# Compare variable of the same individuals measured in different situations

Hi gang,
I measured a temporal variable (dur) of several individuals (batid) in 4 different situations (g_type). I dont have data for each individual in each situation. Now I would like to test if the temporal variable is shorter or longer depending on the situation. I wanted to do a Kruscal Wallis test, because the data is not normally distributed. But since its similar individuals in each situation I think I need to correct for that somehow. What would be the best way to do so? Or can I just do the Kruscal Wallis test? (Results of this below) Would be amazing if I can get help here Thanks for you patience!

``````nuber g_type batid dur
1	lb	1	1.063353
2	lb	2	1.020000
3	lb	4	1.325775
4	lb	5	1.170000
5	lb	7	1.100000
6	lb	8	1.426637
7	lb	9	1.035059
8	mp	1	3.322441
9	mp	2	3.381790
10	mp	3	2.450036
11	mp	4	1.530039
12	mp	5	4.680098
13	mp	6	2.403818
14	mp	7	3.462617
15	mp	8	2.154150
16	mp	9	3.218999
17	mp	10	4.850000
18	mp	11	2.371661
19	mp	12	1.130000
20	mp	13	1.670117
21	tg	1	1.560286
22	tg	2	1.468229
23	tg	3	1.560000
24	tg	4	NA
25	tg	5	1.525176
26	tg	6	1.471191
27	tg	7	1.476790
28	tg	8	1.610000
29	tg	9	1.440869

kruskal.test(df3\$dur ~ df3\$g_type)
dunnTest(df3\$dur ~ df3\$g_type,
method="bh")
ยดยดยด

Kruskal-Wallis rank sum test

data:  df3\$dur by df3\$g_type
Kruskal-Wallis chi-squared = 18.731, df = 2, p-value = 8.561e-05

1    lb - mp -4.277181 1.892751e-05 5.678253e-05
2    lb - tg -1.983965 4.725969e-02 4.725969e-02
3    mp - tg  2.177267 2.946063e-02 4.419094e-02``````

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