research design types

8/3/2019 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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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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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.

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