IDEAS home Printed from https://ideas.repec.org/a/zbw/espost/222432.html
   My bibliography  Save this article

Multiple imputation of binary multilevel missing not at random data

Author

Listed:
  • Hammon, Angelina
  • Zinn, Sabine

Abstract

Summary We introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first equation of the model defines the regression model for the missing data mechanism. The second equation specifies the regression model of the variable to be imputed. The non-random selection of the binary data is mapped by correlations between the error terms of the two regression models. Hierarchical data structures are modelled by random intercepts in both equations. To fit the novel imputation model we use maximum likelihood and adaptive Gauss–Hermite quadrature. A comprehensive simulation study shows the overall performance of the approach. We test its usefulness for empirical research by applying it to a common problem in social scientific research: the emergence of educational aspirations. Our software is designed to be used in the R package mice.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:espost:222432
    DOI: 10.1111/rssc.12401
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/222432/1/HAMMON.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12401?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. Donald B. Rubin, 2003. "Nested multiple imputation of NMES via partially incompatible MCMC," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 3-18, February.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. William Greene, 2004. "Convenient estimators for the panel probit model: Further results," Empirical Economics, Springer, vol. 29(1), pages 21-47, January.
    4. D. Pfeffermann & C. J. Skinner & D. J. Holmes & H. Goldstein & J. Rasbash, 1998. "Weighting for unequal selection probabilities in multilevel models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 23-40.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    6. Eric Delattre & Richard Moussa, 2015. "On the estimation of causality in a bivariate dynamic probit model on panel data with Stata software. A technical review," THEMA Working Papers 2015-04, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    7. Emmanuel Lesaffre & Bart Spiessens, 2001. "On the effect of the number of quadrature points in a logistic random effects model: an example," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 325-335.
    8. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    9. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
    10. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    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. Reginald Masimba Mbona, 2021. "The Impact of China-Africa Trade on Industries in Africa: A Case Study of FOCAC and BRI," Business and Economic Research, Macrothink Institute, vol. 11(2), pages 301-318, June.
    2. Chunhua Chen & Jianwei Ren & Lijun Tang & Haohua Liu, 2020. "Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.

    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. Angelina Hammon & Sabine Zinn, 2020. "Multiple imputation of binary multilevel missing not at random data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 547-564, June.
    2. Belderbos, Rene & Carree, Martin & Diederen, Bert & Lokshin, Boris & Veugelers, Reinhilde, 2004. "Heterogeneity in R&D cooperation strategies," International Journal of Industrial Organization, Elsevier, vol. 22(8-9), pages 1237-1263, November.
    3. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2021. "On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 430-465, August.
    4. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    5. Lara Lopez & Fernando L. Vázquez & Ángela J. Torres & Patricia Otero & Vanessa Blanco & Olga Díaz & Mario Páramo, 2020. "Long-Term Effects of a Cognitive Behavioral Conference Call Intervention on Depression in Non-Professional Caregivers," IJERPH, MDPI, vol. 17(22), pages 1-24, November.
    6. Lorenzo Cappellari & Stephen P. Jenkins, 2006. "Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation," Stata Journal, StataCorp LP, vol. 6(2), pages 156-189, June.
    7. Hoque, Mohammad Mainul & Artz, Georgeanne M. & Jarboe, Darren H. & Martens, Bobby J., 2015. "Producer Participation In Biomass Markets: Farm Factors, Market Factors, And Correlated Choices," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 47(3), pages 317-344, August.
    8. Peter Haan & Arne Uhlendorff, 2006. "Estimation of multinomial logit models with unobserved heterogeneity using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 6(2), pages 229-245, June.
    9. Richard Gates, 2006. "A Mata Geweke–Hajivassiliou–Keane multivariate normal simulator," Stata Journal, StataCorp LP, vol. 6(2), pages 190-213, June.
    10. Moura, Guilherme V. & Richard, Jean-François & Liesenfeld, Roman, 2007. "Dynamic Panel Probit Models for Current Account Reversals and their Efficient Estimation," Economics Working Papers 2007-11, Christian-Albrechts-University of Kiel, Department of Economics.
    11. Alexander Robitzsch, 2021. "A Comprehensive Simulation Study of Estimation Methods for the Rasch Model," Stats, MDPI, vol. 4(4), pages 1-23, October.
    12. Aditya R. Khanal & Ashok K. Mishra & Omobolaji Omobitan, 2019. "Examining organic, agritourism, and agri-environmental diversification decisions of American farms: are these decisions interlinked?," Review of Agricultural, Food and Environmental Studies, Springer, vol. 100(1), pages 27-45, December.
    13. Zamboanga, Byron L. & Merrill, Jennifer E. & Olthuis, Janine V. & Milroy, Jeffrey J. & Sokolovsky, Alexander W. & Wyrick, David L., 2019. "Secondary effects of myPlaybook on college athletes’ avoidance of drinking games or pregaming as a protective behavior strategy: A multisite randomized controlled study," Social Science & Medicine, Elsevier, vol. 228(C), pages 135-141.
    14. Harold Doran, 2023. "A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 37-69, February.
    15. Stephen J. Lepore & Bradley N. Collins & Donna L. Coffman & Jonathan P. Winickoff & Uma S. Nair & Beth Moughan & Tyra Bryant-Stephens & Daniel Taylor & David Fleece & Melissa Godfrey, 2018. "Kids Safe and Smokefree (KiSS) Multilevel Intervention to Reduce Child Tobacco Smoke Exposure: Long-Term Results of a Randomized Controlled Trial," IJERPH, MDPI, vol. 15(6), pages 1-16, June.
    16. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    17. John Mullahy, 2017. "Marginal effects in multivariate probit models," Empirical Economics, Springer, vol. 52(2), pages 447-461, March.
    18. Vitaliy Roud & Valeriya Vlasova, 2016. "Firm-Level Evidence on the Cooperative Innovation Strategies in Russian Manufacturing," HSE Working papers WP BRP 63/STI/2016, National Research University Higher School of Economics.
    19. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    20. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.

    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:zbw:espost:222432. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    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.