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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.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics./
Author:	Schweig, Jonathan David.
Description:	48 p.
Notes:	Source: Masters Abstracts International, Volume: 52-05.
Contained By:	Masters Abstracts International52-05(E).
Subject:	Education, General. -
Online resource:	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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020 $a 9781303782985
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100 1 $a Schweig, Jonathan David. $3 2101975
245 1 0 $a Multilevel factor analysis by model segregation: Comparing the performance of maximum likelihood and robust test statistics.
300 $a 48 p.
500 $a Source: Masters Abstracts International, Volume: 52-05.
500 $a Adviser: Peter M. Bentler.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2014.
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.
590 $a School code: 0031.
650 4 $a Education, General. $3 1019158
650 4 $a Psychology, Psychometrics. $3 1017742
650 4 $a Statistics. $3 517247
690 $a 0463
690 $a 0515
690 $a 0632
710 2 $a University of California, Los Angeles. $b Statistics 0891. $3 2095317
773 0 $t Masters Abstracts International $g 52-05(E).
790 $a 0031
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1553273