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Income missing values imputation: EVS 1999 and 2008


  • SARRACINO Francesco


Missing data is a very frequent obstacle in many social science studies. The absence of values on one or more variables can signi?cantly affect statistical analyses by reducing their precision and by introducing selection biases. Being unable to account for these aspects may result in severe misrepresentation of the phenomenon under analysis. For this reason several approaches have been proposed to impute missing values. In present work I will adopt multiple imputation to impute income missing data for Luxembourg in the European Values Study data-set of 1999 and 2008.

Suggested Citation

  • SARRACINO Francesco, 2011. "Income missing values imputation: EVS 1999 and 2008," LISER Working Paper Series 2011-05, LISER.
  • Handle: RePEc:irs:cepswp:2011-05

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    Cited by:

    1. TOURBEAUX Jérôme, 2012. "Intégration et frontières sociales au Luxembourg," LISER Working Paper Series 2012-21, LISER.

    More about this item


    multiple imputation; missing data; income; EVS; cross-section;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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