IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v42y2017i6p651-677.html
   My bibliography  Save this article

A Cognitive Diagnosis Model for Continuous Response

Author

Listed:
  • Nathan D. Minchen

    (Rutgers, The State University of New Jersey)

  • Jimmy de la Torre

    (The University of Hong Kong)

  • Ying Liu

    (University of Southern California)

Abstract

Nondichotomous response models have been of greater interest in recent years due to the increasing use of different scoring methods and various performance measures. As an important alternative to dichotomous scoring, the use of continuous response formats has been found in the literature. To assess finer-grained skills or attributes and to extract information with diagnostic value from continuous response data, a multidimensional skills diagnosis model for continuous response is proposed. An expectation-maximization implementation of marginal maximum likelihood estimation is developed to estimate its parameters. The viability of the proposed model is shown via a simulation study and a real data example. The proposed model is also shown to provide a substantial improvement in attribute classification when compared to a model based on dichotomized continuous responses.

Suggested Citation

  • Nathan D. Minchen & Jimmy de la Torre & Ying Liu, 2017. "A Cognitive Diagnosis Model for Continuous Response," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 651-677, December.
    • Handle: RePEc:sae:jedbes:v:42:y:2017:i:6:p:651-677
    DOI: 10.3102/1076998617703060
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998617703060
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998617703060?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. 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. Fumiko Samejima, 1973. "Homogeneous case of the continuous response model," Psychometrika, Springer;The Psychometric Society, vol. 38(2), pages 203-219, June.
    3. Fumiko Samejima, 1974. "Normal ogive model on the continuous response level in the multidimensional latent space," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 111-121, March.
    4. Yvonnick Noel, 2014. "A Beta Unfolding Model for Continuous Bounded Responses," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 647-674, October.
    5. Gunter Maris & Han Maas, 2012. "Speed-Accuracy Response Models: Scoring Rules based on Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 615-633, October.
    6. Ying Cheng, 2009. "When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 619-632, December.
    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. C. Glas & Anna Dagohoy, 2007. "A Person Fit Test For Irt Models For Polytomous Items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 159-180, June.
    2. Yi-Hsuan Lee & Zhiliang Ying, 2015. "A Mixture Cure-Rate Model for Responses and Response Times in Time-Limit Tests," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 748-775, September.
    3. Matthias Kloft & Raphael Hartmann & Andreas Voss & Daniel W. Heck, 2023. "The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 888-916, September.
    4. Sandip Sinharay & Peter W. van Rijn, 2020. "Assessing Fit of the Lognormal Model for Response Times," Journal of Educational and Behavioral Statistics, , vol. 45(5), pages 534-568, October.
    5. 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.
    6. Yvonnick Noel, 2014. "A Beta Unfolding Model for Continuous Bounded Responses," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 647-674, October.
    7. Wojciech Łukasz Dragan & Andrzej Śliwerski & Monika Folkierska-Żukowska, 2022. "New data on the validity of the Fazio Laterality Inventory," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
    8. Ying Cheng & Ke-Hai Yuan, 2010. "The Impact of Fallible Item Parameter Estimates on Latent Trait Recovery," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 280-291, June.
    9. Alberto Maydeu-Olivares & Rosa Montaño, 2013. "How Should We Assess the Fit of Rasch-Type Models? Approximating the Power of Goodness-of-Fit Statistics in Categorical Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 116-133, January.
    10. Carolina Navarro & Luis Ayala & José Labeaga, 2010. "Housing deprivation and health status: evidence from Spain," Empirical Economics, Springer, vol. 38(3), pages 555-582, June.
    11. Merkle, Edgar C. & Steyvers, Mark & Mellers, Barbara & Tetlock, Philip E., 2017. "A neglected dimension of good forecasting judgment: The questions we choose also matter," International Journal of Forecasting, Elsevier, vol. 33(4), pages 817-832.
    12. Alexander Robitzsch, 2024. "Bias-Reduced Haebara and Stocking–Lord Linking," J, MDPI, vol. 7(3), pages 1-12, September.
    13. Inhan Kang & Paul Boeck & Roger Ratcliff, 2022. "Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 725-748, June.
    14. Joel A. Martínez-Regalado & Cinthia Leonora Murillo-Avalos & Purificación Vicente-Galindo & Mónica Jiménez-Hernández & José Luis Vicente-Villardón, 2021. "Using HJ-Biplot and External Logistic Biplot as Machine Learning Methods for Corporate Social Responsibility Practices for Sustainable Development," Mathematics, MDPI, vol. 9(20), pages 1-16, October.
    15. Cafarelli Ryan & Rigdon Christopher J. & Rigdon Steven E., 2012. "Models for Third Down Conversion in the National Football League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-26, October.
    16. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    17. Michela Battauz & Ruggero Bellio, 2011. "Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 40-56, January.
    18. Melissa Gladstone & Gillian Lancaster & Gareth McCray & Vanessa Cavallera & Claudia R. L. Alves & Limbika Maliwichi & Muneera A. Rasheed & Tarun Dua & Magdalena Janus & Patricia Kariger, 2021. "Validation of the Infant and Young Child Development (IYCD) Indicators in Three Countries: Brazil, Malawi and Pakistan," IJERPH, MDPI, vol. 18(11), pages 1-19, June.
    19. Alexander Robitzsch, 2023. "Linking Error in the 2PL Model," J, MDPI, vol. 6(1), pages 1-27, January.
    20. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.

    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:42:y:2017:i:6:p:651-677. 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.