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Can Post-Stratification Adjustments Do Enough to Reduce Bias in Telephone Surveys that Do Not Sample Cell Phones? It Depends Kathleen Thiede Call SHADAC AAPOR, Chicago May 15, 2009 Funded by a grant from the Robert Wood Johnson Foundation
Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why this matters ,[object Object],[object Object]
Research question ,[object Object],[object Object],[object Object]
Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview of analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Table 1. Selected outcomes for non-elderly; original public use weights by phone status Non-CPOH subsample (A-B) significantly underestimates all key health related outcomes Non-CPOH and CPOH subsamples are significantly different on all health related estimates (C-B); CPOH rates are higher on all outcomes ^Status of adults (18+) * T-tests corrected for overlapping observations (A-B) and are significant at the p ≤ .05 level or more
Table 2. Contrasting adjustments for selected health outcomes for non-elderly ,[object Object]
Table 2. Contrasting adjustments for selected health outcomes for non-elderly ,[object Object],[object Object]
Table 3. Selected outcomes by weight, non-elderly
Table 3. Selected outcomes by weight, non-elderly
Table 3. Summary of selected outcomes by weight, non-elderly -- Continued ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Table 4. Reweighted estimates for key segments of non-elderly population Bias reduction is greatest among total non-elderly; works least well for young adults Average absolute bias across estimates is relatively small for reweighted data
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary Continued ,[object Object],[object Object],[object Object],[object Object]
Implications and Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contact information ,[object Object],[object Object],[object Object],©2002-2009 Regents of the University of Minnesota. All rights reserved. The University of Minnesota is an Equal Opportunity Employer

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Can Post-Stratification Adjustments Do Enough to Reduce Bias in Telephone Surveys that Do Not Sample Cell Phones? It Depends

  • 1. Can Post-Stratification Adjustments Do Enough to Reduce Bias in Telephone Surveys that Do Not Sample Cell Phones? It Depends Kathleen Thiede Call SHADAC AAPOR, Chicago May 15, 2009 Funded by a grant from the Robert Wood Johnson Foundation
  • 2.
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  • 4.
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  • 7.
  • 8. Table 1. Selected outcomes for non-elderly; original public use weights by phone status Non-CPOH subsample (A-B) significantly underestimates all key health related outcomes Non-CPOH and CPOH subsamples are significantly different on all health related estimates (C-B); CPOH rates are higher on all outcomes ^Status of adults (18+) * T-tests corrected for overlapping observations (A-B) and are significant at the p ≤ .05 level or more
  • 9.
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  • 11. Table 3. Selected outcomes by weight, non-elderly
  • 12. Table 3. Selected outcomes by weight, non-elderly
  • 13.
  • 14. Table 4. Reweighted estimates for key segments of non-elderly population Bias reduction is greatest among total non-elderly; works least well for young adults Average absolute bias across estimates is relatively small for reweighted data
  • 15.
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  • 18.