2. Chapter 12
Experimental Designs

3. Chapter Objectives
understand the role and scope of experimental research in business
distinguish between causal and correlational analysis
explain the difference between laboratory and field experiments
explain the following terms: extraneous variables, manipulation, experimental and control groups, treatment effect, matching and randomisation
discuss the seven possible threats to internal validity in experimental designs
describe the different types of experimental designs
explain the role of simulation in experimental research
describe the ethical issues involved in experimental research

4. Laboratory Experiment Field Experiment
Experimental Designs
Laboratory Experiment
Field Experiment
Cause: - Effect relationships established by:
1. Manipulating treatments
2. Controlling for external or exogenous variables
Manipulation of Treatment:
Example: Three different teaching methods given to three different groups of students
Straight lectures to 10 students
simulation only, to another 10 students
Both lectures and simulations to 10 other students
Assess which results in greatest amount of learning

5. Group Treatment Final Marks Simulation alone is ineffective.
Experimental Group 1
Lectures
70%
Experimental Group 2
Simulation
40%
Experimental Group 3
Lecture & Simulation
100%
Control Group
No treatment
50%
Simulation alone is ineffective.
Lectures are more effective than no treatment at all.
Both lectures and simulation are extremely effective.
Cause: - Effect relationship can be established because of:
Controls for age, etc. through either randomisation or matching of groups
Because of an additional control group

6. Control of Exogenous Variables through;
Random assignment of members to various groups
Matched groups
Control groups
Example: Different treatments may have different effects on people with differing interests, ages, expertise,etc.
So, a) randomly assign members to different treatment groups. The differences will be randomly distributed. Systematic bias will be reduced.
b) match the different groups as closely as possible in terms of age, interest, expertise, etc.
c) have an additional control group of students who ar not exposed to any of the three treatments, and see how they learn and compare.

7. Controlled Variables
Variables that might affect the Cause - Effect relationship among the IVs and DV, and hence need to be controlled.
Example: 1. Age
2. Education levels
3. Length of Service in Organisation
Might affect the relationship between job characteristics and job satisfaction

8. Uncontrolled Variables
Variables or phenomena that occur unexpectedly and can confound the results.
Example:
Advertising
Purchasing
(IV)
(DV)
Age
Life style
Sudden Unemployment
(Uncontrolled Variable)
(Controlled Variables)

9. Lab Experiements can have tight controls and hence the validity of cause – Effect findings is high – ie., they have high internal validity. But their generalisability to real life is low, because of their tight controls – ie., their external validity is low.
Field Experiments (eg, different incentive plans (treatment0 in work organisations for assessing effect on productivity, have high external validity or generalisability (because they represent the actual situations), but have low internal validity (ie., cause – effect relationships are contaminated because of no controls.)

10. Cause and effect relationship after randomisation
Groups
Treatment
Treatment effect
(% increase in production over pre-piece rate system)
Experimental group 1
$1.00 per piece 10
Experimental group 2
$1.50 per piece 15
Experimental group 3
$2.00 per piece 20
Control group
(no treatment)
Old hourly rate

11. FACTORS AFFECTING INTERNAL VALIDITY
HISTORY EFFECTS
MATURATION EFFECTS
TESTING EFFECTS
INSTRUMENTATION EFFECTS
SELECTION BIAS
STATISTICAL REGRESSION
MORTALITY

12. History effects in experimental design

13. Maturation effects on the cause and effect relationship

14. Pre-test and post-test experimental group design
Treatment
Post-test
Experimental group O1 X O2
Treatment effect = (O2 - O1)

15. Post-test only with experimental and control groups
Treatment
Outcome
Experimental group X O1
Control group O2
Treatment effect = (O1 - O2)

16. Pre-test and post-test experimental and control groups
Treatment
Post-test
Experimental group O1 X O2
Control Group O3 O4
Treatment effect = [(O2 - O1) – (O4 - O3)]

17. Solomon four-group design
Pre-test
Treatment
Post-test
1.Treatment O1 X O2
2.Control O3 O4
3. Experimental O5
4. Control O6
Treatment effect (E) could be judged by:
E 1 = (O2 - O1)
E 2 = (O2 - O4 )
E 3 = (O5 - O6)
E 4 = (O5 - O3 )
E 5 = (O2 - O1) – (O4 - O3 )
If all Es are similar, the cause and effect relationship is highly valid.

18. Major threats to internal validity in different experimental designs
Types of experimental designs
Major threats to internal validity
1. Pre-test and post-test with one experimental group only
Testing, history, maturation
2. Post-tests only with one experimental and one control group
Maturation
3. Pre-test and post-test with one experimental and one control group
Mortality
4. Solomon four-group design

19. Simulation as experimentation

20. Example of a management flight simulator

21. Ethical Issues in Experimental Research
The following practices are considered unethical:
pressuring individuals to participate in experiments through coercion or applying social pressure
giving out menial tasks and asking demeaning questions that diminish the subject’s self-respect
deceiving subjects by deliberately misleading them as to the true purpose of the research
exposing participants to physical or mental stress
not allowing subjects to withdraw from the research when they want to

22. Ethical Issues in Experimental Research (cont’d)
using the research results to disadvantage the participants, or for purposes that they would not like
not explaining the procedures to be followed in the experiment
exposing respondents to hazardous and unsafe environments
not debriefing participants fully and accurately after the experiment is over
not preserving the confidentiality of the information given by the participants
withholding benefits from control groups

23. Decision points for embarking on an experimental design

24. A completely randomised design
Routes
Number of passengers before
Treatment
Number of passengers after
Group 1 of nine routes O1 X1 O2
Group 2 of nine routes X2 O4
Group 3 of nine routes O3 X3 O6

25. A randomised block design
Blocking factor: residential areas
Fare reduction
Suburbs
Crowded urban area
Retirement areas 5c X1 7c X2
10c X3
Note that the Xs above indicate only various levels of the blocking factor, and the Os (the number of passengers before and after each treatment at each level) are not shown, although these measures will be taken.

26. The Latin square design
Day of the week
Residential area
Midweek
Weekend
Monday/Friday
Suburbs X1 X2 X3
Urban
Retirement

27. A 3 * 3 factorial design Luxury Express X1Y1 X2Y1 X3Y1
Type of bus 5c 7c
10c
Luxury Express
X1Y1
X2Y1
X3Y1
Standard Express
X2Y2
X1Y2
X3Y2
Regular
X3Y3
X2Y3
X1Y3