Income missing values imputation: EVS 1999 and 2008
AbstractMissing 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.
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Bibliographic InfoPaper provided by CEPS/INSTEAD in its series CEPS/INSTEAD Working Paper Series with number 2011-05.
Length: 28 pages
Date of creation: Jan 2011
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multiple imputation; missing data; income; EVS; cross-section;
Find related papers by 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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- NEP-ALL-2011-02-05 (All new papers)
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- TOURBEAUX Jérôme, 2012. "Intégration et frontières sociales au Luxembourg," CEPS/INSTEAD Working Paper Series 2012-21, CEPS/INSTEAD.
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