Examples of using Two categorical variables in English and their translations into Portuguese
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
Comparison between two categorical variables was performed with Chi-square test.
A mosaic plot, fluctuation diagram, orfaceted bar chart may be used to display two categorical variables.
For analyses of associations between two categorical variables, we applied the chi-squared test;
As there were two categorical variables physical activity and result of the 3MS examination, the Chi-square test and the Fisher exact test were performed.
To such end, we have employed Spearman correlation tests in case both the variables were continuous, orcontingency chi-square test for two categorical variables.
To test the association between two categorical variables, Pearson's chi-square x2 test was used.
For such, Spearman's rank correlation coefficient tests were used in case that both variables were continuous orChi-squared tests of contingency for two categorical variables.
For correlation between two categorical variables the Chi-square test was used and the level of significance adopted was 5.
The statistical analysis between the groups in terms of rate of complications, reoperation, and degree of patient satisfaction was done byapplying Fisher's exact test, with the aim of verifying the association between two categorical variables.
In order to test whether the frequencies of the two categorical variables presented any degree of independence, we used the chi-square test.
Two categorical variables were used:(1) dry season period(May to October) and wet season period(November to April);(2) hypertensive heart diseases, ischemic heart diseases, and cerebrovascular diseases.
The Fisher exact test was utilized to analyze the association between any two categorical variables in order to transform the data into a 2× 2 table for the testing.
The chi-square test was used to investigate possible associations between the aforementioned categories and sample-related variables mothers' socioeconomic and infants' birth data;that test was used to investigate associations between two categorical variables.
The association between two categorical variables was obtained by calculating the odds ratio(or) and risk ratio(rr) with 95% confidence interval 95% ci.
However, since this test does not measure the effect size of the association, for such purpose, we used V-Cramer,which is a measure of the degree of association between two categorical variables, and thus it is considered as a small effect when r 0.1, medium r 0.3 and large r 0.5.
The analysis of association between two categorical variables was performed using the chi-square or the Fisher exact test for expected values lower than 5.
In order to analyze the association of these variables with the V-RQOL results the mean values of the continuous variables age, V-RQOL score, satisfaction andtime of surgery were calculated, thus generating two categorical variables above and below the mean to study the association between them.
To evaluate the association between two categorical variables, we used the statistical test Chi-square test or Fisher's exact when the conditions for the Chi-square test were not verified.
For the variable sex,the Fisher's exact test was utilized to evaluate the association between two categorical variables; and for the variables lesion size and patient's age, the non-parametric Wilcoxon-Mann-Whitney test was utilized.
In bivariate analysis,the Chi-square test was used to compare two categorical variables, based on previous specific assumptions, producing crude Odds Ratios and 95% Confidence Intervals CI; and logistic regression was used in multivariable analysis, considering all the variables with p.
For this we utilized descriptive percentages and statistical tests, such as:Chi-square contingency for the last two categorical variables, means tests to assess the difference between more than two groups in function of one or more continuous dependent variables ANOVA, and multivariate techniques the data described above are about variables that are considered correlated, such as principal component analysis, cluster, and correspondence analysis.
A logistic regression model was constructed including two independent categorical variables(PAL score and air flow) and the performance of the model was assessed by non-parametric receiver operating characteristic curves.