CHAPTER 7
Experimental and
Quasi-Experimental
Research
Chapter Outline
• Sources of invalidity
• Threats to internal validity
• Threats to external validity
• Controlling threats to internal validity
• Controlling threats to external validity
• Types of designs
Experimental Research Tries
to Establish Cause and Effect
• Selection of a good theoretical framework
• Application of appropriate experimental design
• Use of correct statistical model and analysis
• Proper selection and control of independent
variables
• Appropriate selection and measurement of
dependent variables
• Correct interpretation of results
Three Criteria for Cause
and Effect
1. The cause must precede the effect in time.
2. The cause and effect must be correlated with
each other.
3. The correlation between cause and effect cannot
be explained by another variable.
If the condition is necessary and sufficient to
produce the effect, then it is the cause.
Distinguishing Between
Types of Validity
• Internal validity: Did the treatments (IV) cause
the change in the outcome (dv)?
• External validity: To what populations, settings,
or treatments can the outcome be generalized?
• Is there a trade-off between internal and external
validity?
• Can a series of studies address the trade-off?
Threats to Internal Validity
• History: Events that are not part of treatment
• Maturation: Events due to passage of time
• Testing: Effects of more than one test
administration
• Instrumentation: Change in calibration of
measurements
• Statistical regression: Selection based on
extreme score
(continued)
Threats to Internal Validity
• Selection biases: Nonrandom participant
selection
• Experimental mortality: Differential loss of
participants
• Selection–maturation interaction: Passage of
time influencing groups differently
• Expectancy: Influence of experimenters on
participants
Threats to External Validity
• Reactive or interactive effects of testing: Pretest
may make participants sensitive to treatment.
• Reactive effects of experimental arrangements:
Setting constraints may influence generalizability.
• Multiple-treatment interference: One treatment
may influence the next treatment.
Controlling Threats
to Internal Validity
• Randomization
- Real randomization
- Matched pairs (not matched groups)
- Randomizing treatments or counterbalancing
• Placebos
• Blind setups
(continued)
Controlling Threats
to Internal Validity
• Double-blind setups
• Reactive effects of testing: Eliminate pretest.
• Instrumentation
- Calibration and test reliability
- Halo effects
• Experimental mortality: Keeping participants
Controlling Threats
to External Validity
• Selecting from larger populations
- Participants
- Treatments
- Situations
• Ecological validity: Does the setting capture the
essence of the real world?
Types of Designs: Preexperimental Designs
One-shot studies
T
O
One-group pretest-posttest
O1
T
O2
Statistical analysis?
Static group comparison
T
O1
-------------
Statistical analysis?
O2
Types of Designs: True Experimental Designs
Randomized-groups design
R
R
T
O1
O2
Statistical analysis?
Extending the levels—randomized-groups design
R
R
R
T1
T2
O1
O2
O3
Statistical analysis?
(continued)
Types of Designs: True Experimental Designs
(continued)
Types of Designs: True Experimental Designs
Pretest-posttest randomized-groups
R
R
O1
O3
T
O2
O4
Statistical analysis?
Extending the design on the RM factor
R
R
O1
O4
T
O2
O5
T
O3
O6
Statistical analysis?
(continued)
Types of Designs: True Experimental Designs
Solomon four-group design—purpose
R
R
R
R
O1
O3
T
T
O2
O4
O5
O6
Statistical analysis (factorial ANOVA)
Pretested
Unpretested
No treatment
O4
O6
Treatment
O2
O5
(continued)
Quasi-Experimental Designs: Time Series
Campbell and Stanley 1963.
Quasi-Experimental Designs: Reversal
Quasi-Experimental Designs: Ex Post Facto
This is one of the preexperimental designs, but with
the treatment not under the control of the experimenter.
T
O1
----------------------O2
Statistical analysis?
Quasi-Experimental Designs:
Single Participant
Identify participant and follow over time.
• Does the treatment produce the same effect
each time?
• Are treatment effects cumulative, or does
participant return to baseline?
• Does participant’s response become less variable
over treatment times?
(continued)
Quasi-Experimental Designs:
Single Participant
• Is participant’s magnitude of response sensitive
to multiple treatment applications?
• Do varying intensities, frequencies, and lengths
of treatment produce varying responses?