IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i10p2267-2275.html
   My bibliography  Save this article

Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables

Author

Listed:
  • White, Ian R.
  • Daniel, Rhian
  • Royston, Patrick

Abstract

Multiple imputation is a popular way to handle missing data. Automated procedures are widely available in standard software. However, such automated procedures may hide many assumptions and possible difficulties from the view of the data analyst. Imputation procedures such as monotone imputation and imputation by chained equations often involve the fitting of a regression model for a categorical outcome. If perfect prediction occurs in such a model, then automated procedures may give severely biased results. This is a problem in some standard software, but it may be avoided by bootstrap methods, penalised regression methods, or a new augmentation procedure.

Suggested Citation

  • White, Ian R. & Daniel, Rhian & Royston, Patrick, 2010. "Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2267-2275, October.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:10:p:2267-2275
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00149-0
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. 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. Patrick Royston, 2007. "Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring," Stata Journal, StataCorp LP, vol. 7(4), pages 445-464, December.
    3. Patrick Royston, 2005. "Multiple imputation of missing values: update," Stata Journal, StataCorp LP, vol. 5(2), pages 188-201, June.
    4. Bull, Shelley B. & Mak, Carmen & Greenwood, Celia M. T., 2002. "A modified score function estimator for multinomial logistic regression in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 57-74, March.
    5. Patrick Royston & John B. Carlin & Ian R. White, 2009. "Multiple imputation of missing values: New features for mim," Stata Journal, StataCorp LP, vol. 9(2), pages 252-264, June.
    6. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    7. Patrick Royston, 2005. "Multiple imputation of missing values: Update of ice," Stata Journal, StataCorp LP, vol. 5(4), pages 527-536, December.
    8. Patrick Royston, 2009. "Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables," Stata Journal, StataCorp LP, vol. 9(3), pages 466-477, September.
    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. Caroline J. Dodd-Reynolds & Dimitris Vallis & Adetayo Kasim & Nasima Akhter & Coral L. Hanson, 2020. "The Northumberland Exercise Referral Scheme as a Universal Community Weight Management Programme: A Mixed Methods Exploration of Outcomes, Expectations and Experiences across a Social Gradient," IJERPH, MDPI, vol. 17(15), pages 1-21, July.
    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. Rabea Aschenbruck & Gero Szepannek & Adalbert F. X. Wilhelm, 2023. "Imputation Strategies for Clustering Mixed-Type Data with Missing Values," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 2-24, April.
    4. Lars P. Feld & Sarah Necker & Bruno S. Frey, 2015. "Happiness of economists," Applied Economics, Taylor & Francis Journals, vol. 47(10), pages 990-1007, February.
    5. Morris A. Davis & William D. Larson & Stephen D. Oliner & Benjamin Smith, 2019. "Mortgage Risk Since 1990," FHFA Staff Working Papers 19-02, Federal Housing Finance Agency.
    6. Williams, Randi M. & Zhang, Jing & Woodard, Nathaniel & Slade, Jimmie & Santos, Sherie Lou Zara & Knott, Cheryl L., 2020. "Development and validation of an instrument to assess institutionalization of health promotion in faith-based organizations," Evaluation and Program Planning, Elsevier, vol. 79(C).
    7. Ahmad R. Alsaber & Jiazhu Pan & Adeeba Al-Hurban, 2021. "Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)," IJERPH, MDPI, vol. 18(3), pages 1-25, February.
    8. Doidge, James C & Higgins, Daryl J & Delfabbro, Paul & Edwards, Ben & Vassallo, Suzanne & Toumbourou, John W & Segal, Leonie, 2017. "Economic predictors of child maltreatment in an Australian population-based birth cohort," Children and Youth Services Review, Elsevier, vol. 72(C), pages 14-25.
    9. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    10. Templ, Matthias & Kowarik, Alexander & Filzmoser, Peter, 2011. "Iterative stepwise regression imputation using standard and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2793-2806, October.
    11. Ferrari, Pier Alda & Annoni, Paola & Barbiero, Alessandro & Manzi, Giancarlo, 2011. "An imputation method for categorical variables with application to nonlinear principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2410-2420, July.
    12. repec:iab:iabfme:201202(en is not listed on IDEAS
    13. Reza C. Daniels, 2012. "Univariate Multiple Imputation for Coarse Employee Income Data," SALDRU Working Papers 88, Southern Africa Labour and Development Research Unit, University of Cape Town.
    14. Royston, Patrick & White, Ian R., 2011. "Multiple Imputation by Chained Equations (MICE): Implementation in Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i04).
    15. Vincent Bauer & Keven Ruby & Robert Pape, 2017. "Solving the Problem of Unattributed Political Violence," Journal of Conflict Resolution, Peace Science Society (International), vol. 61(7), pages 1537-1564, August.
    16. Zhong, Hua & Hu, Wuyang & Penn, Jerrod M., 2018. "Application of Multiple Imputation in Dealing with Missing Data in Agricultural Surveys: The Case of BMP Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.
    17. Jaenichen, Ursula & Sakshaug, Joseph, 2012. "Multiple imputation of household income in the first wave of PASS," FDZ Methodenreport 201202_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    18. Mingyang Cai & Gerko Vink, 2022. "A note on imputing squares via polynomial combination approach," Computational Statistics, Springer, vol. 37(5), pages 2185-2201, November.

