The purpose of this study is to introduce method on the analysis of Latent Growth Model(LGM) with a Cohort Sequential Design(CSD). LGM is a relatively new statistical model for the analysis of repeated measures data. LGM combines elements of MANOVA and structural equation modeling (SEM) to capture aspects of longitudinal change. Unlike MANOVA, LGM takes into account variance in the latent variable and differs from traditional SEM in that it computes a mean for the latent variable. CSD is linked overlapping measurements of cohorts. (note: The example, CSD depicts the generation of six-year growth trajectory (grades 1 through 6) from four years of data (wave 1=first, second, third, fourth; wave 2= third, fourth, fifth, sixth). Therefore, the follow-up period for data collection is shorter, thus reducing expenses and attrition and unlike panel data that confound age effects (those related to maturation) and period effects (those due to historical events), observations between same-age participants at time 1, time 2, time 3, time 4 can be contrasted. The RM ANOVA tests for differences between the means and for linear trends. But The LGM analysis estimates nonlinear trends, variances of measured and latent variables, relationship between latent variable variances, and means of latent variables (initial level and rate of change). LGM can be a very useful statistical model for the research in exercise science and physical education field.