Computations Contrasts Confidence Intervals Partitioning the SS total The total SS is divided into two sources Cells or Model SS Error SS The model is Partitioning the SS cells ID: 512513
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Slide1
Two-Way Balanced Independent Samples ANOVA
Computations
Confidence
IntervalsSlide2
Partitioning the
SS
total
The total SS is divided into two sourcesCells or Model SSError SSThe model is Slide3
Partitioning the SScells
The cells
SS is divided into three sources
SSA, representing the main effect of factor ASSB, representing the main effect of factor BSSAxB, representing the A x B interactionThese sources will be orthogonal if the design is balanced (equal sample sizes)They sum to SScellsOtherwise the analysis gets rather complicated.Slide4
Gender x Smoking HistoryCell
n
= 10, Y2 = 145,140Slide5
Computing Treatment SS
Square and then sum group totals, divide by the number of scores that went into each total, then subtract the CM
. Slide6
SScells and
SS
error
SSerror is then SStotal minus SS
Cells = 26,115 ‑ 15,405 = 10,710. Slide7
SSgender and
SS
smokeSlide8
SSSmoke x Gender
SS
interaction
= SSCells SSGender SSSmoke
= 15,405 ‑ 9,025 ‑ 5,140 = 1,240. Slide9
Degrees of Freedomdftotal =
N
- 1dfA = a - 1dfB = b - 1
dfAxB = (a - 1)(b -1)dferror = N - abSlide10
Source TableSlide11
Simple Main Effects of Gender
SS
Gender, never smokedSS Gender, stopped < 1mSS
Gender, stopped 1m ‑ 2y Slide12
Simple Main Effects of Gender
SS
Gender, stopped 2y - 7ySS Gender, stopped 7y ‑ 12 y Slide13
Simple Main Effects of GenderMS = SS / df; F = MS
effect
/ MSEMSE from omnibus model = 119 on 90
df Slide14
Interaction PlotSlide15
Simple Main Effects of Smoking
SS
Smoking history for men
SS Smoking history for womenSmoking history had a significant simple main effect for women, F
(4, 90) = 11.97, p < .001, but not for men, F(4, 90) = 1.43,
p
=.23.
Slide16
Multiple Comparisons Involving A Simple Main EffectSmoking had a significant simple main effect for women.
There are 5 smoking groups.
We could make 10 pairwise comparisons.Instead, we shall make only 4 comparisons.We compare each group of ex-smokers with those who never smoked.Slide17
Female Ex-Smokersvs. Never Smokers
There is a special procedure to compare each treatment mean with a control group mean (Dunnett).
I’ll use a Bonferroni procedure instead.The denominator for each
t will be:Slide18
See Obtaining
p
values with SPSSSlide19
Multiple Comparisons Involving a Main Effect
Usually done only if the main effect is significant and not involved in any significant interaction.
For pedagogical purposes, I shall make pairwise comparisons among the marginal means for smoking.Here I use Bonferroni
, usually I would use REGWQ.Slide20
Bonferroni Tests, Main Effect of Smoking
c
= 10, so adj. criterion = .05 / 10 = .005.n’
s are 20: 20 scores went into each mean. Slide21
Smoking History
Mean
< 1 m
25.0
A
1m - 2y
28.5
AB
2y - 7y
35.0
BC
7y - 12y
39.0
CD
Never
45.0
D
Means sharing a superscript do not differ from one another at the .05 level.
Results of
Bonferroni
TestSlide22
(Semipartial) Eta-Squared
2
= SSEffect SSTotalUsing our smoking history data,
For the interaction, For gender, For smoking history,
Slide23
CI.90 Eta-Squared
Compute the
F that would be obtained were all other effects added to the error term.
For gender,Slide24
CI.90 Eta-Squared
Use that
F with my Conf-Interval-R2-Regr.sas
F= 51.752 ;df_num = 1 ;df_den = 98 ;
eta_squared
eta2_lower
eta2_upper
0.34558
0.22117
0.44897
Get the CI with SPSS or RSlide25
CI.90 Eta-Squared
90
% CI [.22, .45] for gender[.005, .15] for smoking[.000, .17] for the interaction
Yikes, 0 in the CI for a significant effect!The MSE in the ANOVA excluded variance due to other effects, that for the CI did not.Slide26
Partial Eta-Squared
The value of
η2 can be affected by the number and magnitude of other effects contributing to variance in the outcome variable.
