Browsing by Author "Kwok, Oi-man"

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Detecting Misspecified Multilevel Structural Equation Models with Common Fit Indices: AMonte Carlo Study
(2015-03-04)
Hsu, Hsien-Yuan
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Kwok, Oi-man
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Lin, Jr Huang
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Acosta, Sandra
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教育研究所
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.
Indirect Effects of Child Reports of Teacher-Student Relationship on Achievement
(2012-05-01)
Hughes, Jan N.
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Wu, Jiun-Yu
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Kwok, Oi-man
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Villarreal, Victor
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Johnson, Audrea Y.
The effect of student-reported teacher student relationship quality (TSRQ) on academic motivation and achievement was investigated among a sample of 690 academically at-risk elementary students (52.8% male). Measures of TSRQ, achievement, and motivation were collected annually for 3 consecutive years, beginning when participants were in Grade 2 (24.8%) or Grade 3 (74.6%). Child-reported conflict was stable across the 3 years, whereas warmth declined. Boys and African American students reported greater conflict than did girls and Caucasian and Hispanic students. Girls and African American students reported higher warmth than boys and non-African American students. Using path analysis, the authors tested the hypothesis that measures of student motivation in Year 2 mediated the effects of conflict and warmth in Year 1 on reading and math achievement in Year 3. Child-perceived conflict predicted cross-year changes in teacher-rated behavioral engagement, which, in turn, predicted cross-year changes in reading and math achievement. Math competence beliefs also mediated the effect of child-perceived warmth on math achievement. Effects controlled for stability of measures across time, the within-wave association between measures, and baseline measures of IQ and economic adversity. Implications of findings for improving the academic achievement of students at-risk for school failure are discussed.
Using SEM to Analyze Complex Survey Data: A Comparison between Design-Based Single-Level and Model-Based Multilevel Approaches
(2012)
Wu, Jiun-Yu
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Kwok, Oi-man
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教育研究所
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Institute of Education
Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between-and within-level model structures (B. O. Muthen & Satorra, 1995), the performances of these 2 approaches for analyzing multilevel data with unequal between-and within-level structures have not yet been systematically examined. In this study, we extended B. O. Muthen and Satorra's (1995) study by comparing these 2 approaches and an additional model-based maximum model for analyzing multilevel data considering number of clusters, cluster size, intraclass correlation, and the equality of different level structures. The simulation results showed the model-based maximum model generally performed well across conditions. This model is also recommended as an alternative for analyzing nonindependent survey data, especially when the information of the higher level model structure is not known.