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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.
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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