2009/02/01 by Matthias Schonlau, Arthur van Soest, Arie Kapteyn +2 · 263 citations
Mathematics · Psychology · Social Sciences · #Advanced Causal Inference Techniques #Artificial intelligence #Computer science #Demography #Econometrics #Health and Retirement Study #Matching (statistics) #Mathematics #Population #Propensity score matching #Psychology #Sample (material) #Sample size determination #Sampling (signal processing) #Sampling bias #Sampling design #Selection (genetic algorithm) #Selection bias #Sociology #Statistics #Survey Methodology and Nonresponse #The Internet #Urban, Neighborhood, and Segregation Studies #World Wide Web
paper · doi:10.1177/0049124108327128
published in Sociological Methods & Research 37(3), 291-318 (SAGE Publishing)
openalex publication_date 2009/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25
Web surveys are a popular survey mode, but the subpopulation with Internet access may not represent the population of interest. The authors investigate whether adjusting using weights or matching on a small set of variables makes the distributions of target variables representative of the population. This application has a rich sampling design; the Internet sample is part of an existing probability sample, the Health and Retirement Study, that is representative of the U.S. population aged 50 and older. For the dichotomous variables investigated, the adjustment helps. On average, the sample means in the Internet access sample differ by 6.5 percent before and 3.7 percent after adjustment. Still, a large number of adjusted estimates remain significantly different from their target estimates based on the complete sample. This casts doubt on the common procedure to use only a few variables to correct for the selectivity of convenience samples.