Lecture 17: Within-Subjects Designs Flashcards
1
Q
within-subjects designs
A
Within-subjects experimental design uses a single group of participants in all conditions
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synonyms of within-subjects designs
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within-group, within-participant design, or repeated-measures design
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what two things does a within-subjects design accomplish?
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- Equating groups by using the same subjects
- Reducing within-group variance by controlling for individual differences
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individual differences in within-subjects designs
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- Individual differences are eliminated
- Controlling for individual differences increases sensitivity and thus the ability to detect a treatment effect
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error variance in within-subjects deisgns
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Error variance is reduced considerably because the participants become their control
6
Q
F-ratio for within-subjects designs
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F= condition effects + error/ error
7
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variability in within-subjects designs
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- Variability associated with individual differences is removed (it contributes equally to the numerator and denominator)
- There is no assumption of independence between condition scores as there is in a between-subjects design because each individual contributes to each condition
- Between-condition variance is based on within-subject comparisons
8
Q
two sources of potential confounding in within-subjects designs
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- confounding from environmental variables
- confounding from time-related variables
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confounding from environmental variables
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characteristics of the environment that may change across the range of conditions that each participant must complete
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Q
confounding from time-related variables
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between the conditions, participants may be influenced by factors other than the treatments being investigated (fatigue, practice, etc.)
11
Q
power of within-subjects designs
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they reduce the within-group variance and gives a more powerful test
12
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environmental variables
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Any characteristic in the environment that may differ between treatment conditions
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example of an environmental variable
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noise, lighting, experimenter
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impact of environmental variables
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- they can become confounds
- we can no longer say the treatment caused the outcome
15
Q
how can we control environmental variables?
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- Standardizing
- Holding constant the environment across conditions
- Matching across treatment conditions
- Randomization
16
Q
big 5 time-related factors
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- history
- maturation
- instrumentation
- regression toward the mean
- testing effects
17
Q
history
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when an outside event changes over time and affects Ps scores in one condition but not the other
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maturation
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changes in Ps’ physical or psychological characteristics between treatments
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instrumentation
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changes in the measuring instrument throughout the study
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regression toward the mean
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extreme scores often move toward the mean on a second test
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testing effects
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when scores are affected by experience in prior condition (fatigue, learning, boredom)
22
Q
order effects
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directly related to the experience obtained in a research study
23
Q
what time-related variables are related to the length of time between conditions?
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history, maturation, and instrumentation
24
Q
length of time between conditions and the impact of environmental variables
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- If short timespan (1 hr) between conditions, less likely that these changes will occur
- If longer timespan (weeks or months), chances increase that time-related changes will influence results
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how to reduce the effects of history, maturation, and instrumentation
1. Decrease the time between conditions to reduce the likelihood of this happening
2. Counterbalance: matching treatments with respect to time
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order effects
- Effects that one treatment may have on another treatment
- Influenced by events or experiences that occurred earlier in the sequence of conditions
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what designs are prone to order effects?
within-subjects designs
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types of order effects
carryover and progressive effects
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carryover effects
exposure to one manipulation that produces persistent consequences influencing the participants’ response to subsequent manipulations
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progressive error
changes to behaviour/performance that are related to general experience in a research study (but not because of the treatment)
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types of progressive error
practice and fatigue effects
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practice effects
progressive improvement through treatment conditions
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fatigue effects
progressive decline in performance through treatment conditions
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problem with order effects
does the change in performance between conditions result from differences in the IV or order effects
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solution for dealing with order effects
counterbalancing
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time-related design challenges
- The possibility of a time-related threat (history, maturation, instrumentation) is directly related to the length of time required to complete the within-subject study.
- Increasing the time between treatments increases the risk of time-related threats to internal validity
- Reducing the time between treatments increases the likelihood that order effects will influence results.
- Between-subjects design may be a better choice for research conditions that are prone to order effects.
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counterbalancing
Changing the order in which conditions are administered from 1 participant to the next so that they are matched overall
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goal of counterbalancing
to use every possible order of treatments with an equal number of subjects participating in each sequence
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purpose of counterbalancing
to eliminate time-related confounding
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impact of counterbalancing
- Disrupts the systematic relationship between treatment order and any order effects
- Prevents order effects from accumulating in a particular treatment condition (spreads evenly)
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complete counterbalancing
- All possible treatment orders are used equally often
- There are equal numbers of participants in each treatment condition
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logical counterbalancing
- A particular series of treatment conditions may create their own unique order effect
- Therefore, include every possible ordering of treatment conditions
- Does not eliminate order effects, just controls them
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requirement of counterbalancing
There must be equal numbers of participants in each counterbalanced order
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issues with complete counterbalancing
- As the number of conditions increases, complete counterbalancing becomes more complex and # of required participants increases!
