From Multiple Modes for Surveys to Multiple Sources for Estimates
- Speaker: Constance F. Citro, Director, Committee on National Statistics, National Academy of Sciences/National Research Council
- Chair: Keith Rust, WSS President
- Date/time: May 22, 2013, 1:00 - 3:00 p.m. Light refreshments will be served following the talk. Video conferencing will not be available.
- Location: Bureau of Labor Statistics Conference Center; 2 Massachusetts Avenue, NE, Washington, DC. (Metrorail Red Line to Union Station. Enter BLS from First Street, NE).
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Abstract: Users of federal statistics want estimates that are “wider, deeper, quicker, better, cheaper” (channeling Tim Holt, former head of the UK Office for National Statistics). Each of these adjectives poses challenges and opportunities for those who produce statistics. I am going to focus on “better.” Since World War II, we have relied on the probability sample survey as the best we could do—and that best being very good, indeed—for estimates on everything from household income to self-reported health status, unemployment, crime victimization, and many other topics. Faced with steadily declining unit and item response rates, we have responded in many ways, including the use of multiple survey modes, more sophisticated imputation methods, etc. In the business sector, we also long ago moved away from relying solely on surveys to produce needed estimates, but, to date, we have not done that for household surveys. I argue that we can and must move from a paradigm of producing the best estimates possible from a survey to that of producing the best possible estimates from multiple data sources, including administrative records and, in the future, perhaps other kinds of data. I use household income as my main example. From my 45 years working in and around the federal statistical system (28 years with CNSTAT), mostly as a user, I also offer some observations about ways in which a user perspective can productively become a more integral part of the DNA of the federal statistical system.