This document discusses experimental research designs. It describes pre-experimental designs like one-shot case studies which lack random assignment and controls. True experimental designs, like pretest-posttest control group designs, manipulate variables and use random assignment and controls. Quasi-experimental designs, such as non-equivalent control groups, lack random assignment. Factorial designs examine effects of manipulating two or more independent variables simultaneously. The document provides examples and discusses threats to validity for different designs.

1. Marilou K. Peralta
Master in Management Engineering
Pangasinan State University

2. PRESENTATION CONTENT
1. What is Experimental Research?
2. Variables and Threats to
Experimental Research
3. Types of Experimental Research
Designs
4. Pros & Cons of Experimental
Research Designs

3. RESEARCH DESIGN
The research design is a logical model
that shows the strategies for sample
selection, development of measurement
tools, data collection as well as methods
of data processing and analysis.

4. QUANTITATIVE RESEARCH
RESEARCH
EXPERIMENTAL
True Experimental Design
Controlled experimentation
Solomon four experimental
design
Delayed effects experimental
designs
NON-EXPERIMENTAL
Correlation studies
Surveys

5. Quasi-experimental designs
 One shot study
 Pretest-posttest design
 Contrasted groups
 Time series

6. EXPERIMENTAL RESEARCH
It is a systematic and scientific
approach to research in which the
researcher manipulates one or more
variables and controls, and measures
any change in other variables
The purpose is to study the cause
and effect relationship

7. EXPERIMENTAL RESEARCH
An experiment is a set of
observations conducted under
controlled circumstances in which
the investigator manipulates
conditions to ascertain what effects
such manipulation has on the
outcome.

8. REQUIREMENTS OF A GOOD
EXPERIMENTAL DESIGN
Requirements of a good experimental
design
1. Subjects are randomly assigned to the
experimental groups (EG) and control
groups (CG)
-presence of a comparison group; random
assignment

9. 2. The independent variable(IV) can
be manipulated such that any change
in the dependent variable (DV) is
attributable to changes in the
independent variable (manipulable
IV)

10. EXPERIMENTAL RESEARCH
VARIABLES:
Independent Variable – is
presumed to cause changes
to occur in another variable,
experimental or treatment
variable
Dependent Variable /
Measured Variable – what
changes because of
another variable, the effect
or outcome variable

11. EXPERIMENTAL RESEARCH
EXTRANEOUS VARIABLES - (EV) are effectively
controlled to avoid confounding or spurious
INTERNAL VALIDITY: indicates whether the independent
variable was the sole cause of the change in the dependent
variable
EXTERNAL VALIDITY: involves the extent to which the
results of a study can be generalized (applied)
beyond the sample
Source:
http://web.mst.edu/~psyworl
d/extraneous.htm

12. VALIDITY
The internal validity of any research
undertaking is strengthened by the
correct choice of research design and the
soundness and appropriateness of
decisions that pertain to sampling,
instrumentation, data collection and
analysis.

13. THREATS TO EXPERIMENTAL VALIDITY
History
Maturation
Testing
Instrumentation
Selection
Regression
Experimental mortality

14. EXPERIMENTAL RESEARCH DESIGN
1. PRE-EXPERIMENTAL DESIGN
One-Shot Case Study Design
One-group Pretest Posttest Design
Static Group Comparison
2. TRUE
EXPERIMENTAL
DESIGN
Posttest only Control Group Design
Pretest Posttest Control Group Design
Solomon Four Group Design
3. QUASI-EXPERIMENTAL
DESIGN
Non-equivalent Control Group
Time Series
4. FACTORIAL DESIGN Multiple Time Series

15. 1. PRE-EXPERIMENTAL DESIGN
One-Shot Case Study Design
One-group Pretest Posttest Design
Static Group Comparison
pre-experimental designs represent the simplest form
of research designs
 considered “pre-,” indicating they are preparatory or
prerequisite to true experimental designs
no randomization procedures are used to control for
extraneous variables
Source: http://srmo.sagepub.com/view/encyc-of-research-design/n330.xml

16. ONE-SHOT CASE STUDY DESIGN
Selected
experimental
group
TREATMEN
T
POST-TEST

17. ONE-SHOT CASE STUDY DESIGN
Another example of one such
design might be a one-off survey
of unemployed people in a
specific local area to assess their
health status and the impact of
unemployment on health.

