In a within-subjects experiment, subjects are assigned to more than one treatment condition
Within-subjects design
Researchers measure subjects on the dependent variable after each treatment
Within-subjects design
Subjects participate in more than one treatment condition and serve as their own control
Within-subjects design
We compare their performance on the dependent variable across conditions to determine whether there is a treatment effect
Two major sources of confounding variables in within-subjects designs
Confounding from environmental variables
Confounding from time-related factors
Confounding from time-related factors
Participants are measured over time
Many events can affect a participant over time, so observed changes may be due to time-related effects rather than treatment differences
Time-related effects
Maturation
Instrumentation
History
Statistical regression
Order effects
Maturation
A third grader may read better after three months because she is maturing rather than because of a reading intervention
Instrumentation
Measuring instruments, including researchers themselves, may affect scores
History
An unrelated event may influence the outcomes
Statistical regression
Extreme scores tend to become less extreme over time due to statistical regression
Order effects
Positive and negative performance changes due to a condition's position in a series of treatments
Progressive error
Encompasses both positive and negative order effects
Order effects
Participation in one condition can directly affect participation in another treatment condition
Holding order constant would confound the experiment
Practice effects
Outcome measure improves on each administration due to the effects of practice taking the test
Fatigue
A person might perform worse on each administration due to fatigue
Carryover effects
The effects of participation on the first treatment carry over into the second treatment, making the outcome better or worse than if it had been the only treatment
Contrast effects
A type of carryover effect where the perception of one treatment is affected by its contrast with another
Dealing with time-related and order effects
Time-related effects like history and maturation are less of a problem in shorter experiments
If order effects are likely to be a major problem, a between-subjects design may be better
Counterbalancing
A method of controlling order effects by distributing progressive error across different treatment conditions
Counterbalancing strategies
Subject-by-subject counterbalancing
Across subjects counterbalancing
Counterbalancing and time-related effects
Half the participants undergo treatment 1 then 2, the other half undergo 2 then 1
Still a within-subjects design, just with different orders
Counterbalancing and order effects
Counterbalancing does not eliminate order effects, but it eliminates them as a confound by spreading them equally across conditions
Counterbalancing and the number of treatments
As the number of treatment conditions increases, the number of possible counterbalancing sequences increases dramatically (n!)
Subject-by-subject counterbalancing
Controls progressive error for each subject by presenting all treatment conditions more than once
Subject-by-subject counterbalancing techniques
Reverse counterbalancing
Block randomization
Reverse counterbalancing
Only controls for linear progressive error, can confound the experiment
Block randomization
Assigns each subject to several complete blocks of treatment, with each block presenting the treatments in a different random order
Advantages of within-subjects designs
Use fewer subjects
Save time on training
Greater statistical power
More completed record of subject's performance
Disadvantages of within-subjects designs
Subjects participate longer
Resetting equipment may take time
Treatment conditions may interfere with each other