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Integration and imputation of survey data in R: the StatMatch package

Author

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  • Marcello D’Orazio

    (Italian National Institute of Statistics)

Abstract

Statistical matching methods permit to integrate two or more data sources with the purpose of investigating the relationship between variables not jointly observed. Recently these methods received much attention as valid alternative to produce new statistical outputs.The paper provides an overview on the statistical matching methods implemented in the package StatMatch for the R environment, focusing on the most widespread methods and how they were improved. Particular attention is devoted to hot deck matching methods, strictly related to the ones developed for the imputation of missing values. The corresponding functions in StatMatch are very powerful and are flexible enough to be applied for imputing missing values in a survey. The paper tackles also the problem of matching data from complex sample surveys, a very important topic in National Statistical Institutes. Finally it is described the concept of uncertainty characterizing the statistical matching framework and how this alternative approach can be exploited for different purposes.

Suggested Citation

  • Marcello D’Orazio, 2015. "Integration and imputation of survey data in R: the StatMatch package," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 57-68, June.
    • Handle: RePEc:rsr:journl:v:63:y:2015:i:2:p:57-68
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    References listed on IDEAS

    as
    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. Gabriella Donatiello & Marcello D’Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical Matching of Income and Consumption Expenditures," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(3), pages 50-65.
    3. Marco Ballin & Marco Di Zio & Marcello D'Orazio & Mauro Scanu & Nicola Torelli, 2008. "File concatenation of survey data: a computer intensive approach to sampling weights estimation," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 10(2), pages 5-12, October.
    4. Gabriella Donatiello & Marcello D'Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical matching of income and consumption expenditures," Proceedings of International Academic Conferences 0100965, International Institute of Social and Economic Sciences.
    5. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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