Facets are similar to the "factors" used in analysis of variance, and may include persons, raters, items/forms, time, and settings among other possibilities.
The answers to these questions will vary from one researcher to the next, and will drive the design of a G study in different ways.
For example, a soft drink company might be interested in assessing the quality of a new product through use of a consumer rating scale.
This may be suitable in the context of highly controlled laboratory conditions, but variance is a part of everyday life.
The advantage of G theory lies in the fact that researchers can estimate what proportion of the total variance in the results is due to the individual factors that often vary in assessment, such as setting, time, items, and raters.
Another important difference between CTT and G theory is that the latter approach takes into account how the consistency of outcomes may change if a measure is used to make absolute versus relative decisions.
In contrast, an example of a relative, or norm-referenced, decision would be when the individual's test score is used to either (a) determine relative standing as compared to his/her peers (i.e. a child's score on a reading subtest is used to determine which reading group he/she is placed in), or (b) make intra-individual comparisons (i.e. comparing previous versus current performance within the same individual).