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Results_1.txt
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print(aspect_data)
aspect aspect_count
1 artistic 15
2 technical 18
3 informative 13
Integration:
negative rather_negative neutral rather_positive positive NA. col_names_vec
1 0 0 2 9 31 0 Enrichment
2 0 3 3 10 26 0 Integration
3 1 2 1 3 35 0 Use again
4 0 2 5 8 21 6 More digital Apps
Usability:
difficult rather_difficult neutral rather_easy easy NA. col_names_vec
1 0 0 4 3 30 5 Operation
2 0 0 12 10 12 8 Help
3 1 0 3 6 27 5 Color\nSelection
4 0 0 2 12 22 6 Tools
5 0 0 15 7 13 7 Artistic\nfreedom
6 1 1 9 10 14 7 Creativity
Knowledge:
neutral no not_really rather_yes yes NA. col_names_vec
1 7 16 2 3 12 2 Used\nInfoscreen
2 4 15 4 3 13 3 Read\nObject Panel
3 8 2 2 11 15 4 Motivation
4 11 1 0 7 17 6 Text Length\nAppropriateness
5 1 29 0 3 7 2 Noticed\nHighlights
6 6 4 8 5 15 4 Background\nKnowledge
integration_score
[1] 4.529762
usability_score
[1] 4.295495
knowledge_score
[1] 3.06125
Kendall's rank correlation tau
data: data_num$a5 and data_num$i3
z = -2.2597, p-value = 0.02384
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau
-0.3207467
Welch Two Sample t-test
data: data_num$i3 and data_num$a5
t = 10.982, df = 80.988, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.735108 2.502987
sample estimates:
mean of x mean of y
4.642857 2.523810
Pearson's Chi-squared test
data: table(data_cut5$q_sum, data_cut5$w1)
X-squared = 18.848, df = 12, p-value = 0.09225
Pearson's Chi-squared test
data: table(data_cut5$q_sum, data_cut5$w2)
X-squared = 25.32, df = 12, p-value = 0.01338