Missing Data

by Paul D. Allison

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. 

 

Genres: Nonfiction, Read For School

104 pages, Kindle Edition

First published August 13, 2001

Reviews

[Connor Oswald · Rating 4 out of 5]

A great introduction to missing data