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Parameter recovery of the explanator...

James Madison University.

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Parameter recovery of the explanatory multidimensional Rasch model.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Parameter recovery of the explanatory multidimensional Rasch model./
作者:	Setzer, J. Carl.
面頁冊數:	140 p.
附註:	Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 4478.
Contained By:	Dissertation Abstracts International69-07B.
標題:	Psychology, Psychometrics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3323373
ISBN:	9780549746355

Parameter recovery of the explanatory multidimensional Rasch model.
Setzer, J. Carl.

Parameter recovery of the explanatory multidimensional Rasch model. - 140 p.

Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 4478.

Thesis (Ph.D.)--James Madison University, 2008.

Recently, there have been two types of model formulations used to demonstrate the utility of explanatory item response models. Specifically, the generalized linear mixed model (GLMM) and hierarchical generalized linear model (HGLM) have expanded item response models to include covariates for item effects, person effects, or both simultaneously. Both frameworks have recently been garnering greater attention in the educational measurement field. Despite these two frameworks being conceptually equivalent, much of the related literature has emphasized one or the other. However, to date, there has been little attempt to associate the frameworks together. In addition, item response models that have been described within the GLMM and HGLM frameworks have mostly been of the unidimensional type. Very little has been done to demonstrate the utility of an explanatory multidimensional item response model.

ISBN: 9780549746355Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

Parameter recovery of the explanatory multidimensional Rasch model.
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245 1 0 $a Parameter recovery of the explanatory multidimensional Rasch model.
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500 $a Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 4478.
502 $a Thesis (Ph.D.)--James Madison University, 2008.
520 $a Recently, there have been two types of model formulations used to demonstrate the utility of explanatory item response models. Specifically, the generalized linear mixed model (GLMM) and hierarchical generalized linear model (HGLM) have expanded item response models to include covariates for item effects, person effects, or both simultaneously. Both frameworks have recently been garnering greater attention in the educational measurement field. Despite these two frameworks being conceptually equivalent, much of the related literature has emphasized one or the other. However, to date, there has been little attempt to associate the frameworks together. In addition, item response models that have been described within the GLMM and HGLM frameworks have mostly been of the unidimensional type. Very little has been done to demonstrate the utility of an explanatory multidimensional item response model.
520 $a As explanatory models become more prevalent in research and practice, it is important to maintain software that can estimate them. SAS is an all-purpose and widely-used program that can estimate explanatory item response models. However, no previous research has examined how well SAS can recover the parameters of an explanatory multidimensional Rasch model (EMRM).
520 $a There were three main goals of this study. First, several types of Rasch models, including both non-explanatory and explanatory models, were summarized within the GLMM and HGLM frameworks. The equivalence of these two frameworks was demonstrated for each model. Second, a parameter recovery study was performed to determine how well SAS PROC NLMIXED can recover the parameters of an EMRM. The effect of sample size and test length on parameter recovery was assessed. The results of the simulation study indicate that very little bias occurs, even with small sample sizes and short test lengths. The final goal was to demonstrate the utility of an EMRM model using empirical data. Using data collected from the Marlowe-Crowne Social Desirability Scale (MCSDS), an EMRM was fit to the data while using gender as a covariate. Interpretations of the model parameter estimates were given and it was concluded that gender did not explain a significant amount of variation in either of the MCSDS subscales.
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