For decades, not only have exclusionary punishment practices forcibly removed children from educational opportunities and fueled the school-to-prison pipeline they have also indirectly negatively affected students who attend high-suspension rate schools. A broad range of studies have documented the suspension inequality that largely affects students of colour, students with disabilities, LGBTQ+ students and students from socioeconomically disadvantaged neighbourhoods. In this study, I attempt to add to the current literature by exploring if any association between school expenditures and suspension rates exists. Although there is some relationship discovered, it is not the best fit model and therefore, causality cannot be found. In the later half of the paper, I discuss how causality can be found with the right study and a discussion on the necessary steps to lower suspension rates for the benefit of all students and staff.
Raw data can be found here: https://www.datalumos.org/datalumos/project/103004/version/V1/view;jsessionid=8B19F77635B6CB11BE66A6B2292453D5
In order to run both the 00-EDA and 00-analysis-and-modelling R script with no changes, download the dataset and save it in the project folder as "inputs/data/CRDC-2015-16-School-Data.csv"
If you want to play with some of the data, please visit this link: https://rachaelal.shinyapps.io/edu-suspension-app/
'Inputs' are everything that will not be edited, which includes raw data and references. 'Outputs' are everything that will be created and further modified, which includes the r markdown and the paper.