 Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation*David Penn () Social Science Quarterly, 2007, vol. 88, issue 2, 573-584 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 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: 2007 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:socsci:v:88:y:2007:i:2:p:573-584 Ordering information: This journal article can be ordered from Access Statistics for this article Social Science Quarterly is currently edited by Robert L. Lineberry More articles in Social Science Quarterly from Southwestern Social Science Association |
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