期刊
SOCIOLOGICAL METHODS & RESEARCH
卷 -, 期 -, 页码 -出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/00491241221113873
关键词
incomplete data; imputation; missing data; weighting
资金
- UK Medical Research Council [MC_UU_00004/07]
This article discusses three common methods for addressing missing data: complete-case analysis, weighting, and multiple imputation. It provides a non-technical discussion of the strengths and weaknesses of these approaches, and when to choose each method. The methods are illustrated using data from the Youth Cohort (Time) Series for England, Wales and Scotland, 1984-2002.
Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c) multiple imputation (MI), where missing values of the variables in the analysis are imputed as draws from their predictive distribution under an implicit or explicit statistical model, the imputation process is repeated to create multiple filled-in data sets, and analysis is carried out using simple MI combining rules. This article provides a non-technical discussion of the strengths and weakness of these approaches, and when each of the methods might be adopted over the others. The methods are illustrated on data from the Youth Cohort (Time) Series (YCS) for England, Wales and Scotland, 1984-2002.
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