In experimental psychology one of the generally accepted practices in data analysis is the aggregation on subject level. For example, several measurements of reaction time is gathered for each subject using different stimuli, then mean reaction time is computed for each subject. Those scores are further analyzed by ANOVA or other methods.
My questions are:
- When this procedure is more appropriate than a simple analysis of raw data (i.e. the analysis without any aggregation)? And when analysis of raw data is more appropriate?
- Has anyone met the discussion of these question in literature?
UPD: To give an example, suppose I'm interested in the effect of a drug on a speed of face detection. So I for each participant I have the data for drug and placebo condition (within subject design). I can analyze a) answers for each face for each subject for each condition, b) answers for each subject aggregated by condition, and, perhaps c) answers for each face aggregated by condition. When (a) is better than (b) and when it is otherwise?