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Hierarchical Diagnostic Classification Modeling of Reading Comprehension

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  • Mona Tabatabaee-Yazdi

Abstract

The Hierarchical Diagnostic Classification Model (HDCM) reflects on the sequences of the presentation of the essential materials and attributes to answer the items of a test correctly. In this study, a foreign language reading comprehension test was analyzed employing HDCM and the generalized deterministic-input, noisy and gate (G-DINA) model to determine and compare respondents’ mastery profiles in the test’s predefined skills and to illustrate the relationships among the attributes involved in the test to capture the influence of sequential teaching of materials on increasing the probability of getting an item a correct answer. Furthermore, Differential Item Functioning (DIF) analysis was applied to detect whether the test functions as a reason for the gender gap in participants’ achievement. Finally, classiï¬ cation consistency and accuracy indices are studied. The results showed that the G-DINA and one of the HDCMs fit the data well. However, although the results of HDCM showed the existence of attribute dependencies in the reading comprehension test, the relative fit indices highlight a significant difference between the G-DINA and HDCM, favoring G-DINA. Moreover, results indicate that there is a significant difference between males and females in six items in favor of females. Besides, classiï¬ cation consistency and accuracy indices specify that the Iranian University Entrance Examination holds a 71% chance of categorizing a randomly selected test taker consistently on two distinct test settings and a 78% likelihood of accurately classifying any randomly selected student into the true latent classes. As a result, it can be concluded that the Iranian University Entrance Examination can be considered as a valid and reliable test.

Suggested Citation

  • Mona Tabatabaee-Yazdi, 2020. "Hierarchical Diagnostic Classification Modeling of Reading Comprehension," SAGE Open, , vol. 10(2), pages 21582440209, June.
  • Handle: RePEc:sae:sagope:v:10:y:2020:i:2:p:2158244020931068
    DOI: 10.1177/2158244020931068
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    References listed on IDEAS

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    1. Jonathan Templin & Laine Bradshaw, 2014. "Hierarchical Diagnostic Classification Models: A Family of Models for Estimating and Testing Attribute Hierarchies," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 317-339, April.
    2. Jimmy Torre & Jeffrey Douglas, 2004. "Higher-order latent trait models for cognitive diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 333-353, September.
    3. E. Maris, 1999. "Estimating multiple classification latent class models," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 187-212, June.
    4. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    5. Jimmy Torre, 2011. "Erratum to: The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 510-510, July.
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