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

David Penn ()

No 200709, Working Papers from Middle Tennessee State University, Department of Economics and Finance

Abstract: 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.

Keywords: subjective well-being; financial well-being; multiple imputation (search for similar items in EconPapers)
JEL-codes: A10 C42 (search for similar items in EconPapers)
Date: 2007-06
New Economics Papers: this item is included in nep-dcm
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Citations: View citations in EconPapers (8)

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http://capone.mtsu.edu/berc/working/GSS3.pdf (application/pdf)

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Journal Article: Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation* (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:mts:wpaper:200709

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