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Flexible Item Response Modeling for Timed Reading Comprehension Assessment.

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Title: Flexible Item Response Modeling for Timed Reading Comprehension Assessment.
Authors: Forthmann, Boris (AUTHOR), Lenhard, Wolfgang (AUTHOR), Lenhard, Alexandra (AUTHOR), Förster, Natalie (AUTHOR)
Source: Journal of Experimental Education. 2025, Vol. 93 Issue 4, p770-786. 17p.
Subjects: Item response theory, Psychometrics, Poisson regression, Statistical models, Comprehension testing
Abstract: While a rich methodology for analyzing response patterns for accuracy and time-on-task is at hand via Item Response Theory (IRT), tests with time cutoffs are so far harder to handle. Given that this test mode is widely applied, especially in the context of paper-and-pencil testing, there is a lack of psychometric techniques for a relevant number of tests. In this context, the original work of Rasch and his Rasch Poisson Counts model indeed offers an approach for this scenario that is adequate to solve the problem but which leads to model violations in many cases. Recent developments in statistical modeling – the so-called Conway Maxwell Poisson Counts Model (CMPCM) – can solve the problem of under- and overdispersion. We apply this model to the norm data of the ELFE II reading comprehension test and analyze patterns of over- and underdispersion with regard to speededness and mode effects. CMPCM with subtest-specific dispersion was adequate to model the raw test data, with underdispersion occurring mainly in highly speeded subtests with low difficulty and overdispersion in less speeded subtests with high difficulty. Thus, the CMPCM could contribute to psychometric methodology to appropriately model tests with time cutoffs on the subtest level. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd 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: Psychology and Behavioral Sciences Collection
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  Data: Flexible Item Response Modeling for Timed Reading Comprehension Assessment.
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  Data: <searchLink fieldCode="AR" term="%22Forthmann%2C+Boris%22">Forthmann, Boris</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lenhard%2C+Wolfgang%22">Lenhard, Wolfgang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lenhard%2C+Alexandra%22">Lenhard, Alexandra</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Förster%2C+Natalie%22">Förster, Natalie</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Experimental+Education%22">Journal of Experimental Education</searchLink>. 2025, Vol. 93 Issue 4, p770-786. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+regression%22">Poisson regression</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Comprehension+testing%22">Comprehension testing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: While a rich methodology for analyzing response patterns for accuracy and time-on-task is at hand via Item Response Theory (IRT), tests with time cutoffs are so far harder to handle. Given that this test mode is widely applied, especially in the context of paper-and-pencil testing, there is a lack of psychometric techniques for a relevant number of tests. In this context, the original work of Rasch and his Rasch Poisson Counts model indeed offers an approach for this scenario that is adequate to solve the problem but which leads to model violations in many cases. Recent developments in statistical modeling – the so-called Conway Maxwell Poisson Counts Model (CMPCM) – can solve the problem of under- and overdispersion. We apply this model to the norm data of the ELFE II reading comprehension test and analyze patterns of over- and underdispersion with regard to speededness and mode effects. CMPCM with subtest-specific dispersion was adequate to model the raw test data, with underdispersion occurring mainly in highly speeded subtests with low difficulty and overdispersion in less speeded subtests with high difficulty. Thus, the CMPCM could contribute to psychometric methodology to appropriately model tests with time cutoffs on the subtest level. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd 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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      – Type: doi
        Value: 10.1080/00220973.2024.2367162
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 770
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      – SubjectFull: Item response theory
        Type: general
      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Poisson regression
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Comprehension testing
        Type: general
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      – TitleFull: Flexible Item Response Modeling for Timed Reading Comprehension Assessment.
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            – D: 01
              M: 10
              Text: 2025
              Type: published
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