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Experimental design 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.

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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.


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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.


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Types of experimental design: (e.g. Campbell and Stanley 1969):

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!


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(b) One group pre-test/post test design: (e.g. Campbell and Stanley 1969):

Now have a baseline against which to measure effects of treatment.

Still prone to time effects.

Statistics marks

2006

Statistics marks

2007

course change


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(c) Interrupted time-series design: (e.g. Campbell and Stanley 1969):

measurement

measurement

time

measurement

treatment

measurement

measurement

measurement

Still prone to time effects.


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(c) Interrupted time-series design (cont.): (e.g. Campbell and Stanley 1969):

Deaths for Friday nights, 10-12 pm; Saturday and Sunday nights, 10 pm - 4 am. Vertical line: implementation of British Road Safety Act, Oct. 1967 (Ross, Campbell & Glass, 1970).


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(c) “Static group comparison" design: (e.g. Campbell and Stanley 1969):

group A:

measurement

treatment

(experimental gp.)

group B:

measurement

no

treatment

(control gp.)

Subjects are not allocated randomly to groups; therefore observed differences may be due to pre-existing group differences.


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2. True experimental designs: (e.g. Campbell and Stanley 1969):

(a) Post-test only/control group" design:

group A:

treatme

nt

measurement

(experimental

random

gp.)

allocation:

group B:

measurement

no treatment

(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!


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(b) Pre-test / post-test control group" design: (e.g. Campbell and Stanley 1969):

measurement

gro

up

measurem

ent

treatment

A:

random

allocation:

group

measurement

no treatment

measurement

B:

Ensures that groups are indeed comparable before the experimental manipulation was administered.


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(c) Solomon four group design: (e.g. Campbell and Stanley 1969):

measurement

treatment

measurement

group

A:

measurement

no treatment

measurement

group

B:

random

allocation:

treatment

measurement

group

C:

no treatment

group

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).


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Between-groups versus within-subjects designs: (e.g. Campbell and Stanley 1969):

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.



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Within-subjects designs and order effects: within-subjects designs:

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.


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A simple within-subjects design: within-subjects designs:

subject 2:

time

treatment A

measurement A

subject 1:

treatment B

treatment B

measurement B

measurement A

treatment A

measurement A


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Disadvantages of the experimental method: within-subjects designs:

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.


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Conclusion: within-subjects designs:

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).