Fractional Imputation in Survey Sampling: A Comparative Review

TL;DR

This paper reviews fractional imputation (FI), a modern method for handling missing data in survey sampling, discussing its concepts, methods, practical implementation, and empirical performance compared to multiple imputation.

Contribution

It provides a comprehensive overview of FI, introduces recent developments, and offers practical guidance and empirical evaluation for its application in survey sampling.

Findings

FI performs well compared to multiple imputation in empirical tests

Practical implementation guidelines enhance FI's usability

Recent developments improve FI's flexibility and robustness

Abstract

Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their fractional weights are created for each missing item. Each fractional weight represents the conditional probability of the imputed value given the observed data, and the parameters in the conditional probabilities are often computed by an iterative method such as EM algorithm. The underlying model for FI can be fully parametric, semiparametric, or nonparametric, depending on plausibility of assumptions and the data structure. In this paper, we give an overview of FI, introduce key ideas and methods to readers who are new to the FI literature, and highlight some new development. We also provide guidance on practical implementation of FI and valid inferential tools after imputation. We demonstrate the empirical performance of FI with respect to multiple imputation using a pseudo finite population generated from a sample in Monthly Retail Trade Survey in US Census Bureau.