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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Richard Carson & Theodore Groves, 2007.
"Incentive and informational properties of preference questions,"
Environmental & Resource Economics,
European Association of Environmental and Resource Economists, vol. 37(1), pages 181-210, May.
- 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.
- Philipson, Tomas & Malani, Anup, 1999. "Measurement errors: A principal investigator-agent approach," Journal of Econometrics, Elsevier, vol. 91(2), pages 273-298, August.
- Hugo Benitez-Silva & Moshe Buchinsky & Hiu Man Chan & Sofia Cheidvasser & John Rust, 2000.
"How Large is the Bias in Self-Reported Disability?,"
2000-01, Brown University, Department of Economics.
- Hugo Ben�tez-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 is Self-Reported Disability?," NBER Working Papers 7526, National Bureau of Economic Research, Inc.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:jhecon:v:27:y:2008:i:5:p:1275-1284. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.