International Journal of Statistical Distributions and Applications
Volume 5, Issue 1, March 2019, Pages: 5-9
Received: Apr. 13, 2019;
Accepted: May 10, 2019;
Published: Jun. 4, 2019
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Miika Honkala, Department of Standards and Methods, Statistics Finland, Helsinki, Finland
Many surveys are carried out yearly, and the implementation of the surveys remains the same from year to year. Experience from a previous survey is useful when planning a new survey, because the response behavior usually remains quite the same in subsequent years. This paper studies how response propensities, estimated using the dataset of the previous survey, predict actual response rates. In this study, two consecutive datasets of the European Social Survey were available. The both datasets contained same register variables. Response propensities were estimated to the older dataset using a logistic regression model. Then the propensities were imputed to the newer dataset using a donor-recipient method. The imputation was based on the explanatory variables of the logistic regression model so that the donor and the recipient had the same values in the variables. Then it was examined if there was a connection between the imputed response propensities and actual response rates. The result was that the imputed response propensities predicted the response behavior quite well. People with low response propensities were often nonrespondents, and people with high response propensities were often respondents. Using the previous survey, it is possible to calculate response propensities for a new sample before the data collection of the survey has been started. Then challenging respondents are known before the data collection, and this information is useful for data collection.
Estimation of Response Propensities Using the Previous Survey, International Journal of Statistical Distributions and Applications.
Vol. 5, No. 1,
2019, pp. 5-9.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Brick, J. M. and Jones, M. E. (2008). Propensity to respond and nonresponse bias. International Journal of Statistics, LXVI, 51-73.
Bethlehem, J., Cobben, F. and Schouten, B. (2011). Handbook of nonresponse in household surveys. Wiley, New Jersey.
Schouten, B., Cobben, F. and Bethlehem, J. (2009). Indicators for the representativeness of survey response. Survey Methodology, 35, 101-113.
Tourangeau, R., Brick, M., Lohr, S. and Li, J. (2017). Adaptive and Responsive Survey Designs: a Review and Assessment. Journal of the Royal Statistical Society, Series A 180, 203-223.
Laaksonen, S. (2016). Anticipation of unit nonresponse or not in the sampling designing From the point of view of the European Social Survey (ESS). International Workshop on Household Survey Nonresponse, 2016, Oslo.
Schouten, B., Peytchev, A. and Wagner, J. (2017). Adaptive survey design. Chapman and Hall, New York.
Chun, A., Heeringa, S. and Schouten, B. (2018). Responsive and Adaptive Design for Survey Optimization. Journal of Official Statistics, 34, 581-597.
Brick, J. M. and Tourangeau, R. (2017). Responsive Survey Designs for Reducing Nonresponse Bias. Journal of Official Statistics, 33, 735-752.
Särndal, C.-E. and Lundquist, P. (2017). Inconsistent Regression and Nonresponse Bias: Exploring Their Relationship as a Function of Response Imbalance. Journal of Official Statistics, 33, 709-734.
Honkala, M. (2017). Responsive design in data collection: practical experiences. Workshop on Survey Statistics Theory and Methodology, Vilnius, 2017.
Walejko, G. and Wagner, J. (2018). A Study of Interviewer Compliance in 2013 and 2014 Census Test Adaptive Designs. Journal of Official Statistics, 34, 649-670.
Luiten, A. and Schouten, B. (2013). Tailored fieldwork design to increase representative household survey response: an experiment in the Survey of Consumer Satisfaction. Journal of the Royal Statistical Society, Series A, 176 (1), 169-189.
Stoop, I., Billiet, J., Koch, A. and Fitzgerald, R. (2010). Improving Survey Response: Lessons Learned from the European Social Survey. Wiley
Laaksonen, S. (2006). Does the choice of link function matter in response propensity modelling? Model Assisted Statistics and Applications, 1, 95-100.
Laaksonen, S. (2018). Survey Methodology and Missing Data. Tools and Techniques for Practitioners. Springer.