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Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation

Listed author(s):
  • David A. Penn

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.

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Paper provided by Middle Tennessee State University, Department of Economics and Finance in its series Working Papers with number 200709.

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Date of creation: Jun 2007
Handle: RePEc:mts:wpaper:200709
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