I'm trying to understand if there is a statistical difference between Policy support scores (PS_score) and Income. I have never done one before so I'm hoping someone can see if I have done it correctly?
Also, if there is anything else I should do to present the findings ie. plots etc I would appreciate the advice! :)
Many thanks
This is the data set:
PS_score EC_score Age Income Own_bags
1 4 4 2 1 1
2 3 4 2 3 1
3 4 4 2 2 1
4 4 4 1 3 2
5 3 4 2 3 1
6 3 3 1 1 2
7 2 2 1 1 2
8 3 4 1 3 2
9 5 2 1 3 1
10 3 5 1 1 1
11 2 2 2 3 2
12 5 4 1 1 2
13 2 3 1 1 1
14 5 1 2 2 3
15 4 4 1 2 1
16 4 3 1 1 1
17 3 4 2 2 1
18 4 4 2 NA 1
19 4 3 1 1 2
20 2 4 1 3 1
21 2 3 1 2 1
22 4 4 1 1 2
23 5 4 1 1 1
24 3 4 2 3 2
25 4 5 1 1 1
26 4 4 1 1 1
27 2 2 1 2 2
28 3 3 1 1 1
29 4 4 1 2 1
30 4 4 1 3 1
31 2 4 1 2 2
32 4 3 1 3 3
33 3 4 1 2 2
34 4 4 1 1 2
35 4 4 2 2 1
36 4 4 1 1 2
37 4 5 1 1 1
38 4 5 1 2 2
39 3 3 1 1 1
40 2 4 1 1 1
41 4 4 2 2 2
42 4 3 2 2 1
43 4 4 1 2 2
44 2 2 1 1 1
45 4 4 1 1 2
46 5 3 1 1 2
47 4 4 1 2 1
48 4 3 2 2 1
49 2 5 2 NA 1
50 4 4 1 3 2
51 5 4 2 2 2
52 4 5 2 3 2
53 4 4 1 1 2
54 4 4 1 1 1
55 4 4 1 1 2
56 5 4 1 1 1
57 3 3 1 2 2
58 4 4 1 2 1
59 4 4 1 1 1
60 4 3 1 1 1
61 4 3 1 2 2
62 4 2 1 2 2
63 4 4 1 1 2
64 4 4 1 2 1
65 4 3 1 2 2
66 4 3 1 1 2
67 3 4 1 1 1
68 4 3 2 2 2
69 5 3 1 1 1
70 3 4 2 NA 1
71 4 4 1 1 1
72 4 5 1 3 1
73 5 4 2 3 2
74 4 4 1 3 1
75 4 3 1 1 2
76 4 3 2 NA 1
77 4 2 2 1 3
78 4 3 1 2 2
79 3 3 2 1 1
80 4 4 2 NA 2
81 4 4 2 1 1
82 2 3 2 1 2
83 4 4 NA NA 1
84 3 4 2 NA 1
85 4 4 2 3 1
86 4 4 2 2 1
87 5 4 2 2 1
88 4 4 2 2 1
89 4 4 2 1 1
90 5 3 2 NA 1
91 4 4 2 2 1
92 3 3 2 NA 1
93 4 4 2 3 2
94 4 4 2 3 2
95 4 3 2 2 2
96 3 3 2 3 1
97 3 4 1 1 2
98 5 4 2 NA 1
99 4 4 2 3 1
100 4 5 2 2 1
101 4 4 2 2 1
102 3 3 2 1 1
103 2 3 2 2 1
104 5 5 2 2 1
105 4 4 2 2 1
106 4 4 2 2 1
107 4 3 2 1 2
108 4 3 2 3 2
109 3 3 2 1 2
110 4 3 2 2 1
111 4 4 2 3 3
112 4 4 2 2 1
113 3 1 2 2 1
114 3 2 1 1 2
115 5 4 2 NA 1
116 5 4 1 2 1
117 4 3 2 NA 2
118 4 4 2 2 1
119 4 3 2 2 1
120 4 4 2 2 1
121 4 3 2 1 1
122 2 3 2 1 3
123 3 4 2 2 1
124 3 3 2 NA 1
125 3 3 2 2 1
126 4 3 2 1 1
127 4 4 2 2 1
128 4 4 2 NA 1
129 4 4 2 2 1
130 3 4 2 2 1
131 3 4 2 NA 1
132 3 4 2 NA 1
133 3 3 2 2 2
134 5 4 2 2 1
135 4 4 2 3 2
136 4 2 2 3 3
137 4 4 2 2 1
138 3 4 2 1 1
139 4 3 2 NA 1
140 3 2 2 2 3
141 5 3 2 3 1
142 4 4 1 1 1
143 5 4 2 3 2
144 3 3 2 2 2
145 4 5 2 2 1
146 3 4 2 3 1
147 5 2 2 NA 1
148 4 5 2 3 1
149 4 4 2 3 1
150 3 3 1 2 2
151 4 4 1 2 1
152 4 4 2 2 1
153 4 3 2 NA 1
154 3 5 2 NA 1
155 4 4 2 2 2
156 4 3 2 3 1
157 4 4 2 1 1
158 5 3 2 2 1
159 5 4 1 2 2
160 4 4 1 2 2
161 4 4 2 1 1
162 3 2 2 1 1
163 5 4 2 NA 1
164 4 3 2 1 1
165 4 4 2 2 2
This is what I did & the results:
kruskal.test(PS_score ~ Income, data = survey)
Kruskal-Wallis rank sum test
data: PS_score by Income
Kruskal-Wallis chi-squared =
1.7261, df = 2, p-value = 0.4219
