3.5 KiB
3.5 KiB
In [7]:
df = read.csv("./DataStudyEvaluation/R_TLX.csv") df$ID <- factor(df$ID) df$Task <- factor(df$Task) friedman.test(Score ~ Task | ID, data = df)
Friedman rank sum test data: Score and Task and ID Friedman chi-squared = 12, df = 1, p-value = 0.000532
In [8]:
df = read.csv("./DataStudyEvaluation/R_SUS.csv") df$ID <- factor(df$ID) df$Task <- factor(df$Task) friedman.test(Score ~ Task | ID, data = df)
Friedman rank sum test data: Score and Task and ID Friedman chi-squared = 8.3333, df = 1, p-value = 0.003892
In [6]:
df = read.csv("./DataStudyEvaluation/R_Quest.csv") df$ID <- factor(df$ID) df$Task <- factor(df$Task) friedman.test(Easiness ~ Task | ID, data = df) friedman.test(Speed ~ Task | ID, data = df) friedman.test(Success ~ Task | ID, data = df) friedman.test(Accuracy ~ Task | ID, data = df) friedman.test(Comfort ~ Task | ID, data = df)
Friedman rank sum test data: Easiness and Task and ID Friedman chi-squared = 8.3333, df = 1, p-value = 0.003892
Friedman rank sum test data: Speed and Task and ID Friedman chi-squared = 12, df = 1, p-value = 0.000532
Friedman rank sum test data: Success and Task and ID Friedman chi-squared = 6.4, df = 1, p-value = 0.01141
Friedman rank sum test data: Accuracy and Task and ID Friedman chi-squared = 11, df = 1, p-value = 0.0009111
Friedman rank sum test data: Comfort and Task and ID Friedman chi-squared = 12, df = 1, p-value = 0.000532
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