Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data

4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained using a modified strategy that we call multiple imputation, then deletion (MID). Under MID, all cases are used for imputation but, following imputation, cases with imputed Y values are excluded from the analysis. When there is something wrong with the imputed Y values, MID protects the estimates from the problematic imputations. And when the imputed Y values are acceptable, MID usually offers somewhat more efficient estimates than an ordinary MI strategy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sociological Methodology SAGE

4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data

Sociological Methodology , Volume 37 (1): 35 – Aug 1, 2007

Loading next page...
 
/lp/sage/4-regression-with-missing-ys-an-improved-strategy-for-analyzing-X4I9E4rbl6

References (24)

Publisher
SAGE
Copyright
© 2007 American Sociological Association
ISSN
0081-1750
eISSN
1467-9531
DOI
10.1111/j.1467-9531.2007.00180.x
Publisher site
See Article on Publisher Site

Abstract

When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained using a modified strategy that we call multiple imputation, then deletion (MID). Under MID, all cases are used for imputation but, following imputation, cases with imputed Y values are excluded from the analysis. When there is something wrong with the imputed Y values, MID protects the estimates from the problematic imputations. And when the imputed Y values are acceptable, MID usually offers somewhat more efficient estimates than an ordinary MI strategy.

Journal

Sociological MethodologySAGE

Published: Aug 1, 2007

There are no references for this article.