Automatic regrouping of strata in the goodness-of-fit chi-square test

  1. Vicente Núñez-Antón
  2. Juan Manuel Pérez-Salamero González
  3. Marta Regúlez-Castillo
  4. Manuel Ventura-Marco
  5. Carlos Vidal-Meliá
Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Any de publicació: 2019

Volum: 43

Número: 1

Pàgines: 113-142

Tipus: Article

DOI: 10.2436/20.8080.02.83 DIALNET GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Sort: Statistics and Operations Research Transactions

Resum

Pearson’s chi-square test is widely employed in social and health sciences to analyse categorical data and contingency tables. For the test to be valid, the sample size must be large enough to provide a minimum number of expected elements per category. This paper develops functions for regrouping strata automatically, thus enabling the goodness-of-fit test to be performed within an iterative procedure. The usefulness and performance of these functions is illustrated by means of a simulation study and the application to different datasets. Finally, the iterative use of the functions is applied to the Continuous Sample of Working Lives, a dataset that has been used in a considerable number of studies, especially on labour economics and the Spanish public pension system.

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Informació de finançament

The authors gratefully acknowledge financial support from Ministerio de Economía y Competitividad (Spain), Agencia Estatal de Investigación (AEI), and the European Regional Development Fund (ERDF), under research grants ECO2015-65826-P (AEI/ ERDF, EU) and MTM2016-74931-P (AEI/ERDF, EU) and from the Department of Education of the Basque Government (UPV/EHU MacLab Research Group and UPV/EHU Econometrics Research Group) under research grants IT 793-13 and IT-642-13, respectively. The authors wish to thank the editor and two anonymous referees for providing thoughtful comments and suggestions which have led to substantial improvement in the presentation of the material in this paper. They also would like to thank Jose M. Pavía, Miguel Angel García Pérez and Fernando Tusell for their comments and suggestions, and Christopher G. Pellow for his help with the English. Any errors are entirely due to the authors.

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