|a.||Assume an addition model, calculate the cell entries for the factorial table, and plot these values, showing that they exhibit parallelism.|
|b.||In this same 2 × 5 design, suppose the row stimuli have values a and b, and the column stimuli have values v, w, x, y, and z. Assume an addition model and write down the entry in each cell of the factorial data table.|
|c.||How does the factorial table for (b) reveal parallelism?|
Get marginal means for the data of Exercise a2 and check that this formula is correct. (As an optional exercise, derive this formula algebraically.)a5.
cell mean = row mean + column mean - overall mean.
|a.||How, according to the parallelism theorem, do the data of Figure 20.3 relate to the subjective values of heaviness?|
|b.||The psychophysical function is the function relating the subjective value of some sensory dimension to the objective, physical magnitude. How does the measurement of (a) solve the problem of determining the psychophysical function for heaviness?|
|c.||Why go to this illusion to get subjective heaviness? Why not eliminate the illusion by concealing the weights behind a screen and ask subjects to judge heaviness of these unseen lifted weights?|
a6. In Figure 21.4: a. Why is the parallelism essential to show that bimodality is real? b. Explain bimodality in terms of propensity to approach and avoid goals. c. Outline an experiment to test bipolarity with another stimulus class.