Dergiler / International Journal of Assessment Tools in Education / 2018 / Cilt: 5 - Sayı: 2

An Iterative Method for Empirically-Based Q-Matrix Validation

Sayfa
248–262
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Finally, the method is applied to real data using fraction-subtraction data.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In cognitive diagnosis modeling, the attributes required for each item are specified in the Q-matrix.
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Temel Bulgular & Veri:The traditional way of constructing a Q-matrix based on expert opinion is inherently subjective, consequently resulting in serious validity concerns.
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Sonuç & Katkı:Finally, the method is applied to real data using fraction-subtraction data.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Cognitive diagnosis model · Q-matrix validation · DINA

Özet

In cognitive diagnosis modeling, the attributes required for each item are specified in the Q-matrix. The traditional way of constructing a Q-matrix based on expert opinion is inherently subjective, consequently resulting in serious validity concerns. The current study proposes a new validation method under the deterministic inputs, noisy “and” gate (DINA) model to empirically validate attribute specifications in the Q-matrix. In particular, an iterative procedure with a modified version of the sequential search algorithm is introduced. Simulation studies are conducted to compare the proposed method with existing parametric and nonparametric methods. Results show that the new method outperforms the other methods across the board. Finally, the method is applied to real data using fraction-subtraction data.

Abstract

In cognitive diagnosis modeling, the attributes required for each item are specified in the Q-matrix. The traditional way of constructing a Q-matrix based on expert opinion is inherently subjective, consequently resulting in serious validity concerns. The current study proposes a new validation method under the deterministic inputs, noisy “and” gate (DINA) model to empirically validate attribute specifications in the Q-matrix. In particular, an iterative procedure with a modified version of the sequential search algorithm is introduced. Simulation studies are conducted to compare the proposed method with existing parametric and nonparametric methods. Results show that the new method outperforms the other methods across the board. Finally, the method is applied to real data using fraction-subtraction data.

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Bu Makaleye Atıf Yap

TORRE, J. D. L., TERZİ, R. (2022). An Iterative Method for Empirically-Based Q-Matrix Validation. International Journal of Assessment Tools in Education, Cilt: 5 - Sayı: 2, 248–262. https://ajindex.com/articles/540199

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Kaynakça (8)

  1. Akbay, L., Terzi, R., Kaplan, M., & Karaaslan, K. G. (2018). Expert-based attribute identification and validation: An application of cognitively diagnostic assessment. Journal on Mathematics Education, 9, 103-120.
  2. Chiu, C. (2013). Statistical refinement of the Q-matrix in cognitive diagnosis. Applied Psychological Measurement 37, 598-618.
  3. Chiu, C., & Douglas, J. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification 37, 225-250.
  4. Chiu, C., Douglas, J., & Li, X. (2009). Cluster analysis for cognitive diagnosis: Theory and applications. Psychometrika. 74, 633-665.
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  6. DeCarlo, L. T. (2011). On the analysis of fraction subtraction data: The DINA model, classification, latent class sizes, and the Q-matrix. Applied Psychological Measurement, 35(1), 8-26. de la Torre, J. (2008). An empirically based method of Q-Matrix validation for the DINA model: development and applications. Journal of Educational Measurement, 45, 343-362. de la Torre, J. (2009a). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33, 163-183. de la Torre, J. (2009b). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34, 115-130. de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76, 179-199. de la Torre, J., & Chiu, C.-Y. (2016). A general method of empirical Q-matrix validation. Psychometrika, 81, 253-273. de la Torre, J., & Douglas, J. A. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333-353. de la Torre, J., & Douglas, J. A. (2008). Model evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data. Psychometrika, 73, 595-624.
  7. Doornik, J. A. (2009). An object-oriented matrix programming language Ox 6. [Computer software]. London, UK: Timberlake Consultants Ltd. Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26, 333–352.
  8. Hou, L., de la Torre, J., & Nandakumar, R. (2014). Differential item functioning assessment in cognitive diagnostic modeling: Application of the wald test to investigate DIF in the DINA model. Journal of Educational Measureme