IBM SPSS Web Report - Output8

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Log
GLM VAR00001 VAR00002 VAR00003 VAR00004 VAR00005 VAR00006 VAR00007 VAR00008 VAR00009 VAR00010 VAR00011 VAR00012 VAR00013 VAR00014 VAR00015 VAR00016 VAR00017 VAR00018 VAR00019 VAR00020 VAR00021 VAR00022 VAR00023 VAR00024 VAR00025 VAR00026 VAR00027 VAR00028 VAR00029 VAR00030 VAR00031 VAR00032 VAR00033 VAR00034 VAR00035 VAR00036
  /WSFACTOR=method 3 Polynomial speaker 4 Polynomial style 3 Polynomial
  /METHOD=SSTYPE(3)
  /PLOT=PROFILE(method speaker style method*speaker method*style speaker*style)
  /EMMEANS=TABLES(method) COMPARE ADJ(BONFERRONI)
  /EMMEANS=TABLES(speaker) COMPARE ADJ(BONFERRONI)
  /EMMEANS=TABLES(style) COMPARE ADJ(BONFERRONI)
  /EMMEANS=TABLES(method*speaker)
  /EMMEANS=TABLES(method*style)
  /EMMEANS=TABLES(speaker*style)
  /EMMEANS=TABLES(method*speaker*style)
  /CRITERIA=ALPHA(.05)
  /WSDESIGN=method speaker style method*speaker method*style speaker*style method*speaker*style.
General Linear Model
Within-Subjects FactorsWithin-Subjects Factors, table, Measure, MEASURE_1, 1 layers, 1 levels of column headers and 3 levels of row headers, table with 4 columns and 39 rows
MEASURE_1 MEASURE_1
method speaker style Dependent Variable
1 1 1 VAR00001
2 VAR00002
3 VAR00003
2 1 VAR00004
2 VAR00005
3 VAR00006
3 1 VAR00007
2 VAR00008
3 VAR00009
4 1 VAR00010
2 VAR00011
3 VAR00012
2 1 1 VAR00013
2 VAR00014
3 VAR00015
2 1 VAR00016
2 VAR00017
3 VAR00018
3 1 VAR00019
2 VAR00020
3 VAR00021
4 1 VAR00022
2 VAR00023
3 VAR00024
3 1 1 VAR00025
2 VAR00026
3 VAR00027
2 1 VAR00028
2 VAR00029
3 VAR00030
3 1 VAR00031
2 VAR00032
3 VAR00033
4 1 VAR00034
2 VAR00035
3 VAR00036
General Linear Model
Multivariate TestsaMultivariate Tests, table, 1 levels of column headers and 2 levels of row headers, table with 7 columns and 32 rows
Effect Value F Hypothesis df Error df Sig.
method Pillai's Trace .683 14.005b 2.000 13.000 .001
Wilks' Lambda .317 14.005b 2.000 13.000 .001
Hotelling's Trace 2.155 14.005b 2.000 13.000 .001
Roy's Largest Root 2.155 14.005b 2.000 13.000 .001
speaker Pillai's Trace .620 6.533b 3.000 12.000 .007
Wilks' Lambda .380 6.533b 3.000 12.000 .007
Hotelling's Trace 1.633 6.533b 3.000 12.000 .007
Roy's Largest Root 1.633 6.533b 3.000 12.000 .007
style Pillai's Trace .742 18.734b 2.000 13.000 .000
Wilks' Lambda .258 18.734b 2.000 13.000 .000
Hotelling's Trace 2.882 18.734b 2.000 13.000 .000
Roy's Largest Root 2.882 18.734b 2.000 13.000 .000
method * speaker Pillai's Trace .842 7.965b 6.000 9.000 .003
Wilks' Lambda .158 7.965b 6.000 9.000 .003
Hotelling's Trace 5.310 7.965b 6.000 9.000 .003
Roy's Largest Root 5.310 7.965b 6.000 9.000 .003
method * style Pillai's Trace .313 1.254b 4.000 11.000 .345
Wilks' Lambda .687 1.254b 4.000 11.000 .345
Hotelling's Trace .456 1.254b 4.000 11.000 .345
Roy's Largest Root .456 1.254b 4.000 11.000 .345
speaker * style Pillai's Trace .742 4.316b 6.000 9.000 .025
Wilks' Lambda .258 4.316b 6.000 9.000 .025
Hotelling's Trace 2.878 4.316b 6.000 9.000 .025
Roy's Largest Root 2.878 4.316b 6.000 9.000 .025
