For your qualitative analysis, you will obtain maximum 4 outputs:
- Chi2.csv: informs which variable is implicated in the factor's levels
- fisher_exact.csv: informs which variable (with low expected frequency) is implacated in the factor's levels
- qualitative_results.csv: informs if the variable is implicated in the factor's levels, this file informs in each factor's level if the variable modality is:
For your qualitative analysis, you may obtain up to 4 output files:
- Chi2.csv: contains the results of the Chi-squared test of independence which assess whether a variable is dependent on the factor.
- fisher_exact.csv: an alternative to the Chi-squared when the expected frequencies in the contingency table between the variable and the factor are very low (<5). It also tests the dependency between the variable and the factor.
- qualitative_results.csv: for dependent variables, this file describes the dependency of the factor levels. For each factor level, it indicates whether the variable modality is:
- over-represented
- under-represented
- not significant
- not present
- weight.csv: informs the qualitative variables contribution to the factor's levels
- weight.csv: indicates the contribution of the qualitative variables to the factor levels
For your quantitative analysis, you will obtain maximum 5 outputs:
- normality.csv: informs if the quantitative variables have a normal distribution in the different factor's levels
- homoscedasticity.csv: informs if the quantitative variables' variance are the same in the different factor's levels
- anova.csv : informs if a variable have a significant higher or lower average than the average of all the groups.
- kruskal_wallis.csv: informs if a variable have a significant higher or lower average than the average of all the groups for variables that are not normal distributed.
- quantitative_results.csv informs for significative variable (to ANOVA or kruskal wallis) if the average of the variable is:
- above from the average for all individuals
- below from the average for all individuals
- Not significantly different from the average for all individuals
For your quantitative analysis, you may obtain up to 5 outputs files:
- normality.csv: contains the results of the Shapiro-Wilk test, which assesses whether each quantitative variable meets the normality assumption within the compared factor levels.
- homoscedasticity.csv: contains the results of Bartlett's test, which verifies the equality of variances between the factor levels.
- anova.csv : when assumptions are met, this test determines whether there is a significance difference in at least one factor level for each quantitative variable.
- kruskal_wallis.csv: the non-parametric alternative of anova, used when at least one assumption is not met.
- quantitative_results.csv: provides information on variables with significant differences (based on anova and Kruskal-Wallis results) and only when the homoscedasticity assumption is verified. If homoscedasticity is not verified, Kruskal-Wallis is applied but, no further description of the factor levels is performed. This file indicates whether the variable mean is:
- above the overall average
- below the overall average
- Not significantly different from the overall average
