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

被引：0

作者：

Liu, Benmei ^{[1
]}

Hennessy, Erin ^{[2
]}

Oh, April ^{[1
]}

Dwyer, Laura A. ^{[1
]}

Nebeling, Linda ^{[1
]}

机构：

[1] NCI, Rockville, MD 20850 USA

[2] Tufts Univ, Medford, MA 02155 USA

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2018年 / 17卷 / 01期

基金：

美国国家卫生研究院;

关键词：

Perceptional categorical data; high missing rates; multiple imputation;

D O I：

10.22237/jmasm/1536146540

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：1 / 21

页数：20

共 21 条

[1] A Review of Hot Deck Imputation for Survey Non-response [J].