Volume 5, Issue 1, March 2019, Page: 5-9
Estimation of Response Propensities Using the Previous Survey
Miika Honkala, Department of Standards and Methods, Statistics Finland, Helsinki, Finland
Received: Apr. 13, 2019;       Accepted: May 10, 2019;       Published: Jun. 4, 2019
DOI: 10.11648/j.ijsd.20190501.12      View  18      Downloads  13
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.
Response Propensity, Response Rate, Representative Set of Respondents, Imputation
To cite this article
Miika Honkala, Estimation of Response Propensities Using the Previous Survey, International Journal of Statistical Distributions and Applications. Vol. 5, No. 1, 2019, pp. 5-9. doi: 10.11648/j.ijsd.20190501.12
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