I am working on a research paper and I need to organize the statistical analysis part. I have two questionnaires with the same number of participants (15 cases). Basically, we asked the participants to play two different games with two different conditions.
Thanks for the revised question.
For Game A. The two conditions were scored very similarly, and you have only 7 scores.
a1 = c(4, 3.5, 3, 4, 4.5, 4, 4)
a2 = c(4, 4, 4, 3, 4, 4, 4)
A paired Wilcoxon signed rank test finds no difference between conditions. The P-value is not exact because of the ties in the data. Tests from R.
wilcox.test(a1, a2, pair=T)
Wilcoxon signed rank test
with continuity correction
data: a1 and a2
V = 5, p-value = 1
alternative hypothesis:
true location shift is not equal to 0
Treating the scores as numerical (even though I wonder if they may be ordinal categorical choices), we find no difference (high P-value), again because the scores are so nearly the same for the two conditions.
t.test(a1, a2)$p.val
[1] 1
For Game B. In all five categories, Condition 2 was preferred. The Wilcoxon signed rank test will not work because of ties in the differences. However, regarding data as numerical, a paired t test is significant at the 5% level (P-value $0.012 < 0.05 = 5\\%.)$
b1 = c(4, 3, 3, 3, 4)
b2 = c(5, 4, 5, 5, 4.5)
t.test(b1, b2, pair=T)
Paired t-test
data: b1 and b2
t = -4.3333, df = 4, p-value = 0.01232
alternative hypothesis:
true difference in means is not equal to 0
95 percent confidence interval:
-2.1329335 -0.4670665
sample estimates:
mean of the differences
-1.3
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