Introduction to Policy
Processes
Dan Laitsch
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Overview (Class meeting 5)
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Sign in
Agenda
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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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Centre Jobs
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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.
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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 Different
Using Inferential Statistics
Chapter 12 
Two Groups Too Many?
Try Analysis of Variance (ANOVA)
What you learned in Chapter
12
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What Analysis of Variance (ANOVA) is
and when it is appropriate to use
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How to compute the F statistic
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How to interpret the F statistic
Analysis of Variance (ANOVA)
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Used when more than two group means are
being tested simultaneously
– Group means differ from one another on a
particular score / variable
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Example: DV = GRE Scores & IV = Ethnicity
Test statistic = F test
– R.A. Fisher, creator
Path to Wisdom & Knowledge
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How do I know if ANOVA is the right test?
Different Flavors of ANOVA
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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
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Single factor (or one way) ANOVA
– Used to study the effects of 2 or more treatment variables
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One-way ANOVA for repeated measures
– Used when subjects subjected to repeated measures.
More Complicated ANOVA
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Factorial Design
– More than one treatment/factor examined
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Multiple Independent Variables
– One Dependent Variable
– Example – 3x2 factorial design
G
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n
d
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Number of Hours in Preschool
5 hours
10 hours
Male
per week
per week
Female
5 hours
per week
10 hours
per week
20 hours
per week
20 hours
per week
Computing the F Statistic
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Rationale…want the within group variance
to be small and the between group variance
large in order to find significance.
Hypotheses
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Null hypothesis
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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…
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– BUT…Type I error increases with every
follow-up test / possible comparison made
Glossary Terms to Know
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Analysis of variance
– Simple ANOVA
– One-way ANOVA
– Factorial design
Omnibus test
 Post Hoc comparisons
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Part IV:
Significantly Different
Chapter 14    
Cousins or Just Good Friends?
Testing Relationships Using the Correlation
Coefficient
What you will learn in Chapter
14
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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
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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
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Positive correlations
– Indirect relationships
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Negative correlations
Path to Wisdom & Knowledge
Computing the Test Statistic
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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!)
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Just because two variables are related has no
bearing on whether there is a causal relationship.
– Example:
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Quality marriage does not ensure a quality parentchild 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!!)
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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!!!
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“What you see is not always what you get.”
Policy (conclusions)
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Analysis
– Frameworks
Organize
 Structure
 Cannot explain
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Theories
 Models
 Theme: Science, research as a framework
 Frame-->theory-->model
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Conclusions
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Common pool resource theory
– Governance from the common pool
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Agenda setting and policy adoption
– Advocacy coalitions
– Policy networks
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Punctuated equalibrium
– Incrementalism
– Major chance
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Rationality and the role of the individual
– Asimov and Seldon
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Micro-policy and the role of the institutions
Conclusions
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Strengthening policy theory
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Building logical coherence
Seeking causality
Empirically falsifiable
Defined scope
Useful (presents more than obvious outcomes)
Developing field (mostly descriptive)
– From qualitative to testable
Conclusions
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Next steps
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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