1. Experimental, Quasi-
Experimental, and Ex Post Facto
(Causal-Comparative) Research

2. Characteristics of
Experimental Research
• There is a control or comparison group
• Subjects are randomly assigned to
groups
• The treatment is randomly assigned to
groups.

3. Characteristics of Quasi-
Experimental Research
• There is a control or comparison group
• Intact groups are used
• The treatment is randomly assigned to
groups.

4. Characteristics of Ex Post
Facto Research
• There is a control or comparison group
• Intact groups are used
• The treatment is not manipulated, it
has already occurred.

6. Diagramming Research
• To illustrate research designs, a
number of symbols are used
– X1 = Treatment
– X2 = Control Group
– O = Observation (pretest or posttest)
– R = Random Assignment

7. A Sample Research Design
• Single-Group Pretest-Treatment-
Posttest Design
R O X1 O This means subjects are
randomly assigned to a
group, which is then given a
pretest, then there is a
treatment, then there is a
posttest.
This means subjects are
randomly assigned to a
group, which is then given a
pretest, then there is a
treatment, then there is a
posttest.

8. R O X1 O
• This is not really an experimental design
because there is no control group
– It is often referred to as a preexperimental design
• Novice researchers often use this research
design
• There are some major problems with this
design – did the treatment really make the
difference or was something else happening.

9. R O X1 O
• What are the threats to the Internal
Validity of this type of research (Did
the treatment really cause a
difference?)

10. Internal Validity Threats
• History
– Another event occurs during the time of the
experiment that might cause the difference
• An experiment to heighten racial awareness was
conducted by a researcher during February. This is
Black History month; so the results might be affected
by events that occur during Black History month and
not the treatment.
R O X1 O

11. Internal Validity Threats
• Maturation
– People naturally change and evolve over time.
This may cause the difference.
• A college develops a new housing plan to promote
more open-mindness and acceptance of others. The
students are tested when they enter college and when
they graduate. The results show they are now more
open-minded and tolerant of others. Did the housing
plan work or do students just mature and grow as a
result of the college experience.
R O X1 O

12. Internal Validity Threats
• Mortality
– Some people drop out during an experiment. This
may affect the outcome.
• I am teaching a new experimental seminar on study
skills. About half of the class stopped coming to the
seminar before the semester was over. The students
who remained improved their study skills. So my
course was effective!
– Probably not. The half that stopped coming might not
have gained anything; that is why they stopped
attending.
R O X1 O

13. Internal Validity Threats
• Testing
– Whenever you give a pretest, the students may
remember the test questions, and get them correct
on the posttest.
• I gave a test to my study skills group on Monday,
presented some unique concepts on Tuesday, then gave
them the posttest on Wednesday. The grades were
significantly higher on the posttest.
– It is possible the grades were higher because the students
still remembered the questions from the pretest.
R O X1 O

14. Internal Validity Threats
• Instrumentation
– To overcome the testing threat to internal
validity, a researcher develops a different form of
the test instrument, but it is not really equivalent.
• I gave a test to my study skills group on Monday,
presented some unique concepts on Tuesday, then gave
them an alternative form of the pretest on Wednesday.
The grades were significantly higher on the posttest.
– It is possible the grades were higher because the second
test was easier than the first.
R O X1 O

15. Internal Validity Threats
• Regression
– When subjects are selected because of extreme
scores on some type of instrument, there is
tendency for their scores to move more toward
the average on subsequent tests.
• An experimenter selected students for a reading
program based on their low test scores. At the end of
the treatment, the test scores had improved.
– Extreme scores naturally move toward the mean on
subsequent tests.
O X1 O

16. How to Handle Internal
Validity Threats
• Have a control group and use
randomization.This design is the Two-
Group Pretest-Treatment-Posttest
Design.
R O X1 O
R O X2 O
R O X1 O
R O X2 O
The Control Group would experience the same
history and maturation. Mortality should be the
same because of random assignment. Random
assignment eliminates the selection threat.
However testing and instrumentation could still
be a threat.

17. Other Research Designs
• Two-Group Treatment-Posttest-Only
Design
R X1 O
R X2 O
R X1 O
R X2 O
There is no pretest so this
eliminates the testing and
instrumentation threat to
internal validly but you
don’t know about their
knowledge or attitude
coming into the study.

18. Other Research Designs
• Solomon 4-Group Design
R O X1 O
R X1 O
R O O
R O
R O X1 O
R X1 O
R O O
R O
Note: A blank
indicates the
control group,
same as X2

19. Quasi-Experimental Designs
• Posttest Only Nonequivalent Group
Design
X1 O
X2 O
X1 O
X2 O
The absence of R indicates
there is no random
assignment. Sometimes you
will see a dotted line
between the two groups.
This indicates the two
groups may not be
equivalent.

20. Quasi-Experimental Designs
• Pretest-Posttest Nonequivalent Group
Design
O X1 O
O X2 O
O X1 O
O X2 O

21. Time Series Designs
O O O X1 O O OO O O X1 O O O
In the next course, AEE 579 Research Design,
many more research designs are examined.

22. External Validity
• Can the research be generalized to
other settings?
– Population Validity
– Personological Variables
– Ecological Validity

23. Population Validity
• Is the sample population similar to the
population the researchers wishes to
generalize to

24. Personological Variables
• Different people have different
personalities, learning styles, etc., so
the results may not be generalizable to
people who are substantially different
on these personological variables.

25. Ecological Validity
• The setting or situation in which the
experiment occurred may be different
than other settings.

26. Social Interaction Validity
Threats
• Diffusion or Imitation of Treatment
– This occurs when a comparison group learns
about the program either directly or indirectly
from program group participants.
• This group may try to imitate or emulate what the
treatment group is getting.

27. Social Interaction Validity
Threats
• Compensatory Rivalry
– The comparison group knows what
the program group is getting and
develops a competitive attitude with
them.

28. Social Interaction Validity
Threats
• Resentful Demoralization
– This is almost the opposite of compensatory
rivalry. Here, students in the comparison
group know what the program group is
getting. But here, instead of developing a
rivalry, they get discouraged or angry and
they give up.

29. Social Interaction Validity
Threats
• Compensatory Equalization of
Treatment
– The researcher is under pressure to
“enrich” the experiences of the control
group. This pressure may come from
parents, school administrators, etc.

30. Ex Post Facto (Causal-
Comparative) Research
• Explores possible causes and effects
• The independent variable is not manipulated,
it has already been applied
• Focuses first on the effect, then attempts to
determine what caused the observed effect.

31. Statistical Analysis
• If we are comparing the scores of two
groups – a t-test is normally used. The
value of t means nothing by itself
(unlike the value of R). We have to
determine if t is statistically significant
Tea for two

32. Statistical Analysis
• If we are comparing the scores of three
(or more) groups – Analysis of
Variance (ANVOA) is used. This test
gives us a f value which means nothing
by itself. We have to determine if it is
statistically significant.

33. Statistical Analysis
• If we want to statistically equate two or more
groups (because one group had a high pretest
score) we use Analysis of Covariance
(ANCOVA). This test gives us a f value
which means nothing by itself. We have to
determine if it is statistically significant.