one-way between groups analysis of variance
DESCRIPTION
One-way Between Groups Analysis of Variance. 320 Ainsworth. Major Points. Problem with t-tests and multiple groups The logic behind ANOVA Calculations Multiple comparisons Assumptions of analysis of variance Effect Size for ANOVA. T-test. - PowerPoint PPT PresentationTRANSCRIPT
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320Ainsworth
One-way Between Groups Analysis of Variance
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Major Points
Problem with t-tests and multiple groupsThe logic behind ANOVACalculationsMultiple comparisonsAssumptions of analysis of varianceEffect Size for ANOVA
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T-test
So far, we have made comparisons between a single group and population, 2-related samples and 2 independent samplesWhat if we want to compare more than 2 groups? One solution: multiple t-tests
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T-test
With 3 groups, you would perform 3 t-testsNot so bad, but what if you had 10 groups?You would need 45 comparisons to analyze all pairsThat’s right 45!!!
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The Danger of Multiple t-TestsEach time you conduct a t-test on a single set of data, what is the probability of rejecting a true null hypothesis?Assume that H0 is true. You are conducting 45 tests on the same set of data. How many rejections will you have?Roughly 2 or 3 false rejections!So, multiple t-tests on the same set of data artificially inflate
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Summary: The Problems With Multiple t-Tests
Inefficient - too many comparisons when we have even modest numbers of groups.Imprecise - cannot discern patterns or trends of differences in subsets of groups.Inaccurate - multiple tests on the same set of data artificially inflate What is needed: a single test for the overall difference among all means e.g. ANOVA
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LOGIC OF THE ANALYSIS OF VARIANCE
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Logic of the Analysis of Variance
Null hypothesis h0: Population means equalm1 = m2 = m3 = m4
Alternative hypothesis: h1 –Not all population means equal.
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Logic
Create a measure of variability among group means–MSBetweenGroups AKA s2
BetweenGroups
Create a measure of variability within groups–MSWithinGroups AKA s2
WithinGroups
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Logic
MSBetweenGroups /MSWithinGroups –Ratio approximately 1 if null true–Ratio significantly larger than 1 if null
false– “approximately 1” can actually be as
high as 2 or 3, but not much higher
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“So, why is it called analysis of variance anyway?”
Aren’t we interested in mean differences?Variance revisited–Basic variance formula
2
2
1iX X SSs
n df
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“Why is it called analysis of variance anyway?”
What if data comes from groups?– We can have different sums of squares
2
1
2
2
2
3
Where represents the individual, represents the groups and
GM represent the ungrouped (grand) mean
i GM
i j
j j GM
SS Y Y
SS Y Y
SS n Y Y
ij
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Y-A
xis
X-Axis
3GroupY1GroupY 2GroupYJohn’s Score
X
Grand Mean(Ungrouped Mean)
Logic of ANOVA
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CALCULATIONS
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Sums of Squares
The total variability can be partitioned into between groups variability and within groups variability.
