# Design Experimental Control. Experimental control allows causal inference (IV caused observed change in DV) Experiment has internal validity when it fulfills.

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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

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

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 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 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 143 145 145 152 156 159 161 162 167 169 170 174 175 181 183

Matching Group 1Group 2Group 3 145143145 152159156 161167162 169170174 181175183 161.8162.8164

Block randomization Group 1Group 2Group 3 143156175 167159152 183169145 170181174 161162145 164.8165.4158.2 156 167 183 170 145 143 152 145 181 162 175 159 169 174 161 2 1 1 1 3 1 3 3 3 2 3 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

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 participant1234 11234 22341 33 44

Latin Squares order participant1234 11234 22341 33 4 44

Latin Squares order participant1234 11234 22341 33 412 44 1

Latin Squares order participant1234 11234 22341 33 412 44 123

Balanced square order participant1234 11243 223 33 44 Rule is first row 1,2,n, 3, n-1, 4,n-2,5…. Second row add one

Balanced square order participant1234 11243 223 1 33 44 Rule is first row 1,2,n, 3, n-1, 4,n-2,5…. Second row add one

Balanced square order participant1234 11243 223 14 33 44 Rule is first row 1,2,n, 3, n-1, 4,n-2,5…. Second row add one

Balanced square order participant1234 11243 223 14 33 421 44 132 Rule is first row 1,2,n, 3, n-1, 4,n-2,5…. Second row add one

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

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 trainingtrainingAfter experimental OXO control OO Before trainingtrainingAfter OXO Before trainingTrainingAfter experimental OXO Control 1 OO 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 trainingTrainingAfter experimental OXO Control 1 OO Control2 XO Control3 O

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

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