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Research into Sports Participation. This graph represents the total hours per week spent on other activities linked to their sport, the most common is 1 hour spent on other activities,

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19-5N01-00L                                                                            

Research Methods                                                                            Oliver Sharp

Research Methods; Data Analysis

Task 1

image00.png

This graph represents the total hours per week spent on other activities linked to their sport, the most common is 1 hour spent on other activities, but surprisingly 33 people do not spend any other time on other activities. (mode=1)

Task 2

Girls

This graph represents the total hours per week spent in competition or matches. 1 hour per week was top with 20 girls. 9 girls participated for 2 hours and 5 girls for zero hours.
(mean = 6.33), (std. dev= 7.420)
image01.png

Boys

This histogram represents the total hours spent per week in competition. The highest amount per week was also 1 hour with 17 boys.
(mean = 6.5), (std. dev= 7.867)
image02.png

Task 3

Gender

Clothing

Equipment

Travelling

Coaching

Other

Male

4

10

8

7

8

Female

10

2

22

2

2

This table shows that there is a vast difference in answers between the two genders. For example, female's most common largest expense was travelling with 22 answers whereas males answered equipment more often with 10. Also males less common answer was clothing with 4 whereas females chose coaching, equipment and other the least all with 2 answers.

Task 4

image03.png

The bar chart is a comparison between male and female responses to how they rated the amount of coaching they received per week. The majority of the boys and girls said it was too much, however more boys than girls said it was too little.

Task 5

Ages

Mean

Median

Mode

Standard Deviation

Girls

193.79

195.00

195

5.438

Boys

190.38

191.00

189

7.889

This table simply shows the mean, median and mode and standard deviation of the age in months between all the males and females that took part in the survey.

Task 6

Girls

image04.png

The pie chart shows the girls rating of coaching quality. It shows the majority thought just OK and only 3% thought it wasn't good enough.

Boys

This pie chart shows the males responses. It shows that the majority thought it was OK with 70% and only 22% thought it was excellent.image05.png

Task 7

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

q5 * q6

75

100.0%

0

.0%

75

100.0%

q5 * q6 Crosstabulation

Count

q6

1

2

3

Total

q5

Too Much

15

25

0

40

OK

4

10

3

17

Too little

4

13

1

18

Total

23

48

4

75

This cross tabulation represents the responses of question "how do you rate the amount of coaching you receive per week?" against "how do you rate the quality of coaching you receive?" OK was the most popular answer for quality of coaching with 25 people agreeing who also answered they had too much coaching per week.

Task 8

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

q9 * q8b

75

100.0%

0

.0%

75

100.0%

q9 * q8b Crosstabulation

q8b

1

2

3

Total

q9

positive

Count

11

24

2

37

Expected Count

9.4

23.7

3.9

37.0

neutral

Count

6

8

4

18

Expected Count

4.6

11.5

1.9

18.0

negative

Count

2

16

2

20

Expected Count

5.1

12.8

2.1

20.0

Total

Count

19

48

8

75

Expected Count

19.0

48.0

8.0

75.0

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

7.695a

4

.103

Likelihood Ratio

7.941

4

.094

Linear-by-Linear Association

2.294

1

.130

N of Valid Cases

75

a. 4 cells (44.4%) have expected count less than 5. The minimum expected count is 1.92.

This table shows that out of the 75 people questioned 37 of them thought the school was good for sport. Out of that 37 11 of them agreed that it was excellent for playing matches. 18 people had a neutral response, answers split across excellent, ok and not good enough. The remaining 20 responded negatively. The chi-square test is used for estimating how closely an observed distribution matches an expected distribution. In this case backing up the hypothesis.

Task 9

GCSE * q5 Crosstabulation

q5

Too Much

OK

Too little

Total

GCSE

not exam year

Count

6

9

10

25

Expected Count

13.5

5.4

6.1

25.0

exam year

Count

34

7

8

49

Expected Count

26.5

10.6

11.9

49.0

Total

Count

40

16

18

74

Expected Count

40.0

16.0

18.0

74.0

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

13.733a

2

.001

Likelihood Ratio

14.182

2

.001

Linear-by-Linear Association

11.200

1

.001

N of Valid Cases

74

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.41.

Of 75 people 25 of them were not in their exam year and therefore not taking GCSEs. 10 of these said the amount of coaching received was too little, however 6 said it was too much. 34 people who are in their exam year believed the coaching received to be too much, possibly because they need more time to study for exams and not play sport. This is what the chi-square test is showing.

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