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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