Using Mx to Analyze Cross-Level Effects in Two-Level Structural Equation Models
Publication in refereed journal

香港中文大學研究人員

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摘要Two-level data sets are frequently encountered in social and behavioral science research. They arise when observations are drawn from a known hierarchical structure, such as when individuals are randomly drawn from groups that are randomly drawn from a target population. Although 2-level data analysis in the context of structural equation modeling can be conducted by easily accessible software such as LISREL, the group- and individual-level effects are usually treated as though they are uncorrelated. When extra group variables are measured and their relationships with individual-level variables are studied, the analysis of cross-level covariance structures is of interest. In this article, we propose a model setup framework in Mx that allows the analysis of cross-level covariance structures. An illustrative example is given and a small-scale simulation study is conducted to examine the performance of the proposed procedure. The results show that the proposed method can produce reliable parameter and standard error estimates, and the goodness-of-fit statistics also follow the chi-square distribution in large samples.
著者Bai Y, Poon WY
期刊名稱Structural Equation Modeling
出版年份2009
月份1
日期1
卷號16
期次1
出版社Taylor & Francis (Routledge): STM, Behavioural Science and Public Health Titles
頁次163 - 178
國際標準期刊號1070-5511
電子國際標準期刊號1532-8007
語言英式英語
Web of Science 學科類別Mathematical Methods In Social Sciences; Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS

上次更新時間 2021-26-02 於 00:02