Missing data /

"Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the ana...

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Bibliographic Details
Main Author: Allison, Paul David
Format: Book
Language:English
Published: Thousand Oaks, Calif. : Sage Publications, [2002], ©2002.
Series:Quantitative applications in the social sciences ; no. 07-136.
Subjects:
Summary:"Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. 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 been relying 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."--Pub. desc.
Description
Item Description:"A SAGE university paper"--Cover.
Physical Description:vi, 93 pages : illustrations ; 22 cm.
ISBN:0761916725
9780761916727