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WK1: ONE-WAY BETWEEN SUBJECTS ANOVA (Other Inter-related Terms (:warning:…
WK1: ONE-WAY BETWEEN SUBJECTS ANOVA
3 Reasons Why We Need Statistics
To test
magnitude
of an effect
To test
assumptions
associated with statistical analysis
No access to
population of interest
Other Inter-related Terms
P-Value
(significance)
Magnitude of Effect
(effect size)
Sample
(to generalise population, but small size = less confidence in result accuracy)
Null Hypo / Alternative Hypo
Population
Coefficient of Determination
:warning: ANOVA Specific
F-Ratio
(Variability
between
mean: Variability
within
individual observation)
Degrees of Freedom (df)
Mean Square Error (MS)
F-Value
(after computing F-ratio if P-value is significant first)
Familywise Error Rate
(chance of type 1 when doing more analysis)
Guidelines to Note
Cohen's Correlation
Large - .50
Medium - .30
Small - .10
Cohen's D (effect size)
Medium - .50
Large - .80
Small - .20
Normal Distribution Assumption Check
Skew
- Up to 2.0
Kurtosis
- Up to 9.0
Levene's F-test
(ideally equal variance) - p > .50
ANOVA F-Ratio
Look at overall significance P < .05
Then look at F-value if P-value is significant
F-ratio > df > MS > F-value
If Variability (between) explained > Variability (within) not explained =
Increased
confidence due to
IV's association with mean difference on DV
Assumptions to Test
DV is measure on interval / ratio scale (continuous)
DV: Normally Distributed
Independence of Observation
Homogeneity of Variance - Levene's F-test
Random Samping
General Purpose of ANOVAs
Tests
all possible comparisons
between the groups
p < .05 =
group means not equal
(difference) = significant
Test
overall difference
between groups (f-value)
Protect from committing
Type 1 Error
Specifically ONE-WAY BETWEEN ANOVA
Eta Squared
(% of variance in DV accounted by the IV)
Omega Squared
(non-SPSS but more accurate than eta as it doesn't
overestimate magnitude of effect size
)
Sample size
less than 20
: Omega Squared
Sample size
more than 30
: No difference, use Eta Squared
????
SPSS Steps and Output
One-way Between ANOVA
One-way ANOVA
DV in Dependent List, IV in Factor box
Compare Means
Options > Descriptive for Homogeneity of Variance + Means Plot
Use
Contrast
for Planned Comparisons, if confident in hypothesis
Or Use
Post Hoc
if less certain
Assumptions
Normally Distributed DV
Frequencies + Histograms to check
Look at skew and kurtosis
Descriptive Stats
Split File by each group
Homogeneity of Variance
In ANOVA, Options:
Under Descriptive
Tick Homogeneity of variance test
Tick Means Plot
:!:Look at Levene's Statistics > significance column
Correlations (P-value)
Correlate
Bivariate
Analyze
Variance Box (Pearson's Correlation)
:star: Use
Welch's or Brown's Test
if Homogeneity of Variance
cannot be assumed / violated