method * speaker * style Pillai's Trace .796 .975b 12.000 3.000 .584
Wilks' Lambda .204 .975b 12.000 3.000 .584
Hotelling's Trace 3.900 .975b 12.000 3.000 .584
Roy's Largest Root 3.900 .975b 12.000 3.000 .584
a. Design: Intercept
Within Subjects Design: method + speaker + style + method * speaker + method * style + speaker * style + method * speaker * style
b. Exact statistic
General Linear Model
Mauchly's Test of SphericityaMauchly's Test of Sphericity, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 1 levels of row headers, table with 8 columns and 14 rows
MEASURE_1 MEASURE_1
Within Subjects Effect Mauchly's W Approx. Chi-Square df Sig. Epsilonb
Greenhouse-Geisser Huynh-Feldt Lower-bound
method .600 6.631 2 .036 .715 .773 .500
speaker .691 4.708 5 .454 .793 .967 .333
style .536 8.098 2 .017 .683 .732 .500
method * speaker .023 44.493 20 .002 .395 .481 .167
method * style .131 25.247 9 .003 .531 .628 .250
speaker * style .225 17.593 20 .630 .673 .980 .167
method * speaker * style .000 142.944 77 .000 .369 .561 .083
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.
a. Design: Intercept
Within Subjects Design: method + speaker + style + method * speaker + method * style + speaker * style + method * speaker * style
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.
General Linear Model
Tests of Within-Subjects EffectsTests of Within-Subjects Effects, table, Measure, MEASURE_1, 1 layers, 1 levels of column headers and 2 levels of row headers, table with 7 columns and 59 rows
MEASURE_1 MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
method Sphericity Assumed 3211.065 2 1605.533 7.199 .003
Greenhouse-Geisser 3211.065 1.429 2247.057 7.199 .008
Huynh-Feldt 3211.065 1.546 2076.974 7.199 .007
Lower-bound 3211.065 1.000 3211.065 7.199 .018
Error(method) Sphericity Assumed 6244.801 28 223.029    
Greenhouse-Geisser 6244.801 20.006 312.144    
Huynh-Feldt 6244.801 21.644 288.518    
Lower-bound 6244.801 14.000 446.057    
speaker Sphericity Assumed 7847.006 3 2615.669 12.168 .000
Greenhouse-Geisser 7847.006 2.380 3296.534 12.168 .000
Huynh-Feldt 7847.006 2.901 2705.156 12.168 .000
Lower-bound 7847.006 1.000 7847.006 12.168 .004
Error(speaker) Sphericity Assumed 9028.299 42 214.960    
Greenhouse-Geisser 9028.299 33.325 270.914    
Huynh-Feldt 9028.299 40.611 222.314    
Lower-bound 9028.299 14.000 644.879    
style Sphericity Assumed 24383.752 2 12191.876 30.991 .000
Greenhouse-Geisser 24383.752 1.366 17844.459 30.991 .000
Huynh-Feldt 24383.752 1.464 16654.300 30.991 .000
Lower-bound 24383.752 1.000 24383.752 30.991 .000
Error(style) Sphericity Assumed 11015.044 28 393.394    
Greenhouse-Geisser 11015.044 19.130 575.786    
Huynh-Feldt 11015.044 20.498 537.383    
Lower-bound 11015.044 14.000 786.789    
method * speaker Sphericity Assumed 3745.441 6 624.240 27.181 .000
Greenhouse-Geisser 3745.441 2.370 1580.471 27.181 .000
Huynh-Feldt 3745.441 2.885 1298.466 27.181 .000
Lower-bound 3745.441 1.000 3745.441 27.181 .000
Error(method*speaker) Sphericity Assumed 1929.132 84 22.966    
Greenhouse-Geisser 1929.132 33.178 58.146    
Huynh-Feldt 1929.132 40.383 47.771    
