<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Schroeder HM</submitter><funding>NIA NIH HHS</funding><pagination>100-127</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11770253</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>13(1)</volume><pubmed_abstract>Typical design-based methods for weighting probability samples rely on several assumptions, including the random selection of sampled units according to known probabilities of selection and ignorable unit nonresponse. If any of these assumptions are not met, weighting methods that account for the probabilities of selection, nonresponse, and calibration may not fully account for the potential selection bias in a given sample, which could produce misleading population estimates. This analysis investigates possible selection bias in the 2019 Health Survey Mailer (HSM), a sub-study of the longitudinal Health and Retirement Study (HRS). The primary HRS data collection has occurred in "even" years since 1992, but additional survey data collections take place in the "off-wave" odd years via maile</pubmed_abstract><journal>Journal of survey statistics and methodology</journal><pubmed_title>Analyzing Potential Non-Ignorable Selection Bias in an Off-Wave Mail Survey Implemented in a Long-Standing Panel Study.</pubmed_title><pmcid>PMC11770253</pmcid><funding_grant_id>U01 AG009740</funding_grant_id><pubmed_authors>West BT</pubmed_authors><pubmed_authors>Schroeder HM</pubmed_authors></additional><is_claimable>false</is_claimable><name>Analyzing Potential Non-Ignorable Selection Bias in an Off-Wave Mail Survey Implemented in a Long-Standing Panel Study.</name><description>Typical design-based methods for weighting probability samples rely on several assumptions, including the random selection of sampled units according to known probabilities of selection and ignorable unit nonresponse. If any of these assumptions are not met, weighting methods that account for the probabilities of selection, nonresponse, and calibration may not fully account for the potential selection bias in a given sample, which could produce misleading population estimates. This analysis investigates possible selection bias in the 2019 Health Survey Mailer (HSM), a sub-study of the longitudinal Health and Retirement Study (HRS). The primary HRS data collection has occurred in "even" years since 1992, but additional survey data collections take place in the "off-wave" odd years via maile</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Feb</publication><modification>2026-05-13T14:40:04.768Z</modification><creation>2026-05-13T14:24:18.935Z</creation></dates><accession>S-EPMC11770253</accession><cross_references><pubmed>39877150</pubmed><doi>10.1093/jssam/smae039</doi></cross_references></HashMap>