Multiple Imputation for Nonignorable Attrition in Panel Studies with Refreshment Samples

• Activity Number: 578
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yajuan Si*+ and Jerome P Reiter
• Companies: Duke University and Duke University
• Keywords: multiple imputation ; large scale ; attrition ; panel data ; refreshment samples

Abstract

Across a wide range of academic disciplines, government agencies, and business sectors, panel surveys are an important source of attitudinal and behavioral data. Panel data provides more complex relationship and more advanced understanding of of mass behavior along time. Examples include the 2007-2008 AP/Yahoo News Election Panel and 2006-2008 General Social Survey with multiple follow-up waves. However, panel studies suffer from attrition, which reduces the effective sample size and can lead to biased estimate if the tendency to drop out is systematically related to the substantive outcome of interest. Refreshment sample as external data source includes new, randomly sampled respondents who are provided with the same questionnaire at the same time as a second or subsequent wave of the panel. We utilize refreshment samples to assess the effects of panel attrition and to correct for biases via Bayesian modeling and multiple imputation. We focus on large scale panel study with categorical responses.