18. ONE-SHOT CASE STUDY DESIGN
Example: the effects of counseling sessions on the
attitudes of identified bullies in school.
Experimental Group Treatment (X) Posttest (O2)
Bully students Counseling Observation
Pretest – O1 Posttest – O2 Treatment – X Randomization – R Control Group - C

19. ONE-SHOT CASE STUDY DESIGN
Example: You want to assess the effects of counseling
sessions on the attitudes of identified bullies in school.
INTERNAL 1. History VALIDITY:
–
History, parents Maturation,
may have
Testing, other Instrumentation,
counselling
Selection, being done Regression,
at other
Experimental mortality
Experimental Group Treatment (X) Posttest (O2)
Bully students Counseling Observation
place
2. Instrumentation
Pretest – O1 Posttest – O2 Treatment – X Randomization – R Control Group - C

20. ONE-SHOT CASE STUDY DESIGN
Example: You want to determine whether praising primary
school children makes them do better in Mathematics.
INTERNAL
VALIDITY:
History, Maturation,
Testing,
Instrumentation,
Selection,
Regression,
Experimental
mortality
Selection – possible
that students
selected were
already good in
Mathematics
Experimental Group Treatment (X) Posttest (O)
Primary school children History Praising
– the school
Attentiveness/Performance
had organized a
motivation course

21. ONE-GROUP PRETEST-POSTTEST
DESIGN
Selected
experimen
tal group
PRETEST
TREATMEN
T
POST-TEST
(Before and After design)

22. Another example
Testing the effectiveness of a DRUG A on
capacity to recall words
EG : # words recalled---exposure to DRUG
A
-----# words recalled
CG: # words recalled---no exposure to
DRUGA -----# words recalled

23. ONE-GROUP PRETEST-POSTTEST
DESIGN
Example: You want to determine whether praising primary
school children makes them do better in Mathematics.
Experimental Group Pretest(O1) Treatment (X)
Posttest(O2)
Primary school children Praising
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

24. ONE-GROUP PRETEST-POSTTEST
DESIGN
Example: You want to determine whether praising primary
school children makes them do better in Mathematics.
Maturation: period between
pretest and posttest is long so
subjects may have matured
because of developmental
changes.
Testing: period between the
pretest and the posttest is too
short and there is the possibility
that subjects can remember the
questions and answers.
INTERNAL VALIDITY:
History, Maturation,
Testing, Instrumentation,
Selection, Regression,
Experimental mortality
Experimental Group Pretest(O1) Treatment (X)
Posttest(O2)
Primary school children Praising
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

25. STATIC GROUP COMPARISON
experimental
group
TREATMEN
T
POST-TEST
control group
POST-TEST

26. STATIC GROUP COMPARISON
Example: Determine whether praising primary school
children makes them do better in Mathematics.
Group A Treatment (X) Posttest(O2)
Praising
Group B Posttest(O2)
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

27. PRE-EXPERIMENTAL
DESIGN
One-Shot Case
Study Design
One-group
Pretest Posttest
Design
Static Group
Comparison
PROs:
As exploratory approaches, pre-experiments can be a cost-effective
way to discern whether a potential explanation is
worthy of further investigation suitable for beginners
CONs:
Lower validity, difficult to assess the significance of an
observed change in the case
Very little control over the research, higher threat to internal
validity
it is difficult or impossible to rule out rival hypotheses or
explanations

28. 2. TRUE EXPERIMENTAL
DESIGN
Posttest only Control Group Design
Pretest Posttest Control Group Design
Solomon Four Group Design
Manipulation – control of independent variable by the researcher
through treatment/intervention
Control – the use of control group and extraneous variable
Randomization – every subjects have equal chance of being
assigned to experimental and control group
Random assignment helps ensure that there is no pre-existing
condition that will influence the variables and mess up the results.