    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. Paul Watts & Gemma Phillips & Mark Petticrew & Richard Hayes & Christian Bottomley & Ge Yu & Elena Schmidt & Patrick Tobi & Derek Moore & Caroline Frostick & Karen Lock & Adrian Renton, 2013. "Physical Activity in Deprived Communities in London: Examining Individual and Neighbourhood-Level Factors," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-8, July.
    2. repec:iab:iabfme:201202(en is not listed on IDEAS
    3. Jaenichen, Ursula & Sakshaug, Joseph, 2012. "Multiple imputation of household income in the first wave of PASS," FDZ Methodenreport 201202_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. HwaJung Choi & Robert F. Schoeni & Kenneth M. Langa & Michele M. Heisler, 2015. "Older Adults’ Residential Proximity to Their Children: Changes After Cardiovascular Events," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 70(6), pages 995-1004.
    5. Andrea Marshall & Lucinda Billingham & Stirling Bryan, 2009. "Can we afford to ignore missing data in cost-effectiveness analyses?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 10(1), pages 1-3, February.
    6. Andrew F Auld & Francisco Mbofana & Ray W Shiraishi & Charity Alfredo & Mauro Sanchez & Tedd V Ellerbrock & Lisa J Nelson, 2013. "Incidence and Determinants of Tuberculosis among Adults Initiating Antiretroviral Therapy – Mozambique, 2004–2008," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    7. Harald Schoen, 2008. "Identity, Instrumental Self-Interest and Institutional Evaluations," European Union Politics, , vol. 9(1), pages 5-29, March.
    8. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2018. "Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 316-353, June.
    9. Francesco Aiello & Graziella Bonanno, 2018. "Multilevel empirics for small banks in local markets," Papers in Regional Science, Wiley Blackwell, vol. 97(4), pages 1017-1037, November.
    10. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.
    11. King, Christian, 2018. "Food insecurity and child behavior problems in fragile families," Economics & Human Biology, Elsevier, vol. 28(C), pages 14-22.
    12. Lynch, Frances L. & Dickerson, John F. & Saldana, Lisa & Fisher, Phillip A., 2014. "Incremental net benefit of early intervention for preschool-aged children with emotional and behavioral problems in foster care," Children and Youth Services Review, Elsevier, vol. 36(C), pages 213-219.
    13. Christian King, 2018. "Food insecurity and housing instability in vulnerable families," Review of Economics of the Household, Springer, vol. 16(2), pages 255-273, June.
    14. Nikita Lyssenko & Roberto Mart󹑺-Espiñeira, 2012. "Respondent uncertainty in contingent valuation: the case of whale conservation in Newfoundland and Labrador," Applied Economics, Taylor & Francis Journals, vol. 44(15), pages 1911-1930, May.
    15. Petrit Gashi & Iraj Hashi & Geoff Pugh, 2014. "Export behaviour of SMEs in transition countries," Small Business Economics, Springer, vol. 42(2), pages 407-435, February.
    16. Lynch, Frances L. & Dickerson, John F. & Pears, Katherine C. & Fisher, Philip A., 2017. "Cost effectiveness of a school readiness intervention for foster children," Children and Youth Services Review, Elsevier, vol. 81(C), pages 63-71.
    17. Martina Eschelbach, 2009. "Crown Princes and Benjamins: Birth Order and Educational Attainment in East and West Germany," Working Papers 085, Bavarian Graduate Program in Economics (BGPE).
    18. Juul Spaan & Ruben Gaalen & Matthijs Kalmijn, 2022. "Disentangling the Long-term Effects of Divorce Circumstances on Father–Child Closeness in Adulthood: A Mediation Analysis," European Journal of Population, Springer;European Association for Population Studies, vol. 38(5), pages 1183-1211, December.
    19. Reza C. Daniels, 2012. "Univariate Multiple Imputation for Coarse Employee Income Data," SALDRU Working Papers 88, Southern Africa Labour and Development Research Unit, University of Cape Town.
    20. Lucia Mangiavacchi & Chiara Rapallini, 2014. "Self-Reported Economic Condition And Home Production: Intra-Household Allocation In Italy," Bulletin of Economic Research, Wiley Blackwell, vol. 66(3), pages 279-304, July.
    21. repec:mpr:mprres:7890 is not listed on IDEAS
    22. Juho Härkönen & Hande Kaymakçalan & Pirjo Mäki & Anja Taanila, 2012. "Prenatal Health, Educational Attainment, and Intergenerational Inequality: The Northern Finland Birth Cohort 1966 Study," Demography, Springer;Population Association of America (PAA), vol. 49(2), pages 525-552, May.

    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:eee:csdana:v:54:y:2010:i:10:p:2267-2275. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.