Contrast A contrast is used to test the hypothesis that the mean of the three smile conditions is different from the mean of the neutral condition. The results of the analysis using SAS JMP are shown below. The coefficients for each of the three smile conditions is 0.333; the coefficient for the neutral condition is -1. Note that the sum of the coefficients is 0.
How this was done The value obtained by applying the coeffients to the sample mean is 0.9461. JMP labels this as the "Estimate" since it is an estimate of what you would get if you applied the coefficents to the population means.
What can be deduced from an 'estimate' of 0.9461? The smile means are lower (on average) than the neutral mean. The smile means are statistically signficantly lower than the neutral mean. The smile means are higher (on average) than the neutral mean. The smile means are statistically signficantly higher than the neutral mean.
Can the null hypothesis that the average of the three smiling conditions is equal to the neutral condition be rejected? Yes No
General Linear Models Procedure Dunnett's T tests for variable: LENIENCY NOTE: This tests controls the type I experimentwise error for
comparisons of all treatments against a control.
Alpha= 0.05 Confidence= 0.95 df= 132 MSE= 2.648897
Critical Value of Dunnett's T= 2.376
Minimum Significant Difference= 0.938
Comparisons significant at the 0.05 level are indicated by '***'.
General Linear Models Procedure
Simultaneous Simultaneous
Lower Difference Upper
COND Confidence Between Confidence
Comparison Limit Means Limit
false - neutral 0.3120 1.2500 2.1880 ***
felt - neutral -0.1439 0.7941 1.7321
miserable - neutral -0.1439 0.7941 1.7321 How this was done
Which comparison of conditions is significant at the 0.05 level? false - neutral felt - neutral miserable - neutral none of the above
Contrast DF Contrast SS Mean Square F Value Pr > F false versus felt 1 3.5330882 3.5330882 1.33 0.2502 false versus miserab 1 3.5330882 3.5330882 1.33 0.2502 felt versus miserabl 1 0.0000000 0.0000000 0.00 1.0000 How this was done