標題: Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: AMonte Carlo Study
作者: Hsu, Hsien-Yuan
Kwok, Oi-man
Lin, Jr Huang
Acosta, Sandra
教育研究所
Institute of Education
公開日期: 4-三月-2015
摘要: 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.
URI: http://dx.doi.org/10.1080/00273171.2014.977429
http://hdl.handle.net/11536/124711
ISSN: 0027-3171
DOI: 10.1080/00273171.2014.977429
期刊: MULTIVARIATE BEHAVIORAL RESEARCH
Volume: 50
起始頁: 197
結束頁: 215
顯示於類別:期刊論文