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Dispersion prediction models in nest...

Yorio, Patrick L.

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Dispersion prediction models in nested data structures: Examining the performance of dispersion indexes in polytomous items.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dispersion prediction models in nested data structures: Examining the performance of dispersion indexes in polytomous items./
作者:	Yorio, Patrick L.
面頁冊數:	202 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
標題:	Psychology, Psychometrics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3582647
ISBN:	9781321209839

Dispersion prediction models in nested data structures: Examining the performance of dispersion indexes in polytomous items.
Yorio, Patrick L.

Dispersion prediction models in nested data structures: Examining the performance of dispersion indexes in polytomous items. - 202 p.

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2014.

This item must not be sold to any third party vendors.

This study was designed to explore two possible causes of and solutions to poor dispersion prediction model performance in polytomous items. First, the impact of the correlation between the 'level' (e.g., the average score of the distribution) and the 'strength' (the dispersion among the data points in a distribution) on the dispersion effect was explored. Second, the extent to which non-linearity and heteroscedasticity influenced the dispersion effect was also explored. In order to explore these two factors, Monte Carlo studies were performed in which the dispersion index, the number of aggregated observations, the number of nested data points, the number of items from which the dispersion index was derived, the shape of the distribution, and the 'level' covariate in the multiple regression model were varied. The studies used a 5 point response polytomous item context. The evaluation criteria included power/Type I error rates, model R2, sr2 for the dispersion index, the VIF of the dispersion index, linearity of the dispersion index, and homoscedasticity of the errors in the dispersion prediction model.

ISBN: 9781321209839Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

Dispersion prediction models in nested data structures: Examining the performance of dispersion indexes in polytomous items.
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245 1 0 $a Dispersion prediction models in nested data structures: Examining the performance of dispersion indexes in polytomous items.
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500 $a Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
500 $a Adviser: Feifei Ye.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2014.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a This study was designed to explore two possible causes of and solutions to poor dispersion prediction model performance in polytomous items. First, the impact of the correlation between the 'level' (e.g., the average score of the distribution) and the 'strength' (the dispersion among the data points in a distribution) on the dispersion effect was explored. Second, the extent to which non-linearity and heteroscedasticity influenced the dispersion effect was also explored. In order to explore these two factors, Monte Carlo studies were performed in which the dispersion index, the number of aggregated observations, the number of nested data points, the number of items from which the dispersion index was derived, the shape of the distribution, and the 'level' covariate in the multiple regression model were varied. The studies used a 5 point response polytomous item context. The evaluation criteria included power/Type I error rates, model R2, sr2 for the dispersion index, the VIF of the dispersion index, linearity of the dispersion index, and homoscedasticity of the errors in the dispersion prediction model.
520 $a The results suggest that none of the dispersion indexes systematically violate the multiple regression assumptions of linearity or homoscedasticity. They also suggest that the choice of the dispersion index, the number of items used, and the central tendency covariate used in the dispersion prediction model are the prominent determinants of good performance in a 5 point response scale polytomous item context. The sample standard deviation (SD) and average deviation indexes (ADm and ADmd) performed equally well and substantially better than the MAD, CV, and awg in terms of the evaluation criteria across the conditions of the study. The performance of the SD, ADm, and ADmd improved substantially when computed from 5 different polytomous items as opposed to a single polytomous item. Finally, the results suggest that in skewed distributions the performance of the SD, ADm, and ADmd decreases due to an increased correlation with the level covariate. This decrease in performance can be counteracted in skewed distribution by controlling for the median as the level covariate as opposed to the mean.
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