1. Experimental design 2:

2. Good experimental designs have high internal validity:
To unequivocally establish causality, we need to ensure that groups in our study differ systematically only on our intended independent variable(s) and not on other confounding variables as well.

3. Threats to the internal validity of an experiment's results (e. g
Threats to the internal validity of an experiment's results (e.g. Campbell and Stanley 1969):
Time threats:
History
Maturation
Selection-maturation interaction
Repeated testing
Instrument change
Group threats:
Initial non-equivalence of groups
Regression to the mean
Differential mortality
Control group awareness of its status.
Participant reactivity threats:
Experimenter effects, reactivity, evaluation apprehension.

4. Types of experimental design:
1. Quasi-experimental designs:
No control over allocation of subjects to groups, or timing of manipulations of the independent variable.
(a) “One-group post-test" design:
Prone to time effects, and no baseline against which to measure effects - pretty useless!

5. (b) "One group pre-test/post-test" design:
Now have a baseline against which to measure effects of treatment.
Still prone to time effects.
Statistics marks
2009
Statistics marks
2010
course change

6. (c) "Interrupted time-series" design:
measurement
measurement
time
measurement
treatment
measurement
measurement
measurement
Still prone to time effects.

7. (c) "Interrupted time-series" design (cont.):
Deaths for Friday nights, pm; Saturday and Sunday nights, 10 pm - 4 am. Vertical line: implementation of British Road Safety Act, Oct (Ross, Campbell & Glass, 1970).

8. (d) “Static group comparison" design:
group A:
treatment
measurement
(experimental gp.)
group B: no
treatment
measurement
(control gp.)
Subjects are not allocated randomly to groups; therefore observed differences may be due to pre-existing group differences.

9. 2. True experimental designs:
(a) "Post-test only/control group" design:
group A:
treatme nt
measurement
(experimental
random
gp.)
allocation:
group B:
no treatment
measurement
(control gp.)
Random allocation of subjects to groups should ensure that observed differences are not due to pre-existing group differences - but can't be certain!

10. (b) "Pre-test/post-test control group" design:
measurement
treatment
measurem
ent A:
random
allocation:
group
measurement
no treatment
measurement B:
Ensures that groups are indeed comparable before the experimental manipulation was administered.

11. (c) "Solomon four group" design:
measurement
treatment
measurement
group A:
measurement
no treatment
measurement
group B:
random
allocation:
treatment
measurement
group C:
group
no treatment
measurement D:
Ensures that groups are indeed comparable before the experimental manipulation was administered, and that pre-testing hasn't affected performance. (Uses lots of subjects, so rarely used).

12. Between-groups versus within-subjects designs:
Between-groups (independent measures) -
Each subject participates in only one condition of the study.
e.g. sex differences in memory.
Within-subjects (repeated measures) -
Each subject does all of the conditions in a study.
e.g. effects of alcohol on memory.
Mixed designs -
Mixture of both.
e.g, sex differences in effects of alcohol on memory.

13. Advantages and disadvantages of between-groups and within-subjects designs:
Ease of design
Straightforward.
Can be more complicated.
Number of subjects required
More
Fewer
Carry-over effects between conditions No
Possible
Sensitivity to experimental effects
Lower
Higher
Reversibility of conditions
Unimportant
Essential

14. Within-subjects designs and order effects:
Order effects: practice, fatigue, boredom.
A fixed order of conditions would cause order to vary systematically with condition - results are uninterpretable, because they could be due to order effects, experimental manipulations or both.
Solutions:
(a) Randomise order of conditions:
e.g. with 3 conditions, subjects randomly get orders ABC, BCA, ACB, CBA, CAB, BAC.
(b) Counterbalance order of conditions:
e.g. equal numbers of subjects get each order.

15. A simple within-subjects design:
time
treatment A
measurement A
subject 1:
treatment B
treatment B
measurement B
measurement B
treatment A
measurement A

16. Are threatening faces detected faster than happy ones?
subject 2:
time
threatening faces
detection time
subject 1:
happy faces
happy faces
detection time
detection time
threatening faces
detection time 16

17. Disadvantages of the experimental method:
Intrusive - participants know they are being observed, and this may affect their behaviour.
Experimenter effects.
Not all phenomena are amenable to experimentation, for practical or ethical reasons (e.g. post-traumatic stress disorder, near-death experiences, effects of physical and social deprivation, etc.).
Some phenomena (e.g. personality, age or sex differences) can only be investigated by methods which are, strictly speaking, quasi-experimental.

18. Conclusion:
Experiments are a useful tool for establishing cause and effect - but other methods (e.g. observation) are also important in science.
A good experimental design ensures that the only variable that varies is the independent variable chosen by the experimenter - the effects of alternative confounding variables are eliminated (or at least rendered unsystematic by randomisation).