Abstract
One of the most salient data problems empirical researchers face is the lack of informative responses in survey data. This contribution briefly surveys the literature on item nonresponse behavior and its determinants before it describes four approaches to address item nonresponse problems: Casewise deletion of observations, weighting, imputation, and model-based procedures. We describe the basic approaches, their strengths and weaknesses and illustrate some of their effects using a simulation study. The paper concludes with some recommendations for the applied researcher.
Keywords
- Multiple Imputation
- Complete Case Analysis
- Single Imputation
- Miss Data Mechanism
- Multiple Imputation Procedure
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
We are grateful to an anonymous referee who provided helpful comments. Also we like to thank Donald B. Rubin for helpful comments and always motivating discussions as well as Ralf Münnich for inspiring discussions about raking procedures.
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Rässler, S., Riphahn, R.T. (2006). Survey Item Nonresponse and its Treatment. In: Hübler, O., Frohn, J. (eds) Modern Econometric Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32693-6_15
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DOI: https://doi.org/10.1007/3-540-32693-6_15
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