IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v48y2023i6p914-941.html
   My bibliography  Save this article

A Diagnostic Tree Model for Adaptive Assessment of Complex Cognitive Processes Using Multidimensional Response Options

Author

Listed:
  • Mark L. Davison
  • David J. Weiss

    (University of Minnesota)

  • Joseph N. DeWeese

    (University of Minnesota)

  • Ozge Ersan

    (Turkish Ministry of National Education)

  • Gina Biancarosa
  • Patrick C. Kennedy

    (University of Oregon)

Abstract

A tree model for diagnostic educational testing is described along with Monte Carlo simulations designed to evaluate measurement accuracy based on the model. The model is implemented in an assessment of inferential reading comprehension, the Multiple-Choice Online Causal Comprehension Assessment (MOCCA), through a sequential, multidimensional, computerized adaptive testing (CAT) strategy. Assessment of the first dimension, reading comprehension (RC), is based on the three-parameter logistic model. For diagnostic and intervention purposes, the second dimension, called process propensity (PP), is used to classify struggling students based on their pattern of incorrect responses. In the simulation studies, CAT item selection rules and stopping rules were varied to evaluate their effect on measurement accuracy along dimension RC and classification accuracy along dimension PP. For dimension RC, methods that improved accuracy tended to increase test length. For dimension PP, however, item selection and stopping rules increased classification accuracy without materially increasing test length. A small live-testing pilot study confirmed some of the findings of the simulation studies. Development of the assessment has been guided by psychometric theory, Monte Carlo simulation results, and a theory of instruction and diagnosis.

Suggested Citation

  • Mark L. Davison & David J. Weiss & Joseph N. DeWeese & Ozge Ersan & Gina Biancarosa & Patrick C. Kennedy, 2023. "A Diagnostic Tree Model for Adaptive Assessment of Complex Cognitive Processes Using Multidimensional Response Options," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 914-941, December.
  • Handle: RePEc:sae:jedbes:v:48:y:2023:i:6:p:914-941
    DOI: 10.3102/10769986231158301
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986231158301
    Download Restriction: no

    File URL: https://libkey.io/10.3102/10769986231158301?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. Laine Bradshaw & Jonathan Templin, 2014. "Combining Item Response Theory and Diagnostic Classification Models: A Psychometric Model for Scaling Ability and Diagnosing Misconceptions," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 403-425, July.
    2. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    3. Timothy R. Johnson & Daniel M. Bolt, 2010. "On the Use of Factor-Analytic Multinomial Logit Item Response Models to Account for Individual Differences in Response Style," Journal of Educational and Behavioral Statistics, , vol. 35(1), pages 92-114, February.
    4. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
    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. Anne Thissen-Roe & David Thissen, 2013. "A Two-Decision Model for Responses to Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 522-547, October.
    2. Nana Kim & Daniel M. Bolt & James Wollack, 2022. "Noncompensatory MIRT For Passage-Based Tests," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 992-1009, September.
    3. repec:zbw:rwidps:0002 is not listed on IDEAS
    4. Dahmann, Sarah C., 2017. "How does education improve cognitive skills? Instructional time versus timing of instruction," Labour Economics, Elsevier, vol. 47(C), pages 35-47.
    5. Andrés López-Sepulcre & Sebastiano De Bona & Janne K. Valkonen & Kate D.L. Umbers & Johanna Mappes, 2015. "Item Response Trees: a recommended method for analyzing categorical data in behavioral studies," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(5), pages 1268-1273.
    6. Torberg Falch & Justina AV Fischer, 2008. "Does a generous welfare state crowd out student achievement? Panel data evidence from international student tests," TWI Research Paper Series 31, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    7. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    8. Steger, Diana & Schroeders, Ulrich & Wilhelm, Oliver, 2019. "On the dimensionality of crystallized intelligence: A smartphone-based assessment," Intelligence, Elsevier, vol. 72(C), pages 76-85.
    9. Janna Niens & Lisa Richter-Beuschel & Tobias C. Stubbe & Susanne Bögeholz, 2021. "Procedural Knowledge of Primary School Teachers in Madagascar for Teaching and Learning towards Land-Use- and Health-Related Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-36, August.
    10. 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.
    11. Xiang Liu & James Yang & Hui Soo Chae & Gary Natriello, 2020. "Power Divergence Family of Statistics for Person Parameters in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 502-525, June.
    12. Chun Wang, 2015. "On Latent Trait Estimation in Multidimensional Compensatory Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 428-449, June.
    13. Marko Böhm & Jan Barkmann & Sabina Eggert & Claus H. Carstensen & Susanne Bögeholz, 2020. "Quantitative Modelling and Perspective Taking: Two Competencies of Decision Making for Sustainable Development," Sustainability, MDPI, vol. 12(17), pages 1-32, August.
    14. repec:zbw:rwidps:0023 is not listed on IDEAS
    15. Hammon, Angelina & Zinn, Sabine, 2020. "Multiple imputation of binary multilevel missing not at random data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 69(3), pages 547-564.
    16. Schmidt, Christoph & Fertig, Michael, 2002. "The Role of Background Factors for Reading Literacy: Straight National scores in the Pisa 2000 Study," CEPR Discussion Papers 3544, C.E.P.R. Discussion Papers.
    17. Yang Liu & Jan Hannig & Abhishek Pal Majumder, 2019. "Second-Order Probability Matching Priors for the Person Parameter in Unidimensional IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 701-718, September.
    18. Robitzsch, Alexander, 2020. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45, Center for Open Science.
    19. Haruhiko Ogasawara, 2013. "Asymptotic properties of the Bayes modal estimators of item parameters in item response theory," Computational Statistics, Springer, vol. 28(6), pages 2559-2583, December.
    20. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence From the PISA 2000 Study," RWI Discussion Papers 2, RWI - Leibniz-Institut für Wirtschaftsforschung.
    21. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    22. Seonghoon Kim, 2012. "A Note on the Reliability Coefficients for Item Response Model-Based Ability Estimates," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 153-162, January.

    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:914-941. 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.