Design Experimental Control Experimental control allows causal inference

Design Experimental Control

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

Specify variables to be controlled • Controlling extraneous variables • • 1) elimination 2) holding conditions constant 3) randomization/balancing 4) counterbalance

1) Elimination 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.

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

3) Randomization/Balance • Especially useful if unsure what extraneous variables may be operating

Between Subjects Design only choice if • a) subject variable eg smoker and nonsmoker • 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 groups sources of variability are • a) the IV • b) confounds –systematic • c) error – unsystematic (individual variability)

Design problems • The equivalent group

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 Randomization use random number table to assign order if have 5 groups then use numbers 1 -5 list the numbers in the order they appear – must finish sequence before repeating a number c) Matching if small N then a few individuals assigned by chance can have a big impact test 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 143 145 152 156 159 161 162 167 169 170 174 175 181 183

Matching Group 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. 2 156 167 183 170 145 143 152 145 181 162 175 159 169 174 161 21113 13332 32221

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

Between Subjects Design problems • The equivalent group • Solution – randomize or balance

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

Within Subjects Design problem • Sequence effects

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

Latin Squares order participant 1 2 3 4 1 1 2 3 4 2 2 3 4 1 3 3 4 4

Latin Squares order participant 1 2 3 4 1 1 2 3 4 2 2 3 4 1 3 3 4 4 4

Latin Squares order participant 1 2 3 4 1 1 2 3 4 2 2 3 4 1 3 3 4 1 2 4 4 1

Latin Squares order participant 1 2 3 4 1 1 2 3 4 2 2 3 4 1 3 3 4 1 2 4 4 1 2 3

Balanced square Rule is first row 1, 2, n, 3, n-1, 4, n-2 , 5…. Second row add one order participant 1 2 3 4 1 1 2 4 3 2 2 3 3 3 4 4

Balanced square Rule is first row 1, 2, n, 3, n-1, 4, n-2 , 5…. Second row add one order participant 1 2 3 4 1 1 2 4 3 2 2 3 1 3 3 4 4

Balanced square Rule is first row 1, 2, n, 3, n-1, 4, n-2 , 5…. Second row add one order participant 1 2 3 4 1 1 2 4 3 2 2 3 1 4 3 3 4 4

Balanced square Rule is first row 1, 2, n, 3, n-1, 4, n-2 , 5…. Second row add one order participant 1 2 3 4 1 1 2 4 3 2 2 3 3 3 4 4 4 1 1 2 3 4 1 2

Within Subjects Design problem • Sequence effects • Solution - counterbalance

Experimental Control Dependant Variable • validity • reliability • multiple measures

Independent Variable Vary in a systematic way • Control confounds related to IV Eliminate Hold constant Balance (groups) Counterbalance (order) Randomize Plan 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

Double pretest 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

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 After O X O Single Group Multiple Groups Before training After experimental O X O control O O Before training Training After experimental O X O Control 1 O Control 2 O O

Solomon 4 group design Before training Training After experimental O X O Control 1 O Control 2 Control 3 O X O O 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.

Determine extraneous variables Will not influence DV Might influence DV Cannot be controlled Can be controlled ignore Randomize Continue experiment Cannot randomize Abandon experiment