Multilevel covariance structure analysis of sibling antisocial behavior

Duncan, Terry E.; Alpert, Anthony; Duncan, Susan C.

doi:10.1080/10705519809540102

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…Muthén (1989Muthén ( , 1994 proposed a simpler estimator called Muthén's maximum-likelihood-based (MUML) estimator. 2 The MUML estimator is probably the most widely used procedure for applied researchers conducting MSEM (e.g., Duncan, Alpert, & Duncan, 1998;Dyer et al, 1997;Kaplan, & Elliott, 1997a, 1997b because it can be easily implemented in nearly all SEM software packages without modifications. Empirical findings suggest that the MUML estimator gives similar results as FIML estimators with rough approximations to the correct chi-square test statistics and standard errors of parameter estimates (Hox, 1993;Hox & Maas, 2001;McDonald, 1994;B.…”

Section: Muthén's Modelmentioning

confidence: 99%

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Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research

Cheung

Au²

2005

Structural Equation Modeling: A Multidisciplinary Journal

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Multilevel structural equation modeling (MSEM) has been proposed as an extension to structural equation modeling for analyzing data with nested structure. We have begun to see a few applications in cross-cultural research in which MSEM fits well as the statistical model. However, given that cross-cultural studies can only afford collecting data from a relatively small number of countries, the appropriateness of MSEM has been questioned. Using the data from the International Social Survey Program (1997; N = 15,244 from 27 countries), we first showed how Muthén's MSEM procedure could be applied to a real data set on cross-cultural research. Given a small country-level sample size (27 countries) we then demonstrated that results on the individual level were quite stable even when using small individual-level sample sizes, whereas the group-level parameter estimates and their standard errors were affected unsystematically by varying individual-level sample sizes. Use of the findings for cross-cultural research and other areas with limited numbers of groups are discussed.Cross-cultural research has initiated many studies on how and why people behave differently across nations. Culture has been widely used as a variable to explain many cross-national behavioral and psychological differences. Conceptually speaking, multilevel structural equation modeling (MSEM) seems to be the immediate choice of statistical model for a variety of research questions in cross-cultural research. However, MSEM may require large group-level samples of about 100 STRUCTURAL EQUATION MODELING, 12(4),

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Section: Muthén's Modelmentioning

confidence: 99%

“…Muthén (1994) used a data set with 3,724 students nested within 197 classes. Duncan et al (1998) analyzed 1,076 adolescents from 450 households, and Elliott (1997a, 1997b) analyzed 1,165 students from 356 schools. These studies satisfy the suggestion by Hox and Maas (2001).…”

Section: Sample Size Issues In Msemmentioning

confidence: 99%

Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research

Cheung

Au²

2005

Structural Equation Modeling: A Multidisciplinary Journal

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“…Group sizes of five are common in data from organizational research where it is a typical size of a working team (Jackson & Joshi, ; Koman & Wolff, ). Cluster sizes of two correspond to a typical cluster size in data from family research (Duncan, Alpert, & Duncan, ; Voorpostel & Blieszner, ).…”

Section: Methodsmentioning

confidence: 98%

On the power of the test for cluster bias

Jak

Oort

2015

Brit J Math & Statis

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Cluster bias refers to measurement bias with respect to the clustering variable in multilevel data. The absence of cluster bias implies absence of bias with respect to any cluster-level (level 2) variable. The variables that possibly cause the bias do not have to be measured to test for cluster bias. Therefore, the test for cluster bias serves as a global test of measurement bias with respect to any level 2 variable. However, the validity of the global test depends on the Type I and Type II error rates of the test. We compare the performance of the test for cluster bias with the restricted factor analysis (RFA) test, which can be used if the variable that leads to measurement bias is measured. It appeared that the RFA test has considerably more power than the test for cluster bias. However, the false positive rates of the test for cluster bias were generally around the expected values, while the RFA test showed unacceptably high false positive rates in some conditions. We conclude that if no significant cluster bias is found, still significant bias with respect to a level 2 violator can be detected with an RFA model. Although the test for cluster bias is less powerful, an advantage of the test is that the cause of the bias does not need to be measured, or even known.

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“…At this point, we also calculate the Intraclass Correlation (ICC) to ensure that multilevel modeling is appropriate for the purpose of this study. An ICC coefficient of 0.06 was considered as a sufficient level of ICC for utilizing multilevel modeling (Cheung & Au, 2005; Duncan et al., 1998; Stapleton, 2006). We presented the M0 (equation (1)) as follows: …”

Section: Methodsmentioning

confidence: 99%

Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

Liu

Xing

Zeng

et al. 2020

Journal of Educational Computing Research

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Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.

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Multilevel covariance structure analysis of sibling antisocial behavior

Abstract: 1998) Multilevel covariance structure analysis of sibling antisocial behavior, Structural Equation Modeling: A Multidisciplinary Journal, 5:3, 211-228,

Cited by 12 publications

References 21 publications

Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research

Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research

On the power of the test for cluster bias

Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

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