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PSYC 306 > Lecture 17: Within-Subjects Designs > Flashcards

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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2
Q

synonyms of within-subjects designs

A

within-group, within-participant design, or repeated-measures design

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3
Q

what two things does a within-subjects design accomplish?

A
  1. Equating groups by using the same subjects
  2. Reducing within-group variance by controlling for individual differences
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4
Q

individual differences in within-subjects designs

A
  • Individual differences are eliminated
  • Controlling for individual differences increases sensitivity and thus the ability to detect a treatment effect
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5
Q

error variance in within-subjects deisgns

A

Error variance is reduced considerably because the participants become their control

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6
Q

F-ratio for within-subjects designs

A

F= condition effects + error/ error

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7
Q

variability in within-subjects designs

A
  • 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
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8
Q

two sources of potential confounding in within-subjects designs

A
  1. confounding from environmental variables
  2. confounding from time-related variables
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9
Q

confounding from environmental variables

A

characteristics of the environment that may change across the range of conditions that each participant must complete

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10
Q

confounding from time-related variables

A

between the conditions, participants may be influenced by factors other than the treatments being investigated (fatigue, practice, etc.)

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11
Q

power of within-subjects designs

A

they reduce the within-group variance and gives a more powerful test

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12
Q

environmental variables

A

Any characteristic in the environment that may differ between treatment conditions

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13
Q

example of an environmental variable

A

noise, lighting, experimenter

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14
Q

impact of environmental variables

A
  • they can become confounds
  • we can no longer say the treatment caused the outcome
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15
Q

how can we control environmental variables?

A
  • Standardizing
  • Holding constant the environment across conditions
  • Matching across treatment conditions
  • Randomization
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16
Q

big 5 time-related factors

A
  1. history
  2. maturation
  3. instrumentation
  4. regression toward the mean
  5. testing effects
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17
Q

history

A

when an outside event changes over time and affects Ps scores in one condition but not the other

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18
Q

maturation

A

changes in Ps’ physical or psychological characteristics between treatments

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19
Q

instrumentation

A

changes in the measuring instrument throughout the study

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20
Q

regression toward the mean

A

extreme scores often move toward the mean on a second test

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21
Q

testing effects

A

when scores are affected by experience in prior condition (fatigue, learning, boredom)

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22
Q

order effects

A

directly related to the experience obtained in a research study

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23
Q

what time-related variables are related to the length of time between conditions?

A

history, maturation, and instrumentation

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24
Q

length of time between conditions and the impact of environmental variables

A
  • 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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25
Q

how to reduce the effects of history, maturation, and instrumentation

A
  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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26
Q

order effects

A
  • 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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27
Q

what designs are prone to order effects?

A

within-subjects designs

28
Q

types of order effects

A

carryover and progressive effects

29
Q

carryover effects

A

exposure to one manipulation that produces persistent consequences influencing the participants’ response to subsequent manipulations

30
Q

progressive error

A

changes to behaviour/performance that are related to general experience in a research study (but not because of the treatment)

31
Q

types of progressive error

A

practice and fatigue effects

32
Q

practice effects

A

progressive improvement through treatment conditions

33
Q

fatigue effects

A

progressive decline in performance through treatment conditions

34
Q

problem with order effects

A

does the change in performance between conditions result from differences in the IV or order effects

35
Q

solution for dealing with order effects

A

counterbalancing

36
Q

time-related design challenges

A
  • 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.
37
Q

counterbalancing

A

Changing the order in which conditions are administered from 1 participant to the next so that they are matched overall

38
Q

goal of counterbalancing

A

to use every possible order of treatments with an equal number of subjects participating in each sequence

39
Q

purpose of counterbalancing

A

to eliminate time-related confounding

40
Q

impact of counterbalancing

A
  • Disrupts the systematic relationship between treatment order and any order effects
  • Prevents order effects from accumulating in a particular treatment condition (spreads evenly)
41
Q

complete counterbalancing

A
  • All possible treatment orders are used equally often
  • There are equal numbers of participants in each treatment condition
42
Q

logical counterbalancing

A
  • 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
43
Q

requirement of counterbalancing

A

There must be equal numbers of participants in each counterbalanced order

44
Q

issues with complete counterbalancing

A
  • 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)
45
Q

latin square counterbalancing

A
  • 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
46
Q

history of Latin square counterbalancing

A
  • 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
47
Q

partial Latin square counterbalancing

A
  • 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
48
Q

alternative method of partial Latin square counterbalancing

A
  • 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
49
Q

limits of counterbalancing

A
  • 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
50
Q

example of asymmetrical carryover

A
  • 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).
51
Q

example of range effects in a within-subjects design

A
  • 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
52
Q

reversibility

A

IVs that permanently alter the development or state of participants in irreversible carry-over effects

53
Q

examples of reversibility

A
  • Learning conditions; particular treatments to improve a skill or behaviour (cannot be “unlearned”)
  • Physiological changes (brain lesions)
  • Some medications or chemicals
54
Q

when are within-subjects designs not appropriate?

A

Within-subjects designs are not appropriate if the experimental conditions produce a lasting effect on the participants that cannot be reversed

55
Q

order of administration and reversibility

A
  • Condition A = measure behaviour at baseline
  • Condition B = measure during praising intervention
  • Condition A’ = measure after intervention stopped
56
Q

advantages of within-subjects designs

A
  • Fewer participants are required (ex. 3 conditions with 30 participants):
  • Eliminates problems of individual differences
  • Can increase the chances of detecting a treatment effect
57
Q

disadvantages of within-subjects designs

A
  • Not suitable when carryover effects are expected
  • Participant attrition may be a problem
  • Ordering of conditions can be time-consuming and require many participants
58
Q

does counterbalancing eliminate order effects?

A
  • Counterbalancing does not eliminate order effects
  • Adds the order effects to some (but not all) of the subjects within each treatment
59
Q

comparing designs at the analysis stage

A

Different analyses (within- or between subjects) can yield similar results but the variability across stimuli can differ from the variability across participants

60
Q

major weakness of between-subjects designs

A

individual differences

61
Q

three factors that differentiate between- and within-subjects designs

A
  • Individual differences
  • Time-related factors and order effects
  • Number of required participants
62
Q

what study design should you choose?

A

Choose the study design by the factors of most interest to the study to avoid problems of validity

63
Q

ABA’ design

A

Allows one to measure the presence or absence of carry-over effects

64
Q

example of an ABA’ design

A

Using praise with children in the classroom to increase participation

65
Q

major weakness of within-subjects designs

A

order effects

66
Q

major strength of between-subjects designs

A

eliminating order effects

67
Q

major strength of within-subjects designs

A

eliminates variability from individual differences; needs fewer participants

PSYC 306 (17 decks)

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