1. 1 Introduction to Policy Processes Dan Laitsch

2. 2 Overview Sign in Business –Crashed blog –Grades and extensions Review last class –Stats –Research –Policy

3. 3 Class 2: Review Stats –Mean, median, mode –Variability –Correlations –Reliability and Validity Research –Thanks tanks, advocacy, scholarship Policy –Institutional rational choice –Multiple streams –Social construction

4. 4 Class 3 Agenda Review Stats –Hypotheses –Probability PBL groups Small groups: Role play prep -Lunch- Role plays Policy readings PBL Groups

5. 5 Part III: Taking Chances for Fun and Profit Chapter 7 Hypotheticals and You: Testing Your Questions

6. 6 What you learned in Chapter 7 –The difference between samples and populations –The importance of… The null hypothesis The research hypotheses –How to judge a good hypothesis

7. 7 What is a hypothesis? An “educated guess” Role is to reflect the general problem statement or question that is driving the research Translates the problem or research question into a form that can be tested. Not all good research requires a hypothesis

8. 8 Samples and Populations Population –The large group to which you would like to generalize your findings Sample –The smaller, representative group of the population that is used to do the research Sampling error – a measure of how well a sample represents the population

9. 9 The Null Hypothesis Statements that contain two or more things that are equal (or unrelated) to one another H 0 :  1 =  2 –E.g. there is no difference between the two groups –Starting point and is accepted as true without knowing more information –Benchmark to compare actual outcomes

10. 10 The Research Hypothesis Statement that there is a relationship between two variables Two Types… –Nondirectional -- H 1 : X 1 ≠ X 2 Reflects a difference; direction is not specified Two-tailed test –Directional -- H 1 : X 1 > X 2 Reflects a difference; direction is specified One-Tailed test

11. 11 Null & Research Hypotheses

12. 12 Differences Between Null and Research Hypotheses NullResearch No relationship between variables Relationship between variables Refers to the populationRefers to the sample Indirectly testedDirectly tested Written using Greek symbolsWritten using Roman symbols Implied hypothesisExplicit hypothesis

13. 13 What Makes a Good Hypothesis? Stated in a declarative form rather than a question Defines an expected relationship between variables Reflects theory or literature on which they are based Brief and to the point Testable – include variables that can be measured

14. 14 Glossary Terms to Know Hypothesis –Null Hypothesis –Research Hypothesis Direction & Non-directional hypotheses One-tailed & Two-tailed test Population Sample –Sampling error

15. 15 Part III: Taking Chances for Fun and Profit Chapter 8 Are Your Curves Normal? Probability and Why it Counts

16. 16 What you learned in Chapter 7 Understanding probability is basic to understanding statistics Characteristics of the “normal” curve –i.e. the bell-shaped curve All about z scores –Computing them –Interpreting them

17. 17 Why Probability? Basis for the normal curve –Provides basis for understanding probability of a possible outcome Basis for determining the degree of confidence that an outcome is “true” –Example: Are changes in student scores due to a particular intervention that took place or by chance alone?

18. 18 The Normal Curve (a.k.a. the Bell-Shaped Curve) Visual representation of a distribution of scores Three characteristics… –Mean, median, and mode are equal to one another –Perfectly symmetrical about the mean –Tails are asymptotic (get closer to horizontal axis but never touch)

19. 19 The Normal Curve

20. 20 Hey, That’s Not Normal! In general, many events occur right in the middle of a distribution with few on each end.

21. 21 More Normal Curve 101

22. 22 More Normal Curve 101 For all normal distributions… –almost 100% of scores will fit between -3 and +3 standard deviations from the mean. –So…distributions can be compared –Between different points on the X-axis, a certain percentage of cases will occur.

23. 23 What’s Under the Curve?

24. 24 The z Score A standard score that is the result of dividing the amount that a raw score differs from the mean of the distribution by the standard deviation. What about those symbols?

25. 25 The z Score Scores below the mean are negative (left of the mean) and those above are positive (right of the mean) A z score is the number of standard deviations from the mean z scores across different distributions are comparable

26. 26 What z Scores Represent The areas of the curve that are covered by different z scores also represent the probability of a certain score occurring. So try this one… –In a distribution with a mean of 50 and a standard deviation of 10, what is the probability that one score will be 75 or above?

27. 27 The Difference between z scores

28. 28 What z Scores Really Represent Knowing the probability that a z score will occur can help you determine how extreme a z score you can expect before determining that a factor other than chance produced the outcome Keep in mind… z scores are typically reserved for populations.

29. 29 Hypothesis Testing & z Scores Any event can have a probability associated with it. –Probability values help determine how “unlikely” the even might be –The key --- less than 5% chance of occurring and you have a significant result

30. 30 Glossary Terms to Know Probability Normal curve –Asymptotic Standard Scores –z scores

31. 31 Small Group Roll Play Break into groups and plan role play Lunch Commence role play

32. 32 Policy Readings Chapters 17-20 –Political Parties –Business Associations –Labour Movement –Voluntary Sector

33. 33 For Next Class Assignments for next class –Research Review. Small group work (as assigned by the group). Readings: –Studying Your Own School, Chapter 5 –Statistics for People Who (think they) Hate Statistics, Part 4 (Chapters 9-11) –Theories of the Policy Process: Part 3 & 4 (Chapters 5, 7, 8, 9) –Policy Analysis in Canada: Part 6 (Chapters 21-23) End of day 2