Lower-bound 1929.132 14.000 137.795    
method * style Sphericity Assumed 449.011 4 112.253 2.450 .057
Greenhouse-Geisser 449.011 2.122 211.560 2.450 .101
Huynh-Feldt 449.011 2.512 178.752 2.450 .089
Lower-bound 449.011 1.000 449.011 2.450 .140
Error(method*style) Sphericity Assumed 2566.086 56 45.823    
Greenhouse-Geisser 2566.086 29.713 86.361    
Huynh-Feldt 2566.086 35.167 72.969    
Lower-bound 2566.086 14.000 183.292    
speaker * style Sphericity Assumed 2399.724 6 399.954 5.244 .000
Greenhouse-Geisser 2399.724 4.039 594.157 5.244 .001
Huynh-Feldt 2399.724 5.881 408.036 5.244 .000
Lower-bound 2399.724 1.000 2399.724 5.244 .038
Error(speaker*style) Sphericity Assumed 6406.066 84 76.263    
Greenhouse-Geisser 6406.066 56.544 113.293    
Huynh-Feldt 6406.066 82.336 77.804    
Lower-bound 6406.066 14.000 457.576    
method * speaker * style Sphericity Assumed 630.632 12 52.553 2.879 .001
Greenhouse-Geisser 630.632 4.430 142.357 2.879 .026
Huynh-Feldt 630.632 6.734 93.643 2.879 .010
Lower-bound 630.632 1.000 630.632 2.879 .112
Error(method*speaker*style) Sphericity Assumed 3066.388 168 18.252    
Greenhouse-Geisser 3066.388 62.019 49.443    
Huynh-Feldt 3066.388 94.282 32.524    
Lower-bound 3066.388 14.000 219.028    
General Linear Model
Tests of Within-Subjects ContrastsTests of Within-Subjects Contrasts, table, Measure, MEASURE_1, 1 layers, 1 levels of column headers and 4 levels of row headers, table with 9 columns and 73 rows
MEASURE_1 MEASURE_1
Source method speaker style Type III Sum of Squares df Mean Square F Sig.
method Linear     3113.264 1 3113.264 14.600 .002
Quadratic     97.801 1 97.801 .420 .527
Error(method) Linear     2985.352 14 213.239    
Quadratic     3259.449 14 232.818    
speaker   Linear   2417.472 1 2417.472 9.075 .009
Quadratic   5401.286 1 5401.286 21.013 .000
Cubic   28.247 1 28.247 .233 .637
Error(speaker)   Linear   3729.382 14 266.384    
Quadratic   3598.582 14 257.042    
Cubic   1700.335 14 121.453    
style     Linear 15199.169 1 15199.169 28.524 .000
Quadratic 9184.584 1 9184.584 36.169 .000
Error(style)     Linear 7459.941 14 532.853    
Quadratic 3555.103 14 253.936    
method * speaker Linear Linear   2272.129 1 2272.129 35.654 .000
Quadratic   671.489 1 671.489 36.826 .000
Cubic   64.727 1 64.727 8.726 .010
Quadratic Linear   384.356 1 384.356 19.492 .001
Quadratic   327.434 1 327.434 20.125 .001
Cubic   25.306 1 25.306 2.036 .175
Error(method*speaker) Linear Linear   892.178 14 63.727    
Quadratic   255.279 14 18.234    
Cubic   103.851 14 7.418    
Quadratic Linear   276.066 14 19.719    
Quadratic   227.784 14 16.270    
Cubic   173.974 14 12.427    
method * style Linear   Linear 53.676 1 53.676 1.423 .253
Quadratic 372.432 1 372.432 4.119 .062
Quadratic   Linear .394 1 .394 .013 .911
Quadratic 22.509 1 22.509 .898 .359
Error(method*style) Linear   Linear 527.999 14 37.714    
Quadratic 1265.832 14 90.417    
Quadratic   Linear 421.207 14 30.086    
Quadratic 351.048 14 25.075    
speaker * style   Linear Linear 57.543 1 57.543 .716 .412
Quadratic 25.926 1 25.926 .480 .500
Quadratic Linear 1606.556 1 1606.556 22.744 .000
