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Parametric Bootstrap Mantel‐Haenszel Statistic for Aggregated Testlet Effects.

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Title: Parametric Bootstrap Mantel‐Haenszel Statistic for Aggregated Testlet Effects.
Authors: Lim, Youn Seon1 (AUTHOR) limyo@ucmail.uc.edu
Source: Journal of Educational Measurement. Dec2025, Vol. 62 Issue 4, p503-530. 28p.
Subject Terms: *Problem solving, Statistical bootstrapping, Simulation methods & models, Statistical hypothesis testing, Mathematical statistics
Abstract: While testlets have proven useful for assessing complex skills, the stem shared by multiple items often induces correlations between responses, leading to violations of local independence (LI), which can result in biased parameter and ability estimates. Diagnostic procedures for detecting testlet effects typically involve model comparisons testing for the inclusion of extra testlet parameters or, at the item level, testing for pairwise LI. Rosenbaum's adaptation of the Mantel‐Haenszel (MH) χ2$\chi ^2$‐statistic belongs to the latter category. The MH χ2$\chi ^2$‐statistic has also been used in cognitive diagnosis for detecting violations of LI and for the identification of testlet effects. However, this approach is not without limitations, as it lacks a rationale for integrating multiple pairwise MH χ2$\chi ^2$‐statistics and any notion of the sampling distribution of such an integrated statistic. In this article, a procedure for integrating multiple pairwise MH χ2$\chi ^2$‐statistics to evaluate testlet effects in cognitive diagnosis is proposed. The unknown sampling distribution issue is addressed by implementing a parametric bootstrap resampling scheme. Results from simulation studies demonstrate the performance of the proposed parametric bootstrap testlet MH χ2$\chi ^2$‐statistic, and its application to the 2015 PISA Collaborative Problem Solving (CPS) data set illustrates the method's practical merits. [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.)
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  Data: Parametric Bootstrap Mantel‐Haenszel Statistic for Aggregated Testlet Effects.
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  Data: <searchLink fieldCode="AR" term="%22Lim%2C+Youn+Seon%22">Lim, Youn Seon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> limyo@ucmail.uc.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Dec2025, Vol. 62 Issue 4, p503-530. 28p.
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  Data: *<searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+bootstrapping%22">Statistical bootstrapping</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink>
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  Data: While testlets have proven useful for assessing complex skills, the stem shared by multiple items often induces correlations between responses, leading to violations of local independence (LI), which can result in biased parameter and ability estimates. Diagnostic procedures for detecting testlet effects typically involve model comparisons testing for the inclusion of extra testlet parameters or, at the item level, testing for pairwise LI. Rosenbaum's adaptation of the Mantel‐Haenszel (MH) χ2$\chi ^2$‐statistic belongs to the latter category. The MH χ2$\chi ^2$‐statistic has also been used in cognitive diagnosis for detecting violations of LI and for the identification of testlet effects. However, this approach is not without limitations, as it lacks a rationale for integrating multiple pairwise MH χ2$\chi ^2$‐statistics and any notion of the sampling distribution of such an integrated statistic. In this article, a procedure for integrating multiple pairwise MH χ2$\chi ^2$‐statistics to evaluate testlet effects in cognitive diagnosis is proposed. The unknown sampling distribution issue is addressed by implementing a parametric bootstrap resampling scheme. Results from simulation studies demonstrate the performance of the proposed parametric bootstrap testlet MH χ2$\chi ^2$‐statistic, and its application to the 2015 PISA Collaborative Problem Solving (CPS) data set illustrates the method's practical merits. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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.12440
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      – SubjectFull: Statistical bootstrapping
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              Text: Dec2025
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