| M3 head tracking | + stereo display |
| M2 head tracking | + mono display |
| M1 no tracking | + stereo display |
| M0 no tracking | + mono display |
NB There is an ambiguity with what is meant by no tracking. Provisionally, we shall mean no head tracking. The wand must be tracked for now since that's the navigation device.
The result of a game, the score, is the numerical output. Since the user can learn the game, we shall need at least 24 naive test subjects. Nonetheless, we cannot control for native navigation and game playing skills regardless of which mode. Maybe one should calibrate the player by a different game which requires similar skills (navigation, aim etc). Or, we need multiple subjects in each of the 24 classes. I suggest that we use the 4! model first to formulate an hypothesis, and then redesign the second experiment with fewer modes but better sampling.
Or, we need multiple subjects in each of the 24 classes. I suggest that we use the 4! model first to formulate an hypothesis, and then redesign the second experiment with fewer modes but better sampling.
Or, Alex suggests, we have each subject do 4 runs but only in one mode. This would reduce the number of subjects needed.
The statistical model is a very simple one, to estimate the additive influence of the two factors on the ability to navigate and manipulate an immersive virtual environment, as measured by the game score. Second, to estimate which of the two factors is the more significant.