Quadratic 8.862 1 8.862 .128 .726
Cubic Linear 693.367 1 693.367 5.567 .033
Quadratic 7.469 1 7.469 .128 .726
Error(speaker*style)   Linear Linear 1125.785 14 80.413    
Quadratic 756.737 14 54.053    
Quadratic Linear 988.915 14 70.637    
Quadratic 971.889 14 69.421    
Cubic Linear 1743.729 14 124.552    
Quadratic 819.012 14 58.501    
method * speaker * style Linear Linear Linear 10.862 1 10.862 .707 .414
Quadratic 46.051 1 46.051 1.464 .246
Quadratic Linear 7.764 1 7.764 .612 .447
Quadratic 30.766 1 30.766 1.769 .205
Cubic Linear 71.297 1 71.297 4.591 .050
Quadratic 68.017 1 68.017 6.556 .023
Quadratic Linear Linear 100.056 1 100.056 2.031 .176
Quadratic 126.714 1 126.714 14.617 .002
Quadratic Linear 41.328 1 41.328 2.008 .178
Quadratic 95.775 1 95.775 7.947 .014
Cubic Linear 1.514 1 1.514 .085 .775
Quadratic 30.489 1 30.489 3.864 .069
Error(method*speaker*style) Linear Linear Linear 214.939 14 15.353    
Quadratic 440.456 14 31.461    
Quadratic Linear 177.502 14 12.679    
Quadratic 243.478 14 17.391    
Cubic Linear 217.413 14 15.530    
Quadratic 145.248 14 10.375    
Quadratic Linear Linear 689.560 14 49.254    
Quadratic 121.364 14 8.669    
Quadratic Linear 288.140 14 20.581    
Quadratic 168.734 14 12.052    
Cubic Linear 249.082 14 17.792    
Quadratic 110.472 14 7.891    
General Linear Model
Tests of Between-Subjects EffectsTests of Between-Subjects Effects, table, Measure, MEASURE_1, Transformed Variable, Average, 1 layers, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 6 rows
MEASURE_1 MEASURE_1
Average Average
Source Type III Sum of Squares df Mean Square F Sig.
Intercept 640563.338 1 640563.338 110.932 .000
Error 80840.966 14 5774.355    
1. method
EstimatesEstimates, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 1 levels of row headers, table with 5 columns and 7 rows
MEASURE_1 MEASURE_1
method Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 37.683 3.401 30.388 44.979
2 33.840 3.257 26.854 40.826
3 31.802 3.518 24.256 39.348
1. method
Pairwise ComparisonsPairwise Comparisons, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 2 levels of row headers, table with 7 columns and 13 rows
MEASURE_1 MEASURE_1
(I) method (J) method Mean Difference (I-J) Std. Error Sig.b 95% Confidence Interval for Differenceb
Lower Bound Upper Bound
1 2 3.844* 1.086 .010 .892 6.795
3 5.881* 1.539 .006 1.698 10.065
2 1 -3.844* 1.086 .010 -6.795 -.892
3 2.038 1.971 .956 -3.319 7.395
3 1 -5.881* 1.539 .006 -10.065 -1.698
2 -2.038 1.971 .956 -7.395 3.319
Based on estimated marginal means
*. The mean difference is significant at the
b. Adjustment for multiple comparisons: Bonferroni.
1. method
Multivariate TestsMultivariate Tests, table, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 8 rows
  Value F Hypothesis df Error df Sig.
Pillai's trace .683 14.005a 2.000 13.000 .001
Wilks' lambda .317 14.005a 2.000 13.000 .001
Hotelling's trace 2.155 14.005a 2.000 13.000 .001
Roy's largest root 2.155 14.005a 2.000 13.000 .001
Each F tests the multivariate effect of method. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.
