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A Study of Statistical Power and Typ...

Olivera Aguilar, Margarita.

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A Study of Statistical Power and Type I Errors in Testing a Factor Analytic Model for Group Differences in Regression Intercepts.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Study of Statistical Power and Type I Errors in Testing a Factor Analytic Model for Group Differences in Regression Intercepts./
Author:	Olivera Aguilar, Margarita.
Description:	75 p.
Notes:	Source: Masters Abstracts International, Volume: 49-02, page: 1417.
Contained By:	Masters Abstracts International49-02.
Subject:	Psychology, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1482999
ISBN:	9781124339375

A Study of Statistical Power and Type I Errors in Testing a Factor Analytic Model for Group Differences in Regression Intercepts.
Olivera Aguilar, Margarita.

A Study of Statistical Power and Type I Errors in Testing a Factor Analytic Model for Group Differences in Regression Intercepts. - 75 p.

Source: Masters Abstracts International, Volume: 49-02, page: 1417.

Thesis (M.A.)--Arizona State University, 2010.

In the past, it has been assumed that measurement and predictive invariance are consistent so that if one form of invariance holds the other form should also hold. However, some studies have proven that both forms of invariance only hold under certain conditions such as factorial invariance and invariance in the common factor variances. The present research examined Type I errors and the statistical power of a method that detects violations to the factorial invariant model in the presence of group differences in regression intercepts, under different sample sizes and different number of predictors (one or two). Data were simulated under two models: in model A only differences in the factor means were allowed, while model B violated invariance. A factorial invariant model was fitted to the data. Type I errors were defined as the proportion of samples in which the hypothesis of invariance was incorrectly rejected, and statistical power was defined as the proportion of samples in which the hypothesis of factorial invariance was correctly rejected. In the case of one predictor, the results show that the chi-square statistic has low power to detect violations to the model. Unexpected and systematic results were obtained regarding the negative unique variance in the predictor. It is proposed that negative unique variance in the predictor can be used as indication of measurement bias instead of the chi-square fit statistic with sample sizes of 500 or more. The results of the two predictor case show larger power. In both cases Type I errors were as expected. The implications of the results and some suggestions for increasing the power of the method are provided.

ISBN: 9781124339375Subjects--Topical Terms:

1018034
Psychology, General.

A Study of Statistical Power and Type I Errors in Testing a Factor Analytic Model for Group Differences in Regression Intercepts.
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100 1 $a Olivera Aguilar, Margarita. $3 1681767
245 1 2 $a A Study of Statistical Power and Type I Errors in Testing a Factor Analytic Model for Group Differences in Regression Intercepts.
300 $a 75 p.
500 $a Source: Masters Abstracts International, Volume: 49-02, page: 1417.
500 $a Adviser: Roger E. Millsap.
502 $a Thesis (M.A.)--Arizona State University, 2010.
520 $a In the past, it has been assumed that measurement and predictive invariance are consistent so that if one form of invariance holds the other form should also hold. However, some studies have proven that both forms of invariance only hold under certain conditions such as factorial invariance and invariance in the common factor variances. The present research examined Type I errors and the statistical power of a method that detects violations to the factorial invariant model in the presence of group differences in regression intercepts, under different sample sizes and different number of predictors (one or two). Data were simulated under two models: in model A only differences in the factor means were allowed, while model B violated invariance. A factorial invariant model was fitted to the data. Type I errors were defined as the proportion of samples in which the hypothesis of invariance was incorrectly rejected, and statistical power was defined as the proportion of samples in which the hypothesis of factorial invariance was correctly rejected. In the case of one predictor, the results show that the chi-square statistic has low power to detect violations to the model. Unexpected and systematic results were obtained regarding the negative unique variance in the predictor. It is proposed that negative unique variance in the predictor can be used as indication of measurement bias instead of the chi-square fit statistic with sample sizes of 500 or more. The results of the two predictor case show larger power. In both cases Type I errors were as expected. The implications of the results and some suggestions for increasing the power of the method are provided.
590 $a School code: 0010.
650 4 $a Psychology, General. $3 1018034
650 4 $a Psychology, Psychometrics. $3 1017742
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710 2 $a Arizona State University. $b Psychology. $3 1677434
773 0 $t Masters Abstracts International $g 49-02.
790 1 0 $a Millsap, Roger E., $e advisor
790 1 0 $a Aiken, Leona S. $e committee member
790 1 0 $a Enders, Craig K. $e committee member
790 $a 0010
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792 $a 2010
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