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Multilevel factor analysis by model ...

Schweig, Jonathan David.

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Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics./
作者:	Schweig, Jonathan David.
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 52-05.
Contained By:	Masters Abstracts International52-05(E).
標題:	Education, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1553273
ISBN:	9781303782985

Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics.
Schweig, Jonathan David.

Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics. - 48 p.

Source: Masters Abstracts International, Volume: 52-05.

Thesis (M.S.)--University of California, Los Angeles, 2014.

Survey measures of classroom climate and instructional practice have become central to policy efforts that assess school and teacher quality. Measures of classroom climate are often formed by aggregating individual survey responses. This has sparked a wide interest in using multilevel factor analysis to test hypotheses about the psychometric properties of classroom climate variables. One approach to multilevel factor analysis is conducted in two steps. First, the total covariance matrix is partitioned into separate between-group and within-group covariance matrices. Second, conventional factor analysis is used to test models separately. This study shows that when using this approach, rescaled and residual-based test statistics provide better inferences about the between group-level measurement structure than Maximum Likelihood test statistics even when the number of groups is large and there is no excess kurtosis in the observed variables. This study presents an empirical example and a simulation study to demonstrate how item intraclass correlations and within group sample sizes influence test statistic performance.

ISBN: 9781303782985Subjects--Topical Terms:

1019158
Education, General.

Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics.
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520 $a Survey measures of classroom climate and instructional practice have become central to policy efforts that assess school and teacher quality. Measures of classroom climate are often formed by aggregating individual survey responses. This has sparked a wide interest in using multilevel factor analysis to test hypotheses about the psychometric properties of classroom climate variables. One approach to multilevel factor analysis is conducted in two steps. First, the total covariance matrix is partitioned into separate between-group and within-group covariance matrices. Second, conventional factor analysis is used to test models separately. This study shows that when using this approach, rescaled and residual-based test statistics provide better inferences about the between group-level measurement structure than Maximum Likelihood test statistics even when the number of groups is large and there is no excess kurtosis in the observed variables. This study presents an empirical example and a simulation study to demonstrate how item intraclass correlations and within group sample sizes influence test statistic performance.
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