2 22
i GM j j GM i j
Total BetweenGroups WithinGroups
T BG WG
T Effect Error
Y Y n Y Y Y Y
SS SS SS
SS SS SSSS SS SS
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Degrees of Freedom (df )Number of “observations” free to vary–dfT = N - 1
• Variability of N observations–dfBG = g - 1
• Variability of g means–dfWG = g (n - 1) or N - g
• n observations in each group = n - 1 df times g groups
–dfT = dfBG + dfWG
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Mean Square (i.e. Variance)
2
2
2
2
2
2
1
# 1
# *( 1)
i GMT T
j j GMBG BG
i jWG WG
Y YMS s
N
n Y YMS s
groups
Y YMS s
groups n
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F-test
MSWG contains random sampling variation among the participantsMSBG also contains random sampling variation but it can also contain systematic (real) variation between the groups (either naturally occurring or manipulated)
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F-test
And if no “real” difference exists between groups
Ratio
Systematic BG Variance Random BG VarianceFRandom WS Variance
1 Ratio
Random BG VarianceFRandom WS Variance
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F-test
The F-test is a ratio of the MSBG/MSWG and if the group differences are just random the ratio will equal 1 (e.g. random/random)
Y-A
xis
X-Axis
Y
Grand Mean(Ungrouped Mean)
Y Y
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F-testY
-Axi
s
X-Axis
3GroupY1GroupY 2GroupY
Grand Mean(Ungrouped Mean)
If there are real differences between the groups the difference will be larger than 1 and we can calculate the probability and hypothesis test
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F distributionP
roba
bilit
y
F-ratio1
There is a separate F distribution for every df like t but we need both dfbg and dfwg to calculate the FCV from the F table D.3 for alpha = .05 and D.4 for alpha = .01
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1-WAY BETWEEN GROUPS ANOVA EXAMPLE
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ExampleA researcher is interested in knowing which brand of baby food babies prefer: Beechnut, Del Monte or Gerber.He randomly selects 15 babies and assigns each to try strained peas from one of the three brandsLiking is measured by the number of spoonfuls the baby takes before getting “upset” (e.g. crying, screaming, throwing the food, etc.)
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Hypothesis Testing1. Ho: mBeechnut = mDel Monte = mGerber 2. At least 2 ms are different3. = .054. More than 2 groups ANOVA F5. For Fcv you need both dfBG = 3 – 1 = 2
and dfWG = g (n - 1) = 3(5 – 1) = 12Table D.3 Fcv(2,12) = 3.89, if Fo > 3.89 reject the null hypothesis
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Step 6 – Calculate F-test
Start with Sum of Squares (SS) –We need:
• SST
• SSBG
• SSWG
Then, use the SS and df to compute mean squares and F
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`Brand Baby Spoonfuls (Y) Group Means 2
..ijY Y 2
.ij jY Y 2
. ..j jn Y Y
Bee
chnu
t
1 3
4.6
2 4 3 4 4 4 5 8
Del
Mon
te 6 7
6
0.445 1
[5 * (6 - 6.333)2] = 0.555
7 4 5.443 4 8 8 2.779 4 9 6 0.111 0 10 5 1.777 1
Ger
ber
11 9
8.4
7.113 0.36
[5 * (8.4 - 6.333)2] = 21.36
12 6 0.111 5.76 13 10 13.447 2.56 14 8 2.779 0.16 15 9 7.113 0.36
Mean 6.333 Sum 71.335 34.4 36.93
Step 6 – Calculate F-test
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ANOVA summary table and Step 7
Remember–MS = SS/df–F = MSBG/MSWG
Step 7 – Since ______ > 3.89, reject the null hypothesis
Source SS df MS FBG 36.93WG 34.4Total 71.335
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Conclusions
The F for groups is significant.–We would obtain an F of this size, when
H0 true, less than 5% of the time.–The difference in group means cannot
be explained by random error.–The baby food brands were rated
differently by the sample of babies.
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ALTERNATIVE COMPUTATIONAL APPROACH
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Alternative Analysis – computational approach to SS
Equations
– Under each part of the equations, you divide by the number of scores it took to get the number in the numerator
22
2 2T
Y TSS Y YN N
2
2j
BG
a TSSn N
2
2 jWG
aSS Y
n
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Computational Approach ExampleBrand Baby Spoonfuls (Y)
1 32 43 44 45 8
Sum 236 77 48 89 610 5
Sum 3011 912 613 1014 815 9
Sum 4295
673Total
Sum Y Squared
Bee
chnu
tD
el M
onte
Ger
ber
2 22 ________ 71.33
15TTSS YN
22 2 2 2 2___ ___ ___ ___
5 15_____ _____ 36.93
jBG
a TSSn N
2
2
2 2 2___ ___ _______ ____ ____ 34.45
jWG
aSS Y
n
Note: You get the same SS using this method 32
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Unequal Sample SizesWith one-way, no particular problem–Multiply mean deviations by appropriate
ni as you go–The problem is more complex with more
complex designs, as shown in next chapter.
