In a parametric framework, the Student t-test (k=2) (respectively Fisher F-test (k>2)) is implemented while the Wilcoxon (k=2) (respectively Kruskal-Wallis (k>2)) test is preferred in a non-parametric one. For each quantitative variable, a test is performed to determine whether it is significantly different from 0. For each category, it consists in determining whether the mean of the quantitative variable to characterize in the group whose members share this category is significantly different from the mean of the quantitative variable to characterize considering the whole sample.Ģ) Characterization of a qualitative variable (with k categories):Ĭharacterization of a qualitative variable by quantitative variables is carried out using the correlation ratio. The more the correlation ratio significantly different from zero, the stronger the link between the quantitative variable to characterize and the characterizing qualitative variable.Ĭharacterization of a quantitative variable by categories is carried out using a mean comparison test. Kruskal-Wallis (k>2)) test is preferred in a non-parametric one. Fisher F-test (k>2)) is implemented while the Wilcoxon (k=2) (resp. In a parametric framework, the Student t-test (k=2) (resp. For each qualitative variable (with k categories), a test is performed to determine whether the latter is significantly different from 0. The more significantly different from 0 the correlation coefficient, the stronger the link between the 2 quantitative variables.Ĭharacterization of a quantitative variable by qualitative variables is carried out using the correlation ratio.
The Pearson correlation test is implemented in a parametric context while the Spearman correlation test is preferred in a non-parametric one. For each characterizing quantitative variable, a test is performed to determine whether the latter is significantly different from 0. Here are the various characterizing possibilities of the procedure:ġ) Characterization of a quantitative variable:Ĭharacterization of a quantitative variable by other quantitative variables is carried out using the correlation coefficient. Variable characterization allows to characterize elements (quantitative variables, qualitative ones or categories of qualitative variables) exploring the links they share with characterizing variables (quantitative variables, qualitative variables or categories of qualitative variables). It is available in Excel using the XLSTAT statistical software. Variable characterization will let you easily describe quantitative or qualitative variables.
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