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The effects of parcels and latent va...

Fletcher, Thomas D.

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The effects of parcels and latent variable scores on the detection of interactions in structural equation modeling.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The effects of parcels and latent variable scores on the detection of interactions in structural equation modeling./
Author:	Fletcher, Thomas D.
Description:	85 p.
Notes:	Directors: Terry L. Dickinson; Debra A. Major.
Contained By:	Dissertation Abstracts International66-05B.
Subject:	Psychology, Industrial. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3176686
ISBN:	9780542157424

The effects of parcels and latent variable scores on the detection of interactions in structural equation modeling.
Fletcher, Thomas D.

The effects of parcels and latent variable scores on the detection of interactions in structural equation modeling. - 85 p.

Directors: Terry L. Dickinson; Debra A. Major.

Thesis (Ph.D.)--Old Dominion University, 2005.

Numerous theories in the behavioral and organizational sciences involve the regression of an outcome variable on component terms and their product to evaluate interaction effects. There are numerous statistical difficulties with this multiple regression approach. The most serious is measurement error, requiring the use of structural equation modeling. Joreskog and Yang (1996) described a nonlinear structural equation modeling procedure that incorporates mean structures in the covariance analysis. They demonstrated that only one indicator for the product term is necessary for model identification. Unfortunately, the Joreskog-Yang procedure leads to biased estimates of the product coefficient. In this dissertation, I propose that (1) the proper use of item parcels can reduce bias in estimates, and (2) that using a relatively new technique of analysis (creation of latent variable scores) can also be fruitful in removing measurement error and improving the estimation of product terms. Two studies investigated these proposals. In Study 1, archival data were analyzed using the proposed techniques. The interaction hypothesis tested by the various techniques is that a competitive climate influences perceptions of coworker support, and that this relationship is moderated by (interacts with) a person's level of trait competitiveness. Study 2 involved a Monte Carlo investigation of methods for estimating an interaction effect. The Monte Carlo research included design factors for (a) effect size, (b) parceling strategy, and (c) method of analysis. Study 1 demonstrated that method of analysis and parceling strategy affected the detection of the moderator effect of competition on two types of coworker support (instrumental and affective). Variability in the t-tests and effect size indices lend credibility for the need for the Monte-Carlo investigation. Study 2 demonstrated that (1) there is greater variability in the estimation of the interaction effect with the Joreskog-Yang method than the latent variable scores method, (2) parceling strategy has the most influence on the interaction effect in the Joreskog-Yang method, and this effect is dependent upon which strategy is used, and (3) the latent variable score method is superior to the Joreskog-Yang method with respect to statistical decision making (i.e., fewer Type II errors). Practical implications and future research directions are considered.

ISBN: 9780542157424Subjects--Topical Terms:

520063
Psychology, Industrial.

The effects of parcels and latent variable scores on the detection of interactions in structural equation modeling.
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100 1 $a Fletcher, Thomas D. $3 1293856
245 1 4 $a The effects of parcels and latent variable scores on the detection of interactions in structural equation modeling.
300 $a 85 p.
500 $a Directors: Terry L. Dickinson; Debra A. Major.
500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2872.
502 $a Thesis (Ph.D.)--Old Dominion University, 2005.
520 $a Numerous theories in the behavioral and organizational sciences involve the regression of an outcome variable on component terms and their product to evaluate interaction effects. There are numerous statistical difficulties with this multiple regression approach. The most serious is measurement error, requiring the use of structural equation modeling. Joreskog and Yang (1996) described a nonlinear structural equation modeling procedure that incorporates mean structures in the covariance analysis. They demonstrated that only one indicator for the product term is necessary for model identification. Unfortunately, the Joreskog-Yang procedure leads to biased estimates of the product coefficient. In this dissertation, I propose that (1) the proper use of item parcels can reduce bias in estimates, and (2) that using a relatively new technique of analysis (creation of latent variable scores) can also be fruitful in removing measurement error and improving the estimation of product terms. Two studies investigated these proposals. In Study 1, archival data were analyzed using the proposed techniques. The interaction hypothesis tested by the various techniques is that a competitive climate influences perceptions of coworker support, and that this relationship is moderated by (interacts with) a person's level of trait competitiveness. Study 2 involved a Monte Carlo investigation of methods for estimating an interaction effect. The Monte Carlo research included design factors for (a) effect size, (b) parceling strategy, and (c) method of analysis. Study 1 demonstrated that method of analysis and parceling strategy affected the detection of the moderator effect of competition on two types of coworker support (instrumental and affective). Variability in the t-tests and effect size indices lend credibility for the need for the Monte-Carlo investigation. Study 2 demonstrated that (1) there is greater variability in the estimation of the interaction effect with the Joreskog-Yang method than the latent variable scores method, (2) parceling strategy has the most influence on the interaction effect in the Joreskog-Yang method, and this effect is dependent upon which strategy is used, and (3) the latent variable score method is superior to the Joreskog-Yang method with respect to statistical decision making (i.e., fewer Type II errors). Practical implications and future research directions are considered.
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790 1 0 $a Major, Debra A., $e advisor
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