Reference no: EM132372193

Research Methods and Statistics in Exercise and Sport AT2 - Assignment

Overview - For AT2 - Assignment you are required to provide responses to the questions below that focus on content covered during weeks 1-9. There are 9 questions (4 questions in Part A, 5 questions in Part B).

Submission expectations -

Structure: Your assignment should include 1) a title page, 2) your answers to the questions, 3) references page and 4) a pdf copy of your SPSS output - the latter as a separate file.

Word length - You should carefully construct your answers to ensure that your report conforms with the word limit of 1,000 words maximum.

Part A - Questions to be answered

1. A researcher is interested in the relationship between physical activity and cognitive function among older adults. A sample of 200 participants completed a short survey where they reported their age (in years), gender, and number of close friends (0,1,2 ...) as well as the amount of time (recorded in hours) they spent in low, moderate and vigourous physical activity over the past 7 days (ie. capturing weekdays and weekends). A total weekly physical activity measure ('Weekly PA' = total weekly PA time / hours) was computed from the low, moderate and vigourous physical activity measures. At the time of completing the survey, respondents also completed The Abbreviated Mental Test Score (AMTS), a 10 item screening tool where they completed 10 cognitive tasks such as recalling specific dates, times, names and locations as well as performing a basic counting task. Each of the 10 tasks completed correctly was scored as a '1' yielding a score on cognitive function ranging from 0 (low cognitive function) through to 10 (high cognitive function). Having scored, compiled, checked and screened the data, the researcher performed several correlation analyses and obtained the following results. The correlation between Age (years) and Cognitive Function (0-10) was r = -0.42, p < 0.05 while the correlation between Weekly PA (hours) and Cognitive Function (0-10) was r = 0.51, p < 0.05. State what each of these results means.

2. A researcher is interested in exploring patterns of communication among local junior football coaches. She decides to use a qualitative approach, as she believes that this will provide her with a deeper understanding of the contextual factors that influence coach communications however she is uncertain about what method of data collection to use and is seeking guidance from you.

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

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

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

3. Qualitative researchers must ensure 'trustworthiness' of their data and there are a number of techniques that researchers can use to ensure that they gather good data and objective conclusions. Triangulation is one technique that a researcher could use to ensure 'trustworthiness' of their study.

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

ii) Describe the technique of triangulation.

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

4. Braun and Clarke (2013) have noted that qualitative research questions can be clustered into a number of different 'types'. List and briefly describe three different types of qualitative research questions that they believe are suited for thematic analysis.

Part B - SPSS analyses and reporting and interpreting results

The International Ice Hockey Federation (IIHF) is the organization that runs the World Ice Hockey Championships that are held annually. The IIHF records information about players (e.g., age, height, weight ...) from each of the competing countries and provide public access to this data. Kashnitsky (2016) has compiled this data for the period 2001-2016 and made this data available for use. For our purposes, we use a subsample of the full 2001-2016 (population) dataset to perform a number of (exploratory) analyses. The subsample includes data from four (of 16) countries that competed in the 2016 Championships.

This data is recorded in the datafile 'HSE104-AT2-IceHockey.sav'. For the following questions, you will need to download the datafile 'HSE104-AT2-IceHockey.sav' from the Assignment 2 folder on CloudDeakin. You should download and save a copy of this file to a location where you can navigate to the file and successfully open it from within SPSS (e.g., location on your Deakin student directory).

The datafile contains 14 variables and these are described as follows.

'country' - player country [1 = Denmark; 2 = Finland; 3 = France; 4 = USA]

'country_2gp' - player country - recoded (into two groups) [1 = Nordic; 2 = Not Nordic]

'name' - player name

'club' - player club

'no' - player number

'birth' - player date of birth

'cohort' - player age cohort (birth year)

'position' - player position [1 = Forward; 2 = Defender; 3 = Goalkeeper]

'side' - player side (left/right) [1 = Left; 2 = Right]

'height' - player height (cm)

'weight' - player weight (kg)

'bmi' - player BMI (weight-kg/height-m2)

'age' - player age (years)

'age_2gp' - player age (years) - recoded (into two groups) [1 = Less than 30 years; 2 = 30 years or older]

Using SPSS you should open the 'HSE104-AT2-IceHockey.sav' datafile (File menu ? Open ? Data) and then familiarise yourself with the datafile, variables, variable labels, and actual data-points. You should then perform analyses to answer the following questions.

1. 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.

Table 2. Summary text reporting on sample characteristics.

2. We want to examine the relationship between a number of key variables - specifically player age, player weight and player height. As a preliminary step, you should check the distribution of scores on each of these variables for normality and report your results in Table 3. You should then perform correlation analyses to investigate relationships among these variables and then complete Table 4. Finally, in Table 5 you should report the coefficient of determination for height and weight and then state what this result means.

Table 3. Normality testing results.

Table 4. Results of correlation analyses.

Table 5. Coefficient of determination.

3. There is research indicating that the height of professional ice hockey players has increased over recent time. We (indirectly) investigate this hypothesis by testing to see whether players from the 2016 subsample are significantly different (taller) than the known population mean height of ice hockey players for the period 1904-2016 that is reported to be 183.31 cm. Run an appropriate t test to determine if the 2016 subsample is different to the known population height. (Note we have previously checked the normality for Height so there is no need to check this prior to running the t test analysis.) Once you have completed the analysis, you should refer to the SPSS output to complete Table 6.

Table 6. Results of t test analysis.

4. 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.

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

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

5. Body mass index is known to be distorted by a number of factors. One of these factors is height. World Tables for Height indicate that adults from Nordic countries (which includes Denmark and Finland) are taller than adults from numerous other (non-Nordic) countries. Conduct an analysis to test whether players from Nordic countries and those from non-Nordic countries have different heights. You should use SPSS for this task. Note, distributions for the 'height' variable have already been assessed (using a combination of the Shapiro-Wilk and Kolmogorov-Smirnov statistics) and found to be acceptable. You should perform a t test to test for differences in player height for the two groups. Once you have completed your analysis you should refer to the SPSS output to complete Table 10.

Table 10: Results of t test analysis.

Presentation, referencing and SPSS output file -

i. Presentation (spelling, grammar, clarity, adherence to word limits/format requirements) and Referencing (accuracy of referencing; i.e., citing sources appropriately, correct reference list).

ii. SPSS output file - a pdf copy (NOT 'spv' SPSS output file) of your own SPSS output.

Note - All tables are in attached file.

Attachment:- Research Methods and Statistics Assignment Files.rar