This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These students are in turn nested within a school. Under the circumstance, this model attempts to estimate three performance indicators: initial status, growth rate, and educational gap parameters across different cohorts. Specifically, one can see a longitudinal pattern where each cohort of students within a school starts or ends, how much it gains/grows within a specific period of time, and how much the initial gap between initially-low performing students and initially-high performing students is magnified or diminished. Furthermore, these three performance indicators of different cohorts are also modeled as a function of cohort and school background characteristics, in order to examine the extent to which differences or fluctuations across different cohorts within schools. are related to differences in cohorts' and schools' characteristics. This longitudinal and multiple-cohorts perspective is important because particular school reform efforts or changes in school characteristics that may impact school-wide or particular grade level achievement might take place in some years and not others (e.g., increment of school budget, numbers of qualified teachers, student demographic composition, etc.). As such, this model is distinguished from the current value-added models in a way that provides us with a more comprehensive picture of school's performance based on student growth over time and the distribution of student growth across cohorts within a school. Appended are: (1) The percentage of students (by cohort and school) eligible to receive free/reduced price lunch; and (2) Observed mean gain scores by cohort and school. (Contains 5 tables and 4 figures.)