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Introduction to Policy
Processes
Dan Laitsch
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Overview
Sign in
 Business
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– Crashed blog
– Grades and extensions
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Review last class
– Stats
– Research
– Policy
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Class 2: Review
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Stats
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Mean, median, mode
Variability
Correlations
Reliability and Validity
Research
– Thanks tanks, advocacy, scholarship
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Policy
– Institutional rational choice
– Multiple streams
– Social construction
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Class 3 Agenda
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Review
Stats
– Hypotheses
– Probability
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PBL groups
Small groups: Role play prep
-LunchRole plays
Policy readings
PBL Groups
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Part III: Taking Chances for Fun
and Profit
Chapter 7   
Hypotheticals and You: Testing Your Questions
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What you learned in Chapter 7
– The difference between samples
and populations
– The importance of…
 The
null hypothesis
 The research hypotheses
– How to judge a good hypothesis
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What is a hypothesis?
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An “educated guess”
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Role is to reflect the general problem statement
or question that is driving the research
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Translates the problem or research question into
a form that can be tested.
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Not all good research requires a hypothesis
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Samples and Populations
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Population
– The large group to which you would like to
generalize your findings
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Sample
– The smaller, representative group of the
population that is used to do the research
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Sampling error – a measure of how well a sample
represents the population
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The Null Hypothesis
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Statements that contain two or more things that are
equal (or unrelated) to one another
H0 : m1 = m2
– E.g. there is no difference between the two groups
– Starting point and is accepted as true without knowing
more information
– Benchmark to compare actual outcomes
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The Research Hypothesis
Statement that there is a relationship between
two variables
 Two Types…
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– Nondirectional -- H1 : X1 ≠ X2
Reflects a difference; direction is not specified
 Two-tailed test
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– Directional -- H1 : X1 > X2
Reflects a difference; direction is specified
 One-Tailed test
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Null & Research Hypotheses
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Differences Between Null and
Research Hypotheses
Null
Research
No relationship between
variables
Refers to the population
Relationship between variables
Indirectly tested
Directly tested
Written using Greek symbols
Written using Roman symbols
Implied hypothesis
Explicit hypothesis
Refers to the sample
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What Makes a Good Hypothesis?
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Stated in a declarative form rather than a
question
Defines an expected relationship between
variables
Reflects theory or literature on which they are
based
Brief and to the point
Testable – include variables that can be measured
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Glossary Terms to Know
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Hypothesis
– Null Hypothesis
– Research Hypothesis
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Direction & Non-directional hypotheses
One-tailed & Two-tailed test
Population
Sample
– Sampling error
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Part III: Taking Chances for Fun
and Profit
Chapter 8   
Are Your Curves Normal? Probability
and Why it Counts
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What you learned in Chapter 7
Understanding probability is basic to
understanding statistics
 Characteristics of the “normal” curve
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– i.e. the bell-shaped curve
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All about z scores
– Computing them
– Interpreting them
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Why Probability?
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Basis for the normal curve
– Provides basis for understanding probability of
a possible outcome
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Basis for determining the degree of
confidence that an outcome is “true”
– Example:
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Are changes in student scores due to a particular
intervention that took place or by chance alone?
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The Normal Curve
(a.k.a. the Bell-Shaped Curve)
Visual representation of a distribution of
scores
 Three characteristics…
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– Mean, median, and mode are equal to one
another
– Perfectly symmetrical about the mean
– Tails are asymptotic (get closer to horizontal
axis but never touch)
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The Normal Curve
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Hey, That’s Not Normal!
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In general, many events occur right in the
middle of a distribution with few on each
end.
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More Normal Curve 101
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More Normal Curve 101
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For all normal distributions…
– almost 100% of scores will fit between -3 and +3
standard deviations from the mean.
– So…distributions can be compared
– Between different points on the X-axis, a certain
percentage of cases will occur.
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What’s Under the Curve?
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The z Score
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A standard score that is the result of dividing the
amount that a raw score differs from the mean of
the distribution by the standard deviation.
(X  X )
z
,
s
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What about those symbols?
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The z Score
Scores below the mean are negative (left of
the mean) and those above are positive
(right of the mean)
 A z score is the number of standard
deviations from the mean
 z scores across different distributions are
comparable
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What z Scores Represent
The areas of the curve that are covered by
different z scores also represent the
probability of a certain score occurring.
 So try this one…
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– In a distribution with a mean of 50 and a
standard deviation of 10, what is the
probability that one score will be 75 or above?
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The Difference between z scores
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What z Scores Really
Represent
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Knowing the probability that a z score will
occur can help you determine how extreme
a z score you can expect before
determining that a factor other than chance
produced the outcome
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Keep in mind… z scores are typically
reserved for populations. 
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Hypothesis Testing & z Scores
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Any event can have a probability
associated with it.
– Probability values help determine how
“unlikely” the even might be
– The key --- less than 5% chance of occurring
and you have a significant result
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Glossary Terms to Know
Probability
 Normal curve
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– Asymptotic
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Standard Scores
– z scores
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Small Group Roll Play
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Break into groups and plan role play
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Lunch
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Commence role play
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Policy Readings
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Chapters 17-20
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Political Parties
Business Associations
Labour Movement
Voluntary Sector
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For Next Class
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Assignments for next class
– Research Review. Small group work (as assigned by
the group).
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Readings:
– Studying Your Own School, Chapter 5
– Statistics for People Who (think they) Hate Statistics,
Part 4 (Chapters 9-11)
– Theories of the Policy Process: Part 3 & 4 (Chapters
5, 7, 8, 9)
– Policy Analysis in Canada: Part 6 (Chapters 21-23)
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End of day 2
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