Transcript Analysis of Variance (ANOVA)

Analysis of Variance
Mean Comparison Procedures
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Learning Objectives
 Understand what to do when we have a significant ANOVA
 Know the difference between
 Planned Comparisons
 Post-Hoc Comparisons
 Be aware of different approaches to controlling Type I error
 Be able to calculate Tukey’s HSD
 Be able to calculate a linear contrast
 Understand issue of controlling type 1 error versus power
 Deeper understanding of what F tells us.
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What does F tell us?
 If F is significant…
 Will at least two means be significantly different?
 What is F testing, exactly?
 Do we always have to do an F test prior to
conducting group comparisons?
 Why or why not?
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When F is significant…
 Typically want to know which groups are significantly
different
 May have planned some comparisons in advance
 e.g., vs. control group
 May have more complicated comparisons in mind
 e.g., does group 1 & 2 combined differ significantly from group
3 & 4?
 May wish to make comparisons we didn’t plan
 Can potentially make lots of comparisons
 Consider a 1-way with 6 groups!
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Decisions in ANOVA
True World Status of Hypothesis
Our Decision
Reject H0
Don’t Reject H0
H0 True
H0 False
Type I error
p=
Correct decision
p=1–β
Correct decision
p=1–
Type II error
p=β
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Concerns over Type I Error
 How it becomes inflated in post-hoc comparisons
 Familywise / Experimentwise errors
 Type I error can have undesirable consequences
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Wasted additional research effort
Monetary costs in implementing a program that doesn’t work
Human costs in providing ineffective treatment
Other…
 Type II error also has undesirable consequences
 Closing down potentially fruitful lines of research
 Loss costs, for not implementing a program that does work
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Beginning at the end, posthocs…
 Fisher’s solution – a no-nonsense approach
 The Bonferroni solution – keeping it simple
 The Dunn–Šidák solution – one-upping
Bonferroni
 Scheffé’s solution – keeping it very tight
 Tukey’s solution – keeping a balance
 Newman-Keuls Solution – keeping it clever
 Dunn’s test – what’s up with the control group?
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Recall our ongoing
example…
One-Way Analysis of Variance Example
Subject #
1
2
3
4
5
6
7
8
T
ΣX2
n
SS
Mean
Method 1
3
5
2
4
8
4
3
9
Method 2
4
4
3
8
7
4
2
5
Method 3
6
7
8
6
7
9
10
9
38
224
8
43.5
4.75
37
199
8
27.88
4.63
62
496
8
15.5
7.75
Total
n=8
k=3
137
919
24
136.96
5.71
Source Table
SSwithin
SS
86.88
Df
21
MS
4.14
SSmethod
50.08
2
25.04
136.96
23
5.95
SStotal
Example taken from Winer et al. (1991). Page 75
F
6.05
p<
0.01
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The Bonferroni Inequality
 The multiplication rule in probability
 For any two independent events, A and B…
 the probability that A and B will occur is…
 P(A&B)=P(A)xP(B)
 Applying this to group comparisons…
 The probability of a type 1 error = .05
 Therefore the probability of a correct decision = .95
 The probability of making three correct decisions = .953
Bonferroni’s solution: α/c
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Bonferroni t’ / Dunn’s Test
 Appropriate when making a few comparisons
 Attributes
 Excellent control of type 1 error
 Lower power, especially with high c
 Can be used for comparison of groups, or
more complex comparisons
 Linear contrasts
Dunn-Šidák test is a refined version of the Bonferroni test where
alpha is controlled by taking into account the more precise
estimate of type 1 error: ind  1  c 1  overall
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Tukey’s approach
1) Determine r: number of groups (3 in our teaching method example)
2) Look up q from table (B.2): using r and dfW (3 & 21  rounding down to
20 = 3.578).
4) Determine HSD: HSD  q
MSW
n
4) Check for significant differences
3.578
4.14
 2.574
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M1
M1
M2
M3
4.75
4.63
7.75
M2
M3
0.12 -3.00
-3.12
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Student Newman-Keuls
Example from One-Way ANOVA where k=7
Means
C
A
B
D
G
E
F
2.0
C
2.4
A
0.4
2.6
B
0.6
0.2
3.6
D
1.6
1.2
1.0
4.4
G
2.4
2.0
1.8
0.8
4.8
E
2.8
2.4
2.2
1.2
0.4
5.0
F
3.0
2.6
2.4
1.4
0.6
0.2
r Intrvl.
7 1.82
6 1.75
5 1.67
4 1.56
3 1.41
2 1.17
 4.54
.80
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MSW = 0.80, dfW = 28, n=5
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S-N-K cont’d
Means
2.0
C
2.4
A
0.4
2.6
B
0.6
0.2
3.6
D
1.6
1.2
1.0
4.4
G
2.4
2.0
1.8
0.8
4.8
E
2.8
2.4
2.2
1.2
0.4
5.0
F
3.0
2.6
2.4
1.4
0.6
0.2
C
A
B
D
G
*
*
*
E
*
*
*
F
*
*
*
C
A
B
D
G
E
F
C
A
B
D
G
E
F
r
7
6
5
4
3
2
Intrvl.
1.82
1.75
1.67
1.56
1.41
1.17
r
7
6
5
4
3
2
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And, finally…
C
A
B
D
G
E
F
Homogenous subsets of means…
Problems with S-N-K and alternatives
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Post-Hoc Comparison
Approaches
 The Bonferroni Inequality
 Flexible Approaches for Complex Contrasts
 Simultaneous Interval Approach
 Taking magnitude into account
 If you have a control group
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What if we were clever enough
to plan our comparisons?
 Linear Contrasts
 Simple Comparisons
 To correct or not correct…
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Orthogonal Contrasts
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What are they?
How many are there?
How do I know if my contrasts are orthogonal?
When would I use one?
What if my contrasts aren’t orthogonal?
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Simple Example
 Three treatment levels
 Wish to compare A & B with C
1.
A B
C1 :
C
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In words, A & B combined aren’t
significantly different from C
2. Next we need to derive contrast coefficients, thus we need to get
coefficients that sum to zero. First, multiply both sides by 2…
C1 : A  B  2C
3. Then, subtract 2C from both sides.
C1 : A  B  2C  0
A & B have an implied coefficient of “1”
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SS for C1
SSC 
T
 (C j T j ) 2
C 2j / n
[(1x4.75)  (1x4.62)  (2 x7.75)]2 6.132
SSC1 

 50.103
2
2
2
(1  1  2 ) / 8
0.75
 All contrasts have 1 df
 Thus, SS = MS
 Error term is common MSW, calculated before
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Why?
 Imagine another outcome…
Subject # Method 1 Method 2 Method 3
1
3
4
6
2
5
4
7
3
2
3
5
4
4
8
6
5
8
7
7
6
4
4
9
7
3
2
6
8
9
5
7
T
Sigma X2
n
SS
Mean
38
224
8
43.50
4.75
37
199
8
27.88
4.63
53
361
8
9.88
6.63
Total
n=8
Now, F = 2.60, p > .05
k=3
MSW = 3.87
128
784
24
101.33
5.33
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Other types of contrasts
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Special
Helmert
Difference
Repeated
Deviation
Simple
Trend (Polynomial)
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Final thoughts/questions
 Do we need to do an ANOVA / F test?
 What is your strategy for determining group
differences?
 Which methods are best suited to your strategy /
questions?
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