© John M. Abowd 2007, all rights reserved General Methods for Missing Data John M. Abowd March 2007.

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Presentation transcript:

© John M. Abowd 2007, all rights reserved General Methods for Missing Data John M. Abowd March 2007

© John M. Abowd 2007, all rights reserved Outline General principles Missing at random Weighting procedures Imputation procedures Hot decks Introduction to model-based procedures

© John M. Abowd 2007, all rights reserved General Principles Most of today’s lecture is taken from Statistical Analysis with Missing Data, 2 nd edition, Roderick J. A. Little and Donald B. Rubin (New York: John Wiley & Sons, 2002). The basic insight is that missing data should be modeled using the same probability and statistical tools that are the basis of all data analysis. Missing data are not an anomaly to be swept under the carpet. They are an integral part of very analysis.

© John M. Abowd 2007, all rights reserved Missing Data Patterns Univariate non-response Multivariate non-response Monotone General File matching Latent factors, Bayesian parameters

© John M. Abowd 2007, all rights reserved Missing Data Mechanisms The complete data are defined as the matrix Y (n  K). The pattern of missing data is summarized by a matrix of indicator variables M (n  K). The data generating mechanism is summarized by the joint distribution of Y and M.

© John M. Abowd 2007, all rights reserved Missing Completely at Random In this case the missing data mechanism does not depend upon the data Y. This case is called MCAR.

© John M. Abowd 2007, all rights reserved Missing at Random Partition Y into observed and unobserved parts. Missing at random means that the distribution of M depends only on the observed parts of Y. Called MAR.

© John M. Abowd 2007, all rights reserved Not Missing at Random If the condition for MAR fails, then we say that the data are not missing at random, NMAR. Censoring and more elaborate behavioral models often fall into this category.

© John M. Abowd 2007, all rights reserved The Rubin and Little Taxonomy Analysis of the complete records only Weighting procedures Imputation-based procedures Model-based procedures

© John M. Abowd 2007, all rights reserved Analysis of Complete Records Only Assumes that the data are MCAR. Only appropriate for small amounts of missing data. Used to be common in economics, less so in sociology. Now very rare.

© John M. Abowd 2007, all rights reserved Weighting Procedures Modify the design weights to correct for missing records. Provide an item weight (e.g., earnings and income weights in the CPS) that corrects for missing data on that variable. See complete case and weighted complete case discussion in Rubin and Little.

© John M. Abowd 2007, all rights reserved Imputation-based Procedures Missing values are filled-in and the resulting “Completed” data are analyzed –Hot deck –Mean imputation –Regression imputation Some imputation procedures (e.g., Rubin’s multiple imputation) are really model- based procedures.

© John M. Abowd 2007, all rights reserved Imputation Based on Statistical Modeling Hot deck: use the data from related cases in the same survey to impute missing items (usually as a group) Cold deck: use a fixed probability model to impute the missing items Multiple imputation: use the posterior predictive distribution of the missing item, given all the other items, to impute the missing data

© John M. Abowd 2007, all rights reserved Current Population Survey Census Bureau Imputation Procedures: Relational Imputation Longitudinal Edit Hot Deck Allocation Procedure

© John M. Abowd 2007, all rights reserved “Hot Deck” Allocation Labor Force Status Employed Unemployed Not in the Labor Force

© John M. Abowd 2007, all rights reserved “Hot Deck” Allocation BlackNon-Black Male 16 – ID #0062 Female

© John M. Abowd 2007, all rights reserved “Hot Deck” Allocation BlackNon-Black Male 16 – 24ID #3502ID # ID #8177ID #0062 Female 16-24ID #9923ID # ID #4396ID #2271

© John M. Abowd 2007, all rights reserved CPS Example Effects of hot-deck imputation of labor force status.

© John M. Abowd 2007, all rights reserved Public Use Statistics

© John M. Abowd 2007, all rights reserved Allocated v. Unallocated

© John M. Abowd 2007, all rights reserved Model-based Procedures A probability model based on p(Y, M) forms the basis for the analysis. This probability model is used as the basis for estimation of parameters or effects of interest. Some general-purpose model-based procedures are designed to be combined with likelihood functions that are not specified in advance.

© John M. Abowd 2007, all rights reserved Little and Rubin’s Principles Imputations should be –Conditioned on observed variables –Multivariate –Draws from a predictive distribution Single imputation methods do not provide a means to correct standard errors for estimation error.