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MANOVA - Equations
Lecture 11Psy524Andrew Ainsworth
Data design for MANOVAIV1 IV2 DV1 DV2 DVM
S01(11) S01(11) S01(11) S02(11) S02(11) S02(11) 1 S03(11) S03(11) S03(11) S04(12) S04(12) S04(12) S05(12) S05(12) S05(12)
1
2 S06(12) S06(12) S06(12) S07(21) S07(21) S07(21) S08(21) S08(21) S08(21) 1 S09(21) S09(21) S09(21) S10(22) S10(22) S10(22) S11(22) S11(22) S11(22)
2
2 S12(22) S12(22) S12(22)
Data design for MANOVA
Steps to MANOVA
MANOVA is a multivariate generalization of ANOVA, so there are analogous parts to the simpler ANOVA equations
Steps to MANOVA
ANOVA –
( )22 2( ) ( )( ) ( )ij y j y ij j
i j j i jY GM n Y GM Y Y− = − + −∑∑ ∑ ∑∑
( ) ( ) ( )Total y bg y wg ySS SS SS= +
Steps to MANOVA
When you have more than one IV the interaction looks something like this:
( ) ( )
( ) ( ) ( )
2 22( ) ( ) ( )
2 2 2
( ) ( ) ( )
( )km km DT k k D m m Tk m k m
km km DT k k D m m Tk m k m
n DT GM n D GM n T GM
n DT GM n D GM n T GM
− = − + − +
− − − − −
∑∑ ∑ ∑
∑∑ ∑ ∑
bg D T DTSS SS SS SS= + +
Steps to MANOVA
The full factorial design is:
( ) ( )
( ) ( ) ( )
2 22( ) ( ) ( )
2 2 2
( ) ( ) ( )
2
( )
( )
ikm ikm k k D m m Ti k m k m
km km DT k k D m m Tk m k m
ikm kmi k m
Y GM n D GM n T GM
n DT GM n D GM n T GM
Y DT
− = − + − +
− − − − −
+ −
∑∑∑ ∑ ∑
∑∑ ∑ ∑
∑∑∑
Steps to MANOVA
MANOVA - You need to think in terms of matrices
Each subject now has multiple scores, there is a matrix of responses in each cellMatrices of difference scores are calculated and the matrix squaredWhen the squared differences are summed you get a sum-of-squares-and-cross-products-matrix (S) which is the matrix counterpart to the sums of squares.The determinants of the various S matrices are found and ratios between them are used to test hypotheses about the effects of the IVs on linear combination(s) of the DVsIn MANCOVA the S matrices are adjusted for by one or more covariates
Matrix Equations
If you take the three subjects in the treatment/mild disability cell:
11
115 98 107108 105 98iY
=
Matrix Equations
You can get the means for disability by averaging over subjects and treatments
1 2 3
95.83 88.83 82.6796.50 88.00 77.17
D D D
= = =
Matrix Equations
Means for treatment by averaging over subjects and disabilities
1 2
99.89 78.3396.33 78.11
T T
= =
Matrix Equations
The grand mean is found by averaging over subjects, disabilities and treatments.
89.1187.22
GM
=
Matrix Equations
Differences are found by subtracting the matrices, for the first child in the mild/treatment group:
111
115 89.11 25.89( )
108 87.22 20.75Y GM
− = − =
Matrix Equations
Instead of squaring the matrices you simply multiply the matrix by its transpose, for the first child in the mild/treatment group:
[ ]111 111
25.89 670.29 537.99( )( )' 25.89 20.75
20.75 537.99 431.81Y GM Y GM
− − = =
Matrix Equations
This is done on the whole data set at the same time, reorganaizing the data to get the appropriate S matrices. The full break of sums of squares is:
( )( ) ' ( )( ) '
( )( ) ' [ ( )( ) '
( )( ) ' ( )( ) ']
( )( ) '
ikm ikm k k ki k m k
m m m km km kmm km
k k k m m mk m
ikm km ikm kmi k m
Y GM Y GM n D GM D GM
n T GM T GM n DT GM DT GM
n D GM D GM n T GM T GM
Y DT Y DT
− − = − −
+ − − + − −
− − − − − −
+ − −
∑∑∑ ∑
∑ ∑
∑ ∑
∑∑∑
Matrix Equations
If you go through this for every combination in the data you will get four S matrices (not including the S total matrix):
/
570.29 761.72 2090.89 1767.56761.72 1126.78 1767.56 1494.22
2.11 5.28 544.00 31.005.28 52.78 31.00 539.33
D T
DT s DT
S S
S S
= =
= =
Test Statistic – Wilk’s Lambda
Once the S matrices are found the diagonals represent the sums of squares and the off diagonals are the cross productsThe determinant of each matrix represents the generalized variance for that matrixUsing the determinants we can test for significance, there are many ways to this and one of the most is Wilk’s Lambda
Test Statistic – Wilk’s Lambda
this can be seen as the percent of non-overlap between the effect and the DVs.
error
effect error
SS S
Λ =+
Test Statistic – Wilk’s Lambda
For the interaction this would be:
/
/
/
292436.52
2.11 5.28 544.00 31.00 546.11 36.285.28 52.78 31.00 539.33 36.28 529.11
322040.95292436.52 .908068322040.95
s DT
DT s DT
DT s DT
S
S S
S S
=
+ = + =
+ =
Λ= =
Test Statistic – Wilk’s LambdaApproximate Multivariate F for Wilk’sLambda is
21 2
1
2 21/
2 2
1
2
1( , )
( ) 4 , ,
( ) 5
, ( )
1 ( ) 2( )
2 2
effects
effect
effect
effect effecterror
dfyF df dfy df
p dfwhere y s
p df
p number of DVs df p df
p df p dfdf s df
−=
−= Λ =
+ −
= =
− + − = − −
Test Statistic – Wilk’s LambdaSo in the example for the interaction:
2 2
2 2
1/ 2
1
2
(2) (2) 4 2(2) (2) 5
.908068 .9529262(2) 4
( 1) 3(2)(3 1) 122 2 1 2(2) 22 12 22
2 2.047074 22F(4,22)= 0.2717.952926 4
error
s
ydfdf dt n
df
Approximate
−= =
+ −
= == == − = − =
− + − = − − = =
Eta Squared
2 1η = −Λ
Partial Eta Squared
2 1/ sη = −Λ