29. POSTTEST ONLY CONTROL GROUP
Randomly
selected
experimen
tal group
TREATMEN
T
POST-TEST
Randomly selected
control group
POST-TEST

30. POSTTEST ONLY CONTROL GROUP
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

31. PRETEST POSTTEST CONTROL GROUP
DESIGN
Randomly
selected
experimental
group
PRE-TEST
TREATMEN
T
POST-TEST
Randomly selected
control group
PRE-TEST
POST-TEST

32. PRETEST POSTTEST CONTROL
GROUP DESIGN
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

33. SOLOMON FOUR GROUP DESIGN
Experimental
Group A
PRE-TEST TREATMEN
T
POST-TEST
Experimental
Group B
Control Group A
PRE-TEST
POST-TEST
POST-TEST
POST-TEST
Control Group B

34. SOLOMON FOUR GROUP DESIGN
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

35. TRUE EXPERIMENTAL DESIGN
Posttest only
Control Group
Pretest Posttest
Control Group
Design
Solomon Four
Group Design
PROs:
Greater internal validity,
Control over extraneous variables is usually greater
CONs:
Ethical Problems,
less external validity
It may be unethical or impossible to randomly assign people to groups

36. 3. QUASI-EXPERIMENTAL
DESIGN
Non-equivalent Control Group
Time Series
Multiple Time Series
is simply defined as not a true experiment since it does
not have randomly assigned groups the
comparison/control group is predetermined to be
comparable to the treatment group in critical ways.
Matching, comparing the same participants over time
and pre-existing groups are used.
These designs are frequently used when it is not
logistically feasible or ethical to conduct a randomized
controlled trial.
Source: http://education-portal.com/academy/lesson/quasi-experimental-designs-definition-characteristics-types-examples.
html#lesson

37. NON-EQUIVALENT CONTROL GROUP
DESIGN
Subjects are tested in existing group or intact group rather than being
randomly selected
O1 X O2
O1 O2
This design should only be used when random assignment is
impossible
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

38. NON-EQUIVALENT CONTROL GROUP
DESIGN
Subjects are tested in existing group or intact group rather than being
randomly selected
Selection
Maturation
Regression
O1 X O2
O1 O2
Testing
This design should only be used when random assignment is
impossible
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

39. TIME SERIES
A single group is pretested repeatedly until pretest scores are stable ,
exposed to treatment and, then, repeatedly post tested
O1 > O1 > O1 > O1 >O1 >X > O2 >O2 >O2 >O2>O2
Pretest – O1 Posttest – O2 Treatment – X Random Assignment – R Control Group -
C

40. QUASI-EXPERIMENTAL DESIGN
Non-equivalent
Control Group
Time Series
Multiple
Time Series
PROs:
can be very useful in generating results for general trends
Greater external validity(more like real world conditions)
Much more feasible given time and logistical constraints
CONs:
Without proper randomization, statistical tests can be meaningless
Not as many variables controlled
Selection could be biased
Source: https://explorable.com/quasi-experimental-design

41. FACTORIAL DESIGN
Used to examine the effects that the manipulation of at least 2
independent variables (simultaneously at different levels) has
upon the dependent variable.
PROs: more precision on each factor than with single factor experimentation,
broadening the scope of an experiment, possible to estimate the interaction
effect
CONs: could be complex, the experiment can be very large with a number of
factors each at several levels
Source: http://www.stat.ncsu.edu/people/nelson/courses/st512/Factorial%20Experiments.pdf

42. FACTORIAL DESIGN
Example: Driver frustration under low, medium, and high density traffic
conditions and under traffic flow controlled by a police officer or a traffic
signal was investigated. The measure of frustration was the number of
horns
honked by drivers before receiving the right-of-way at a controlled
intersection. The mean number of horns honked in each condition were:
Source: http://www.csupomona.edu/~dhorner/webpages/UOW-Res/094/Exr-2x3-table1-ans.pdf

43. FACTORIAL DESIGN
Source: http://www.csupomona.edu/~dhorner/webpages/UOW-Res/094/Exr-2x3-table1-ans.pdf

44. EXPERIMENTAL RESEARCH DESIGN
1. PRE-EXPERIMENTAL DESIGN
One-Shot Case Study Design
One-group Pretest Posttest Design
Static Group Comparison
2. TRUE
EXPERIMENTAL
DESIGN
Posttest only Control Group Design
Pretest Posttest Control Group Design
Solomon Four Group Design
3. QUASI-EXPERIMENTAL
DESIGN
Non-equivalent Control Group
Time Series
4. FACTORIAL DESIGN Multiple Time Series

45. procedures
materials
measures participants