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Assessing Model Fit of the Generalized Graded Unfolding Model

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Title: Assessing Model Fit of the Generalized Graded Unfolding Model
Language: English
Authors: Abdulla Alzarouni (ORCID 0000-0003-3757-7383), R. J. De Ayala (ORCID 0009-0002-9246-7670)
Source: Practical Assessment, Research & Evaluation. 2025 30(1).
Availability: University of Massachusetts Amherst Libraries. 154 Hicks Way, Amherst, MA 01003. e-mail: pare@umass.edu; Web site: https://openpublishing.library.umass.edu/pare/
Peer Reviewed: Y
Page Count: 22
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Goodness of Fit, Models, Statistical Analysis, Sample Size, Test Length, Item Response Theory, Test Items
ISSN: 1531-7714
Abstract: The assessment of model fit in latent trait modeling is an integral part of correctly applying the model. Still the assessment of model fit has been less utilized for ideal point models such as the Generalized Graded Unfolding Models (GGUM). The current study assesses the performance of the relative fit indices "AIC" and "BIC," and the absolute fit adjusted chi-square statistic for the GGUM for both dichotomous and polytomous data. Factors included data generation model, sample size, instrument length, and screening value. Results show that relative fit indices performed well in identifying the GGUM when at least 20-items were used. For polytomous data the correct generation model was identified as the best fitting mode irrespective of the number of items and sample size. The adjusted chi-square statistic performed well in correctly identifying GGUM as the best fit for the GGUM dichotomous data generation, but performed poorly with the dominance models. With polytomous data case these fit indices always correctly identified GGUM as the best fit for the GGUM data. An explanation for this performance is provided.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1491691
Database: ERIC
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  Availability: 0
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  Data: <searchLink fieldCode="AR" term="%22Abdulla+Alzarouni%22">Abdulla Alzarouni</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3757-7383">0000-0003-3757-7383</externalLink>)<br /><searchLink fieldCode="AR" term="%22R%2E+J%2E+De+Ayala%22">R. J. De Ayala</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-9246-7670">0009-0002-9246-7670</externalLink>)
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  Data: University of Massachusetts Amherst Libraries. 154 Hicks Way, Amherst, MA 01003. e-mail: pare@umass.edu; Web site: https://openpublishing.library.umass.edu/pare/
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  Data: The assessment of model fit in latent trait modeling is an integral part of correctly applying the model. Still the assessment of model fit has been less utilized for ideal point models such as the Generalized Graded Unfolding Models (GGUM). The current study assesses the performance of the relative fit indices "AIC" and "BIC," and the absolute fit adjusted chi-square statistic for the GGUM for both dichotomous and polytomous data. Factors included data generation model, sample size, instrument length, and screening value. Results show that relative fit indices performed well in identifying the GGUM when at least 20-items were used. For polytomous data the correct generation model was identified as the best fitting mode irrespective of the number of items and sample size. The adjusted chi-square statistic performed well in correctly identifying GGUM as the best fit for the GGUM dichotomous data generation, but performed poorly with the dominance models. With polytomous data case these fit indices always correctly identified GGUM as the best fit for the GGUM data. An explanation for this performance is provided.
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      – Text: English
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        PageCount: 22
    Subjects:
      – SubjectFull: Goodness of Fit
        Type: general
      – SubjectFull: Models
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      – SubjectFull: Statistical Analysis
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      – SubjectFull: Sample Size
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      – SubjectFull: Test Length
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      – SubjectFull: Item Response Theory
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      – SubjectFull: Test Items
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