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## Introduction to Policy Processes

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**Introduction to Policy Processes**Dan Laitsch • 1**Overview (Class meeting 5)**Sign in Agenda PBL break out, final project polishing Centre Jobs Review last class Stats PBL planning (presentations) Policy Conclusions [Lunch] Action research Course review Evaluation PBL and dismiss • 2**Centre Jobs**Program Assistant (CSELP) Identify, organize, and provide an overview of electronic education policy resources in Canada, including Federal and provincial government resources; think tanks, policy centres, professional organizations, and NGOs; judicial decisions and resources; research resources and data repositories; and news and information sources. Graduate Student Editor (IJEPL) Assist with review of articles; responsible for article layout and posting.**Class : Review**Cohort break outs Mid term assessment results Significance and t-tests Policy and unifying content Action research • 4**Part IV:Significantly DifferentUsing Inferential Statistics**Chapter 12 Two Groups Too Many? Try Analysis of Variance (ANOVA)**What you learned in Chapter 12**• What Analysis of Variance (ANOVA) is and when it is appropriate to use • How to compute the F statistic • How to interpret the F statistic**Analysis of Variance (ANOVA)**• Used when more than two group means are being tested simultaneously • Group means differ from one another on a particular score / variable • Example: DV = GRE Scores & IV = Ethnicity • Test statistic = F test • R.A. Fisher, creator**Path to Wisdom & Knowledge**• How do I know if ANOVA is the right test?**Different Flavors of ANOVA**• ANOVA examines the variance between groups and the variances within groups • These variances are compared against each other • Similar to t Test. ANOVA has more than two groups • Single factor (or one way) ANOVA • Used to study the effects of 2 or more treatment variables • One-way ANOVA for repeated measures • Used when subjects subjected to repeated measures.**More Complicated ANOVA**• Factorial Design • More than one treatment/factor examined • Multiple Independent Variables • One Dependent Variable • Example – 3x2 factorial design**Computing the F Statistic**• Rationale…want the within group variance to be small and the between group variance large in order to find significance.**Hypotheses**• Null hypothesis • Research hypothesis**Omnibus Test**• F test is an “omnibus test” and only tells you that a difference exist • Must conduct follow-up t tests to find out where the difference is… • BUT…Type I error increases with every follow-up test / possible comparison made**Glossary Terms to Know**• Analysis of variance • Simple ANOVA • One-way ANOVA • Factorial design • Omnibus test • Post Hoc comparisons**Part IV: Significantly Different**Chapter 14 Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient**What you will learn in Chapter 14**• How to test the significance of the correlation coefficient • The interpretation of the correlation coefficient • The distinction between significance and meaningfulness (Again!)**The Correlation Coefficient**• Remember…correlations examine the relationship between variables they do not attempt to determine causation • Examine the “strength” of the relationship • Range -1 to +1 • Direct relationships • Positive correlations • Indirect relationships • Negative correlations**Computing the Test Statistic**• Use the Pearson formula**So How Do I Interpret…**• r(27) = .393, p < .05? • r is the test statistic • 27 is the degrees of freedom • .393 is the obtained value • p < .05 is the probability • Critical value (Table B4) for r(27) is .3494**Causes and Associations (Again!)**• Just because two variables are related has no bearing on whether there is a causal relationship. • Example: • Quality marriage does not ensure a quality parent-child relationship • Two variables may be correlated because they share something in common…but just because there is an “association” does not mean there is “causation.”**Significance Versus Meaningfulness(Again, Again!!)**• Even if a correlation is significant, it doesn’t mean that the amount of variance accounted for is meaningful. • Example • Correlation of .393 • Squaring .393 shows that the variance accounted for .154 or 15.4% • 84.6% remains unexplained!!! • “What you see is not always what you get.”**Policy (conclusions)**• Analysis • Frameworks • Organize • Structure • Cannot explain • Theories • Models • Theme: Science, research as a framework • Frame-->theory-->model**Conclusions**• Common pool resource theory • Governance from the common pool • Agenda setting and policy adoption • Advocacy coalitions • Policy networks • Punctuated equalibrium • Incrementalism • Major chance • Rationality and the role of the individual • Asimov and Seldon • Micro-policy and the role of the institutions**Conclusions**• Strengthening policy theory • Building logical coherence • Seeking causality • Empirically falsifiable • Defined scope • Useful (presents more than obvious outcomes) • Developing field (mostly descriptive) • From qualitative to testable**Conclusions**• Next steps • Clarify and specify (ability to be proven wrong) • Broad in scope • Defines the causal process • Develop a coherent model of the individual • Resolve internal inconsistencies • Develop a research program • Respect and use multiple theories when appropriate