IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v48y2023i6p690-718.html
   My bibliography  Save this article

Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach

Author

Listed:
  • Qianru Liang
  • Jimmy de la Torre
  • Nancy Law

    (The University of Hong Kong)

Abstract

To expand the use of cognitive diagnosis models (CDMs) to longitudinal assessments, this study proposes a bias-corrected three-step estimation approach for latent transition CDMs with covariates by integrating a general CDM and a latent transition model. The proposed method can be used to assess changes in attribute mastery status and attribute profiles and to evaluate the covariate effects on both the initial state and transition probabilities over time using latent (multinomial) logistic regression. Because stepwise approaches generally yield biased estimates, correction for classification error probabilities is considered in this study. The results of the simulation study showed that the proposed method yielded more accurate parameter estimates than the uncorrected approach. The use of the proposed method is also illustrated using a set of real data.

Suggested Citation

  • Qianru Liang & Jimmy de la Torre & Nancy Law, 2023. "Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 690-718, December.
    • Handle: RePEc:sae:jedbes:v:48:y:2023:i:6:p:690-718
    DOI: 10.3102/10769986231163320
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986231163320
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986231163320?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    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. Hwan Chung & Theodore Walls & Yousung Park, 2007. "A Latent Transition Model With Logistic Regression," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 413-435, September.
    4. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    5. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    6. Matthew J. Madison & Laine P. Bradshaw, 2018. "Assessing Growth in a Diagnostic Classification Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 963-990, December.
    7. Elizabeth Ayers & Sophia Rabe-Hesketh & Rebecca Nugent, 2013. "Incorporating Student Covariates in Cognitive Diagnosis Models," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 195-224, July.
    8. Jimmy Torre, 2011. "Erratum to: The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 510-510, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peida Zhan & Wen-Chung Wang & Xiaomin Li, 2020. "A Partial Mastery, Higher-Order Latent Structural Model for Polytomous Attributes in Cognitive Diagnostic Assessments," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 328-351, July.
    2. Yinghan Chen & Ying Liu & Steven Andrew Culpepper & Yuguo Chen, 2021. "Inferring the Number of Attributes for the Exploratory DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 30-64, March.
    3. Steven Andrew Culpepper, 2023. "A Note on Weaker Conditions for Identifying Restricted Latent Class Models for Binary Responses," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 158-174, March.
    4. Kazuhiro Yamaguchi & Jonathan Templin, 2022. "A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 24-54, March.
    5. Matthew S. Johnson & Sandip Sinharay, 2020. "The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 5-31, February.
    6. James Joseph Balamuta & Steven Andrew Culpepper, 2022. "Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA–GAMMA Data Augmentation," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 903-945, September.
    7. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference for the DINA Model," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 569-597, October.
    8. Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    9. Chen-Wei Liu & Björn Andersson & Anders Skrondal, 2020. "A Constrained Metropolis–Hastings Robbins–Monro Algorithm for Q Matrix Estimation in DINA Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 322-357, June.
    10. Chun Wang & Jing Lu, 2021. "Learning Attribute Hierarchies From Data: Two Exploratory Approaches," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 58-84, February.
    11. Hans Friedrich Köhn & Chia-Yi Chiu, 2021. "A Unified Theory of the Completeness of Q-Matrices for the DINA Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 500-518, October.
    12. Xuliang Gao & Wenchao Ma & Daxun Wang & Yan Cai & Dongbo Tu, 2021. "A Class of Cognitive Diagnosis Models for Polytomous Data," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 297-322, June.
    13. Jimmy de la Torre & Xue-Lan Qiu & Kevin Carl Santos, 2022. "An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 693-724, June.
    14. Wenyi Wang & Lihong Song & Teng Wang & Peng Gao & Jian Xiong, 2020. "A Note on the Relationship of the Shannon Entropy Procedure and the Jensen–Shannon Divergence in Cognitive Diagnostic Computerized Adaptive Testing," SAGE Open, , vol. 10(1), pages 21582440198, January.
    15. Steven Andrew Culpepper, 2019. "An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 921-940, December.
    16. Kazuhiro Yamaguchi & Jonathan Templin, 2022. "Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1390-1421, December.
    17. 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.
    18. Kazuhiro Yamaguchi, 2023. "Bayesian Analysis Methods for Two-Level Diagnosis Classification Models," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 773-809, December.
    19. Jürgen Heller & Luca Stefanutti & Pasquale Anselmi & Egidio Robusto, 2015. "On the Link between Cognitive Diagnostic Models and Knowledge Space Theory," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 995-1019, December.
    20. Motonori Oka & Kensuke Okada, 2023. "Scalable Bayesian Approach for the Dina Q-Matrix Estimation Combining Stochastic Optimization and Variational Inference," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 302-331, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:48:y:2023:i:6:p:690-718. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.