Introduction to Policy Processes

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Introduction to Policy Introduction to Policy ProcessesProcesses

Dan Laitsch

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Overview (Class meeting 5)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

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.

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Class : Review Class : Review

– Cohort break outs

– Mid term assessment results

– Significance and t-tests

– Policy and unifying content

– Action research

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 / variableExample: DV = GRE Scores & IV = Ethnicity

Test statistic = F test– R.A. Fisher, creator

Path to Wisdom & KnowledgeHow 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

Number of Hours in Preschool

Gender

Male

5 hours per week

10 hours per week

20 hours per week

Female 5 hours per week

10 hours per week

20 hours per week

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 testPost 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

Path to Wisdom & Knowledge

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

OrganizeStructureCannot explain

TheoriesModelsTheme: Science, research as a frameworkFrame-->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