1. KAHS 6020 Multivariate analysis and design Dr. Alison Macpherson Website www.yorku.ca/alison3

2. Primary course objectives 1.To learn about multivariate statistical techniques 2.To apply these techniques in a situation that is meaningful to you and your research

3. Secondary course objectives 1.To improve on presentation skills 2.To learn how to prepare a report on data analysis 3.To prepare students to write the methods for data analysis and results sections of their theses

4. Course philosophy This course will use a problem-based learning approach to multivariate statistical techniques. It is designed to be applied to real life situations that you may encounter as you conduct your research.

5. Course overview Format 1.Initial lecture on current topic 2.Questions 3.One complete example 4.Case study (ongoing example of analysis of a data set) 5.Overview and questions

6. Week 1 An overview Research questionsResearch questions Exposure and outcomeExposure and outcome Types of variablesTypes of variables Basic statistical measures -continuous variables -categorical variablesBasic statistical measures -continuous variables -categorical variables An example: body checking injuries in childrenAn example: body checking injuries in children Case study 1: Research questionCase study 1: Research question

7. There was this statistics professor who, when driving his car, would always accelerate hard before coming to any Intersection, whip straight through it, then slow down again once he'd got past it. One day, he took a passenger, who was understandably unnerved by his driving style, and asked him why he went so fast over intersections. The statistics professor replied, "Well, statistically speaking, you are far more likely to have an accident at an intersection, so I just make sure that I spend less time there."

8. Research Design and Data Analysis All analysis starts with a research question The question drives the analysis process Questions should be: -specific (what are you planning to measure?) -answerable using the data you have Should include both an exposure and outcome variable

9. Methodological Considerations: Exposure and Outcomes Exposure No exposure Outcome No outcome Causal pathway

10. What is exposure? Some examples from Kinesiology: - Programs to promote activity - Exercise - Balance training - Use of protective equipment - Others?

11. What is an outcome? Some examples from Kinesiology: Usually related to improved health - Weight loss ( BMI) - Fewer injuries - Healthier lifestyle - Increased participation - Others?

12. Exposure and Outcomes Exposure No exposure Outcome No outcome Causal pathway Potential for bias, other explanations

13. Types of variables Continuous variables -variables for which there is a range of responses e.g., age, blood pressure, weight Categorical variables –Variables that fall into categories –e.g, gender, smoking status

14. Basic statistical measures for continuous variables Mean (the average number) -calculated by summing all the numbers and dividing by n Median (the number in the middle) -calculated by going to the 50 th percentile Mode (the most frequent response) -calculated by counting the number of times each number occurs

15. Did you hear about the statistician who had his head in an oven and his feet in a bucket of ice? When asked how he felt, he replied, "On the average I feel just fine."

16. More about statistical measures for continuous variables Standard deviation Assesses the variability in the data Measure is the square root of the variance Variance is calculated by the distance of each measure from the mean Accuracy depends on the normal distribution

17. Statistical measures for categorical variables Counts (how many fall within each category) Proportions (what percentage fall within each category) Frequency distributions (comparing counts and percentages between categories)

18. Example # 1 Is a change in the policy related to body-checking associated with a change in injuries in youth ice hockey? Reporting of frequencies and proportions

19. Background In 1998/1999 Ontario Hockey Federation changed policy to allow body checking among Atom rep players (elite hockey players ages 10 and 11) Ontario allows body checking at the Pee Wee level (players ages 12 and 13) Québec does not allow any body checking until Bantam level (players ages 14 and 15)

20. Methods All children presenting to participating hospital Emergency Departments with a hockey related injury Children in Ontario were compared to children in Québec Hospitals participating in the Canadian Hospitals Injury Reporting and Prevention Program

21. Methods Exposure variable: Playing hockey in Ontario compared to Québec Outcome variable: Injury due to body checking compared to other hockey injury

22. Atom Level: Body Checking Injuries Proportion Checking

23. Pee Wee Level: Body Checking Injuries Proportion Checking

24. Bantam Level: Body Checking Injuries Proportion Checking

25. Implications for Prevention Rule change allowing Atom players to body check was associated with an increase in checking injuries Increased injuries attributable to body checking were observed in all age groups where checking was allowed Allowing body checking among younger players was not associated with a decrease in injuries later on

26. Overview All analysis starts with a research question Examine the exposure/outcome relationship Different types of variables are measured and presented differently

27. For next week Read chapters 1, 2 and 4 in the text Start thinking about possible data sets