IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v35y2010i4p422-452.html
   My bibliography  Save this article

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

Author

Listed:
  • Carolyn J. Anderson
  • Jay Verkuilen
  • Buddy L. Peyton

Abstract

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of items from a large political psychology survey. In particular, LMA models are shown to provide a useful analysis of items when the proper scoring rule for them is unclear. They also clearly reveal the performance of the items when instructions were altered to suppress “Don’t know†responses. LMA models can be fit rapidly using commonly available software.

Suggested Citation

  • Carolyn J. Anderson & Jay Verkuilen & Buddy L. Peyton, 2010. "Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 422-452, August.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:4:p:422-452
    DOI: 10.3102/1076998609353117
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998609353117
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998609353117?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. Darrell Bock, 1972. "Estimating item parameters and latent ability when responses are scored in two or more nominal categories," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 29-51, March.
    2. Anderson, Carolyn J. & Li, Zhushan & Vermunt, Jeroen K., 2007. "Estimation of Models in a Rasch Family for Polytomous Items and Multiple Latent Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i06).
    3. Carolyn Anderson & Hsiu-Ting Yu, 2007. "Log-Multiplicative Association Models as Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 72(1), pages 5-23, March.
    4. Joe, Harry & Liu, Ying, 1996. "A model for a multivariate binary response with covariates based on compatible conditionally specified logistic regressions," Statistics & Probability Letters, Elsevier, vol. 31(2), pages 113-120, 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. Carolyn Anderson, 2013. "Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 276-303, July.
    2. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    3. Gunter Maris & Timo Bechger & Ernesto Martin, 2015. "A Gibbs Sampler for the (Extended) Marginal Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 859-879, December.
    4. repec:jss:jstsof:20:i06 is not listed on IDEAS
    5. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
    6. Bas Hemker & Klaas Sijtsma & Ivo Molenaar & Brian Junker, 1996. "Polytomous IRT models and monotone likelihood ratio of the total score," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 679-693, December.
    7. Sijia Huang & Li Cai, 2024. "Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching," Journal of Educational and Behavioral Statistics, , vol. 49(3), pages 311-341, June.
    8. 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.
    9. David J. Hessen, 2023. "Fitting and Testing Log-Linear Subpopulation Models with Known Support," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 917-939, September.
    10. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    11. 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.
    12. Javier Revuelta, 2004. "Analysis of distractor difficulty in multiple-choice items," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 217-234, June.
    13. repec:jss:jstsof:20:i01 is not listed on IDEAS
    14. Ulf Böckenholt, 2012. "The Cognitive-Miser Response Model: Testing for Intuitive and Deliberate Reasoning," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 388-399, April.
    15. Albert Yu & Jeffrey A. Douglas, 2023. "IRT Models for Learning With Item-Specific Learning Parameters," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 866-888, December.
    16. de Leeuw, Jan & Mair, Patrick, 2007. "An Introduction to the Special Volume on "Psychometrics in R"," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i01).
    17. John Hsu & Tom Leonard & Kam-Wah Tsui, 1991. "Statistical inference for multiple choice tests," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 327-348, June.
    18. Ghattas Badih & Michel Pierre & Boyer Laurent, 2019. "Assessing variable importance in clustering: a new method based on unsupervised binary decision trees," Computational Statistics, Springer, vol. 34(1), pages 301-321, March.
    19. David Andrich, 2010. "Sufficiency and Conditional Estimation of Person Parameters in the Polytomous Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 292-308, June.
    20. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2018. "Robust Measurement via A Fused Latent and Graphical Item Response Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 538-562, September.
    21. repec:hal:journl:hal-03533356 is not listed on IDEAS
    22. Zachary F. Fisher & Kenneth A. Bollen, 2020. "An Instrumental Variable Estimator for Mixed Indicators: Analytic Derivatives and Alternative Parameterizations," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 660-683, September.
    23. 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.

    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:35:y:2010:i:4:p:422-452. 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.