IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v35y2010i4p474-488.html
   My bibliography  Save this article

Two Simple Approaches to Overcome a Problem With the Mantel-Haenszel Statistic: Comments on Wang, Bradlow, Wainer, and Muller (2008)

Author

Listed:
  • Sandip Sinharay
  • Neil J. Dorans

Abstract

The Mantel-Haenszel (MH) procedure (Mantel and Haenszel) is a popular method for estimating and testing a common two-factor association parameter in a 2 × 2 × K table. Holland and Holland and Thayer described how to use the procedure to detect differential item functioning (DIF) for tests with dichotomously scored items. Wang, Bradlow, Wainer, and Muller showed that the MH procedure unexpectedly often found DIF where there was none for very easy items. They showed that their suggested Bayesian procedure for DIF detection did not have the same problem. We discuss a simpler solution of the problem—the use of the standardized difference in proportion correct (STD P-DIF) procedure in conjugation with the MH procedure—that has been used by DIF analysts since 1988. We also show, using results from real and simulated data, that it is possible to overcome the problem using the empirical Bayes (EB) procedure of Zwick, Thayer, and Lewis, which also is computationally simpler than the procedure suggested by Wang et al.

Suggested Citation

  • Sandip Sinharay & Neil J. Dorans, 2010. "Two Simple Approaches to Overcome a Problem With the Mantel-Haenszel Statistic: Comments on Wang, Bradlow, Wainer, and Muller (2008)," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 474-488, August.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:4:p:474-488
    DOI: 10.3102/1076998609359789
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998609359789
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998609359789?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. Eric Bradlow & Howard Wainer & Xiaohui Wang, 1999. "A Bayesian random effects model for testlets," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 153-168, 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. Howard Wainer & Eric Bradlow & Xiaohui Wang, 2010. "Detecting DIF: Many Paths to Salvation," Journal of Educational and Behavioral Statistics, , vol. 35(4), pages 489-493, August.
    2. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2013. "Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 32-60, February.

    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. Quinn N. Lathrop & Ying Cheng, 2017. "Item Cloning Variation and the Impact on the Parameters of Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 245-263, March.
    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. Martijn G. de Jong & Jan-Benedict E. M. Steenkamp & Bernard P. Veldkamp, 2009. "A Model for the Construction of Country-Specific Yet Internationally Comparable Short-Form Marketing Scales," Marketing Science, INFORMS, vol. 28(4), pages 674-689, 07-08.
    4. Alexander Robitzsch, 2023. "Linking Error in the 2PL Model," J, MDPI, vol. 6(1), pages 1-27, January.
    5. Jean-Paul Fox & Cees Glas, 2001. "Bayesian estimation of a multilevel IRT model using gibbs sampling," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 271-288, June.
    6. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    7. Sun-Joo Cho & Paul Boeck & Susan Embretson & Sophia Rabe-Hesketh, 2014. "Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 84-104, January.
    8. Xin Xu & Guanhua Fang & Jinxin Guo & Zhiliang Ying & Susu Zhang, 2024. "Diagnostic Classification Models for Testlets: Methods and Theory," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 851-876, September.
    9. Martijn G. de Jong & Donald R. Lehmann & Oded Netzer, 2012. "State-Dependence Effects in Surveys," Marketing Science, INFORMS, vol. 31(5), pages 838-854, September.
    10. Michela Gnaldi & Silvia Bacci & Thiemo Kunze & Samuel Greiff, 2020. "Students’ Complex Problem Solving Profiles," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 469-501, June.
    11. Li Cai, 2010. "A Two-Tier Full-Information Item Factor Analysis Model with Applications," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 581-612, December.
    12. Alexander Robitzsch, 2024. "A Comparison of Limited Information Estimation Methods for the Two-Parameter Normal-Ogive Model with Locally Dependent Items," Stats, MDPI, vol. 7(3), pages 1-16, June.
    13. 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.
    14. Hamdollah Ravand, 2015. "Assessing Testlet Effect, Impact, Differential Testlet, and Item Functioning Using Cross-Classified Multilevel Measurement Modeling," SAGE Open, , vol. 5(2), pages 21582440155, May.
    15. repec:jss:jstsof:36:c01 is not listed on IDEAS
    16. 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.
    17. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2013. "Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 32-60, February.
    18. Yang Liu & Jan Hannig, 2017. "Generalized Fiducial Inference for Logistic Graded Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1097-1125, December.
    19. Heleno Bolfarine & Jorge Luis Bazan, 2010. "Bayesian Estimation of the Logistic Positive Exponent IRT Model," Journal of Educational and Behavioral Statistics, , vol. 35(6), pages 693-713, December.
    20. John R. Busenbark & Jonathan Bundy & M.K. Chin, 2023. "Director departure following political ideology (in)congruence with an incoming CEO," Strategic Management Journal, Wiley Blackwell, vol. 44(7), pages 1698-1732, July.
    21. Johan Braeken & Francis Tuerlinckx & Paul Boeck, 2007. "Copula Functions for Residual Dependency," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 393-411, September.

    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:474-488. 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.