Transcript Hypotheses & Research Design

Hypotheses
&
Research Design
Review & Overview
• You can now:
– concepts  measurement
– operationalize & recode variables
– Describe variables: central tendency and spread
• Now we learn to:
– Formulate hypotheses
– Choose a proper research design
– Understand threats to validity
A theory is a fairly general explanation
of some political phenomenon.
• Theories must be testable
A hypothesis is less general than a theory.
• Theories and hypotheses describe causal
relationships between two or more
variables.
Dependent and Independent Variables
Dependent variable – what you’re trying to explain
Independent variable – what we think causes the dependent
variable.
Independent Variable  Dependent Variable
Hypothesis
Characteristics of Hypotheses
1. Hypotheses should be as SPECIFIC as
possible and provide a clear “tendency” between
the variables
• Example: A person’s vote is related to their
annual income
Better: American voters with higher income are
more likely to vote Republican than are voters
with lower income.
Causal Relationships and Associations
For example:
• Across countries per capita ice cream
consumption is associated with per capital
television ownership. Is this a causal
relationship or an association?
• Statistics can tell you if there is an
association between two variables but not
if that is causal.
Spurious Relationship
Hypothesis expects a negative relationship,
but we observe a positive one.
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Number of Fire Trucks
Amount of Fire Damage
Spurious Relationship
Initial Reporting of
Severity of Fire
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Number of Fire Trucks
Amount of Fire Damage
Problems in Determining Causality
• Reciprocal
Relationships
– Example:
Likelihood of
Winning Election
Amount of
Campaign Funds
Given to Candidate
Choosing Among Competing Theories
Or
Eliminating Alternative Explanations
We can never prove a theory or a
hypothesis, we can only disprove.
Research Design
Experimental Designs
Ideal Design:
• Examines a causal relationship stated in
our hypothesis.
• Allows us to determine if the expected
association is present.
• Eliminates alternative explanations.
Research Design I
R
Grp 1
Grp 2
X
R= random assignment to groups
X= treatment
O= observation
Oi= subscript on O refers to time of observation
Grp 1= treatment group
Grp 2= control group
O1
O1
Research Design II
R
Grp 1
Grp 2
O1
O1
X
O2
O2
Research Design III
Solomon 4 Group Design
R
Grp 1
Grp 2
Grp 3
Grp 4
O1
O1
X
X
O2
O2
O2
O2
Why are these designs superior to:
O1
X
O2
or to:
X
O1
Threats to Internal Validity (C&S)
• History:
– Other contemporaneous event(s) cause change in Y, not treatment.
– Ex: Rise in DVD and game purchases.
• Maturation
– Natural aging/process causes change in Y.
– Ex: Getting smarter or more experienced over time.
• Testing:
– Change in Y due to testing or pretest reactivity
– Pretest may bias subsequent responses
• Instrumentation:
– Something happens to the observers or measurement device that
causes a change in Y
– Ex: fatigue, change in observers (different people)
Threats to Internal Validity
(Campbell & Stanley)
• Experimental Mortality
– Effect is due to a differential loss in comparison groups.
• Statistical Regression
– Regression to the mean. Groups selected based on extreme scores will
tend to display lower scores next time.
• Sample Selection
– Difference in comparison groups is not due to treatment, but to the fact
that the groups were different from the start.
Threats to Validity
• Internal Validity
Are our claims concerning the effects
of the treatments valid in this
particular experiment?
• External Validity
Can our results from this experiment
be generalized to other populations
and settings?
Threats to External Validity
In general, anything that makes the effects found for the
experimental groups unrepresentative of effects on a larger
population or in a different setting.
• Generalize to a different setting: For example, are
results in a lab realistic enough to apply outside the lab?
• Selection: For example are the survey groups
representative of the larger population, or was the
original population defined too narrowly to be of real
interest?
Why do properly constructed research
designs eliminate other explanations?
Alternative Cause
0
Treatment
Result
Quasi-Experimental Designs
• Typically in political science we can’t do proper
experimental designs. Usually we can’t
manipulate who gets the treatment and who
does not.
• Instead we must observe what nature presents
to us. That makes it the responsibility of the
research to statistically control for all variables
that may affect the relationship between our
dependent and our independent variable.