Statistical Modelling for University Administrators using R
Are you working in Student Analytics?
Ever been asked if the average mark is changing over academic years, or if the rate of change is different for females and males?
Or which factors are associated with non-continuation?
Or if the chance of achieving a first class honours degree is associated with tariff points on entry?
This one-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees. The focus of the course is on the use of two statistical modelling techniques:
The course also illustrates how these modelling techniques may be used for one-step-ahead forecasting into next year.
Presentations, demonstrations and hands-on computer practicals are based around the free statistical software R; see https://www.r-project.org/. Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.
All training is online and will be delivered live on each day between 09:30 and 16:00 (GMT+1). The delivery platform is Zoom, which may be freely accessed. Questions may be asked using Zoom's chat box. Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support. During presentations, the team member who is not speaking can take questions in addition to the presenter.
Who Should Attend?
Administrators in educational establishments working in Policy, Planning and Strategy units; Business Intelligence units; those involved in extracting actionable insights from student records and in reporting to policy makers or committees. Anyone in these positions needing to answer questions around how student outcomes may be associated with different factors will benefit greatly from this course.
It is assumed that participants will, prior to the course, have:
How You Will Benefit
By the end of the course you will be familiar with two common statistical modelling methods for investigating associations and extracting actionable insights, be able to report the results in plain language, and be able to perform analyses using free statistical software. You will also be able to follow official guidance on the use of such models, e.g. the Office for Students’ guidance on the use of binary logistic regression for investigating the effectiveness of financial support with respect to student outcomes.
What Do We Cover?
Practical work will be done in R.
The R software is used on the course for two reasons:
Related Courses: This is part of a series of short courses for HE professionals working in administrative, planning and support roles: