Post on 05-Jan-2016
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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