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The sgpData package offers several percentile rank tables that compare student performance relative to academic peers, making this data set especially valuable when including multiple years of assessments. For instance, the sgpData_CONTENTS table can show how much each content area was covered over five years of testing; this may serve as a good indication of gaps in learning or areas needing additional instruction as well as serve as the starting point for conversations among curriculum coordinators and principals about ways of meeting such needs.
Student Growth Percentiles (SGPs) are calculated using a statistical technique that compares current test scores with students’ historical growth trajectories. Quantile regression can be used to describe this relationship between previous score and current one; academic peers refers to all students in the same grade, subject, and assessment year who have experienced a similar progression of scores; growth percentiles indicate how far apart their current test score lies from this mean value of these academic peers.
The Data SGP Package includes four exemplar data sets to aid users when preparing data for SGP analyses: SgpData, sgpData_LONG, sgptData_LONG and sgptData_LONG as well as INSTRUCTOR_NUMBER used by the SGP Package as models for users preparing their own SGP analyses. These contain historical Star examinees with their associated growth trajectories along with teacher level summary information using INSTRUCTOR_NUMBER’s teacher lookup table that allows teachers to be assigned per test record. WIDE formatted data works better when used with lower level functions like studentGrowthPercentiles or studentGrowthProjections while operational analyses should use LONG formatted data sets as these are easier to manage than their counterparts WIDE counterparts.
In addition to Standard Growth Parameters (SGPs), the sgpData packages offer window specific SGPs calculated for Star examinees who have taken multiple tests over different windows. These window specific SGPs take into account both recent tests taken during one window as well as prior exams from prior windows that can be selected when creating reports in Star Report Builder.
The sgpData packages are intended to be used with R, an open-source and free software environment. Although no prior knowledge of R is necessary to perform SGP analyses, it is strongly encouraged. CRAN provides many resources that can help you learn R. The packages can be downloaded for both Windows and OSX computers – though Windows tends to provide faster performance due to being more CPU intensive for running complex SGP analyses than standard Star reports.
