Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating.

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Title: Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating.
Authors: Jiang, Yanlin1, von Davier, Alina A.1, Chen, Haiwen1
Source: Journal of Educational Measurement. Mar2012, Vol. 49 Issue 1, p39-58. 18p. 4 Charts, 6 Graphs.
Subject Terms: Kernel functions, Population, Complex variables, Geometric function theory, Error
Abstract: This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating effectiveness. The study compared the PRE results for chained and poststratification equating. The results indicated that the chained method transformed the new form score distribution to the reference form scale more effectively than the poststratification method. In addition, the study found that in chained equating, the population weight had impact on score distributions over the target population but not on the equating and PRE results. [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: Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating.
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  Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Yanlin%22">Jiang, Yanlin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22von+Davier%2C+Alina+A%2E%22">von Davier, Alina A.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Haiwen%22">Chen, Haiwen</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Mar2012, Vol. 49 Issue 1, p39-58. 18p. 4 Charts, 6 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Kernel+functions%22">Kernel functions</searchLink><br /><searchLink fieldCode="DE" term="%22Population%22">Population</searchLink><br /><searchLink fieldCode="DE" term="%22Complex+variables%22">Complex variables</searchLink><br /><searchLink fieldCode="DE" term="%22Geometric+function+theory%22">Geometric function theory</searchLink><br /><searchLink fieldCode="DE" term="%22Error%22">Error</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating effectiveness. The study compared the PRE results for chained and poststratification equating. The results indicated that the chained method transformed the new form score distribution to the reference form scale more effectively than the poststratification method. In addition, the study found that in chained equating, the population weight had impact on score distributions over the target population but not on the equating and PRE results. [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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RecordInfo BibRecord:
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        Value: 10.1111/j.1745-3984.2011.00159.x
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 39
    Subjects:
      – SubjectFull: Kernel functions
        Type: general
      – SubjectFull: Population
        Type: general
      – SubjectFull: Complex variables
        Type: general
      – SubjectFull: Geometric function theory
        Type: general
      – SubjectFull: Error
        Type: general
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      – TitleFull: Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating.
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              Text: Mar2012
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              Y: 2012
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