Download - Research Design Types
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RESEARCH DESIGN
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TOPICS
Non experimental research design
Experimental vs. quasi-experimental (natural)
research designs Research designs with limited power to
assess cause and effect
Research design with more power to assesscause and effect
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Non experimental
Cross sectional studies
Panel (somewhat rare) Longitudinal (pooled time series)
Case (s) study
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DEVELOPMENT & INCOME
DISTRIBUTION The economic development theory suggests
that as countries get richer income
distribution worsens but eventually incomedistribution improves.
LEVELOF DEVELOPMENT
GAP
DIST.
LOW HIGH
LOW
HIGH
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NON EXPERIMENTAL
RESEARCH
CROSS SECTIONAL: The researcher
would take a sample of countries withdifferent levels of development (low,
medium, high) and analyze the match
between the theory and facts.
This approach is the weakest to determine
causality, but strong in generalizing.
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NON EXPERIMENTAL
RESEARCH
LONGITUDINAL: The researcher would
select a country or countries that have move
from lower to higher levels of development(eg. USA, Canada, etc.) and determine
whether income distribution over time
followed the trend suggested by the theory
Causality can be infer but lacks detail aboutthe true causes as well as weak on
generalization.
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Longitudinal studies are also known as
Pooled time series.
Each unit is measured for series of time
points.
Example, I study all countries annually (over
decades) to see if they become involved in a war.
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Panel Studies
Between cross-sectional and longitudinal,
there are panel studies.
These studies only measure a few timeperiods and are similar to a pretest/post-test
type format.
These studies are rare and are usually used
when data is missing for pooled time series
studies.
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CASE (S) STUDY
The researcher will take a case (s) study andanalyze in depth what factors (policies,institutions, political systems, culture, etc.)
are associated with improving incomedistribution in a given country.
Why income distribution is better in Chilethan in Mexico and Brazil despite having thesame level of development?
Improves details and understanding ofcauses and effect but lacks generalization.
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Experimental vs. Quasi-
experimental Research Designs
Experimental research design: The researcher has
control over the experiment in terms of sample
selection, treatment, environment, etc.
Experimental designs are typical in psychology,
medicine, education, etc.
Quasi-experiments: The researcher does not have
control over the experiment, rather the experiment
occurs in a natural setting.
Quasi-experimental design are typical in economics,
sociology, public administration, urban planning,
political sciences, etc.
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RESEARCH DESIGN
0t = Observation in time t of
experimental groupX = Treatment
0c = Control group
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Research design with limited
power
POST-TEST ONLY
X O 1
POST-TESTWITH CONTROL GROUP
X O1
O2
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PRE-TEST POST-TEST
O1
X O2
PRE-TEST POST-TEST WITH CONTROL GROUP
O1 X O2
O1 O2
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Research designs with more
causal power
CONTROL WITH MORE OBSERVATION
IN THE PRETEST
O1 O2 O3 X O4
O1 O2 O3 O4
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PRE-TEST POST-TEST REMOVING THE
TREATMENT
O1 X O2 X O3 X 04
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CHANGES TO LOOKFOR
CONVERGENCE-DIVERGENCE
1. Positive change in the treatment groupwithout change in the control group
Treatment
Control
Treatment
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Divergence
Positive increments at a different rate
Treatment
Control
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Convergence
The treatment group catches up with the
control group
Control
Treatment
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Cross pattern
The treatment group overpass the control
group
Treatment
Control
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Research design with more
power (time series)
Pre-test post-test
O1 O2 O3 X O4 O5 O6
Pre-test post-test with control group
O1 O2 O3 X O4 O5 O6
O1 O2 O3 O4 O5 O6
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Changes to look for
TIMEX
No effect
Change in the rate or slope
Change in the intercept
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We need to take care that our theory is not
missing other factors that may undermine the
validity of our theory and tests.
Our inferences will be flawed if we are
actually capturing other processes through
our variables. This is why we use control
groups and variables depending on the type of
study.
Without controlling for confounding or
alternative processes, the internal validity of
our studies would be undermined.
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Several possible problems arise that are
related to model misspecification and
spurious relationships.
Thus, we need to control for
confounding factors and alternativeexplanations!!!
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Model Misspecification and Spuriousness
Antecedent variable: A variable that indirectlyaffecting the relationship between two othervariables.
For example, Ivy league education increasesincome.
However, parental wealth and legacy admissionsaffect Ivy league education. Thus, income ofgraduates from Ivy League schools may not berandom.
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Model Misspecification and Spuriousness
Intervening Variable: These may bespuriously related to another relationship. Drinking coffee causes cancer..
Drinking coffee may not be the cause of cancer,but rather the fact that smokers are also coffeedrinkers.
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Model Misspecification and Spuriousness
Alternative Variables: We also want to
control for variables that would bias our
results if omitted.
In this case, the X variables in a model
would produce biased estimates,
undermining their validity and producing errorthat leads to inaccurate inferences.