Longitudinal data were introduced as a special case of repeated measures in \nChapter 5. Such data arise when subjects are measured on the same variable (or, \nin some cases, variables), on…
Longitudinal data were introduced as a special case of repeated measures in \nChapter 5. Such data arise when subjects are measured on the same variable (or, \nin some cases, variables), on several different occasions. Because the repeated \nmeasures arise, in this case, solely from the passing of time, there is no possibility of randomizing the "occasions," and it is this that essentially differentiates \nlongitudinal data from other repeated measure situations arising in psychology, \nwhere the subjects are observed under different conditions, or combinations of \nconditions, which are usually given in different orders to different subjects. The \nspecial structure of longitudinal data makes the sphericity condition described in \nChapter 5, and methods that depend on it for their validity, very difficult to justify. \nWith longitudinal data it is very unlikely that the measurements taken close to \none another in time will have the same correlation as measurements made at more \nwidely spaced time intervals.