a. Exact statistic
2. speaker
EstimatesEstimates, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 1 levels of row headers, table with 5 columns and 8 rows
MEASURE_1 MEASURE_1
speaker Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 40.341 3.828 32.131 48.550
2 32.532 3.406 25.227 39.838
3 30.026 3.166 23.236 36.816
4 34.868 3.358 27.666 42.070
2. speaker
Pairwise ComparisonsPairwise Comparisons, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 2 levels of row headers, table with 7 columns and 19 rows
MEASURE_1 MEASURE_1
(I) speaker (J) speaker Mean Difference (I-J) Std. Error Sig.b 95% Confidence Interval for Differenceb
Lower Bound Upper Bound
1 2 7.809* 1.922 .007 1.909 13.708
3 10.315* 2.263 .003 3.369 17.260
4 5.473 1.893 .071 -.336 11.282
2 1 -7.809* 1.922 .007 -13.708 -1.909
3 2.506 1.471 .663 -2.007 7.019
4 -2.336 1.549 .923 -7.091 2.419
3 1 -10.315* 2.263 .003 -17.260 -3.369
2 -2.506 1.471 .663 -7.019 2.007
4 -4.842* 1.464 .031 -9.334 -.350
4 1 -5.473 1.893 .071 -11.282 .336
2 2.336 1.549 .923 -2.419 7.091
3 4.842* 1.464 .031 .350 9.334
Based on estimated marginal means
*. The mean difference is significant at the
b. Adjustment for multiple comparisons: Bonferroni.
2. speaker
Multivariate TestsMultivariate Tests, table, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 8 rows
  Value F Hypothesis df Error df Sig.
Pillai's trace .620 6.533a 3.000 12.000 .007
Wilks' lambda .380 6.533a 3.000 12.000 .007
Hotelling's trace 1.633 6.533a 3.000 12.000 .007
Roy's largest root 1.633 6.533a 3.000 12.000 .007
Each F tests the multivariate effect of speaker. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.
a. Exact statistic
3. style
EstimatesEstimates, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 1 levels of row headers, table with 5 columns and 7 rows
MEASURE_1 MEASURE_1
style Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 38.023 3.656 30.182 45.865
2 40.274 3.753 32.225 48.323
3 25.028 3.000 18.593 31.462
3. style
Pairwise ComparisonsPairwise Comparisons, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 2 levels of row headers, table with 7 columns and 13 rows
MEASURE_1 MEASURE_1
(I) style (J) style Mean Difference (I-J) Std. Error Sig.b 95% Confidence Interval for Differenceb
Lower Bound Upper Bound
1 2 -2.251 1.181 .232 -5.462 .960
3 12.995* 2.433 .000 6.382 19.608
2 1 2.251 1.181 .232 -.960 5.462
3 15.246* 2.408 .000 8.703 21.790
3 1 -12.995* 2.433 .000 -19.608 -6.382
2 -15.246* 2.408 .000 -21.790 -8.703
Based on estimated marginal means
*. The mean difference is significant at the
b. Adjustment for multiple comparisons: Bonferroni.
3. style
Multivariate TestsMultivariate Tests, table, 1 levels of column headers and 1 levels of row headers, table with 6 columns and 8 rows
  Value F Hypothesis df Error df Sig.
Pillai's trace .742 18.734a 2.000 13.000 .000
Wilks' lambda .258 18.734a 2.000 13.000 .000
Hotelling's trace 2.882 18.734a 2.000 13.000 .000
Roy's largest root 2.882 18.734a 2.000 13.000 .000
Each F tests the multivariate effect of style. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.
a. Exact statistic
Estimated Marginal Means
4. method * speaker4. method * speaker, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 2 levels of row headers, table with 6 columns and 16 rows
MEASURE_1 MEASURE_1
method speaker Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 1 47.089 4.085 38.327 55.851
2 35.452 3.519 27.904 43.000
3 31.959 3.321 24.837 39.082
4 36.233 3.547 28.626 43.841
2 1 42.578 4.073 33.843 51.313
