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Small Sample Estimation in Dichotomous Item Response Models: Effect of Priors Based on Judgmental Information on the Accuracy of Item Parameter Estimates. LSAC Research Report Series.

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Title: Small Sample Estimation in Dichotomous Item Response Models: Effect of Priors Based on Judgmental Information on the Accuracy of Item Parameter Estimates. LSAC Research Report Series.
Language: English
Authors: Swaminathan, Hariharan, Hambleton, Ronald K., Sireci, Stephen G., Xing, Dehui, Rizavi, Saba M., Law School Admission Council, Newtown, PA.
Peer Reviewed: N
Page Count: 28
Publication Date: 2003
Document Type: Reports - Research
Descriptors: Estimation (Mathematics), Item Response Theory, Sample Size, Sampling
Abstract: The primary objective of this study was to investigate how incorporating prior information improves estimation of item parameters in two small samples. The factors that were investigated were sample size and the type of prior information. To investigate the accuracy with which item parameters in the Law School Admission Test (LSAT) are estimated, the item parameter estimates were compared with known item parameter values. By randomly drawing small samples of varying sizes from the population of test takers, the relationship between sample size and the accuracy with which item parameters are estimated was studied. Data used were from the Reading Comprehension subtest of the LAST. Results indicate that the incorporation of ratings of item difficulty provided by subject matter specialists/test developers produced estimates of item difficulty statistics that were more accurate than that obtained without using such information. The improvement was observed for all item response models, including the model used in the LSAT. (SLD)
Entry Date: 2004
Accession Number: ED481818
Database: ERIC
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  Data: Small Sample Estimation in Dichotomous Item Response Models: Effect of Priors Based on Judgmental Information on the Accuracy of Item Parameter Estimates. LSAC Research Report Series.
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  Data: <searchLink fieldCode="AR" term="%22Swaminathan%2C+Hariharan%22">Swaminathan, Hariharan</searchLink><br /><searchLink fieldCode="AR" term="%22Hambleton%2C+Ronald+K%2E%22">Hambleton, Ronald K.</searchLink><br /><searchLink fieldCode="AR" term="%22Sireci%2C+Stephen+G%2E%22">Sireci, Stephen G.</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Dehui%22">Xing, Dehui</searchLink><br /><searchLink fieldCode="AR" term="%22Rizavi%2C+Saba+M%2E%22">Rizavi, Saba M.</searchLink><br /><searchLink fieldCode="AR" term="%22Law+School+Admission+Council%2C+Newtown%2C+PA%2E%22">Law School Admission Council, Newtown, PA.</searchLink>
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  Data: 2003
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  Data: The primary objective of this study was to investigate how incorporating prior information improves estimation of item parameters in two small samples. The factors that were investigated were sample size and the type of prior information. To investigate the accuracy with which item parameters in the Law School Admission Test (LSAT) are estimated, the item parameter estimates were compared with known item parameter values. By randomly drawing small samples of varying sizes from the population of test takers, the relationship between sample size and the accuracy with which item parameters are estimated was studied. Data used were from the Reading Comprehension subtest of the LAST. Results indicate that the incorporation of ratings of item difficulty provided by subject matter specialists/test developers produced estimates of item difficulty statistics that were more accurate than that obtained without using such information. The improvement was observed for all item response models, including the model used in the LSAT. (SLD)
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      – Text: English
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        PageCount: 28
    Subjects:
      – SubjectFull: Estimation (Mathematics)
        Type: general
      – SubjectFull: Item Response Theory
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      – SubjectFull: Sample Size
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      – SubjectFull: Sampling
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      – TitleFull: Small Sample Estimation in Dichotomous Item Response Models: Effect of Priors Based on Judgmental Information on the Accuracy of Item Parameter Estimates. LSAC Research Report Series.
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              Type: published
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