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Statistics & Data Analysis > Repeated measure one-way ANOVA > Flashcards

Repeated measure one-way ANOVA Flashcards

1
Q

repeated measures one-way ANOVA

A

within participants design

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2
Q

one-way ANOVA

A

when we have 1 IV with more than 2 levels

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3
Q

t^2

A

F value

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4
Q

between IV level variance

A
  • manipulation of IV
  • experimental error (random)
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5
Q

within IV level

A
  • experimental error (random)
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6
Q

what does variance within IV levels for repeated measures NOT include

A

variance due to individual differences
- includes ONLY error variance

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7
Q

H0

A
  • no difference between population means under different levels of IV
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8
Q

F formula

A
  • repeated measures designs will result in higher F values compared to independent design as smaller variance within levels due to not including individual differences … dividing variance between by a smaller number… further from 0
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9
Q

assumptions of repeated measures one-way ANOVA

A
  • normality
  • sphericity (homogeneity of covariance) (RM only)
  • equivalent sample size
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10
Q

Sphericity

A
  • homogeneity of covariance
  • the variance in difference scores under each IV level pair should be reasonably equivalent
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11
Q

Mauchly’s

A
  • tests Sphericity
  • H0: no difference between the co variances under each IV level pair (homogeneity)
  • p < .05 reject null (shows heterogeneity)
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12
Q

what is the correction for Mauchly’s test

A

Greenhouse-Geisser

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13
Q

what is the non-parametric equivalent if assumptions are violated

A

Friedman Test

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14
Q

RM ANOVA: SPSS OUTPUT Mauchly’s

A
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15
Q

RM ANOVA: SPSS OUTPUT TABLE : IF M IS NOT SIGNIFICANT

A
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16
Q

RM ANOVA: SPSS OUTPUT TABLE : IF M IS SIGNIFICANT

17
Q

SPSS OUTPUT - GENERAL

18
Q

Model sum of squares (SSm)

A

variance between IV levels (incl. variance due to manipulation of he IV and error)

19
Q

Residual sum of squares (SSr)

A

variance within IV levels (incl. only error variance, but not variance due to individual differences)

20
Q

MSm formula

21
Q

F : SPSS output

22
Q

degrees of freedom for repeated measures one-way ANOVA

A

(dfM = k -1)
dfR = dfM x (n-1)
k = number of IV levels
n = number of participants

  • note if you use corrected d.f.
23
Q

post-hoc test repeated measures one-way ANOVA

A
  • to asses which IV level means differ
  • only when F value significant
  • Bonferroni
24
Q

Bonferroni SPSS output

25
Q

effect size table

26
Q

partial eta^2 formula for repeated measures

A

be able to do by hand

27
Q

Cohen’s d formula

A

be able to do by hand
- always report even if not significant, if not significant don’t talk about effect size

28
Q

advantages of repeated measures vs independent

A
  • requires fewer participants
  • error variance within IV levels reduced as no individual differences
  • more sensitive/powerful than independent so easier to find significant difference (void type II error)
29
Q

disadvantages of repeated measures vs independent

A
  • order effects e.g. practice effect, fatigue effects, sensitization, carry-over effects
  • counterbalance to spread out impact of order effects, not get rid
30
Q

sensitization

A

p’s start to work out aim of study and behave in a particular way to annoy or please experimenter
may be unconscious

31
Q

alternatives to counterbalancing

A
  • fatigue- shorter experiments
  • sensitization - intervals between IV level exposure
  • practice - so order effects already exist when start study do pre-study practice
  • carry-over effects - include a control group
32
Q

note

A

can be used for infinite number of levels as long as only one IV

33
Q

write up one way ANOVA: design

A
  • IV: its levels, sunject design, steps taken to eliminate confounds (e.g. counterbalancing, random allocation)
  • DV
34
Q

one-way ANOVA results: step one: descriptive statistics in the table

A
  • measure of central tendency: mean
  • measure of spread: standard deviation
  • interval estimate: CIs for upper and lower limit
35
Q

one-way ANOVA: step 2: results write up

A
  • refer to descriptive statistics table
  • state test used
  • test statistic: : F(dfM, dfR) = _.__, p = .___ (*)
  • if assumption for homogeneity has been violated (Levene’s for independent, Mauchly’s for repeated measures) state what correction you use after statistic (Welch or Greenhouse-Geisser)
    • state if results were significantin next sentance with directionality if so
  • effect size as partial eta squared as percentage if significant *if not report after test statistic
36
Q

one-way ANOVA: step 3: post hoc as part of results

A
  • if significant do post-hoc write up
  • Tukey HSD for between-subjects
  • Bonferroni for within-subject

Statistics & Data Analysis (13 decks)

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