Statistical matching en la práctica – Una aplicación a la evaluación del sistema educativo mediante PISA y TALIS

  1. Leunda Iztueta, Ixiar 1
  2. Garmendia Navarro, Ines
  3. Etxeberria Murgiondo, Juan 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Journal:
Revista de investigación educativa, RIE

ISSN: 0212-4068,  1989-9106

Year of publication: 2017

Volume: 35

Issue: 2

Pages: 371-388

Type: Article

DOI: 10.6018/RIE.35.2.262171 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

More publications in: Revista de investigación educativa, RIE

Sustainable development goals

Abstract

Statistical matching methods are aimed at the integration of information collected through multiple sources, usually, surveys drawn from some target population. As opposed to record linkage methods -where we search for identical units-, in statistical matching we search for similar units in order to find statistical relations across databases. Methods: Statistical matching is feasible provided that the independent surveys share a common block of variables. A particular solution is based on imputation methods for missing data: first, the distinct files are concatenated (i.e. rows and columns are joined together to form a unique file); next, empty cells corresponding to non-observed values are interpreted as missing data, and they are imputed according to observed data. Results: The fundamental concepts of statistical matching are shown, and the process is illustrated with the PISA (2012) and TALIS (2013) educational studies with Spain’s data. Imputations are carried out using mice package from the free R software. A first validation of the results is performed. Conclusions: Statistical matching offers high potential benefits for the social sciences since it enables to relate information from independent information sources. These techniques can now be applied with relative ease thanks to the development of tools such as R computing environment.

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Data source: Dialnet