Week 8 Flashcards

1
Q

ANOVA H0

A

means for all groups are identical

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2
Q

Why use ANOVA instead of t-tests

A

cannot compare each pair with a t test
cannot look at more than one IV at a time
inflates T1 error rate
When assumptions are met, ANOVA is more powerful than t-tests for more than two groups
ANOVA allows us to evaluate all the means in a single hypothesis test, and keep our a at .05

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3
Q

family wise alpha level

A

probability of making at least one T1 error among a series of comparisons

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4
Q

decisionwise alpha level

A

alpha level for each comparison

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5
Q

SST

A

total variability between scores

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6
Q

SSM

A

variability between group means (our model)
how much variability is accounted for by IV

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7
Q

SSR

A

residual variability
unexplained variance due to chance (within groups variability or error of SS)

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8
Q

SST=

A

SSM+SSR

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9
Q

In an ANOVA, we need to determine

A

whether thee model explains more variability than the residuals, using the F-ratio
SSM>SSR

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10
Q

F=

A

MS model/MS residual
MS between tx/ MS within tx

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11
Q

MSM=

A

SSM/df model
SSM/k-1

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12
Q

MSR=

A

SSR/df residual
SSR/n-k

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13
Q

No treatment effect

A

MSR>MSM
F<1
non significant

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14
Q

Treatment has an effect

A

MSM>MSR
F>1
significant

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15
Q

ANOVA assumptions

A

independence of observations
interval/ratio data
normality
homogeneity of variance

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16
Q

checking homogeneity of variance

A

boxplots between treatment groups

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17
Q

if normality is violated

A

n equal across groups + large, then ok
if not:
transform data
use a non-parametric test (Kruskal-Wallis test)

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18
Q

homogeneity of variance is violated when

A

Levene’s test p<.05
so Levene’s should be non sig for assumption to be met

19
Q

If homogeneity of variance is violated

A

use Brown-Forsythe or Welch F, df, and p-values instead of F

20
Q

If independent observations violated

A

use repeated measures ANOVA

21
Q

for RM ANOVA SSerror=

A

SSwithin groups-SS subjects

22
Q

Within subjects/RM ANOVA assumptions

A

continuous DV, categorical IV
normality
homogeneity of variance between groups

23
Q

post hoc tests

A

conducting analysis after original a-priori hypothesis was rested and we know results

24
Q

orthogonal contrasts/comparisons

A

planned a priori (have to have a strong justified reason for looking at specific groups only and hypothesis driven)

25
post hoc tests
not planned or hypothesised compare all pairs of means
26
post hoc Bonferroni
must use stricter alpha DW (decision wise alpha= T1 error) to accept effects as significant bonferroni adw= afw/number of tests/comparisons
27
eta squared
biased effect size estimate overestimates proportion of variability accounted for
28
eta squared= n2
SSM/SST
29
Small eta squared
0.01
30
Medium eta squared
0.09
31
Large eta squared
0.25
32
Eta squared for repeated measures ANOVA
report partial eta squared SS of subject variability removed from denominator
33
Omega squared= w2
better than eta, especially for smaller n as it is unbiased uses more info from data including df, so more accurate
34
small w2
0.01
35
medium w2
0.06
36
large w2
0.14
37
cohens d
degree of separation between two distributions how far apart in standardised units the means of the two distributions are
38
small cohens d
0.20
39
medium cohens d
0.50
40
large cohens d
0.80
41
cohens d =
mean difference/ SD
42
effect sizes tell us
the proportion of variance accounted for
43
effect size examples
r2 eta squared n2 omega squared w2 cohens d