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Computation and Accuracy Evaluation of Comparable Scores on Culturally Responsive Assessments.

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Title: Computation and Accuracy Evaluation of Comparable Scores on Culturally Responsive Assessments.
Authors: Sinharay, Sandip1 (AUTHOR) ssinharay@ets.org, Johnson, Matthew S.1 (AUTHOR)
Source: Journal of Educational Measurement. Mar2024, Vol. 61 Issue 1, p5-46. 42p.
Subject Terms: *Computer adaptive testing, Rasch models, Multidimensional databases, Data modeling, Fairness
Abstract: Culturally responsive assessments have been proposed as potential tools to ensure equity and fairness for examinees from all backgrounds including those from traditionally underserved or minoritized groups. However, these assessments are relatively new and, with few exceptions, are yet to be implemented in large scale. Consequently, there is a lack of guidance on how one can compute comparable scores on various versions of these assessments. In this paper, the multigroup multidimensional Rasch model is repurposed for modeling data originating from various versions of a culturally responsive assessment and for analyzing such data to compute comparable scores. Two simulation studies are performed to evaluate the performance of the model for data simulated from hypothetical culturally responsive assessments and to find the conditions under which the computed scores are accurate. Recommendations are made for measurement practitioners interested in culturally responsive assessments. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Computation and Accuracy Evaluation of Comparable Scores on Culturally Responsive Assessments.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Mar2024, Vol. 61 Issue 1, p5-46. 42p.
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  Data: *<searchLink fieldCode="DE" term="%22Computer+adaptive+testing%22">Computer adaptive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Rasch+models%22">Rasch models</searchLink><br /><searchLink fieldCode="DE" term="%22Multidimensional+databases%22">Multidimensional databases</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Fairness%22">Fairness</searchLink>
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  Data: Culturally responsive assessments have been proposed as potential tools to ensure equity and fairness for examinees from all backgrounds including those from traditionally underserved or minoritized groups. However, these assessments are relatively new and, with few exceptions, are yet to be implemented in large scale. Consequently, there is a lack of guidance on how one can compute comparable scores on various versions of these assessments. In this paper, the multigroup multidimensional Rasch model is repurposed for modeling data originating from various versions of a culturally responsive assessment and for analyzing such data to compute comparable scores. Two simulation studies are performed to evaluate the performance of the model for data simulated from hypothetical culturally responsive assessments and to find the conditions under which the computed scores are accurate. Recommendations are made for measurement practitioners interested in culturally responsive assessments. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/jedm.12381
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        Text: English
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      – SubjectFull: Data modeling
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      – SubjectFull: Fairness
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              Text: Mar2024
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