Data imputation

Publication Type

Book Chapter

Publication Date

11-2017

Abstract

Data imputation involves representing missing values in a dataset. Missing data create a number of potential challenges for statistical analysis. Missing values can increase the chances of making Type I and Type II errors, reduce statistical power, and limit the reliability of confidence intervals. There are a number of statistical procedures available for researchers to replace missing values with reasonable estimations. Basic methods, such as mean substitution, regression imputation, and hot deck imputation may bias imputed values depending on the mechanism of missingness. Advanced methods include expectation maximization, full information maximum likelihood, and multiple imputation. These methods produce more reliable estimations of missing values, particularly when missingness is not at random.

Keywords

imputation, linear regression, maximum likelihood, measurement error, missing values

Discipline

Communication | Critical and Cultural Studies

Research Areas

Integrative Research Areas

Publication

International Encyclopedia of Communication Research Methods

Editor

J. Matthes

First Page

1

Last Page

12

ISBN

9781118901762

Identifier

10.1002/9781118901731.iecrm0058

Publisher

Wiley

City or Country

Hoboken, NJ

Additional URL

https://doi.org/10.1002/9781118901731.iecrm0058

This document is currently not available here.

Share

COinS