Missing Data [Paperback]

Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data. 

 

Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data. 

 

&an excellent resource for researchers who are conducting multivariate statistical studies.

Series Editor's Introduction
1. Introduction
2. Assumptions
Missing Completely at Random
Missing at Random
Ignorable
Nonignorable
3. Conventional Methods
Listwise Deletion
Pairwise Deletion
Dummy Variable Adjustment
Imputation
Summary
4. Maximum Likelihood
Review of Maximum Likelihood
ML With Missing Data
Contingency Table Data
Linear Models With Normally Distributed Data
The EM Algorithm
EM Example
Direct ML
Direct ML Example
Conclusion
5. Multiple Imputation: Bascis
Single Random Imputation
Multiple Random Imputation
Allowing for Ranl#Ä