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A Nondiagnostic Assessment for Diagnostic Purposes: Q-Matrix Validation and Item-Based Model Fit Evaluation for the TIMSS 2011 Assessment

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  • Ragip Terzi
  • Sedat Sen

Abstract

Large-scale assessments are generally designed for summative purposes to compare achievement among participating countries. However, these nondiagnostic assessments have also been adapted in the context of cognitive diagnostic assessment for diagnostic purposes. Following the large amount of investments in these assessments, it would be cost-effective to draw finer-grained inferences about the attribute mastery. Nonetheless, the correctness of attribute specifications in the Q-matrix has not been verified, despite being designed by domain experts. Furthermore, the underlying process of TIMSS (Trends in International Mathematics and Science Study) assessment is unknown as it was not developed for diagnostic purposes. Thus, this study suggests an initial validating attribute specifications in the Q-matrix and thereafter defining specific reduced or saturated models for each item. In doing so, the two analyses were validated across 20 countries that were selected randomly for TIMSS 2011 data. Results show that attribute specifications can differ from expert opinions and the underlying model for each item can vary.

Suggested Citation

  • Ragip Terzi & Sedat Sen, 2019. "A Nondiagnostic Assessment for Diagnostic Purposes: Q-Matrix Validation and Item-Based Model Fit Evaluation for the TIMSS 2011 Assessment," SAGE Open, , vol. 9(1), pages 21582440198, February.
    • Handle: RePEc:sae:sagope:v:9:y:2019:i:1:p:2158244019832684
    DOI: 10.1177/2158244019832684
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    References listed on IDEAS

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    1. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    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. Ying Cheng, 2009. "When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 619-632, December.
    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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