A Comparison of Three Popular Methods for Handling Missing Data: Complete-Case Analysis, Inverse Probability Weighting, and Multiple Imputation

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Bibliographic Details
Title: A Comparison of Three Popular Methods for Handling Missing Data: Complete-Case Analysis, Inverse Probability Weighting, and Multiple Imputation
Language: English
Authors: Roderick J. Little (ORCID 0000-0001-9878-6977), James R. Carpenter, Katherine J. Lee
Source: Sociological Methods & Research. 2024 53(3):1105-1135.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 31
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Foreign Countries, Probability, Robustness (Statistics), Responses, Statistical Inference, Statistical Distributions, Evaluation Methods, Comparative Testing
Geographic Terms: United Kingdom (England), United Kingdom (Scotland), United Kingdom (Wales)
DOI: 10.1177/00491241221113873
ISSN: 0049-1241
1552-8294
Abstract: 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.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1434927
Database: ERIC
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