Author's Keywords: Imputation, Bias, Current Population Survey, Health Insurance Coverage, Missing Data	Convention Need a solution for abstract management? All Academic can help! Contact us today to find out how our system can help your annual meeting. Submission - Custom fields, multiple submission types, tracks, audio visual, multiple upload formats, automatic conversion to pdf. Review - Peer Review, Bulk reviewer assignment, bulk emails, ranking, z-score statistics, and multiple worksheets! Reports - Many standard and custom reports generated while you wait. Print programs with participant indexes, event grids, and more! Scheduling - Flexible and convenient grid scheduling within rooms and buildings. Conflict checking and advanced filtering. Communication - Bulk email tools to help your administrators send reminders and responses. Use form letters, a message center, and much more! Management - Search tools, duplicate people management, editing tools, submission transfers, many tools to manage a variety of conference management headaches! Click here for more information.
Association: Name: American Association For Public Opinion Association URL: http://www.aapor.org
Citation: URL: http://www.allacademic.com/meta/p16941_index.html Direct Link: HTML Code:

Davern, Michael., Blewett, Lynn., Thiede Call, Kathleen . and Rodin, Holly . "Does Imputation Bias Lead to Finding Significantly More Uninsured in the Current Population Survey’s Estimates of Health Insurance Coverage?" Paper presented at the annual meeting of the American Association For Public Opinion Association, Fontainebleau Resort, Miami Beach, FL, <Not Available>. 2009-05-25 <http://www.allacademic.com/meta/p16941_index.html>

Davern, M. , Blewett, L. , Thiede Call, K. and Rodin, H. "Does Imputation Bias Lead to Finding Significantly More Uninsured in the Current Population Survey’s Estimates of Health Insurance Coverage?" Paper presented at the annual meeting of the American Association For Public Opinion Association, Fontainebleau Resort, Miami Beach, FL <Not Available>. 2009-05-25 from http://www.allacademic.com/meta/p16941_index.html

Publication Type: Paper/Poster Proposal Abstract: Missing data in the form of item or entire survey module non-response is a common problem in survey research (Groves, Dillman, Eltinge, and Little 2001). Approximately 10 percent of the Current Population Survey (CPS) sample refuses to take the demographic supplement. These “full supplement” refusals have the variable values for the entire demographic supplement imputed. Although properly specified imputation can alter basic distributional summary statistics (e.g., means, rates and variances) from the statistics calculated using complete cases only, it should not transform the relationships among variables. In other words, imputation should not create significant correlations between variables that were not there before the imputation, nor should it reduce the magnitude of significant correlations between variables that were there prior to imputation. With this in mind, there are two questions we attempt to answer in this analysis: 1) Is there a difference between the imputed cases and the non-imputed cases with respect to health insurance coverage? 2) Does hot deck imputation create a significant bias in health insurance coverage estimates? In the 2003 Current Population Survey’s Demographic Supplement, 59.7 percent of 18-64 year old adults have commercial health insurance coverage if they have the full supplement imputed. However, 72.4 percent of the non-full supplement imputations have commercial health insurance coverage. Furthermore, full supplement imputations have a 26.7 percent uninsurance rate while all other 18-64 year old adults in the CPS have an uninsurance rate of 14.6 percent. We examine the relationships among key correlates to see whether this difference is due to the characteristics of the full supplement imputations or is due to the Census Bureau’s missing data imputation routines introducing/reducing relationships among variables.

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