Automatic regrouping of strata in the chi-square test

  1. Juan Manuel Pérez-Salamero González 1
  2. Marta Regúlez-Castillo 2
  3. Manuel Ventura-Marco 1
  4. Carlos Vidal-Meliá 13
  1. 1 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

  3. 3 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

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Revista:
Documentos de Trabajo (ICAE)

ISSN: 2341-2356

Año de publicación: 2017

Número: 24

Páginas: 1-25

Tipo: Documento de Trabajo

DIALNET GOOGLE SCHOLAR lock_openDocta Complutense editor

Otras publicaciones en: Documentos de Trabajo (ICAE)

Resumen

Pearson´s chi-square test is widely employed in social and health science to analyze categorical data and contingency tables and to assess sample representativeness. For the test to be valid the sample size must be big enough to provide a minimum number of expected elements per category. If the researcher chooses to regroup the strata in order to solve the failure on the minimum size requirement, the existence of automatic re-grouping procedures in statistical software would be very useful, especially when tests are applied sequentially. After comprehensively reviewing the software that can carry out this test, we find that, with a few exceptions, there is no automatic regrouping of the strata to meet this requirement, although it would be very useful if this were available. This paper develops some functions for regrouping strata automatically no matter where they are located, thus enabling the test to be performed within an iterative procedure. The functions are written in Excel VBA (Visual Basic for Applications) and in Mathematica, so it would not be hard to implement them in other languages. The utility of these functions is shown by using three 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 labor economics and the Spanish public pension system.

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Información de financiación

We gratefully acknowledge financial support from Ministerio de Economía y Competitividad (Spain) and from the Basque Government via projects ECO2015-65826-P and IT 793-13 respectively.

Financiadores

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