# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 5 Results
### Further Analysis at the Item Level  
After confirming that numeracy and need for cognition were influential cognitive characteristics in visualization literacy, we conducted a further analysis at the item level of the VLAT, because each item in the VLAT represented a specific visualization task with an associated data visualization (see Table 2 in [PERSON] et al. 's paper [1] for details). As we did in Sections 5.2 and 5.3, we divided the participants into high- and low-numeracy user groups and high- and low-need-for-cognition user groups based on the median split method. We calculated the percentages of the groups who answered Item \(i\) in the VLAT correctly. Then, we ran a two-proportion \(Z\)-test for each of the 41 items in order to test the difference in the two proportions between the high and low groups. The results are represented in Figures 6 and 7. The asterisk marks indicate items that have a significant difference between high and low groups at the significance level of 0.05 based on the two-proportion \(Z\)-tests. Besides statistical tests, we also reviewed the general tendency of visualization literacy scores, especially in terms of visualization types and task types, between the high and low groups of cognitive measures. First, we confirmed the general trends in the correct answer rates of the high-numeracy user group and the low-numeracy user group (Figure 6). The high-numeracy user group had higher rates of correct answers across all items in the VLAT as compared to the low-numeracy user group. In other words, the high-numeracy user group generally performed all data visualization tasks with associated visualizations better than the low-numeracy user group. In addition, the overall tendencies between the correct answer rates of the high-numeracy user group and the low-numeracy user group were very similar. Both groups showed relatively high correct answer rates for some items (e.g., Items 6, 17, 38, 61); they also showed relatively low correct answer rates for other items (e.g., Items 10, 40, 45). More specifically, compared to other tasks, users in the both groups showed low performance with the tasks of Make Comparisons (Items 9, 45, 46), Find Anomalies (Item 31), Find Extremum (Item 36), and Determine Range (Items 37, 49). Figure 5: Difference in VLAT scores between visualizer user group and verbalizer user group. According to the results of \(Z\)-tests, we fond significant differences in 23 items between the high- and low-numeracy user groups. Among them, five items (Items 6, 17, 22, 27, 38) had relatively very high correct answer rates (\(\geq\)80%) in the both high- and low-numeracy user groups even though there were significant differences between them. Among the remaining items that had significant differences between two groups, seven items (Items 10, 11, 19, 35, 40, 41, 47) were associated with the task of Retrieve Value with Stacked Bar Chart, Pie Chart, Area Chart, Stacked Area Chart, and Bubble Chart. The participants with low-numeracy performed the Retrieve Value tasks with the five data visualizations significantly worse than the participants with high-numeracy; furthermore, the correct answer rates for the items were not high. In addition, nine items that were associated with tasks that required simple arithmetic operations using retrieved values from visualizations also had significant differences between the two groups. For example, Items 3, 8, 29, 37, and 49 were associated with the task of Determine Range, and Items 5, 18, 34, and 54 were associated with the task of Make Comparisons. The participants with low numeracy performed these tasks significantly worse than the participants with high numeracy. We also confirmed the general trends in correct answer rates of the high-need-for-cognition user group and the low-need-for-cognition user group (Figure 7). We observed few differences in correct answer rates between the high- and low-need-for-cognition groups.