(1)What Are the Assumptions?

Within-Subjects Factors
Measure: MEASURE_1
lecturer / Dependent Variable
1 / tutor1
2 / tutor2
3 / tutor3
4 / tutor4
Descriptive Statistics
M / SD / N
Dr. Field / 68.88 / 5.643 / 8
Dr. Smith / 64.25 / 4.713 / 8
Dr.Scrote / 65.25 / 6.923 / 8
Dr. Death / 57.38 / 7.909 / 8
Descriptive Statistics
N / Minimum / Maximum / M / SD
Dr. Field / 8 / 62 / 78 / 68.88 / 5.643
Dr. Smith / 8 / 58 / 73 / 64.25 / 4.713
Dr. Scrote / 8 / 54 / 75 / 65.25 / 6.923
Dr. Death / 8 / 45 / 65 / 57.38 / 7.909
Valid N (listwise) / 8
Multivariate Testsa
Effect / Value / F / Hypothesis df / Error df / Sig. / Partial Eta Squared
Lecturer / Pillai's Trace / .741 / 4.760b / 3.000 / 5.000 / .063 / .741
Wilks' Lambda / .259 / 4.760b / 3.000 / 5.000 / .063 / .741
Hotelling's Trace / 2.856 / 4.760b / 3.000 / 5.000 / .063 / .741
Roy's Largest Root / 2.856 / 4.760b / 3.000 / 5.000 / .063 / .741
a. Design: Intercept
Within Subjects Design: lecturer
b. Exact statistic
Mauchly's Test of Sphericitya
Measure: MEASURE_1
Within Subjects Effect / Mauchly's W / Approx. Chi-Square / df / Sig. / Epsilonb
Greenhouse-Geisser / Huynh-Feldt / Lower-bound
Lecturer / .131 / 11.628 / 5 / .043 / .558 / .712 / .333
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: lecturer
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.
Tests of Within-Subjects Effects
Measure: MEASURE_1
Source / Type III Sum of Squares / df / Mean Square / F / Sig. / Partial Eta Squared
Lecturer / Sphericity Assumed / 554.125 / 3 / 184.708 / 3.700 / .028 / .346
Greenhouse-Geisser / 554.125 / 1.673 / 331.245 / 3.700 / .063 / .346
Huynh-Feldt / 554.125 / 2.137 / 259.329 / 3.700 / .047 / .346
Lower-bound / 554.125 / 1.000 / 554.125 / 3.700 / .096 / .346
Error(Lecturer) / Sphericity Assumed / 1048.375 / 21 / 49.923
Greenhouse-Geisser / 1048.375 / 11.710 / 89.528
Huynh-Feldt / 1048.375 / 14.957 / 70.091
Lower-bound / 1048.375 / 7.000 / 149.768
Tests of Within-Subjects Contrasts
Measure: MEASURE_1
Source / Lecturer / Type III Sum of Squares / df / Mean Square / F / Sig. / Partial Eta Squared
Lecturer / Linear / 448.900 / 1 / 448.900 / 4.938 / .062 / .414
Quadratic / 21.125 / 1 / 21.125 / .586 / .469 / .077
Cubic / 84.100 / 1 / 84.100 / 3.689 / .096 / .345
Error(Lecturer) / Linear / 636.400 / 7 / 90.914
Quadratic / 252.375 / 7 / 36.054
Cubic / 159.600 / 7 / 22.800
Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source / Type III Sum of Squares / df / Mean Square / F / Sig. / Partial Eta Squared
Intercept / 130816.125 / 1 / 130816.125 / 8858.166 / .000 / .999
Error / 103.375 / 7 / 14.768

Estimated Marginal Means

1. Grand Mean
Measure: MEASURE_1
M / Std. Error / 95% Confidence Interval
Lower Bound / Upper Bound
63.938 / .679 / 62.331 / 65.544

2. Lecturer

Estimates
Measure: MEASURE_1
lecturer / M / Std. Error / 95% Confidence Interval
Lower Bound / Upper Bound
1 / 68.875 / 1.995 / 64.158 / 73.592
2 / 64.250 / 1.666 / 60.310 / 68.190
3 / 65.250 / 2.448 / 59.462 / 71.038
4 / 57.375 / 2.796 / 50.763 / 63.987
Pairwise Comparisons
Measure: MEASURE_1
(I) lecturer / (J) lecturer / Mean Difference (I-J) / Std. Error / Sig.b / 95% Confidence Interval for Differenceb
Lower Bound / Upper Bound
1 / 2 / 4.625* / 1.085 / .022 / .682 / 8.568
3 / 3.625 / 2.841 / 1.000 / -6.703 / 13.953
4 / 11.500 / 4.675 / .261 / -5.498 / 28.498
2 / 1 / -4.625* / 1.085 / .022 / -8.568 / -.682
3 / -1.000 / 2.563 / 1.000 / -10.320 / 8.320
4 / 6.875 / 4.377 / .961 / -9.039 / 22.789
3 / 1 / -3.625 / 2.841 / 1.000 / -13.953 / 6.703
2 / 1.000 / 2.563 / 1.000 / -8.320 / 10.320
4 / 7.875 / 4.249 / .637 / -7.572 / 23.322
4 / 1 / -11.500 / 4.675 / .261 / -28.498 / 5.498
2 / -6.875 / 4.377 / .961 / -22.789 / 9.039
3 / -7.875 / 4.249 / .637 / -23.322 / 7.572
Based on estimated marginal means
*. The mean difference is significant at the
b. Adjustment for multiple comparisons: Bonferroni.
Multivariate Tests
Value / F / Hypothesis df / Error df / Sig. / Partial Eta Squared
Pillai's trace / .741 / 4.760a / 3.000 / 5.000 / .063 / .741
Wilks' lambda / .259 / 4.760a / 3.000 / 5.000 / .063 / .741
Hotelling's trace / 2.856 / 4.760a / 3.000 / 5.000 / .063 / .741
Roy's largest root / 2.856 / 4.760a / 3.000 / 5.000 / .063 / .741
Each F tests the multivariate effect of lecturer. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.
a. Exact statistic