The Impact of Intraclass Correlation on the Effectiveness of Level-Specific Fit Indices in Multilevel Structural Equation Modeling

Hsien-Yuan Mark Hsu · Jr-Hung Lin · Oi‐Man Kwok · Sandra T. Acosta · Victor L. Willson

37 citations0/0 files storedEducational and Psychological Measurement

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Mental Health Research Topics

Abstract

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific fit indices. Our study proposed to fill this gap in the methodological literature. A Monte Carlo study was conducted to investigate the performance of (a) level-specific fit indices derived by a partially saturated model method (e.g., [Formula: see text] and [Formula: see text]) and (b) [Formula: see text] and [Formula: see text] in terms of their performance in multilevel structural equation models across varying ICCs. The design factors included intraclass correlation (ICC: ICC1 = 0.091 to ICC6 = 0.500), numbers of groups in between-level models (NG: 50, 100, 200, and 1,000), group size (GS: 30, 50, and 100), and type of misspecification (no misspecification, between-level misspecification, and within-level misspecification). Our simulation findings raise a concern regarding the performance of between-level-specific partial saturated fit indices in low ICC conditions: the performances of both [Formula: see text] and [Formula: see text] were more influenced by ICC compared with [Formula: see text] and SRMR B . However, when traditional cutoff values ( RMSEA≤ 0.06; CFI, TLI≥ 0.95; SRMR≤ 0.08) were applied, [Formula: see text] and [Formula: see text] were still able to detect misspecified between-level models even when ICC was as low as 0.091 (ICC1). On the other hand, both [Formula: see text] and [Formula: see text] were not recommended under low ICC conditions.

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References

2012Principles and Practice of Structural Equation Modeling 1990Comparative fit indexes in structural models.1998Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.1973A Reliability Coefficient for Maximum Likelihood Factor Analysis 2004Latent Variable Models 2003Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis 1996CFI versus RMSEA: A comparison of two fit indexes for structural equation modeling 2009Structural Equation Modeling (2nd ed.): Foundations and Extensions

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