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Taking "Don't Knows" as Valid Responses: A Complete Random Imputation of Missing Data

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  • Martin Kroh

Abstract

Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts' awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores. In many instances, however, non-response to a stimulus does not result from measurement problems that inhibit accurate surveying of empirical reality, but from the inapplicability of the survey question. In such cases, existing imputation techniques replace valid non-response with counterfactual estimates of a situation in which the stimulus is applicable to all respondents. This paper suggests an alternative imputation procedure for incomplete data for which no true score exists: multiple complete random imputation, which overcomes the biasing effects of missing data and allows analysts to model respondents' valid "I don't know" answers.

Suggested Citation

  • Martin Kroh, 2004. "Taking "Don't Knows" as Valid Responses: A Complete Random Imputation of Missing Data," Discussion Papers of DIW Berlin 442, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp442
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    References listed on IDEAS

    as
    1. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    2. 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.
    3. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Missing data; Incomplete data; Non-response; Multiple imputation; Survey methodology; Mixture regression models; Vote choice;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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