Consider a study addressing the consequences of adding white noise to the comprehension of words presented auditorily over headphones to a group of subjects, using auditory lexical decision latencies as a measure of speed of lexical access. In such a study, the presence or absence of white noise would be the treatment factor, with two levels (noise versus no noise). In addition, we would need identifiers for the individual words (items), and identifiers for the individual participants (or subjects) in the experiment. The item and subject factors, however, differ from the treatment factor in that we would normally only regard the treatment factor as repeatable.

A factor is repeatable, if the set of possible levels for that factor is fixed, and if, moreover, each of these levels can be repeated. In our example, the treatment factor is repeatable, because we can take any new acoustic signal and either add or not add a fixed amount of white noise. We would not normally regard the identifiers of items or subjects as repeatable. Items and subjects are sampled randomly from populations of words and participants, and replicating the experiment would involve selecting other words and other participants. For these new units, we would need new identifiers. In other words, we would be introducing new levels of these subject and item factors in the experiment that had not been seen previously.