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We use descriptive statistics to organize and summarize data. We can generate single numbers to summarize data (means, SDs, SEMs) or we can summarize data in a picture including all data points (histograms). Prior to running inferential statistics, it is a good idea to get “a feel” for your data (general idea of what it looks like).
Fujiwara is a renowned downhill drifting instructor. There are 15 men and 15 women in his Downhill Drifting 101 class. Below are the scores from Fujiwara-sensei’s most recent midterm exam (based on drift technique and speed of completion). He wants to assess the performance of his students both statistically and visually. To help him with this task, your job is to set up a file in SPSS for the exam data and analyze the following:
1. Compute descriptive statistics on the overall exam scores (mean, median, SEM, variance, SD)
2. Create a histogram for overall midterm scores.
3. Compute descriptive statistics (same as above) for male and female scores separately
4. Create a histogram for each gender’s scores separately
5. Create a bar graph comparing the mean midterm score (with CI error bars) of genders
6. Fujiwara-sensei wants to place those who scored below average by half of one SD or more (z=-0.5) into intensive training bootcamp. Which students should receive extra training?
Midterm Exam Scores:
Student
Gender
Score
Student
Gender
Score
1
male
87
16
female
89
2
male
53
17
female
73
3
male
92
18
female
91
4
male
70
19
female
85
5
male
78
20
female
75
6
male
73
21
female
98
7
male
91
22
female
91
8
male
60
23
female
83
9
male
77
24
female
95
10
male
82
25
female
86
11
male
85
26
female
90
12
male
33
27
female
89
13
male
88
28
female
89
14
male
98
29
female
70
15
male
88
30
female
93
ON YOUR OWN:
Complete steps 1-6 again for the class’s second exam:
Exam 2 scores:
Student
Gender
Score
Student
Gender
Score
1
male
90
16
female
77
2
male
91
17
female
73
3
male
81
18
female
70
4
male
77
19
female
85
5
male
80
20
female
75
6
male
88
21
female
98
7
male
92
22
female
88
8
male
77
23
female
83
9
male
98
24
female
81
10
male
89
25
female
86
11
male
82
26
female
82
12
male
88
27
female
89
13
male
77
28
female
89
14
male
65
29
female
89
15
male
85
30
female
69
