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Webinar Details

A Total Error Framework for Generic Datasets and Estimates

Paul Biemer

Thursday, October 18, 2018
12:00 - 1:30 PM CDT/ 1:00 - 2:30 PM EDT/ 10:00 - 11:30 AM PDT

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About This Course:

The course reviews the familiar total survey error framework and discusses how this framework can be generalized and extended for generic probability and nonprobability datasets. The framework considers all the major sources of error and their associated mean squared error components and how they combine to produce an expression for the overall accuracy of a data set mean. Because of its generality, this framework is particularly useful for comparing estimates from surveys with estimates from massive data sets, often with surprising results. An example based upon real data illustrates how the accuracy of estimates from an administrative data set covering 80% of US households may be inferior to a sample survey of 6000 households with a 55% response rate.  The example will also illustrate why error mitigation to improve the comparison may be futile in many situations. Finally, the implications for evaluating the accuracy of hybrid estimates based upon integrated survey and non-survey (Big Data) data sets are discussed.

 

Webinar Level:
Introductory

 

Learning Objectives:

   
  • The processes by which data are generated and errors are introduced in generic data sets and estimates.
  • The concepts of data encoding error, sample recruitment error, their associated subcomponents and their relative importance.
  • Understanding what information is needed to evaluate total estimator accuracy and to develop mitigation strategies for improving accuracy of estimates from nonprobability samples.

About the Instructor:

 
Dr. Paul P. Biemer
Distinguished Fellow, RTI International
Associate Director for Survey R&D, The Odum Institute, UNC-CH


Dr. Biemer is Distinguished Fellow in Statistics at RTI International and Associate Director of Survey Research and Development in the Odum Institute at the University of North Carolina at Chapel Hill.  He is an internationally recognized expert and researcher in survey methodology with many publications and books including  Introduction to Survey Quality, Latent Class Analysis of Survey Error, Measurement Error in Surveys and Total Survey Error in Practice. Dr. Biemer is a Fellow of the ASA and the AAAS and an Elected Member of the ISI.  He holds a number of awards for his contributions to the field of statistics and survey methodology, including the including the Connor Award, Morris Hansen Award and the Roger Herriot Award.