Comparison of Multiple Imputation Methods for Categorical Survey Items with High Missing Rates: Application to the Family Life, Activity, Sun, Health and Eating (FLASHE) Study

Authors

Benmei Liu, National Cancer InstituteFollow
Erin Hennessy, Tufts UniversityFollow
April Oh, National Cancer InstituteFollow
Laura A. Dwyer, National Cancer InstituteFollow
Linda Nebeling, National Cancer InstituteFollow

Abstract

Two multiple imputation methods, the Sequential Regression Multivariate Imputation Algorithm and the Cox-Lannacchione Weighted Sequential Hotdeck, were examined and compared to impute highly missing categorical variables from the Family Life, Activity, Sun, Health and Eating (FLASHE) study. This paper describes the imputation approaches and results from the study.

DOI

10.22237/jmasm/1536146540