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Panel design effects on response rates and response quality

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  • Rene Segers
  • Philip Hans Franses

Abstract

type="main"> To understand changes in individuals' opinions and attitudes, it would be best to collect data through panels. Such panels, however, often cause irritation among respondents, resulting in low response rates and low response quality. We address whether this problem can be alleviated by designing a panel survey in an alternative way. For this purpose, we perform two field studies where we measure the effects of several panel design characteristics on response rates and response quality. These characteristics include the number of waves and the time between subsequent waves, which may be either fixed or random. Our findings suggest that response rates and response quality can be improved significantly by surveying at random time intervals. It is then crucial that panel members are not informed about the dates they will be surveyed, because in this case, respondents are less likely to develop expectations as to when they will be surveyed again. The methodology we put forward can be used to improve the efficiency of a panel study by carefully calibrating the studies' panel design parameters.

Suggested Citation

  • Rene Segers & Philip Hans Franses, 2014. "Panel design effects on response rates and response quality," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 1-24, February.
    • Handle: RePEc:bla:stanee:v:68:y:2014:i:1:p:1-24
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    File URL: http://hdl.handle.net/10.1111/stan.12019
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    1. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
    2. Simmons, Carolyn J & Bickart, Barbara A & Lynch, John G, Jr, 1993. "Capturing and Creating Public Opinion in Survey Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(2), pages 316-329, September.
    3. Dholakia, Utpal M & Morwitz, Vicki G, 2002. "The Scope and Persistence of Mere-Measurement Effects: Evidence from a Field Study of Customer Satisfaction Measurement," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(2), pages 159-167, September.
    4. Verbeek, Marno & Vella, Francis, 2005. "Estimating dynamic models from repeated cross-sections," Journal of Econometrics, Elsevier, vol. 127(1), pages 83-102, July.
    5. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    6. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    7. Nicoletti, Cheti, 2006. "Nonresponse in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 461-489, June.
    8. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
    9. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    10. Verbeek, Marno & Nijman, Theo, 1993. "Minimum MSE estimation of a regression model with fixed effects from a series of cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 125-136, September.
    11. Nijman, Theo & Verbeek, Marno, 1990. "Estimation of time-dependent parameters in linear models using cross-sections, panels, or both," Journal of Econometrics, Elsevier, vol. 46(3), pages 333-346, December.
    12. Marcel Das & Vera Toepoel & Arthur van Soest, 2011. "Nonparametric Tests of Panel Conditioning and Attrition Bias in Panel Surveys," Sociological Methods & Research, , vol. 40(1), pages 32-56, February.
    13. Elisabeth Deutskens & Ko de Ruyter & Martin Wetzels & Paul Oosterveld, 2004. "Response Rate and Response Quality of Internet-Based Surveys: An Experimental Study," Marketing Letters, Springer, vol. 15(1), pages 21-36, February.
    14. Dolores Collado, M., 1997. "Estimating dynamic models from time series of independent cross-sections," Journal of Econometrics, Elsevier, vol. 82(1), pages 37-62.
    15. Nevo, Aviv, 2003. "Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
    16. Nijman, Theo & Verbeek, Marno & van Soest, Arthur, 1991. "The efficiency of rotating-panel designs in an analysis-of-variance model," Journal of Econometrics, Elsevier, vol. 49(3), pages 373-399, September.
    17. Ugo Trivellato, 1999. "Issues in the Design and Analysis of Panel Studies: A Cursory Review," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(3), pages 339-351, August.
    18. Nijman, T.E. & Verbeek, M.J.C.M., 1990. "Estimation of time-dependent parameters in linear models using cross-sections, panels, or both," Other publications TiSEM 1b042f06-e9e2-4712-8460-c, Tilburg University, School of Economics and Management.
    19. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    20. DeStavola, Bianca L, 1986. "Sampling Designs for Short Panel Data," Econometrica, Econometric Society, vol. 54(2), pages 415-424, March.
    21. Nijman, T.E., 1990. "Estimation of time dependent parameters in linear models using cross sections, panels or both," Other publications TiSEM 3efbf7de-1ca7-4f9f-b515-3, Tilburg University, School of Economics and Management.
    22. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    23. Bhattacharya, Debopam, 2008. "Inference in panel data models under attrition caused by unobservables," Journal of Econometrics, Elsevier, vol. 144(2), pages 430-446, June.
    24. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
    25. Das, M., 2004. "Simple estimators for nonparametric panel data models with sample attrition," Journal of Econometrics, Elsevier, vol. 120(1), pages 159-180, May.
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    Cited by:

    1. Segers, Rene & Franses, Philip Hans & de Bruijn, Bert, 2017. "A novel approach to measuring consumer confidence," Econometrics and Statistics, Elsevier, vol. 4(C), pages 121-129.
    2. Segers, R. & Franses, Ph.H.B.F., 2008. "Measuring weekly consumer confidence," Econometric Institute Research Papers EI 2008-01, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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