Sage Journals: Discover world-class research

Abstract

Large-scale surveys typically exhibit data structures characterized by rich mutual dependencies between surveyed variables and individual-specific skip patterns. Despite high efforts in fieldwork and questionnaire design, missing values inevitably occur. One approach for handling missing values is to provide multiply imputed data sets, thus enhancing the analytical potential of the surveyed data. To preserve possible nonlinear relationships among variables and incorporate skip patterns that make the full conditional distributions individual specific, we adapt a full conditional multiple imputation approach based on sequential classification and regression trees. Individual-specific skip patterns and constraints are handled within imputation in a way ensuring the consistency of the sequence of full conditional distributions. The suggested approach is illustrated in the context of income imputation in the adult cohort of the National Educational Panel Study.

Keywords

nonparametric multiple imputation CART missing income values skip patterns

Get full access to this article

View all access options for this article.

References

Allison

2002. Missing Data. Thousand Oaks, CA: Sage.

Azur

M. J.

Stuart

E. A.

Frangakis

Leaf

P. J.

. 2011. “Multiple Imputation by Chained Equations: What Is It and How Does It Work?” International Journal of Methods in Psychiatric Research 20:40–49.

Blossfeld

H.-P.

Roßbach

H.-G.

Maurice

J. V.

, eds. 2011. Education as a Lifelong Process—The German National Educational Panel Study (NEPS). Number Special Issue 14 . Wiesbaden, Germany: VS Verlag.

Breiman

Friedman

Olshen

Stone

. 1984. Classification and Regression Trees. New York: Chapman and Hall.

Burgette

L. F.

Reiter

J. P.

. 2010. “Multiple Imputation for Missing Data via Sequential Regression Trees.” American Journal of Epidemiology 172:1070–76.

Burgette

L. F.

Reiter

J. P.

. 2012. “Nonparametric Bayesian Multiple Imputation for Missing Data due to Mid-study Switching of Measurement Methods.” Journal of the American Statistical Association 107:439–49.

Doove

van Buuren

Dusseldorp

. 2014. “Recursive Partitioning for Missing Data Imputation in the Presence of Interaction Effects.” Computational Statistics & Data Analysis 72:92–104.

Drechsler

2011. “Multiple Imputation in Practice—A Case Study Using a Complex German Establishment Survey.” Advances in Statistical Analysis 95:1–26.

Duffy

2011. “Income and Wage Imputation Methodology.” Technical Series Paper, Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor.

10.

Frick

J. R.

Grabka

M. M.

. 2005. “Item Non-response on Income Questions in Panel Surveys: Incidence, Imputation and the Impact on Inequality and Mobility.” Allgemeines Statistisches Archiv 89:49–61.

11.

Frick

J. R.

Grabka

M. M.

. 2007. “Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-national Perspective.” IZA Discussion Paper, Interdisziplinäres Forschungszentrum Sozialwissenschaften, Bonn, Germany.

12.

Hapfelmeier

Hothorn

Ulm

. 2012. “Recursive Partitioning on Incomplete Data Using Surrogate Decisions and Multiple Imputation.” Computational Statistics & Data Analysis 56:1552–65.

13.

Hayes

Watson

. 2009. “Hilda Imputation Methods.” HILDA Project Technical Paper Series, University of Melbourne, Melbourne, Australia.

14.

Zaslavsky

A. M.

. 2012. “Diagnosing Imputation Models by Applying Target Analyses to Posterior Replicates of Completed Data.” Statistics in Medicine 31:1–18.

15.

Jenkins

S. P.

2010. “The British Household Panel Survey and its Income Data.” IZA Discussion Paper, 5242 , Bonn, Germany.

16.

Jones

M. P.

1996. “Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression.” Journal of the American Statistical Association 91:222–30.

17.

Leopold

Raab

Skopek

. 2011. “Data Manual: Starting Cohort 6: Adult Education and Lifelong Learning.” NEPS Research Data Paper, National Educational Panel Study, Bamberg, Germany.

18.

Lipps

2010. “Income Imputation in the Swiss Household Panel 1999-2007.” FORS Working Paper Series, paper 2010 -1, Lausanne, Switzerland.

19.

Little

R. J. A.

1988. “Missing-data Adjustments in Large Surveys.” Journal of Business & Economic Statistics 6:287–96.

20.

Little

R. J. A.

1992. “Regression with Missing x’s: A Review.” Journal of the American Statistical Association 87:1227–37.

21.

Little

R. J. A.

Raghunathan

T. E.

. 1997. “Should Imputation of Missing Data Condition on All Observed Variables?” Pp. 617–22 in Proceedings of the Section on Survey Research Methods of the American Statistical Association edited by American Statistical Association. Alexandria, VA: American Statistical Association.

22.

Little

R. J. A.

Rubin

. 2002. Statistical Analysis with Missing Data. Hoboken, NJ: Wiley.

23.

Little

R. J. A.

H. L.

. 1989. Item Nonresponse in Panel Surveys. New York: John Wiley.

24.

Raghunathan

T. E.

Lepkowski

J. M.

van Hoewyk

Solenberger

. 2001. “A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models.” Survey Methodology 27:85–95.

25.

Riphahn

R. T.

Serfling

. 2005. “Item Non-response on Income and Wealth Questions.” Empirical Economics 30:521–38.

26.

Rubin

D. B.

1976. “Inference with Missing Data.” Biometrika 63:581–92.

27.

Rubin

D. B.

1987. Multiple Imputation for Nonresponse in Surveys. New York: Wiley.

28.

Rubin

D. B.

2004. “The Design of a General an Flexible System for Handling Nonresponse in Sample Surveys.” Journal of the American Statistical Association 48:298–302.

29.

Schenker

Raghunathan

Chiu

P.-L.

Makuc

Zhang

Cohen

. 2006. “Multiple Imputation of Missing Income Data in the National Health Interview Survey.” Journal of the American Statistical Association 101:924–33.

30.

Reiter

J. P.

. 2013. “Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-scale Assessment Surveys.” Journal of Educational and Behavioral Statistics 38:499–521.

31.

van Buuren

2007. “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification.” Statistical Methods in Medical Research 16:219–42.

32.

van Buuren

2012. Flexible Imputation of Missing Data. Boca Raton, FL: Chapman & Hall.

33.

van Buuren

Groothuis Oudshoorn

. 2011. “Mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software 45:1–67.

34.

Watson

Starick

. 2011. “Evaluation of Alternative Income Imputation Methods for a Longitudinal Survey.” Journal of Official Statistics 27:693–715.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.50 MB

Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study

Abstract

Keywords

Get full access to this article

References

Supplementary Material