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Experimental control allows causal inference (IV caused observed change in DV)
Experiment has internal validity when it fulfills 3 conditions for causal inference
1) covariation
2) time-order relationship
3) elimination of plausible alternatives
• Controlling extraneous variables
• 1) elimination
• 2) holding conditions constant
• 3) randomization/balancing
• 4) counterbalance
Specify variables to be controlled
If possible eliminate the extraneous variable
Eg noise
a) As a confound; group A measured during high traffic Group B low traffic noises
b) Nuisance variable (may not be a confound). Random noises from heating system.
1) Elimination
2) Hold conditions constant
Minimize variability
• Time of day
• Lighting
• Instructions
• Stimuli
• Procedure….
Loftus and Burns ( 1982)
• Two groups both saw a film of a bank robbery. Only the ending differed.
• Group A violent ending• Group B nonviolent• Both groups asked questions about events that
happened prior to end scenes• Eg the number on a t-shirt worn by a bystander• Correct recall group A 4% Group B 28%• Same film, same instructions, same questions, same
room… • Did not control same temperature or weather…• Only factors thought to impact DV
Between Subjects Design
only choice if• a) subject variable eg smoker and non-
smoker• b) if manipulation of IV makes repeats
impossible or undesirable (deception or carryover effects)
the number of groups = the number of levels of IV
disadvantages:• many subjects needed• individual variation and selection effects
statistical tests • compare variability between groups to variability
within groupssources of variability are • a) the IV• b) confounds –systematic• c) error – unsystematic (individual variability)
Equivalent Groups
• - try to compensate for selection effect
• - groups are equal to each other in important ways
• - the number of groups = the number of levels of IV
Random Assignment
a)Every participant has equal chance of being in each group, the individual variation is spread through the groups evenly
this works well with big N
b) Block Randomizationuse random number table to assign orderif have 5 groups then use numbers 1-5 list the numbers in the order they appear – must finish sequence
before repeating a number
c) Matchingif small N then a few individuals assigned by chance can have a
big impacttest participants on a variable and pair scores – each group gets
similar scores• -you need a priori reason to match on a variable• -it adds logistical complexity• -may give away hypothesis ( bias and reactivity problem)
Example
weights
156 167 183 170 145
143 152 145 181 162
175 159 169 174 161
order
143 145 145 152 156 159 161 162 167 169 170 174 175 181 183
MatchingGroup 1 Group 2 Group 3
145 143 145
152 159 156
161 167 162
169 170 174
181 175 183
161.8 162.8 164
Block randomization
Group 1 Group 2 Group 3
143 156 175
167 159 152
183 169 145
170 181 174
161 162 145
164.8 165.4 158.2
156 167 183 170 145143 152 145 181 162175 159 169 174 161
2 1 1 1 31 3 3 3 23 2 2 2 1
Balancing
• Cannot control characteristics of participants.
• Try to evenly spread the individual differences between the levels of IV
• Random assignment
• Eg if in the Loftus and Burns study groups differed in attention or memory then problem
Within Subjects Design (repeated measures)
• Each participant exposed to each level of the IV
• Fewer people needed (economical)
• Individual variability removed as source of error (more power in testing)
Great for rare events/species/diseases
BUT sequence or order effects can be problematic
Progressive effects
Practice improves performance
Fatigue worsens performance
Carryover effects
Doing task A has bigger impact on task B than the reverse
Uneven impact
4) Counterbalance
a) complete counterbalancing – use all possible sequences of orders at least once
good if few conditions (3 or less) (n! possible)
3 groups gives ? possible combinations
4 groups ? possible….
4) Counterbalance
a) complete counterbalancing – use all possible sequences of orders at least once
good if few conditions (3 or less) (n! possible)
3 groups gives 6 possible combinations
4 groups 24 possible….
b) partial counterbalancing
- take random sample of all possible sequences , reduces systematic bias
c) Latin squares
every condition appears equally often in every sequential position
- if balanced Latin square then each condition precedes and follows every other once
Balanced square
order
participant 1 2 3 4
1 1 2 4 3
2 2 3
3 3
4 4
Rule is first row 1,2,n, 3, n-1, 4,n-2 ,5….Second row add one
Balanced square
order
participant 1 2 3 4
1 1 2 4 3
2 2 31
3 3
4 4
Rule is first row 1,2,n, 3, n-1, 4,n-2 ,5….Second row add one
Balanced square
order
participant 1 2 3 4
1 1 2 4 3
2 2 31 4
3 3
4 4
Rule is first row 1,2,n, 3, n-1, 4,n-2 ,5….Second row add one
Balanced square
order
participant 1 2 3 4
1 1 2 4 3
2 2 31 4
3 34 2 1
4 41 3 2
Rule is first row 1,2,n, 3, n-1, 4,n-2 ,5….Second row add one
Independent Variable
Vary in a systematic way• Control confounds related to IV
EliminateHold constantBalance (groups)Counterbalance (order)RandomizePlan for experimenter bias
Participant Effects
• Random assignment
• Pilot measures for social desirability
• Consider floor/ceiling
• Yes/no bias
Single group
A single group threat includes history, maturation, testing, instrumentation, mortality and regression to mean threats.
Multiple Groups
• These multiple group threats are called a selection bias or selection threat.
• These include selection history, selection maturation, selection testing, selection instrumentation, selection mortality and selection regression threats
The design includes two measures as denoted by two "Os" prior to the program.
This design can rule out selection maturation threat and a selection regression threat. It will help to make sure that the two groups are comparable before the treatment
Double pretest
Switching Replication Design
Good at solving the social threats to internal validity
compensatory rivalry,compensatory equalization, resentful demoralization.
Both groups get same program so no inequity
• control group – assumes extraneous variables operate on both experimental and control equally
• more than one control group can be used to assess different variables
Before training training After
experimental O X O
control O O
Before training training After
O X O
Before training Training After
experimental O X O
Control 1 O O
Control2 O
Single Group
Multiple Groups
Solomon 4 group design
testing threat The design consists of four groups of randomly assigned. Two of them receive the treatment as denoted by " X" and the other two do not.
Before training Training After
experimental O X O
Control 1 O O
Control2 X O
Control3 O