Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation
Objectives. Most researchers who use survey data must grapple with the problem of how best to handle missing information. This article illustrates multiple imputation, a technique for estimating missing values in a multivariate setting. Methods. I use multiple imputation to estimate missing income data and update a recent study that examines the influence of parents’ standard of living on subjective well-being. Using data from the 1998 General Social Survey, two ordered probit models are estimated; one using complete cases only, and the other replacing missing income data with multiple imputation estimates. Results. The analysis produces two major findings: 1) parents’ standard of living is more important than suggested by the complete cases model, and 2) using multiple imputation can help to reduce standard errors. Conclusions. Multiple imputation allows a researcher to use more of the available data, thereby reducing biases that may occur when observations with missing data are simply deleted.
|Date of creation:||Jun 2007|
|Contact details of provider:|| Web page: http://www.mtsu.edu/~berc/working/Economics_Working_Papers.html|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:mts:wpaper:200709. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (E. Anthon Eff)
If references are entirely missing, you can add them using this form.