Work

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

Year: 2018

Type: article

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.

Source: Journal of Modern Applied Statistical Methods

DOI: 10.22237/jmasm/1536146540

Authors: Benmei Liu, Erin Hennessy, April Oh, Laura A. Dwyer, Linda Nebeling

Institutions: National Cancer Institute, Tufts University

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute