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HSE104 - Research Methods and Statistics in Exercise and Sport Assignment - Deakin University, Australia

Part A - Questions to be answered

Q1. A researcher is interested in the relationship between physical activity and cognitive function among older adults. State what each of these results means.

The correlation between age and cognitive function exhibited r = - 0.42, p< 0.05, which means, there is no statistical significant relationship between age of the older adults and their cognitive function. The r value is the correlation coefficient, when r value is negative, it means, there exists a negative correlation between dependent and independent variables. Cognitive function differs with the age of the adults.

The correlation coefficient between weekly physical activity hours and cognitive function, r = 0.51, p<0.05. This infers that there exists a positive relationship between physical activity and cognitive function. It can be concluded that weekly physical activity of the adult exhibit statistical significant relationship.

Q2. i) State one method of data collection that you would recommend the researcher use.

Focus group interview would be suitable method for data collection to understand the contextual factors of coach communication.

ii) Briefly describe how this method of data collection could be used by the researcher to address her research issue.

Qualitative interview method is best methodology for gathering rich and brief information about the personal experiences and to understand as well as explain the specific concept. Interview is one of the specialized form of communication between people associated with specific subject matter. While comparing with other methods such as survey, observation etc. it aids to understand one's inner feelings, attitudes and beliefs. Focus group interviews provides holistic approach to understand and investigate the quality of the relationship between contextual factors, certain activities and materials. A focus group is defined as a team of compromised people with certain common traits focused on the given topic of issue for discussion. (Cohen and Manion, 2007).

Researcher should identify local football coaching academy and approach them to obtain consent for the research study after explaining aim and objective of the study. He or she should organise a discussion session at three levels among local junior football coaches for data collection. A detailed questionnaire must be developed for conducting focus group interview. All sessions should be recorded using video or audio recorder. (Cohen and Manion, 2007).

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iii) Indicate one advantage and one disadvantage / consideration associated with this method of data collection.

Advantages of Interview method

1. It aids in collecting high quality data related to the topic of research interest

2. It is valuable research instrument when researcher lacks substantial data about the subjects

3. It is beneficial to understand people's feeling, attitude, experiences on specific issues and reasons behind their thoughts and beliefs (Cohen and Manion, 2007)

Disadvantages of Interview method

1. While comparing with interviews, focus group interviews fail to cover the depth of the issue.

2. There is a likelihood for participants not disclosing their honest opinion and feelings

3. When comparing with other qualitative methods, focus group interviews are much costly for the execution (Cohen and Manion, 2007)

Q3. i) Define what is meant by the term 'trustworthiness'.

The trustworthiness in qualitative research relies on four key concepts namely credibility, dependability, transferability and confirmability. (Kennedy, 2009).

ii) Describe the technique of triangulation.

Triangulation involves usage of one or more methods for collecting data on same research topic of interest. It is a way of assuming validity for the collected data. This method enables the verification of data gathered by cross validating from two or more sources. It evaluates the consistency of the results obtained via different research instruments. It is not about cross validating but widening and deepening the understanding of the research. (Kennedy, 2009)

iii) Provide an example that illustrates how a researcher could use triangulation to provide evidence that a study is trustworthy.

Triangulation inquires same research questions to the different participants of the study and gathers data. This method of collecting data using same research questions using different methods for answering same questions enhances the trustworthiness of the responses. This reduces the measurement bias and procedural bias. (Kennedy, 2009).

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Q4. List and briefly describe three different types of qualitative research questions that they believe are suited for thematic analysis.

The research questions for thematic analysis should always focus on the:

i) Relevant questions related to the theme of the research

ii) Research questions related to the contextual factors affecting the research theme

iii) Research questions related to the importance of theme to the society (Guest et al, 2012)

Part B - SPSS analyses and reporting and interpreting results

Q1. Summarise the characteristics of the sample. Once you have finished running analyses on the variables indicated below, refer to the SPSS output to complete Tables 1 and 2.

Table 1. Summary statistics for sample.

Variable

n

%

Country - Denmark

25

25.3

Country - Finland

25

25.3

Country - France

25

25.3

Country - USA

24

24.2

Position - Forward

57

57.6

Position - Defender

30

30.3

Position - Goalkeeper

12

12.1

Sided - Right

22

22.2

Sided - Left

77

77.8


M

SD

Age (years)

26.56

5.02

Height (cm)

184.61

5.58

Weight (kg)

87.92

6.50

n = number of participants, % = n represented as a proportion of the whole sample, M = mean, SD = standard deviation.

Table 2. Summary text reporting on sample characteristics.

Summarise this information in sentence form below (~80-100 words)

The sample comprises of ice hockey players, 25.3% from Denmark, 25.3% from Finland, 25.3% from France and 24% from United States. The study participants aged between 18 and 40 years. The mean age of the players is 26.56 (SD = 5.02). The mean height of players is 184.61 cm (SD = 5.58) and mean weight of the players is 87.92 (SD = 6.50).

77.8 percent players were left sided and 22.2 percent players were right sided. Also, 57.6% players were positioned forward, 30.3 % players were defenders and 12.1 % were goal keepers.

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Table 3. Normality testing results.

DV (Variable)

Kolmogorov-Smirnov test results

Interpretation (i.e. is the distribution normal or non-normal?)

Test statistic

p value

Age (years)

0.074

0.200

The p value <0.05 and hence the variable is not normally distributed

Height (cm)

0.078

0.147

The p value <0.05 and hence the variable is not normally distributed

Weight (kg)

0.082

0.102

The p value <0.05 and hence the variable is not normally distributed

Numbers for the normality testing for each variable are > 50 so we use the Kolmogorov-Smirnov statistic.

All three variables such as age, height and weight of the players has p value > 0.05 and hence the variable is not normally distributed.

Table 4. Results of correlation analyses.

Variables

r

p value

Interpretation

Age (years) and Height (cm)

-0.93

0.360

There is a large negative correlation between age and height ( r = -0.93, p = 0.360), as increase in age do not affect height of the players

Age (years) and Weight (kg)

-0.19

0.851

There is a large negative correlation between age and weight ( r = -0.19, p = 0.851), as increase in age do not affect weight of the players

Height (cm) and Weight (kg)

0.606

0.000

There is a statistical significant large positive correlation between height and weight of the players (r = 0.606, p = 0.000), as increase in height simultaneously increases weight of the individuals. 

r = Pearson's correlation coefficient

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Table 5. Coefficient of determination.

Variables

Coefficient of determination value

Interpretation

Height (cm) and Weight (kg)

0.36

36% variability is observed between height and weight of the players

 

Table 6. Results of t test analysis.

Height (cm)

Population

2016 subsample

t

df

p value

M

M

SD

183.31

184.61

5.58

2.30

98

0.02

Interpretation of t test results for comparison of height

<< Provide a short paragraph that summarises what was done and what was found. This should include: the (specific) type of statistical test that was performed; the results of the statistical test; and descriptive statistical information. >>

Student's t test was performed to test the mean height differences.There exists a statistical significant difference in average players height of players in 1904-2016 (M = 183.31) and 2016 subset population (M = 184.61), since t(98) = 2.30, p = 0.02. Hence, it can concluded that height of ice hockey players have been increased over recent times.

M = mean, SD = standard deviation, t = t statistic, df = degrees of freedom

2. We are also interested in testing for differences in body size among players from the four countries. To do this we use the 'bmi' variable that has been computed from the 'height' and 'weight' variables. You should use SPSS for this task. As preliminary step, you should check the 'bmi' variable for normality for the four levels of 'country' (i.e., Denmark, Finland, France, USA). You should then conduct a one-way ANOVA to test for i) equal variances and ii) differences in 'bmi' scores of players from the four countries. You should use the obtained F statistic and corresponding probability (p value) to determine whether or not post-hoc testing is required. Where you determine that post-hoc testing is required then you should conduct these analyses using the Tukey's HSD test. Once you have completed these analyses you should refer to the SPSS output to complete Tables 7, 8 and 9.

Table 7. Results for tests of normality.

Country

Shapiro-Wilk test results

Interpretation

Test statistic

p value

Denmark

0.97

0.83

The p value > 0.05, hence the variable is not normally distributed.

Finland

0.93

0.13

The p value > 0.05, hence the variable is not normally distributed.

France

0.95

0.27

The p value > 0.05, hence the variable is not normally distributed.

USA

0.93

0.13

The p value > 0.05, hence the variable is not normally distributed.

Group numbers for the normality testing are < 50 so we use the Shapiro-Wilk statistic.

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Table 8. Results for tests for homogeneity of variance for bmi variable.

Variable

Levene statistic

p value

Interpretation

bmi

1.38

0.25

The t(1.38) = 0.25>0.05, hence the variations are insignificant.

This infers that mean differences among players bmi from different nations are insignificant.

Table 9. Results of one-way ANOVA for bmi variable.

Variable

Denmark

Finland

France

USA

F

dfs

(df between, df within)

p

Post-hoc tests

State: 1) whether post-hoc testing is appropriate / not appropriate; 2)if appropriate, briefly summarise the results of these comparisons


M ±SD

M ±SD

M ±SD

M ±SD

BMI

25.11±1.52

25.38±1.50

26.34±1.05

26.37±1.75

4.75

3, 95

0.004

1) Appropriate

2) No significant difference between any four nations

M = mean, SD = standard deviation, F = F statistic, df = degrees of freedom

Table 10: Results of t test analysis.

height

Nordic

Non-Nordic

Mean

difference

t

df

p value

M

SD

M

SD

185.50

5.51

183.69

5.56

1.806

1.622

97

0.56

Interpretation of t test results for 'height'

 

Independent sample t-test was performed to assess the average differences of height among Nordic and non - Nordic players. The t(97) = 1.622, 0.56>0.05, hence, average height of Nordic players is higher than Non-Nordic players.

M = mean, SD = standard deviation, t = t statistic, df = degrees of freedom

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