Ch. 7 Repeated Measures Design Flashcards
What is the difference between the independent variable for repeated measures design and independent groups design?
independent groups: each participant experiences only one condition of the IV
repeated measures: each participant experiences every condition of the IV
What is balanced across conditions to rule out alterative explanations for findings?
independent groups: individual differences variable
repeated measures: practice effects
Why use a repeated measures design?
- no need to balance individual differences across conditions of experiment
- fewer participants needed
- convenient and efficient
- more sensitive (can detect the effect of an independent variable, even if the effect is small)
What are practice effects?
the main disadvantage of repeated measures designs; people change as they are tested repeatedly.
- performance may improve over time
- people may become bored or tired over time
practice effects become a potential confounding variable if not controlled
What must practice effects be?
Balanced, or averaged, across all conditions
- counterbalancing the order of conditions distributes practice effect equally across conditions
What is a complete design?
each participant experiences each condition several times, using different orders each time; used when each condition is brief. practice effects are balanced within each participant in the complete repeated measures design
What is block randomization?
- a block consists of all conditions
- generate a random order of the block (ACBD)
- participant completes condition A, then C, then B, then D
- generate a new order for each time the participant completes the conditions of the experiment (DACB, CDBA, ACBD)
Block randomization and practice effects
- balances practice effects only when conditions are presented many times
- practice effects are averaged across the many presentations of the conditions
- practice effects are not balanced if conditions are presented only a few times to each participant
What is ABBA counterbalancing?
used when conditions are presented only a small number of times to each participant. present one random sequence of conditions (DABC), then present the opposite of the sequence (CBAD). each condition has the same amount of practice effects
Linear practice effects
suppose participants gain “one unit” of practice with each administration (“trial”) of a conditions. there are zero practice effects with the first administration
Nonlinear practice effects
suppose a participant figures out a method for completing the task on the third trial, then uses the new method for subsequent trials. practice effects are not balanced across the conditions
How do nonlinear practice effects impact ABBA counterbalancing?
they create a confounding; differences in scores on the DV may not be caused by the IV, or differences on DV may be due to different amounts of practice effects associated with each condition
When should ABBA counterbalancing not be used?
when practice effects are likely to vary or change over time (nonlinear practice effects). use block randomization instead
What is an incomplete experiment design?
each participant experiences each condition of the experiment exactly once. practice effects are balanced across participants in the incomplete design
What is the general rule for balancing practice effects in an incomplete design?
each condition must appear in each ordinal position equally often. if this rule is follow, practice effects will be balanced across conditions and will not confound the experiment. there are two techniques: all possible orders and selected orders
All possible orders
use when there are four or fewer conditions.
- two conditions (A, B), two possible orders (AB, BA)
- half of the participants would be randomly assigned to do condition A first, followed by B
- other half would complete condition B first, followed by A
Selected orders
select particular orders of conditions to balance practice effects. two methods: Latin square and random starting order with rotation.
Latin square
each position appears in each ordinal condition exactly once.
- randomly order the conditions of the experiment
- number the conditions
- use this general rule for generating 1st order: 1, 2, N, 3, N-1, 4, N-2, 5, N-3, 6, etc. with N = last number of conditions
Random starting order with rotation
each participant is randomly assigned to one of the orders of conditions
Data analysis
- main benefit to repeated measures design is that each participant essentially servs as their own matched control
- can remove a large amount of variance due to individual differences in the analyses
- data can be ‘rescaled’, based on measurements for each individual
- left only with individual variability in the differential effects on each condition
Problem of differential transfer
repeated measures design should not be used when differential transfer is possible
- occurs when the effects of one condition persist and affect participants’ experience of subsequent conditons
- use independent groups design instead
- assess whether differential transfer is a problem by comparing results for repeated measures design and random groups design
