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Estimating Missing Values from the General Social Survey: An Application of Multiple Imputation *
Author(s) -
Penn David A.
Publication year - 2007
Publication title -
social science quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.482
H-Index - 90
eISSN - 1540-6237
pISSN - 0038-4941
DOI - 10.1111/j.1540-6237.2007.00472.x
Subject(s) - imputation (statistics) , missing data , multivariate statistics , statistics , survey data collection , computer science , econometrics , general social survey , standard error , data mining , mathematics
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.