Dear list,

Sorry for posting a borderline statistical question on the list, but hte
SPSS people around me just stares at me blankly when refering to tests with
any term other than ANOVA and post-hoc. I would appreciate any insight on
how this all is possible:

I have a model fitted by aov() stored in "ppdur", which gives this result
when using ANOVA:

> anova(ppdur)
Analysis of Variance Table

Response: PAPositionPercentOfVoweldur
                                      Df  Sum Sq Mean Sq F value  Pr(>F)
UtteranceType                           4   24731    6183  2.7642 0.02696 *
SyllLable                               1   14584   14584  6.5202 0.01094 *
Cycle                                   1     798     798  0.3566 0.55067
Speaker                                 2    9975    4987  2.2297 0.10855
Label                                   1    2008    2008  0.8979 0.34377
UtteranceType:SyllLable                 4   15210    3803  1.7001 0.14854
UtteranceType:Cycle                     4   13192    3298  1.4745 0.20855
SyllLable:Cycle                         1   11306   11306  5.0545 0.02497 *
UtteranceType:Speaker                   7   13721    1960  0.8764 0.52488
SyllLable:Speaker                       2    1291     645  0.2885 0.74951
Cycle:Speaker                           2   10753    5377  2.4038 0.09135 .
UtteranceType:Label                     4    3579     895  0.4000 0.80871
SyllLable:Label                         1    4499    4499  2.0114 0.15670
Cycle:Label                             1     229     229  0.1022 0.74929
Speaker:Label                           2    1241     620  0.2774 0.75788
UtteranceType:SyllLable:Cycle           3     473     158  0.0705 0.97571
UtteranceType:SyllLable:Speaker         6   13919    2320  1.0372 0.40006
UtteranceType:Cycle:Speaker             3    1221     407  0.1820 0.90865
SyllLable:Cycle:Speaker                 2    1457     729  0.3258 0.72210
UtteranceType:SyllLable:Label           2    3823    1911  0.8545 0.42607
UtteranceType:Cycle:Label               3    8566    2855  1.2766 0.28160
SyllLable:Cycle:Label                   1    3575    3575  1.5983 0.20669
UtteranceType:Speaker:Label             4    2658     664  0.2970 0.87990
SyllLable:Speaker:Label                 2     139      70  0.0311 0.96938
Cycle:Speaker:Label                     2   13599    6800  3.0400 0.04866 *
UtteranceType:SyllLable:Cycle:Speaker   2    2015    1008  0.4505 0.63757
UtteranceType:SyllLable:Cycle:Label     1      11      11  0.0051 0.94328
UtteranceType:SyllLable:Speaker:Label   1     603     603  0.2695 0.60386
Residuals                             539 1205605    2237
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Ok now, when I want to know where the differences are, I get this result
from TukeyHSD:


> TukeyHSD(ppdur,c("Cycle:Speaker:Label" ),ordered=TRUE)
  Tukey multiple comparisons of means
    95% family-wise confidence level
    factor levels have been ordered

Fit: aov(formula = PAPositionPercentOfVoweldur ~ UtteranceType * SyllLable *
Cycle * Speaker * Label, data =PABTSub)

