Optimal recall length in survey design
Self-reported data collected via surveys are a key input into a wide range of research conducted by economists. It is well known that such data are subject to measurement error that arises when respondents are asked to recall past utilisation. Survey designers must determine the length of the recall period and face a trade-off as increasing the recall period provides more information, but increases the likelihood of recall error. A statistical framework is used to explore this trade-off. Finally we illustrate how optimal recall periods can be estimated using hospital use data from Sweden's Survey of Living Conditions.
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- Carson, Richard T & Groves, Theodore, 2010.
"Incentive and Information Properties of Preference Questions,"
University of California at San Diego, Economics Working Paper Series
qt88d8644g, Department of Economics, UC San Diego.
- Richard Carson & Theodore Groves, 2007. "Incentive and informational properties of preference questions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 37(1), pages 181-210, May.
- Hugo Benítez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2004.
"How large is the bias in self-reported disability?,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 19(6), pages 649-670.
- Hugo Benitez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2000. "How Large is the Bias in Self-Reported Disability?," Working Papers 2000-01, Brown University, Department of Economics.
- Hugo Benitez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2000. "How Large is the Bias is Self-Reported Disability?," NBER Working Papers 7526, National Bureau of Economic Research, Inc.
- Philipson, Tomas & Malani, Anup, 1999. "Measurement errors: A principal investigator-agent approach," Journal of Econometrics, Elsevier, vol. 91(2), pages 273-298, August.
- Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
- Griliches, Zvi, 1986. "Economic data issues," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 25, pages 1465-1514 Elsevier.
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