# Digital Competence and Gender: Teachers in Training. A Case Study
## 3 Results  
As noted earlier, the questionnaire contained both qualitative and quantitative items; consequently, we performed different tests depending on the type of variable. We used contingency tables to analyse qualitative items, taking correlation coefficients as a reference to determine the significance of the relationship between variables. For quantitative items, it was important to determine whether to use parametric or non-parametric tests in order to identify differences related to gender. We performed a Kolmogorov-Smirnov test to assess goodness of fit, i.e., if the sample presented a normal distribution. We found that all items presented a non-normal distribution, indicating the need to use non-parametric tests. Even so, the non-normal distribution did not present a severe problem for the ANOVA test, which is fairly robust in the event of non-normal distribution. Nonetheless, we initially attempted to identify differences using non-parametric tests such as the Mann-Whitney U test. Besides, to further investigate our data we performed an ANOVA to conduct a more in-depth analysis (using [PERSON]'s test). Items which obtained a significance level lower than 0.005 in the ANOVA and non-parametric test were highlighted; those which did not meet the two conditions were considered less statistically robust. Thus, we only considered those results that performed satisfactorily in both statistical tests. For qualitative variables, we only found a relationship between gender and the devices used to access the internet (0.023 significance). Our results showed that women made greater use than men of mobile devices, in computer and mobile device use (78.7% vs. 64.6%) (see Table 2). In contrast, 32.3% of men reported only using a computer, compared with 18.7% of women. Concerning scale variables, 38 of the 89 COTAEDU items distributed across the dimensions correlated with the variable of gender, excluding three items (28, 31 and 90) that it should be considered questionable since they were significant according to the non-parametric but not the ANOVA test (see Table 3). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Contingency Table**} & \multicolumn{3}{c}{**(73) What Device Do You Use to Access the Internet?**} & \multirow{2}{*}{**Total**} \\ \cline{3-3} \cline{5-6}
**Item 1 Gender/Item 73 What Device Do You** & & & **Mobile Device (e.g.,** & **Computer and** & **Mobile Device** & \\ \cline{3-6}
**Use to Access the Internet?** & & & & **Mobile Device (e.g.,** & **Computer and** & **Mobile Device** & \\ \hline \multirow{6}{*}{Women} & Number & 43 & 6 & 181 & 230 \\ \cline{2-6}  & \% in (1) Gender & 18.7\% & 2.6\% & 78.7\% & 100.0\% \\ \cline{2-6}  & \% in (73) What device do you & 57.3\% & 66.7\% & 73.9\% & 69.9\% \\ \cline{2-6}  & \% of total & 13.1\% & 1.8\% & 55.0\% & 69.9\% \\ \hline \multirow{6}{*}{Men} & Number & 32 & 3 & 64 & 99 \\ \cline{2-6}  & \% in (1) Gender & 32.3\% & 3.0\% & 64.6\% & 100.0\% \\ \cline{2-6}  & \% in (73) What device do you & 42.7\% & 33.3\% & 26.1\% & 30.1\% \\ \cline{2-6}  & \% of total & 9.7\% & 0.9\% & 19.5\% & 30.1\% \\ \cline{2-6}  & Number & 75 & 9 & 245 & 329 \\ \cline{2-6}  & \% in (1) Gender & 22.8\% & 2.7\% & 74.5\% & 100.0\% \\ \cline{2-6}  & \% in (73) What device do you & 100.0\% & 100.0\% & 100.0\% & 100.0\% \\ \cline{2-6}  & \% of total & 22.8\% & 2.7\% & 74.5\% & 100.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gender and devices. For significant items, we conducted a comparison of means obtained from the ANOVA for the variable of gender to obtain measures of central tendency (means and standard deviations) (see Table 4) and found that men usually scored higher than women.