KAHS 6020 Multivariate analysis and design

• Dr. Alison Macpherson

• Website www.yorku.ca/alison3

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

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

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.

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

Week 1An overview

• Research questionsResearch questions• Exposure and outcomeExposure and outcome• Types of variablesTypes of variables• Basic statistical measuresBasic statistical measures

-continuous variables-continuous variables-categorical variables-categorical variables

• An example: body checking injuries in An example: body checking injuries in childrenchildren

• Case study 1: Research questionCase study 1: Research question

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."

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

Methodological Considerations:Exposure and Outcomes

Exposure

No exposure

Outcome

No outcome

Causal pathway

What is exposure?

Some examples from Kinesiology:- Programs to promote activity

- Exercise

- Balance training

- Use of protective equipment

- Others?

What is an outcome?

Some examples from Kinesiology:

• Usually related to improved health- Weight loss ( BMI)

- Fewer injuries

- Healthier lifestyle

- Increased participation

- Others?

Exposure and Outcomes

Exposure

No exposure

Outcome

No outcome

Causal pathway

Potential for Potential for bias, other bias, other

explanationsexplanations

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

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 50th percentile• Mode (the most frequent response)• -calculated by counting the number of times

each number occurs

• 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."

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

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)

Example # 1Is a change in the policy related to

body-checking associated with a change in injuries in youth ice

hockey?

Reporting of frequencies and proportions

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)

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

Methods

• Exposure variable:Playing hockey in Ontario compared to Québec

• Outcome variable:Injury due to body checking compared to other hockey injury

Atom Level: Body Checking Injuries

0102030405060

Hockey Season

Ontario Quebec

Proportion Checking

Pee Wee Level: Body Checking Injuries

0

1020

3040

5060

Hockey Season

Ontario Quebec

Proportion Checking

Bantam Level: Body Checking Injuries

010

20

30

40

50

60

Hockey Season

Ontario Quebec

Proportion Checking

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

Overview

• All analysis starts with a research question

• Examine the exposure/outcome relationship

• Different types of variables are measured and presented differently

For next week

• Read chapters 1, 2 and 4 in the text

• Start thinking about possible data sets