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

Authors


  • *Direct correspondence to David A. Penn, Business and Economic Research Center, Middle Tennessee State University, 1301 E. Main St., Box 102, Murfreesboro, TN 37132 〈dpenn@mtsu.edu〉. SAS coding and data will be shared on request. The article benefits from the insightful comments and suggestions offered by the reviewers.

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.

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