The data sgp package offers an efficient means of organizing longitudinal (time dependent) student assessment data into statistical growth plots. This package uses quantitativeile regression to estimate the conditional density associated with each student’s achievement history and then generates percentile growth projections or trajectories that show the growth needed to reach future achievement targets.
The lower level functions that do the calculations, such as studentGrowthPercentiles and studentGrowthProjections, require WIDE formatted data. However, the higher level functions that provide wrappers for the lower level functions, such as summarizeSGP and studentSGPAverageGrowths, can use LONG formatted data. If you are running anything but the most basic analyses, we recommend that you supply your data in the LONG format.
In addition to the function to generate statistical growth plots, data sgp contains other functions for analyzing student achievement data and creating descriptive statistics such as average scores, class means, and student distributions. These functions are very helpful in evaluating the quality of student assessment systems and making decisions about where to focus resources.
For example, students who score below a certain threshold can be identified for special assistance. The goal of this process is to identify students who need extra support and then work with their teachers to help them improve their performance. This is particularly important for low performing schools.
Other purposes of the SGP package are to create and visualize longitudinal statistical models and to evaluate educational policies. To this end, the SGP package includes a number of functions to create statistical models and graphics such as the linear model, graphical mean comparisons, and covariance matrix. These tools allow researchers to easily examine and compare the impact of different policy interventions on student outcomes.
Moreover, the SGP package also allows users to visualize the relationship between student variables by displaying them in correlation and scatterplot graphs. Using these graphs, users can see how student variables are related and which ones are most important for student achievement. Lastly, the SGP package can also help researchers make informed choices about educational policies and programs by estimating student growth and forecasting the impact of various policy options. This information can be very valuable to school districts, state governments, and federal agencies. It can also be used to guide decisions about funding and resource allocation. In addition, it can be useful to research organizations and individual researchers. The SGP package also includes examples of WIDE and LONG formatted data sets sgptData_WIDE and sgpData_LONG, to assist in setting up your own data set.