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Investigations of differential item ...

University of California, Berkeley.

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Investigations of differential item functioning: Comparisons among approaches, and extension to a *multidimensional context /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Investigations of differential item functioning: Comparisons among approaches, and extension to a *multidimensional context // Insu Paek.
作者:	Paek, Insu,
面頁冊數:	1 electronic resource (201 pages)
附註:	Source: Dissertations Abstracts International, Volume: 65-01, Section: A.
Contained By:	Dissertations Abstracts International65-01A.
標題:	Educational evaluation. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3082356
ISBN:	9780496302079

Investigations of differential item functioning: Comparisons among approaches, and extension to a *multidimensional context /
Paek, Insu,

Investigations of differential item functioning: Comparisons among approaches, and extension to a *multidimensional context /Insu Paek. - 1 electronic resource (201 pages)

Source: Dissertations Abstracts International, Volume: 65-01, Section: A.

Differential item functioning (DIF) has been one of the major topics in the measurement field for over the last thirty years. As a result, research on DIF-detecting methodology has proliferated. Built on the accumulated work in DIF, a unidimensional parametric item response theory (IRT) DIF method based on multidimensional random coefficient multinomial logit (MRCML) model (Adams, Wilson, & Wang, 1997) and Moore's (1996) work is suggested. It is capable of estimating a DIF index and its standard error directly from the data, which is a unique feature of this DIF modeling compared other proposed parametric IRT DIF modeling. Its performance is compared with the simultaneous bias test (SIBTEST) model (Shealy and Stout, 1993a) by simulation studies. SIBTEST is a recently developed DIF method that is a non-parametric IRT DIF model. Since there is no prior work on which type of approach for detection of DIF is better, this comparison study to reveal the strength and weakness of different methods in various situations would be of help to theory and practice of DIF detection methodology. The controlled variables that are given prime roles to simulate data for comparison are item response function (IRF) conditions and population distribution conditions. The foci of the comparisons are the behavior of DIF estimators and significance test results such as Type I error power. For the Type II error and power investigation, (asymptotic) z-test and likelihood ratio test are used in the suggested parametric method. Using the flexibility of the MRCML model, a multidimensional extension of the unidimensional parametric DIF modeling is attempted and its performance is investigated (in rather limited conditions compared to the unidimensional study) by a simulation study. And the importance of using a multidimensional DIF model in DIF investigation for a multidimensional test is shown by a simulation study as well.

English

ISBN: 9780496302079Subjects--Topical Terms:

526425
Educational evaluation.
Subjects--Index Terms:

Differential item functioning

Investigations of differential item functioning: Comparisons among approaches, and extension to a *multidimensional context /
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520 $a Differential item functioning (DIF) has been one of the major topics in the measurement field for over the last thirty years. As a result, research on DIF-detecting methodology has proliferated. Built on the accumulated work in DIF, a unidimensional parametric item response theory (IRT) DIF method based on multidimensional random coefficient multinomial logit (MRCML) model (Adams, Wilson, & Wang, 1997) and Moore's (1996) work is suggested. It is capable of estimating a DIF index and its standard error directly from the data, which is a unique feature of this DIF modeling compared other proposed parametric IRT DIF modeling. Its performance is compared with the simultaneous bias test (SIBTEST) model (Shealy and Stout, 1993a) by simulation studies. SIBTEST is a recently developed DIF method that is a non-parametric IRT DIF model. Since there is no prior work on which type of approach for detection of DIF is better, this comparison study to reveal the strength and weakness of different methods in various situations would be of help to theory and practice of DIF detection methodology. The controlled variables that are given prime roles to simulate data for comparison are item response function (IRF) conditions and population distribution conditions. The foci of the comparisons are the behavior of DIF estimators and significance test results such as Type I error power. For the Type II error and power investigation, (asymptotic) z-test and likelihood ratio test are used in the suggested parametric method. Using the flexibility of the MRCML model, a multidimensional extension of the unidimensional parametric DIF modeling is attempted and its performance is investigated (in rather limited conditions compared to the unidimensional study) by a simulation study. And the importance of using a multidimensional DIF model in DIF investigation for a multidimensional test is shown by a simulation study as well.
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653 $a Multidimensional
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