拟合优度卡方检验中分层的自动重组

Vicente Nunez Anton, Juan Manuel Pérez Salamero González, Marta Regúlez-Castillo, M. Ventura-Marco, Carlos Vidal-Meliá
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引用次数: 1

摘要

皮尔逊卡方检验在社会科学和健康科学中广泛用于分析分类数据和列联表。为了使测试有效,样本量必须足够大,以提供每个类别所需元素的最小数量。本文开发了自动重新分组地层的功能,从而使拟合优度测试能够在迭代过程中进行。通过仿真研究和在不同数据集上的应用,说明了这些函数的有效性和性能。最后,将函数的迭代使用应用于工作生活的连续样本,这是一个已在相当多的研究中使用的数据集,特别是在劳动经济学和西班牙公共养老金制度方面。
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Automatic Regrouping of Strata in the Goodness-of-Fit Chi-Square Test
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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