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A split questionnaire survey design applied to German media and consumer surveys

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  • Rässler, Susanne
  • Koller, Florian
  • Mäenpää, Christine

Abstract

On the basis of real data sets it is shown that splitting a questionnaire survey according to technical rather than qualitative criteria can reduce costs and respondent burden remarkably. Household interview surveys about media and consuming behavior are analyzed and splitted into components. Following the matrix sampling approach, respondents are asked only the varying subsets of the components inducing missing data by design. These missing data are imputed afterwards to create a complete data set. In an iterative algorithm every variable with missing values is regressed on all other variables which either are originally complete or contain actual imputations. The imputation procedure itself is based on the socalled predictive mean matching. In this contribution the validity of split and imputation is discussed based on the preservation of empirical distributions, bivariate associations, conditional associations and on regression inference. Finally, we find that many empirical distributions of the complete data are reproduced well in the imputed data sets. Concerning these long media and consumer questionnaires we like to conclude that nearly the same inference can be achieved by means of such a split design with reduced costs and minor respondent burden

Suggested Citation

  • Rässler, Susanne & Koller, Florian & Mäenpää, Christine, 2002. "A split questionnaire survey design applied to German media and consumer surveys," Discussion Papers 42b/2002, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
  • Handle: RePEc:zbw:faucse:42b2002
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    References listed on IDEAS

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    1. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    2. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
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    1. Rässler, Susanne, 2006. "Der Einsatz von Missing Data Techniken in der Arbeitsmarktforschung des IAB," IAB-Forschungsbericht 200618, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Rässler, Susanne & Schnell, Rainer, 2004. "Multiple imputation for unit-nonresponse versus weighting including a comparison with a nonresponse follow-up study," Discussion Papers 65/2004, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.

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