlexW4205115460
LanguageEN
Citations received1
References cited29

A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition, various design factors and interaction effects between intraclass correlation (ICC) and misspecification type (MT) on their performance were considered. The simulation results demonstrate that the LS outperformed the SI in detecting model misspecification at the between-group level even in the MCFA model with different factor structures across levels. Especially, the performance of LS fit indices depended on the ICC, group size (GS), or MT. More specifically, the results are as follows. First, the performance of root mean square error of approximation (RMSEA) was more promising in detecting misspecified between-level models as GS or ICC increased. Second, the effect of ICC on the performance of comparative fit index (CFI) or Tucker–Lewis index (TLI) depended on the MT. Third, the performance of standardized root mean squared residual (SRMR) improved as ICC increased and this pattern was more clear in structure misspecification than in measurement misspecification. Finally, the summary and implications of the results are discussed

Algorithm · Confirmatory factor analysis · Econometrics · Goodness of fit · Index (typography) · Intraclass correlation · Mean squared error · Monte Carlo method · Multilevel model · Psychometrics · Residual · Statistics · Structural equation modeling · Computer Science · Mathematics · Mental Health Research Topics · Psychometric Methodologies and Testing · Sensory Analysis and Statistical Methods

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