2 30.952 3.340 23.789 38.115
3 28.200 3.330 21.058 35.342
4 33.630 3.154 26.865 40.394
3 1 31.356 3.878 23.037 39.674
2 31.193 3.682 23.296 39.089
3 29.919 3.393 22.641 37.196
4 34.741 3.748 26.702 42.780
Estimated Marginal Means
5. method * style5. method * style, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 2 levels of row headers, table with 6 columns and 13 rows
MEASURE_1 MEASURE_1
method style Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 1 41.097 3.978 32.565 49.630
2 44.750 3.950 36.278 53.222
3 27.203 3.021 20.724 33.682
2 1 37.264 3.490 29.779 44.749
2 40.081 3.899 31.717 48.444
3 24.175 2.996 17.749 30.601
3 1 35.708 3.895 27.355 44.061
2 35.992 4.176 27.034 44.949
3 23.706 3.212 16.817 30.594
Estimated Marginal Means
6. speaker * style6. speaker * style, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 2 levels of row headers, table with 6 columns and 16 rows
MEASURE_1 MEASURE_1
speaker style Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 1 45.870 4.154 36.962 54.779
2 46.333 4.654 36.350 56.316
3 28.819 3.369 21.594 36.043
2 1 35.770 3.708 27.817 43.724
2 38.907 3.882 30.582 47.233
3 22.919 3.672 15.043 30.794
3 1 29.544 3.923 21.131 37.958
2 35.678 3.388 28.412 42.944
3 24.856 3.288 17.803 31.908
4 1 40.907 4.181 31.941 49.874
2 40.178 3.738 32.161 48.195
3 23.519 2.935 17.225 29.813
Estimated Marginal Means
7. method * speaker * style7. method * speaker * style, table, Measure, MEASURE_1, 1 layers, 2 levels of column headers and 3 levels of row headers, table with 7 columns and 40 rows
MEASURE_1 MEASURE_1
method speaker style Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 1 1 52.133 4.437 42.617 61.650
2 53.956 4.963 43.312 64.599
3 35.178 3.543 27.579 42.776
2 1 38.600 4.283 29.415 47.785
2 44.222 4.093 35.444 53.001
3 23.533 3.654 15.695 31.371
3 1 31.344 4.420 21.866 40.823
2 37.700 3.709 29.744 45.656
3 26.833 3.430 19.477 34.189
4 1 42.311 4.543 32.567 52.055
2 43.122 3.917 34.722 51.523
3 23.267 3.151 16.508 30.025
2 1 1 48.211 4.678 38.177 58.245
2 51.333 5.182 40.218 62.448
3 28.189 3.722 20.207 36.171
2 1 34.533 3.521 26.982 42.085
2 36.689 3.891 28.344 45.034
3 21.633 3.768 13.552 29.715
3 1 26.944 3.692 19.026 34.863
2 33.622 3.765 25.547 41.697
3 24.033 3.570 16.376 31.691
4 1 39.367 3.838 31.134 47.599
2 38.678 3.732 30.673 46.683
3 22.844 2.890 16.645 29.044
3 1 1 37.267 4.376 27.880 46.653
2 33.711 4.458 24.150 43.272
3 23.089 3.845 14.843 31.335
2 1 34.178 3.896 25.822 42.533
2 35.811 4.523 26.110 45.512
3 23.589 3.730 15.590 31.588
3 1 30.344 3.913 21.951 38.737
2 35.711 3.984 27.166 44.256
3 23.700 3.491 16.213 31.187
4 1 41.044 4.694 30.976 51.113
2 38.733 4.539 28.998 48.468
3 24.444 3.019 17.969 30.920
Profile Plots
method: 3 Estimated Marginal Means: 31.802 method: 2 Estimated Marginal Means: 33.84 method: 1 Estimated Marginal Means: 37.683 method: 2 Estimated Marginal Means: 33.84 method: 1 Estimated Marginal Means: 37.683 32.00 34.00 36.00 38.00 38.00 36.00 34.00 32.00 1 2 3 3 2 1
Profile Plots
speaker: 4 Estimated Marginal Means: 34.868 speaker: 3 Estimated Marginal Means: 30.026 speaker: 2 Estimated Marginal Means: 32.532 speaker: 1 Estimated Marginal Means: 40.341 speaker: 3 Estimated Marginal Means: 30.026 speaker: 2 Estimated Marginal Means: 32.532 speaker: 1 Estimated Marginal Means: 40.341 30.00 32.00 34.00 36.00 38.00 40.00 42.00 42.00 40.00 38.00 36.00 34.00 32.00 30.00 1 2 3 4 4 3 2 1
Profile Plots
