Introduction to Policy Processes

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Introduction to Policy Processes

• Dan Laitsch

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Overview (Class meeting 4)

• Sign in
• Agenda
• Cohort break outs
• Review last class
• Mid term assessment
• PBL Groups
• Significance dismiss
• Policy and unifying content
• T-tests
• PBL groups
• Action research
• PBL and dismiss

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Class Review

• Stats
• Hypothesis testing
• Z scores
• PBL
• Topic determined
• Policy
• Role Play

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Cohort Break Out

• Courses and dates (summer session)
• EDUC 813 organizational Theory (Drescher)
• April24/25, May 8/9, May 22/23, June 6/7, June
  19/20, and June 26/27
• Summer Institute
• EDUC 822 Evaluation of Educational Programs
• July 2, 3, 6, 7, 8, 9, 10, 13, 14, 15, 16.
  (Mornings 830 to 130 or Evenings 430 to
  930). SI public lecture times included as part
  of class hours (July 6, Evening 7,9,14 and 16,
  100 pm to 300 pm).
• Action Research Time Frame
• Comprehensive exams

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Midterm Assessment

• Data drive decision making
• What do the following data tell you?
• What questions do they leave unanswered?
• Analysis and response

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Response

• Heavy workload
• Addressing past student concerns
• Creating balance
• Unifying vision
• Possible solutions
• Goals meet course description (policy processes)
• Prepare students for Action Research
• Continued tomorrow

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PBL groups

• Touch base
• Status check
• Group functioning?
• Forming, storming, norming, performing?
• Topic identified?
• Action plan?
• Turn in report (handout)
• Plan for tomorrow
• 2-3 hours of group time (2 break out 1 hour to
  1.5 hours each)

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Part IV Significantly Different Using
Inferential Statistics

• Chapter 9 ? ?
• Significantly Significant
• What it Means for You and Me

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What you learned in Chapter 9

• What significance is and why it is important
• Significance vs. Meaningfulness
• Type I Error
• Type II Error
• How inferential statistics works
• How to determine the right statistical test for
  your purposes

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The Concept of Significance

• Any difference between groups that is due to a
  systematic influence rather than chance
• Must assume that all other factors that might
  contribute to differences are controlled

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If Only We Were Perfect

• Significance level
• The risk associated with not being 100 positive
  that what occurred in the experiment is a result
  of what you did or what is being tested
• The goal is to eliminate competing reasons for
  differences as much as possible.
• Statistical Significance
• The degree of risk you are willing to take that
  you will reject a null hypothesis when it is
  actually true.

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The Worlds Most Important Table
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Type I Errors (Level of Significance)

• The probability of rejecting a null hypothesis
  when it is true
• Conventional levels are set between .01 and .05
• Usually represented in a report as
• p lt .05

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Type II Errors

• The probability of rejecting a null hypothesis
  when it is false
• As your sample characteristics become closer to
  the population, the probability that you will
  accept a false null hypothesis decreases

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Significance Versus Meaningfulness

• A study can be statistically significant but not
  very meaningful
• Statistical significance can only be interpreted
  for the context in which it occurred
• Statistical significance should not be the only
  goal of scientific research
• Significance is influenced by sample sizewell
  talk more about this later.

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How Inference Works

• A representative sample of the population is
  chosen.
• A test is given, means are computed and compared
• A conclusion is reached as to whether the scores
  are statistically significant
• Based on the results of the sample, an inference
  is made about the population.

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Deciding What Test to Use
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Test of Significance

• 1. A statement of null hypothesis.
• 2. Set the level of risk associated with the null
  hypothesis.
• 3. Select the appropriate test statistic.
• 4. Compute the test statistic (obtained) value
• 5. Determine the value needed to reject the null
  hypothesis using appropriate table of critical
  values
• 6. Compare the obtained value to the critical
  value
• 7. If obtained value is more extreme, reject null
  hypothesis
• 8. If obtained value is not more extreme, accept
  null hypothesis

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The Picture Worth a Thousand Words
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Glossary Terms to Know

• Significance level
• Statistical significance
• Type I error
• Type II error
• Obtained value
• Test statistic value
• Critical value