$`Cycle:Speaker:Label`
                                    diff        lwr       upr     p adj
3:Andrea:!H*L-1:Lavinia:!H*L   2.6069140 -37.499300  42.71313 1.0000000
1:Vito:!H*L-1:Lavinia:!H*L     8.7764075 -85.090794 102.64361 1.0000000
3:Andrea:H*L-1:Lavinia:!H*L   12.3411960 -18.883688  43.56608 0.9792814
1:Vito:H*L-1:Lavinia:!H*L     15.0416018 -32.746962  62.83017 0.9968844
1:Andrea:H*L-1:Lavinia:!H*L   15.2934976 -17.987036  48.57403 0.9379977
1:Lavinia:H*L-1:Lavinia:!H*L  16.9297832 -14.670124  48.52969 0.8394874
3:Lavinia:H*L-1:Lavinia:!H*L  18.3218965 -13.445765  50.08956 0.7631935
3:Lavinia:!H*L-1:Lavinia:!H*L 20.9338365 -19.932636  61.80031 0.8761167
3:Vito:!H*L-1:Lavinia:!H*L    24.3874104 -18.890036  67.66486 0.7894161
3:Vito:H*L-1:Lavinia:!H*L     27.8865684  -6.758302  62.53144 0.2589397
1:Andrea:!H*L-1:Lavinia:!H*L  28.8093072 -18.979256  76.59787 0.7077134
1:Vito:!H*L-3:Andrea:!H*L      6.1694934 -87.982875 100.32186 1.0000000
3:Andrea:H*L-3:Andrea:!H*L     9.7342820 -22.337674  41.80624 0.9977466
1:Vito:H*L-3:Andrea:!H*L      12.4346877 -35.911602  60.78098 0.9995138
1:Andrea:H*L-3:Andrea:!H*L    12.6865836 -21.389960  46.76313 0.9870844
1:Lavinia:H*L-3:Andrea:!H*L   14.3228692 -18.114318  46.76006 0.9529437
3:Lavinia:H*L-3:Andrea:!H*L   15.7149825 -16.885651  48.31562 0.9149770
3:Lavinia:!H*L-3:Andrea:!H*L  18.3269225 -23.190369  59.84421 0.9530401
3:Vito:!H*L-3:Andrea:!H*L     21.7804964 -22.112036  65.67303 0.8978922
3:Vito:H*L-3:Andrea:!H*L      25.2796544 -10.130570  60.68988 0.4470040
1:Andrea:!H*L-3:Andrea:!H*L   26.2023932 -22.143897  74.54868 0.8288881
3:Andrea:H*L-1:Vito:!H*L       3.5647885 -87.160916  94.29049 1.0000000
1:Vito:H*L-1:Vito:!H*L         6.2651943 -91.407252 103.93764 1.0000000
1:Andrea:H*L-1:Vito:!H*L       6.5170902 -84.936471  97.97065 1.0000000
1:Lavinia:H*L-1:Vito:!H*L      8.1533757 -82.702082  99.00883 1.0000000
3:Lavinia:H*L-1:Vito:!H*L      9.5454891 -81.368450 100.45943 1.0000000
3:Lavinia:!H*L-1:Vito:!H*L    12.1574291 -82.321291 106.63615 0.9999996
3:Vito:!H*L-1:Vito:!H*L       15.6110030 -79.935308 111.15731 0.9999948
3:Vito:H*L-1:Vito:!H*L        19.1101609 -72.848673 111.06899 0.9999396
1:Andrea:!H*L-1:Vito:!H*L     20.0328997 -77.639547 117.70535 0.9999471
1:Vito:H*L-3:Andrea:H*L        2.7004057 -38.577297  43.97811 1.0000000
1:Andrea:H*L-3:Andrea:H*L      2.9523016 -20.019330  25.92393 0.9999996
1:Lavinia:H*L-3:Andrea:H*L     4.5885872 -15.872500  25.04967 0.9998713
3:Lavinia:H*L-3:Andrea:H*L     5.9807005 -14.738525  26.69993 0.9985708
3:Lavinia:!H*L-3:Andrea:H*L    8.5926405 -24.425090  41.61037 0.9994563
3:Vito:!H*L-3:Andrea:H*L      12.0462144 -23.912642  48.00507 0.9946379
3:Vito:H*L-3:Andrea:H*L       15.5453724  -9.361836  40.45258 0.6595505
1:Andrea:!H*L-3:Andrea:H*L    16.4681112 -24.809592  57.74581 0.9777282
1:Andrea:H*L-1:Vito:H*L        0.2518959 -42.601917  43.10571 1.0000000
1:Lavinia:H*L-1:Vito:H*L       1.8881814 -39.673935  43.45030 1.0000000
3:Lavinia:H*L-1:Vito:H*L       3.2802948 -38.409509  44.97010 1.0000000
3:Lavinia:!H*L-1:Vito:H*L      5.8922348 -43.086576  54.87105 0.9999998
3:Vito:!H*L-1:Vito:H*L         9.3458087 -41.661963  60.35358 0.9999831
3:Vito:H*L-1:Vito:H*L         12.8449666 -31.076810  56.76674 0.9983895
1:Andrea:!H*L-1:Vito:H*L      13.7677055 -41.119471  68.65488 0.9996173
1:Lavinia:H*L-1:Andrea:H*L     1.6362855 -21.842569  25.11514 1.0000000
3:Lavinia:H*L-1:Andrea:H*L     3.0283989 -20.675753  26.73255 0.9999996
3:Lavinia:!H*L-1:Andrea:H*L    5.6403389 -29.327804  40.60848 0.9999955
3:Vito:!H*L-1:Andrea:H*L       9.0939128 -28.663734  46.85156 0.9997414
3:Vito:H*L-1:Andrea:H*L       12.5930707 -14.847220  40.03336 0.9385504
1:Andrea:!H*L-1:Andrea:H*L    13.5158096 -29.338003  56.36962 0.9968280
3:Lavinia:H*L-1:Lavinia:H*L    1.3921134 -19.888090  22.67232 1.0000000
3:Lavinia:!H*L-1:Lavinia:H*L   4.0040534 -29.368559  37.37667 0.9999998
3:Vito:!H*L-1:Lavinia:H*L      7.4576273 -28.827357  43.74261 0.9999460
3:Vito:H*L-1:Lavinia:H*L      10.9567852 -14.418986  36.33256 0.9598798
1:Andrea:!H*L-1:Lavinia:H*L   11.8795240 -29.682592  53.44164 0.9986943
3:Lavinia:!H*L-3:Lavinia:H*L   2.6119400 -30.919560  36.14344 1.0000000
3:Vito:!H*L-3:Lavinia:H*L      6.0655139 -30.365659  42.49669 0.9999937
3:Vito:H*L-3:Lavinia:H*L       9.5646718 -16.019698  35.14904 0.9866445
1:Andrea:!H*L-3:Lavinia:H*L   10.4874107 -31.202393  52.17721 0.9996066
3:Vito:!H*L-3:Lavinia:!H*L     3.4535739 -41.134704  48.04185 1.0000000
3:Vito:H*L-3:Lavinia:!H*L      6.9527318 -29.316321  43.22179 0.9999733
1:Andrea:!H*L-3:Lavinia:!H*L   7.8754707 -41.103340  56.85428 0.9999956
3:Vito:H*L-3:Vito:!H*L         3.4991580 -35.466379  42.46469 1.0000000
1:Andrea:!H*L-3:Vito:!H*L      4.4218968 -46.585875  55.42967 1.0000000
1:Andrea:!H*L-3:Vito:H*L       0.9227388 -42.999038  44.84452 1.0000000

As you can see, I don't get a significant p-value for this interaction
effect  anymore. How could that be?
(For the other variables showing a significant effect TykeyHSD gives me
information about where the effect may come from, so I did not include them
in my example. Also, maybe I should point out that the names in the example
are coded ones. They are NOT the acctual names of hte participants.).

I would be happy to get any insight into how this could come about.

/Fredrik
-- 
"Life is like a trumpet - if you don't put anything into it, you don't get
anything out of it."

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