Mplus Users Guide v6.. - Muthén & Muthén

CHAPTER 6y1 y2 y3 y4isx a21 a22 a23 a24In this example, the multiple group multiple cohort growth model shownin the picture above is estimated. Longitudinal research studies oftencollect data on several different groups of individuals defined by theirbirth year or cohort. This allows the study of development over a widerage range than the length of the study and is referred to as an acceleratedor sequential cohort design. The interest in these studies is thedevelopment of an outcome over age not measurement occasion. Thiscan be handled by rearranging the data so that age is the time axis usingthe DATA COHORT command or using a multiple group approach asdescribed in this example. The advantage of the multiple groupapproach is that it can be used to test assumptions of invariance ofgrowth parameters across cohorts.In the multiple group approach the variables in the data set represent themeasurement occasions. In this example, there are four measurementoccasions: 2000, 2002, 2004, and 2006. Therefore there are fourvariables to represent the outcome. In this example, there are threecohorts with birth years 1988, 1989, and 1990. It is the combination ofthe time of measurement and birth year that determines the agesrepresented in the data. This is shown in the table below where rowsrepresent cohort and columns represent measurement occasion. The130