For example, if our data were only from women, SSTotal would not include SSGender and SSInteraction.This would increase η2.Partial eta-squared estimates what the effect would be if the other effects were all zero.Slide27
Partial Eta-Squared
For the interaction,
For gender,
For smoking history, Slide28
CI.90 on Partial Eta-Squared
If you use the source table
F-ratios and df with my Conf-Interval-R2-Regr.sas, it will return confidence intervals on partial eta-squared.Gender: [.33, .55]
Smoking: [.17, .41]Interaction [.002, .18] note that it excludes 0Slide29
Partial 2
and
F For Interaction
Notice that the denominator of both includes error but
excludes
the effects of Gender and Smoking History
.Slide30
Semi-Partial
2
Notice that, unlike partial
2 and
F
,
the denominator of semi-partial
2
includes
the effects of Gender and Smoking History
.Slide31
Omega-Squared
For the interaction,
For gender,
For smoking history, Slide32
2 for Simple Main Effects
For the women, SStotal = 11,055and SS
smoking = 5,7002 = 5,700/11,055 = .52To construct confidence interval, need compute an F using data from women only.The SSE is 11,055 (total) – 5,700 (smoking) = 5,355. Slide33
90% CI [.29, .60]For the men,
2
= .11, 90% CI [0, .20] Slide34
SAS EFFECTSIZEPROC
GLM; CLASS Age Condition; MODEL Items=Age|Condition / EFFECTSIZE alpha=
0.1;This will give you eta-squared, partial eta-squared, omega-squared, and confidence intervals for each.Slide35
Presenting the Results
Participants were given a test of their ability to detect the scent of a chemical thought to have
pheromonal
properties in humans. Each participant had been classified into one of five groups based on his or her smoking history. A 2 x 5, Gender x Smoking History, ANOVA was employed, using a .05 criterion of statistical significance and a MSE of 119 for all effects tested. There were significant main effects of gender,
F(1, 90) = 75.84, p < .001, 2 = .346, 90% CI [.22, .45], and smoking history,
F
(4, 90) = 10.80,
p
< .001,
2
= .197, 90% CI [.005, .15], as well as a significant interaction between gender and smoking history,
F
(4, 90) = 2.61,
p
= .041,
2
= .047, 90% CI [.00, .17
].
As shown in Table 1, women were better able to detect this scent than were men, and smoking reduced ability to detect the scent, with recovery of function being greater the longer the period since the participant had last smoked.Slide36Slide37
The
significant interaction was further investigated with tests of the simple main effect of smoking history. For the men, the effect of smoking history fell short of statistical significance,
F
(4, 90) = 1.43, p = .23, 2 = .113, 90% CI [.00, .20]. For the women, smoking history had a significant effect on ability to detect the scent,
F(4, 90) = 11.97, p < .001,
2
= .516, 90% CI [.29, .60]. This significant simple main effect was followed by a set of four contrasts. Each group of female ex-smokers was compared with the group of women who had never smoked. The
Bonferroni
inequality was employed to cap the familywise error rate at .05 for this family of four comparisons. It was found that the women who had never smoked had a significantly better ability to detect the scent than did women who had quit smoking one month to seven years earlier, but the difference between those who never smoked and those who had stopped smoking more than seven years ago was too small to be statistically significant
.Slide38
Interaction PlotSlide39
2 or Partial
2
?
I generally prefer 2Kline says you should exclude an effect from standardizer only if it does not exist in the natural population.Values of partial 2 can sum to greater than 100%. Can one really account for more than all of the variance in the outcome variable?Slide40
For every
effect,Slide41
For every
effect,
These sum to 150%Slide42
AssumptionsNormality within each cellHomogeneity of variance across cellsSlide43
Advantages of Factorial ANOVAEconomy -- study the effects of two factors for (almost) the price of one.
Power -- removing from the error term the effects of Factor B and the interaction gives a more powerful test of Factor A.
Interaction -- see if effect of A varies across levels of B.Slide44
One-Way ANOVA
Consider the partitioning of the sums of squares illustrated to the right.
SS
B = 15 and SSE = 85. Suppose there are two levels of B (an experimental manipulation) and a total of 20 cases. Slide45
Treatment Not Significant
MSB
= 15,
MSE = 85/18 = 4.722. The F(1, 18) = 15/4.72 = 3.176, p
= .092. Woe to us, the effect of our experimental treatment has fallen short of statistical significance. Slide46
Sex Not Included in the Model
Now suppose that the subjects here consist of both men and women and that the sexes differ on the dependent variable.
Since sex is not included in the model, variance due to sex is error variance, as is variance due to any interaction between sex and the experimental treatment.Slide47
Add Sex to the Model
Let us see what happens if we include sex and the interaction in the model.
SS
Sex = 25, SSB = 15,
SSSex*B
= 10, and
SSE
= 50. Notice that the
SSE
has been reduced by removing from it the effects of sex and the interaction.
Slide48
Enhancement of Power
The
MSB
is still 15, but the MSE is now 50/16 = 3.125 and the F(1, 16) = 15/3.125 = 4.80,
p = .044. Notice that excluding the variance due to sex and the interaction has reduced the error variance enough that now the main effect of the experimental treatment is significant.