–Equal samples only simplify the equation because when n1= n2 =… = ng
2 2
j j GM j GMn Y Y n Y Y
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MULTIPLE COMPARISONS
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Multiple Comparisons
Significant F only shows that not all groups are equal–We want to know what groups are
different.Such procedures are designed to control familywise error rate.–Familywise error rate defined–Contrast with per comparison error rate
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More on Error Rates
Most tests reduce significance level () for each t test.The more tests we run the more likely we are to make Type I error.–Good reason to hold down number of
tests
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TukeyHonestly Significant DifferenceThe honestly significant difference (HSD) controls for all possible pairwise comparisonsThe Critical Difference (CD) computed using the HSD approach
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TukeyHonestly Significant Difference
where q is the studentized range statistic (table), MSerror is from the ANOVA and nA is equal n for both groups
for the unequal n case38
error
A
MSCD qn
1 1 / 2errori j
CD q MSn n
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Tukey
Comparing Beechnut and Gerber–To compute the CD value we need to
first find the value for q–q depends on alpha, the total number of
groups and the DF for error.–We have 3 total groups, alpha = .05 and
the DF for error is 12–q = 3.77
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TukeyWith a q of 3.77 just plug it in to the formula
This give us the minimum mean differenceThe difference between gerber and beechnut is 3.8, the difference is significant 40
2.8673.77 2.865
error
A
MSCD qn
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Fisher’s LSD ProcedureRequires significant overall F, or no testsRun standard t tests between pairs of groups.–Often we replace s2
pooled with MSerror from overall analysis• It is really just a pooled error term, but with
more degrees of freedom (pooled across all treatment groups)
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Fisher’s LSD ProcedureComparing Beechnut and Gerber
tcv(5+5-2=8) = .05 = 1.860Since 3.55 > 1.860, the 2 groups are significantly different.
*
*
2.867 2.8675 5
8.4 4.6 3.551.071
Beechnut Gerber
Beechnut Gerber
WG WGX X
Beechnut Gerber
Beechnut Gerber
X X
MS MSs
n n
X Xt
s
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Bonferroni t TestRun t tests between pairs of groups, as usual–Hold down number of t tests–Reject if t exceeds critical value in
Bonferroni tableWorks by using a more strict value of for each comparison
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Bonferroni tCritical value of for each test set at .05/c, where c = number of tests run–Assuming familywise = .05–e. g. with 3 tests, each t must be
significant at .05/3 = .0167 level.With computer printout, just make sure calculated probability < .05/c
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Assumptions for Analysis of Variance
Assume:–Observations normally distributed within
each population–Population variances are equal
• Homogeneity of variance or homoscedasticity
–Observations are independent
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ASSUMPTIONS
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Assumptions
Analysis of variance is generally robust to first two–A robust test is one that is not greatly
affected by violations of assumptions.
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EFFECT SIZE
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Magnitude of EffectEta squared (h2)–Easy to calculate–Somewhat biased on the high side–Formula
–Percent of variation in the data that can be attributed to treatment differences
2 BG
Total
SSSS
h
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Magnitude of EffectOmega squared (w2)–Much less biased than h2
–Not as intuitive–We adjust both numerator and
denominator with MSerror –Formula
2 ( 1)BG WG
T WG
SS k MSSS MS
w
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h2 = .52: 52% of variability in preference can be accounted for by brand of baby food w2 = .42: This is a less biased estimate, and note that it is 20% smaller.
2
2
36.93 .51871.335( 1) 36.93 2(2.867) .420
71.335 2.867
BG
T
BG WG
T WG
SSSSSS k MS
SS MS
h
w
h2 and w2 for Baby Food
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Other Measures of Effect SizeWe can use the same kinds of measures we talked about with t tests (e.g. d and d-hat)Usually makes most sense to talk about 2 groups at a time or effect size between the largest and smallest groups, etc.And there are methods for converting h2 to d and vice versa
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