IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v35y2010i2p154-173.html
   My bibliography  Save this article

A Modified General Location Model for Noncompliance With Missing Data

Author

Listed:
  • Hui Jin

    (Harvard University)

  • John Barnard

    (The Cleveland Clinic Foundation)

  • Donald B. Rubin

    (Harvard University)

Abstract

Missing data, especially when coupled with noncompliance, are a challenge even in the setting of randomized experiments. Although some existing methods can address each complication, it can be difficult to handle both of them simultaneously. This is true in the example of the New York City School Choice Scholarship Program, where both the covariates and the outcomes were sometimes missing, and there was complicated noncompliance. The authors propose a modified general location model to integrate the ideas of missing data techniques and principal stratification and then analyze the same data as in Barnard, Frangakis, Hill, and Rubin (2003) , where a pattern-mixture model was used. Their results are presented and compared with those in Barnard et al.

Suggested Citation

  • Hui Jin & John Barnard & Donald B. Rubin, 2010. "A Modified General Location Model for Noncompliance With Missing Data," Journal of Educational and Behavioral Statistics, , vol. 35(2), pages 154-173, April.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:2:p:154-173
    DOI: 10.3102/1076998609346968
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998609346968
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998609346968?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. Jin, Hui & Rubin, Donald B., 2008. "Principal Stratification for Causal Inference With Extended Partial Compliance," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 101-111, March.
    2. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    3. Barnard J. & Frangakis C.E. & Hill J.L. & Rubin D.B., 2003. "Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 299-323, January.
    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. von Hinke, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2016. "Genetic markers as instrumental variables," Journal of Health Economics, Elsevier, vol. 45(C), pages 131-148.
    2. Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
    3. Michael E. Sobel & Bengt Muthén, 2012. "Compliance Mixture Modelling with a Zero-Effect Complier Class and Missing Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1037-1045, December.
    4. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.
    5. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    6. Lindsay C. Page, 2012. "Understanding the Impact of Career Academy Attendance," Evaluation Review, , vol. 36(2), pages 99-132, April.
    7. Qi Long & Roderick J. A. Little & Xihong Lin, 2010. "Estimating causal effects in trials involving multitreatment arms subject to non‐compliance: a Bayesian framework," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 513-531, May.
    8. Bartolucci, Francesco & Grilli, Leonardo, 2011. "Modeling Partial Compliance Through Copulas in a Principal Stratification Framework," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 469-479.
    9. John Engberg & Dennis Epple & Jason Imbrogno & Holger Sieg & Ron Zimmer, 2014. "Evaluating Education Programs That Have Lotteried Admission and Selective Attrition," Journal of Labor Economics, University of Chicago Press, vol. 32(1), pages 27-63.
    10. VanderWeele, Tyler J., 2008. "Simple relations between principal stratification and direct and indirect effects," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2957-2962, December.
    11. van Hasselt, Martijn & Ferland, Timothy & Bray, Jeremy & Aldridge, Arnie, 2017. "Bayesian Estimation of the Complier Average Casual Effect," UNCG Economics Working Papers 17-14, University of North Carolina at Greensboro, Department of Economics.
    12. Julian Wolfson & Peter Gilbert, 2010. "Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials," Biometrics, The International Biometric Society, vol. 66(4), pages 1153-1161, December.
    13. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: evidence from an instrumental variable analysis of China's one‐child policy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1615-1635, October.
    14. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy," Papers 2005.09130, arXiv.org, revised Jun 2020.
    15. Corwin M. Zigler & Thomas R. Belin, 2012. "A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint," Biometrics, The International Biometric Society, vol. 68(3), pages 922-932, September.
    16. Li He & Yu-Bo Wang & William C. Bridges & Zhulin He & S. Megan Che, 2023. "Bayesian Framework for Causal Inference with Principal Stratification and Clusters," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 114-140, April.
    17. Zhai, Fuhua & Raver, C. Cybele & Jones, Stephanie M., 2012. "Academic performance of subsequent schools and impacts of early interventions: Evidence from a randomized controlled trial in Head Start settings," Children and Youth Services Review, Elsevier, vol. 34(5), pages 946-954.
    18. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    19. Michael R. Elliott & Anna Conlon & Yun Li, 2013. "Discussion on “Surrogate Measures and Consistent Surrogates”," Biometrics, The International Biometric Society, vol. 69(3), pages 565-569, September.
    20. Bubb, Ryan & Kaufman, Alex, 2014. "Securitization and moral hazard: Evidence from credit score cutoff rules," Journal of Monetary Economics, Elsevier, vol. 63(C), pages 1-18.

    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:2:p:154-173. 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.