Political Research and Statistics

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Transcript Political Research and Statistics

Significance Testing
• Chapter 3 Proposing Explanations, Framing
Hypotheses, and Making Comparisons
(Pollock) (pp. 58-76)
• Chapter 5 Making Controlled Comparisons
• Chapter 4 Making Comparisons (Pollock
Homework and Exams
• Homework 3 due today
• Exam 2 on Thursday
Office Hours For the Week
• When
– Wednesday 10-12
– Thursday 8-12
– And by appointment
Course Learning Objectives
1. Students will learn the basics of research
design and be able to critically analyze the
advantages and disadvantages of different
types of design.
2. Students will achieve competency in
conducting statistical data analysis using the
SPSS software program.
Bivariate Data Analysis
Running a Test
• Select and Open a Dataset in SPSS
• Run either
– A cross tab with column %’s (two categorical
– A compare means test (involves a categorical and
continuous variable)
What are Cross Tabs?
• a simple and effective way to measure
relationships between two variables.
• also called contingency tables- because it
helps us look at whether the value of one
variable is "contingent" upon that of another
When To Use Compare Means?
• A way to compare ratio
variables by controlling
for an ordinal or nominal
– One ordinal vs. a ratio or
– One nominal vs. a ratio or
• This shows the average of
each category
Running Cross Tabs
• Select, Analyze
– Descriptive Statistics
– Cross Tabulations
Running Cross-Tabs
• Dependent variable is
usually the row
• Independent variable is
usually the column.
We have to use the
measures available
Lets Add Some Percent's
Click on Cells
Cell Display
• Open the States.SAV
• Analyze
– Compare Means
– Means
Where the Stuff Goes
• Your categorical
variable goes in the
independent List
• Your continuous
variable goes in the
Dependent List
Hypothesis Testing
Why Hypothesis Testing
• To determine whether a relationship exists
between two variables and did not arise by
chance. (Statistical Significance)
• To measure the strength of the relationship
between an independent and a dependent
variable? (association)
What is Statistical Significance?
• The ability to say that that an observed
relationship is not happening by chance. It is
not causality
• It doesn't mean the finding is important or
that it has any real world application (beware
of large samples)
• Practical significance is often more important
Determining Statistical Significance
• Establishing parameters or “confidence intervals”
• Are we confident that our relationship is not
happening by chance?
• We want to be rigorous (we usually use the 95%
confidence interval any one remember why)
How do we establish confidence
• Establishing a “p” value or alpha value
• This is the amount of error we are willing to
accept and still say a relationship exists
P-values or Alpha levels
• p<.05 (95% confidence level) - There
is less than a 5% chance that we will
be wrong.
• p<.01. (99% confidence level) 1%
chance of being wrong
• p<.001 (99.9 confidence level) 1 in
1000 chance of being wrong
Problems of the Alpha level (p-value)
• Setting it too high (e.g.
• Setting it too low (.001)
• We have to remember
our concepts and our
units of analysis
You should always use the 95%
Confidence interval (p<.05) unless
there is a good reason not to.
Testing a hypothesis
• Before we can test it, we have to state it
– The Null Hypothesis- There is no
relationship between my independent and
dependent variable
– The Alternate Hypothesis
• We are testing for Significance: We are
trying to disprove the null hypothesis and
find it false!
About the Null
The Alternate Hypothesis
• Also called the research hypothesis
• State it clearly
• State an expected direction
After testing, the Null is either
• True- no relationship between the groups, in
which case the alternate hypothesis is false---Nothing is going on (except by chance)!
• False- there is a relationship and the
alternative hypothesis is correct-- something is
going on (statistically)!
It seems pretty obvious whether or
not you have a statistically significant
relationship, but we can often goof
things up.
Keep or Reject the Null?
Errors and Decisions
A Type I Error
• Type I Error- the
incorrect or mistaken
rejection of a true null
hypothesis (a false
A Type II error
• A Type II Erroraccepting a nullhypothesis when it
should have been
rejected. (denial)
Type I and II (Climate Change)
You do not want to make either