# Evaluating Progressive Web App Accessibility for People with Disabilities
## 5 Results
The results after the descriptive analysis, both for new and older technology, are presented in Tables 5 and 6, respectively. The \(x\)-axis scale in both Tables 5 and 6 presents the auditing tools undertaken to perform the accessibility checks on sample websites, while the \(y\)-axis scale presents the metrics used by the tool to perform the descriptive analysis. To clarify the results shown in Tables 5 and 6 we provide some additional information bellow:  
* The count() function used to count the sum of the data. * The mean() function used to count the average value. * The std() function used to compute the standard deviation along the specified axis. * The 25%, 50%, and 75% are three quartile values (Q1, Q2, and Q3, respectively). The second quartile (Q2) is the median of the whole data, the first quartile (Q1) is the median of the upper half of the data, while the third quartile (Q3) is the median of the lower half of the data. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **T1 (\%)** & **T2** & **T3** & **T4 (\%)** & **T5** & **T6** & **T7** & **T8** & **T9** & **T10** \\ \hline count & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 \\ mean & 20.40 & 23.50 & 37 & 16.90 & 51.55 & 155.75 & 297.65 & 58.65 & 172.95 & 137.15 \\ std & 19.07 & 49.94 & 45.42 & 9.65 & 98.84 & 187.42 & 476.80 & 101.11 & 258.52 & 122.92 \\ min & 5 & 1 & 0 & 0 & 0 & 6 & 14 & 1 & 7 & 14 \\
25\% & 9 & 3 & 3.75 & 11 & 11 & 24.25 & 61.75 & 9.25 & 49.75 & 54.25 \\
50\% & 12.50 & 5 & 17 & 18 & 22.50 & 80.50 & 132 & 28 & 76 & 110.50 \\
75\% & 23.50 & 26.25 & 64 & 22 & 48.50 & 245.25 & 295.75 & 47.25 & 163 & 170.50 \\ max & 89 & 227 & 145 & 33 & 454 & 739 & 2134 & 431 & 1067 & 530 \\ \hline \end{tabular}
\end{table} 
Table 6: Descriptive analysis for older technology (non-PWA)—old_tech_describe(). Figure 4: Import required in Python Libraries. Figure 5: Read data into Pandas Data Frame. * The max() and min() functions used to present the max and min values of errors per auditing tool. A way to start the descriptive analysis is by looking at what kind of ranges each tool has and how much they vary around their average values (mean). We can observe from the above plots that CSS Validation Service by W3 (T5) error value reduced to a significant level in new technology. The average error value in new technology is 13.40, while it is 51.55 in older technology. Similarly, in new technology, the maximum axe DevTool (T7) error value is 450, while it is 2134 in older technology, showing that PWAs are operating significantly better and that error rates in websites have been lowered to a larger extent. Both Tables 5 and 6 demonstrate that conventional non-PWA websites retain more errors than PWA websites in checks performed by accessibility auditing tools. As a result, the PWA websites of the dataset follow the accessibility guidelines more strictly than conventional non-PWA websites. Examining the mean values in Tables 5 and 6, we notice that nine out of ten auditing tools return less errors on average for PWAs than non-PWA websites, concluding that websites that use the PWA technology are more compatible with the existing accessibility guidelines. To further support our findings, we graphically present the results for each tool into plots in Figures 6-8. Figures 7 and 8 compare errors/bugs in the IBM Equal Access toolkit (T9). The \(y\)-axis scale shows that error values in modern technologies are substantially lower than in older technologies, as calculated by several auditing tools. The last column in both Tables 3 and 4 presents the niche--industry for each website checked, grouping for extension of our datasets. The calculation of errors per industry is depicted in Figure 9, while the corresponding plot is presented in Figure 10, based on Axe DevTools (T7). The plot in Figure 10 illustrates that the Axes Devtool (T7) error rate is reduced in PWA when compared to the corresponding non-PWA performance.