One-way between-subjects ANOVA Flashcards

1
Q

assumptions

A

DV is interval or ratio level data
Data is normally distributed (histogram)
Homogeneity of variance (same variance)
Independent-random samples taken from each population

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2
Q

SPSS outputs

A

between subjects factors
descriptives table
levenes test for equality of error variances
tests of between-subjects effects
profile plots

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3
Q

between subjects factors

A

identify levels of factors and number of ppts

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4
Q

descriptives table

A

mean and SD to report results
shows where differences occur if significant result

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5
Q

levenes test for equality of error variances

A

use based on mean
want a non-significant value (p>0.05)
suggests variances aren’t significantly different

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6
Q

tests of between-subjects effects

A

shows DF, mean square, F at significant level

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7
Q

reporting the ANOVA (main effect) results
(what do we use?)

A

use tests of between subjects effects table
F( , )= . ,p<.001

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8
Q

effect size calculation

A

sum of squares for BG condition (hypothesis)/total sum of squares (hypothesis+error)

partial eta square
eta square

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9
Q

Cohen 1988 guidelines

A

effect sizes

small = 0.01
medium = 0.059
large = 0.138

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10
Q

steps for formally reporting ANOVA

A
  1. State ANOVA type, effect of DV
  2. Present means and SD in table or text
  3. Mention assumptions
  4. Report ANOVA results (F ratio)
  5. Report effect sizes and meaning
  6. Report comparisons
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11
Q

Next steps after the anova

A

planned/unplanned comparisons

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12
Q

when to use planned comparisons for one way between subjects anova

A

when no significant main effect

used when hypothesised difference made in advance is explored

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13
Q

planned comparisons for one way between subjects anova

A

Contrasts tests table (assumes equal variances line)
Contrasts effects sizes table (point estimate is effect size)
- Find both the above for each comparison

Report analysis resultsInterpret results

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14
Q

reporting results of planned comparisons for one way between subjects anova

A
  • Planned comparisons were performed to test hypotheses.
  • See table 1 to see means and SD for each groups DV (draw table)
  • Comment on means for DV and whether they were significantly higher or lower in each condition.
  • t( )= . ,p<.001
  • state effect and size

interpret in words

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15
Q

when to use unplanned comparisons for one way between subjects anova

A

used if no hypothesised difference is explained

main effect needs to be significant to be run

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16
Q

another name for unplanned comparisons

A

post hoc comparisons

17
Q

unplanned comparisons for one way between subjects anova

A
  • Bonferroni post hoc test to reduce type 1 error
  • multiple comparisons table shows significance between groups (p<.001 is significant, p>0.05 is non-significant)
  • effect sizes (mean condition 1 - mean condition 2)/mean SD (see cohens effect sizes)
18
Q

reporting results of unplanned comparisons for one way between subjects anova

A

bonferroni post hoc tests revealed the DV of factor was significantly higher/lower than factor with a s/m/l effect size (p<.001), d= . (state for all comparisons)
interpret results in words