scispace - formally typeset
Journal ArticleDOI

Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data

Teresa A. Myers
- 09 Dec 2011 - 
- Vol. 5, Iss: 4, pp 297-310
TLDR
A computational tool for SPSS (Statistical Package for the Social Sciences) is presented that will enable communication researchers to easily implement hot deck imputation in their own analyses.
Abstract
Missing data are a ubiquitous problem in quantitative communication research, yet the missing data handling practices found in most published work in communication leave much room for improvement. In this article, problems with current practices are discussed and suggestions for improvement are offered. Finally, hot deck imputation is suggested as a practical solution to many missing data problems. A computational tool for SPSS (Statistical Package for the Social Sciences) is presented that will enable communication researchers to easily implement hot deck imputation in their own analyses.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

The somatic symptom scale-8 (SSS-8): a brief measure of somatic symptom burden.

TL;DR: The SSS-8 is a reliable and valid self-report measure of somatic symptom burden and associations with measures of construct validity and health care visits are investigated.
Journal ArticleDOI

Imputation with the R Package VIM

TL;DR: The graphical user interface of VIM has been re-implemented from scratch resulting in the package VIMGUI to enable users without extensive R skills to access these imputation and visualization methods.
Journal ArticleDOI

Ten Steps in Scale Development and Reporting: A Guide for Researchers

TL;DR: Despite the central role that scales play in our predictions, scholars often apply measurement buildin... as mentioned in this paper The authors of this paper focus on scale development involves numerous theoretical, methodological, and statistical competencies.
Journal ArticleDOI

An attack on science? Media use, trust in scientists, and perceptions of global warming.

TL;DR: Using within-subject panel data from a nationally representative sample of Americans, this study finds that trust in scientists mediates the effect of news media use on perceptions of global warming.
Journal ArticleDOI

A bird's eye view of civilians killed by police in 2015: further evidence of implicit bias

TL;DR: In this article, the authors analyzed 990 police fatal shootings using data compiled by The Washington Post in 2015 and examined the data for evidence of implicit bias by using multivariate regression models that predict two indicators of threat perception failure: (1) whether the civilian was not attacking the officer(s) or other civilians just before being fatally shot and (2) whether a civilian was unarmed when fatally shot.
References
More filters
Book

Multiple imputation for nonresponse in surveys

TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
Journal ArticleDOI

Missing data: Our view of the state of the art.

TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Journal ArticleDOI

Multiple imputation: a primer:

TL;DR: Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
Journal Article

A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models

TL;DR: In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.
Journal ArticleDOI

ALS issues in clinical trials. Missing data.

TL;DR: The importance of missing data in RCTs is emphasized, and how the problem can be handled in an unbiased way by imputation procedures is discussed, and some recommendations for trial design and conduct are made that are tailored to R CTs for ALS.