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Paul P. Biemer

Distinguished Fellow, Statistics

Paul Biemer, PhD, has more than 30 years of postdoctoral experience in survey methods and statistics. He joined RTI in 1991, serving as director of the survey methods program until 1994 and of the Center for Survey Methods and Research from 1994 to 2000. Dr. Biemer's scientific contributions to survey methodology and statistics include developing methodologies for using computer audio-recorded interviewing, using latent class analysis as a survey error evaluation tool, and applying continuous quality improvement to the coding of industry and occupation question responses. He holds a joint appointment with the Odum Institute for Research in Social Sciences at the University of North Carolina at Chapel Hill where he is associate director for survey research and director of the certificate program in survey methodology. He has written five books, 35 peer-reviewed publications, 17 book chapters, and numerous papers and presentations.


PhD, Statistics, Texas A&M University; MS, Statistics, Texas A&M University; BS, Mathematics, Texas A&M University.

Latest Publications

McCormick, M.C., Baker, D.B., Biemer, P.P., Carlson, B.L., Diez Roux, A.V., Lesser, V.M., et al. (2014). The National Children's Study 2014: Commentary on a Recent National Research Council/Institute of Medicine Report. Academic Pediatrics, 14 (6):545-546.
Biemer, P., Trewin, D., Bergdahl, H., & Japec, L. (2014). A System for Managing the Quality of Official Statistics. Journal of Official Statistics, 30 (3):381-415.
Berzofsky, M.E., Biemer, P.P., & Kalsbeek, W.D. (2014). Local Dependence in Latent Class Analysis of Rare and Sensitive Events. Sociological Methods & Research, 43 (1):137-170.
Park, H., Thissen, R., & Biemer, P.P. (2013). Implementation of CARI technology to improve data quality. Presented at 5th International Workshop on Internet Survey and Survey Methodology, Daejeon, Republic of Korea, Sept, 2013.
View all publications by Paul P. Biemer (84)


  • Survey Design and Analysis
  • General Survey Methodology
  • Nonsampling Error Modeling and Evaluation