style: 3 Estimated Marginal Means: 25.028 style: 2 Estimated Marginal Means: 40.274 style: 1 Estimated Marginal Means: 38.023 style: 2 Estimated Marginal Means: 40.274 style: 1 Estimated Marginal Means: 38.023 25.00 30.00 35.00 40.00 45.00 45.00 40.00 35.00 30.00 25.00 1 2 3 3 2 1
Profile Plots
method: 3 Estimated Marginal Means: 34.741 speaker: 4 method: 2 Estimated Marginal Means: 33.63 speaker: 4 method: 1 Estimated Marginal Means: 36.233 speaker: 4 method: 3 Estimated Marginal Means: 29.919 speaker: 3 method: 2 Estimated Marginal Means: 28.20 speaker: 3 method: 1 Estimated Marginal Means: 31.959 speaker: 3 method: 3 Estimated Marginal Means: 31.193 speaker: 2 method: 2 Estimated Marginal Means: 30.952 speaker: 2 method: 1 Estimated Marginal Means: 35.452 speaker: 2 method: 3 Estimated Marginal Means: 31.356 speaker: 1 method: 2 Estimated Marginal Means: 42.578 speaker: 1 method: 1 Estimated Marginal Means: 47.089 speaker: 1 method: 2 Estimated Marginal Means: 33.63 speaker: 4 method: 1 Estimated Marginal Means: 36.233 speaker: 4 method: 2 Estimated Marginal Means: 28.20 speaker: 3 method: 1 Estimated Marginal Means: 31.959 speaker: 3 method: 2 Estimated Marginal Means: 30.952 speaker: 2 method: 1 Estimated Marginal Means: 35.452 speaker: 2 method: 2 Estimated Marginal Means: 42.578 speaker: 1 method: 1 Estimated Marginal Means: 47.089 speaker: 1 25.00 30.00 35.00 40.00 45.00 50.00 50.00 45.00 40.00 35.00 30.00 25.00 1 2 3 3 2 1
Profile Plots
method: 3 Estimated Marginal Means: 23.706 style: 3 method: 2 Estimated Marginal Means: 24.175 style: 3 method: 1 Estimated Marginal Means: 27.203 style: 3 method: 3 Estimated Marginal Means: 35.992 style: 2 method: 2 Estimated Marginal Means: 40.081 style: 2 method: 1 Estimated Marginal Means: 44.75 style: 2 method: 3 Estimated Marginal Means: 35.708 style: 1 method: 2 Estimated Marginal Means: 37.264 style: 1 method: 1 Estimated Marginal Means: 41.097 style: 1 method: 2 Estimated Marginal Means: 24.175 style: 3 method: 1 Estimated Marginal Means: 27.203 style: 3 method: 2 Estimated Marginal Means: 40.081 style: 2 method: 1 Estimated Marginal Means: 44.75 style: 2 method: 2 Estimated Marginal Means: 37.264 style: 1 method: 1 Estimated Marginal Means: 41.097 style: 1 20.00 25.00 30.00 35.00 40.00 45.00 45.00 40.00 35.00 30.00 25.00 20.00 1 2 3 3 2 1
Profile Plots
speaker: 4 Estimated Marginal Means: 23.519 style: 3 speaker: 3 Estimated Marginal Means: 24.856 style: 3 speaker: 2 Estimated Marginal Means: 22.919 style: 3 speaker: 1 Estimated Marginal Means: 28.819 style: 3 speaker: 4 Estimated Marginal Means: 40.178 style: 2 speaker: 3 Estimated Marginal Means: 35.678 style: 2 speaker: 2 Estimated Marginal Means: 38.907 style: 2 speaker: 1 Estimated Marginal Means: 46.333 style: 2 speaker: 4 Estimated Marginal Means: 40.907 style: 1 speaker: 3 Estimated Marginal Means: 29.544 style: 1 speaker: 2 Estimated Marginal Means: 35.77 style: 1 speaker: 1 Estimated Marginal Means: 45.87 style: 1 speaker: 3 Estimated Marginal Means: 24.856 style: 3 speaker: 2 Estimated Marginal Means: 22.919 style: 3 speaker: 1 Estimated Marginal Means: 28.819 style: 3 speaker: 3 Estimated Marginal Means: 35.678 style: 2 speaker: 2 Estimated Marginal Means: 38.907 style: 2 speaker: 1 Estimated Marginal Means: 46.333 style: 2 speaker: 3 Estimated Marginal Means: 29.544 style: 1 speaker: 2 Estimated Marginal Means: 35.77 style: 1 speaker: 1 Estimated Marginal Means: 45.87 style: 1 20.00 25.00 30.00 35.00 40.00 45.00 50.00 50.00 45.00 40.00 35.00 30.00 25.00 20.00 1 2 3 4 4 3 2 1
IBM SPSS Web Report
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