- Complete counterbalancing requires too many conditions (and subjects per condition)
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latin square counterbalancing
- Each condition occurs equally often in each order in the experiment (ex, for 3 conditions: ABC, BCA, CAB)
- Each condition occurs exactly once in each order
- Equal numbers of participants are assigned to each order
- Instead of all 6 possible orders (3 x 2 x 1), the Latin Square requires only 3 orders
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history of Latin square counterbalancing
- Developed from the agricultural rotation of crops across plots of land to avoid draining the soil of crop-specific nutrients
- Latin squares attributed to Euler (1750s) and Fisher (1935)
- Named after Euler’s use of Latin characters as a symbol
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partial Latin square counterbalancing
- Each treatment condition occurs equally often in different sequence positions across the orders
- In partial counterbalancing, a Latin square can be constructed to decide which sequences to select
- In this counterbalancing, each condition is preceded and succeeded equally often by the same conditions
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alternative method of partial Latin square counterbalancing
- Changes the condition order so that it is preceded and succeeded by different conditions
- In this counterbalancing, the Latin square is adjusted to balance the order of conditions that precede and succeed each condition
- In this counterbalancing, each condition is NOT preceded and succeeded by the same conditions
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limits of counterbalancing
- Carryover effects can be asymmetrical
- Counterbalancing the Condition orders (A, B versus B, A) does not yield a similar carryover effect
- Asymmetries mean that the counterbalancing order can interact with the IV to influence the DV
- Range effects in within-subjects designs: participants may be influenced by the range of tasks they are given
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example of asymmetrical carryover
- Pilots were tested on 2 ground steering methods for airplanes (manual): "Rudder pedal" and "Steering handle"
- Dependent variable: accuracy of steering
- 2 counterbalancing orders:
1) Rested, then Fatigued
2) Fatigued, then Rested
- Results: Pilots performed worse overall in the counterbalanced order fatigued, then rested than in the order rested, the fatigued
- Pilots performed worse on the Rudder pedal in order = Fatigued first. They performed the same on the Rudder pedal and Steering handle in order = Rested first
- Possible explanation: Less learning (causing carryover) occurs during Fatigue and so… More carryover (learning) occurs in Order 1 (Rested, then Fatigued) than in Order 2 (Fatigued, then Rested)
- Counterbalancing the Condition orders (Fatigued / Rested versus Rested / Fatigued) did not yield similar carryover effects.
- Asymmetries mean that the counterbalancing order can interact
with the IV (type of brake) to influence the DV (performance).
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example of range effects in a within-subjects design
- A manual dexterity test performed on a table that has a changing height
- Absolute table height = between-subjects variable (High or Low)
- Relative Table height = within-subjects variable
- High: Relative Table height was centred at 0 inches relative to their elbow
- Low: Relative Table height was centred at 6 inches height below their elbow
- Range effects in within-subject designs: Participants may be influenced by the range of tasks they are given
- Results: Peak performance for each group is influenced by the range of values they experience
- High Group performs best around -1 inch (near elbow height)
- Low Group performs best around 6 inches (below elbow height)
- Both groups were influenced by the within-subject IV (Relative table heights):
- But also influenced by between-subject IV (Absolute table height):
- High Group is best at 0 inches above the elbow
- Low Group is best at 6 inches below the elbow
- Implication: Range effects can be reduced by keeping as many variables constant as possible between subjects when using within-subject designs
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reversibility
IVs that permanently alter the development or state of participants in irreversible carry-over effects
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examples of reversibility
- Learning conditions; particular treatments to improve a skill or behaviour (cannot be "unlearned")
- Physiological changes (brain lesions)
- Some medications or chemicals
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when are within-subjects designs not appropriate?
Within-subjects designs are not appropriate if the experimental conditions produce a lasting effect on the participants that cannot be reversed
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order of administration and reversibility
- Condition A = measure behaviour at baseline
- Condition B = measure during praising intervention
- Condition A' = measure after intervention stopped
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advantages of within-subjects designs
- Fewer participants are required (ex. 3 conditions with 30 participants):
- Eliminates problems of individual differences
- Can increase the chances of detecting a treatment effect
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disadvantages of within-subjects designs
- Not suitable when carryover effects are expected
- Participant attrition may be a problem
- Ordering of conditions can be time-consuming and require many participants
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does counterbalancing eliminate order effects?
- Counterbalancing does not eliminate order effects
- Adds the order effects to some (but not all) of the subjects within each treatment
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comparing designs at the analysis stage
Different analyses (within- or between subjects) can yield similar results but the variability across stimuli can differ from the variability across participants
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major weakness of between-subjects designs
individual differences
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three factors that differentiate between- and within-subjects designs
- Individual differences
- Time-related factors and order effects
- Number of required participants
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what study design should you choose?
Choose the study design by the factors of most interest to the study to avoid problems of validity
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ABA' design
Allows one to measure the presence or absence of carry-over effects
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example of an ABA' design
Using praise with children in the classroom to increase participation
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major weakness of within-subjects designs
order effects
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major strength of between-subjects designs
eliminating order effects
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major strength of within-subjects designs
eliminates variability from individual differences; needs fewer participants
