IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v83y2018i4d10.1007_s11336-018-9638-5.html
   My bibliography  Save this article

Assessing Growth in a Diagnostic Classification Model Framework

Author

Listed:
  • Matthew J. Madison

    (Clemson University)

  • Laine P. Bradshaw

    (University of Georgia)

Abstract

A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time. In a diagnostic classification model (DCM) framework, assessing growth translates to measuring changes in attribute mastery status over time, thereby providing a categorical, criterion-referenced interpretation of growth. This study introduces the Transition Diagnostic Classification Model (TDCM), which combines latent transition analysis with the log-linear cognitive diagnosis model to provide methodology for analyzing growth in a general DCM framework. Simulation study results indicate that the proposed model is flexible, provides accurate and reliable classifications, and is quite robust to violations to measurement invariance over time. The TDCM is used to analyze pre-test/post-test data from a diagnostic mathematics assessment.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:83:y:2018:i:4:d:10.1007_s11336-018-9638-5
    DOI: 10.1007/s11336-018-9638-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-018-9638-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-018-9638-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Erling Andersen, 1985. "Estimating latent correlations between repeated testings," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 3-16, March.
    2. Rolf Langeheine & Jeroen Pannekoek & Frank Van De Pol, 1996. "Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis," Sociological Methods & Research, , vol. 24(4), pages 492-516, May.
    3. Jonathan Templin & Laine Bradshaw, 2013. "Measuring the Reliability of Diagnostic Classification Model Examinee Estimates," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 251-275, July.
    4. Gerhard Fischer, 1989. "An irt-based model for dichotomous longitudinal data," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 599-624, September.
    5. Nambury Raju, 1988. "The area between two item characteristic curves," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 495-502, December.
    6. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    7. Shiyu Wang & Yan Yang & Steven Andrew Culpepper & Jeffrey A. Douglas, 2018. "Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 57-87, February.
    8. Susan Embretson, 1991. "A multidimensional latent trait model for measuring learning and change," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 495-515, September.
    9. William Meredith & Roger Millsap, 1992. "On the misuse of manifest variables in the detection of measurement bias," Psychometrika, Springer;The Psychometric Society, vol. 57(2), pages 289-311, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Joemari Olea & Kevin Carl Santos, 2024. "A General Mixture Model for Cognitive Diagnosis," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 268-307, April.
    6. 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.
    7. 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.

    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 & 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.
    2. Shiyu Wang & Yan Yang & Steven Andrew Culpepper & Jeffrey A. Douglas, 2018. "Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 57-87, February.
    3. Minjeong Jeon & Sophia Rabe-Hesketh, 2016. "An autoregressive growth model for longitudinal item analysis," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 830-850, September.
    4. 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.
    5. Chun Wang & Steven W. Nydick, 2020. "On Longitudinal Item Response Theory Models: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 339-368, June.
    6. Jerry Welkenhuysen-Gybels, 2004. "The Performance of Some Observed and Unobserved Conditional Invariance Techniques for the Detection of Differential Item Functioning," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(6), pages 681-702, December.
    7. 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.
    8. Kazuhiro Yamaguchi & Kensuke Okada, 2020. "Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 973-995, December.
    9. Timo Bechger & Gunter Maris, 2015. "A Statistical Test for Differential Item Pair Functioning," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 317-340, June.
    10. Muhamad Firdaus Mohd Noh & Mohd Effendi Ewan bin Mohd Matore, 2024. "Conundrum and Considerations in Cognitive Diagnostic Assessment for Language Proficiency Evaluation," Information Management and Business Review, AMH International, vol. 16(2), pages 63-72.
    11. Jonathan Templin & Laine Bradshaw, 2014. "Hierarchical Diagnostic Classification Models: A Family of Models for Estimating and Testing Attribute Hierarchies," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 317-339, April.
    12. Matthias Davier & Xueli Xu & Claus Carstensen, 2011. "Measuring Growth in a Longitudinal Large-Scale Assessment with a General Latent Variable Model," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 318-336, April.
    13. Gerhard Fischer, 1995. "Some neglected problems in IRT," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 459-487, December.
    14. David Hessen, 2005. "Constant latent odds-ratios models and the mantel-haenszel null hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 497-516, September.
    15. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 209-224, June.
    16. Susan Embretson, 1999. "Generating items during testing: Psychometric issues and models," Psychometrika, Springer;The Psychometric Society, vol. 64(4), pages 407-433, December.
    17. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2017. "Regularized Latent Class Analysis with Application in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 660-692, September.
    18. Guanhua Fang & Jingchen Liu & Zhiliang Ying, 2019. "On the Identifiability of Diagnostic Classification Models," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 19-40, March.
    19. Hans-Friedrich Köhn & Chia-Yi Chiu, 2017. "A Procedure for Assessing the Completeness of the Q-Matrices of Cognitively Diagnostic Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 112-132, March.
    20. Meulders, Michel & De Boeck, Paul & Vandenberk, Miek, 2009. "Going beyond the detection of differential item functioning in tests for personnel selection," Working Papers 2009/40, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.

    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:spr:psycho:v:83:y:2018:i:4:d:10.1007_s11336-018-9638-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.