Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics

TL;DR

This paper demonstrates that multiple imputation (MI) can effectively handle missing data in massive, complex datasets like the PSID, improving data quality and analysis over traditional hot deck methods.

Contribution

It applies a sequential regression/chained-equation MI approach to large-scale survey data, addressing operational challenges and validating its effectiveness.

Findings

MI preserves correlation structures better than hot deck methods

Imputed data shows improved relationships between wealth components and socio-demographics

MI increases efficiency and is recommended for large missing data fractions

Abstract

\Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the U.S. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/ chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and socio-demographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.