Handling Missing Data in Social Research

By Scott M. Lynch, J. Scott Brown, Sarah A. Mustillo

© 2016 – Chapman and Hall/CRC

320 pages | 50 B/W Illus.

Purchasing Options:
Hardback: 9781439873960
pub: 2016-03-14
e–Inspection Copy

About the Book

For social scientists, it is often confusing how to determine when missing data is a problem in analyses and how to handle it. This book presents a comprehensive overview of the available methods, focusing on which method should be used for specific problems. It features numerous real and simulated data examples to illustrate how the methods can be applied once the appropriate technique has been determined. It also includes exercises, computer-based problems, and suggestions for further reading. R code and the data sets are available on the book’s website.

Table of Contents

Introduction. Simulation Design. Data-Based Solutions: Listwise Deletion. Data-Based Solutions: Mean/Mode Imputation. Data-Based Solutions: Regression-Based Imputation. Data-Based Solutions: Multiple Imputation. Method/Model-Based Solutions: EM Algorithms.

About the Series

Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General