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End of Class

• PBL Work if time allows
• Clarifying grades
• Journal, portfolio, stats notebook
• Homework
• Thinking about research
• What areas are you thinking about?
• What questions do you have?
• Prepare to chat with colleagues tomorrow

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Agenda

• Policy and unifying content
• T-tests
• PBL groups
• Action research
• PBL and dismiss

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Unifying themes

• Diffusion models
• Communication networks
• Diffusion of innovation
• Adoption
• Internal (policy window)
• Severity (crisis)
• Opportunity
• External (policy borrowing)
• National
• Regional
• Leader-Laggard
• Isomorphism (similar states)
• Vertical

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Unifying themes

• Internal (policy window)
• Severity (crisis)
• Opportunity
• Evidence/Data (my insert)
• Research
• Statistics
• External (policy borrowing)
• Governments (CMEC)
• Organizations (CTF, JCSH, CERC-CA)

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Unifying themes

• Problems
• Identification (what is the problem)
• Analysis (what is the cause)
• Solutions
• Research (what has been done)
• Context (how does it fit here)
• Policies
• Action (what are the rules and procedures)
• Evaluation
• Analysis (what happened)
• Refinement (what might we change)

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Unifying themes

• Leadership
• Identifying context (observation and data
  gathering)
• Data gathering and synthesis (problem
  identification)
• Identifying parameters (policy analysis)
• Setting direction (goals and outcomes)
• Research (identify interventions)
• Policy (identify rules and procedures for action)
• Analysis (identify consequences)
• Achieving Goals (problem solving)
• Implementation of actions and activities
• Application of rules and procedures (policy)
• Evaluation (refining context)

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Part IV Significantly DifferentUsing
Inferential Statistics

• Chapter 10 ? ? ?
• t(ea) for Two
• Tests Between the Means of Different Groups

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What you learned in Chapter 10

• When to use a t test
• How to compute the observed t value
• Interpreting the t value and what it means

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t Tests for Independent Samples

• Determining the correct statistic

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Computing the Test Statistic

• Numerator is the difference between the means
• Denominator is the amount of variation within and
  between each of the two groups

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Degrees of Freedom

• Degrees of freedom approximate the sample size
• Degrees of freedom can vary based on the test
  statistic selected
• For this procedure
• n1 1 n2 1

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So How Do I Interpret

• t (58) -.14, p gt .05
• t represents the test statistic used
• 58 is the number of degrees of freedom
• -.14 is the obtained value (from the formula)
• p gt .05 indicates the probability (n.s.)
• p n.s.
• p lt .05 indicates the probability (sig.)

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Special Effects

• Effect size is a measure of how different two
  groups are from one another
• Standardized difference between to group means
• Jacob Cohen

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Computing Effect Size

• Small 0.0 - .20
• Medium .20 - .50
• Large .50 and above

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Effect Size Calculator

• http//web.uccs.edu/lbecker/Psy590/escalc3.htm

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Glossary Terms to Know

• Degrees of freedom
• t Test
• Independent t Test
• Obtained value
• Critical value
• Effect size

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Part IV Significantly DifferentUsing
Inferential Statistics

• Chapter 11 ? ? ?
• t(ea) for Two (Again)
• Tests Between the Means of Related Groups

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What you learned in Chapter 11

• When to use a t test for dependent means
• How to compute the observed t value
• Interpreting the t value and what it means

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t Tests for Dependent Samples

• Determining the correct statistic

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Computing the Test Statistic

• Numerator reflects the sum of the differences
  between two groups

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Degrees of Freedom

• Degrees of freedom approximate the sample size
• Degrees of freedom can vary based on the test
  statistic selected
• For this procedure
• n 1 (where n is the number of observations)

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So How Do I Interpret

• t (24) 2.45, p gt .05
• t represents the test statistic used
• 24 is the number of degrees of freedom
• 2.45 is the obtained value (from the formula)
• p gt .05 indicates the probability (n.s.)
• p n.s.
• p lt .05 indicates the probability (sig.)

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PBL Groups

• Break into groups
• Lunch

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Action Research

• Pair share
• Model and paper process
• Observations
• Questions
• Data
• Methods
• Analysis
• Discuss

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PBL Groups

• PBL Work if time allows

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