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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Middle Tennessee State University, Department of Economics and Finance in its series Working Papers with number
200709.
Find related papers by JEL classification: A10 - General Economics and Teaching - - General Economics - - - General C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
This paper has been announced in the following NEP Reports:
Did you know? All full texts are decentralized with the publishers, none reside on this server, thus making it possible to offer this service for free to all parties.