This course comprises a series of introductory workshops for students facing the challenges of using ‘real-world’ datasets.
The course commences the week beginning 6th March 2023 and includes three 60-minute webinars on consecutive weeks: Wednesday 8th March; Wednesday 15th March and Thursday 23rd March 2023 from 13.00-14.00 GMT.
Are you new to using longitudinal population study data, or are you looking to use ‘real-world’ datasets for the first time? Perhaps you are looking to use longitudinal population study datasets for your Dissertation but are unsure where to start?
This course was designed to help you become familiar with the basics of data access, how to clean and prepare datasets, and how to use, report and interpret outputs of analysis. It used real datasets from longitudinal population studies as guided examples, with interactive quizzes and hands-on analysis tasks to help you become confident in using real-world longitudinal datasets.
Prerequisites: This course was aimed at undergraduates or recent post-graduates with some knowledge of Stata and a basic understanding of statistical techniques.
Timing and access
The course commences the week beginning 6th March 2023 and includes three 60-minute webinars on consecutive weeks: Wednesday 8th March; Wednesday 15th March and Thursday 23rd March 2023 from 13.00-14.00 GMT. Requires 6-8 hours study, including participation in the webinars.
Who is this course for?
The course is designed for undergraduate and master’s students in social sciences or epidemiology/health research who are interested in learning more about how to access, prepare and analyse real-world longitudinal quantitative data. It is designed as a refresher on existing learning to provide guidance on undertaking quantitative data research in practice.
What prior knowledge do I need?
The content of the course assumes some knowledge of basic statistical techniques (descriptive statistics, regression modelling) and requires some previous experience of using Stata (some familiarity with basic commands). It is suitable for undergraduate-level students and would be particularly useful for students with some quantitative experience. There will be some preparatory reading to do before each session.
How do I use the course?
You will need to register and log-in in advance of the sessions to confirm your participation and to access the course materials. Once you have registered you will be instructed on how to create a free UKDS account so that you can access and download the datasets used in the course.
The course is divided into three sessions: 1) Understanding and Accessing Longitudinal Study Data, 2) Preparing a Dataset for Analysis; and 3) Producing and Reporting Descriptive Statistics and Regression Analyses in Stata. In addition to the interactive webinars, course materials will include quizzes to test your knowledge, assignments to practise in your own time and access to web-based resources (reading lists, useful links, FAQs).
Course team
- Dr Neil Kaye, Research Fellow at CLOSER, UCL Institute of Education
- Beate Lichtwardt, Senior Training and Support Officer at UK Data Service
- Charlotte Campbell, Research Fellow at CLOSER, UCL Institute of Education
Further information
If you have any questions or require further information about this course, please contact CLOSER Digital Communications and Events Manager, Jennie Blows at j.blows@ucl.ac.uk
CLOSER Learning Hub
Interested in finding out more about longitudinal studies and research in general?
Take a look at the CLOSER Learning Hub – an educational resource designed to introduce longitudinal studies to beginners.
The online platform includes information and resources covering basics of longitudinal studies, study design, and analysis techniques and has recently been refreshed to include more content, a new, focused look and additional subject areas. Users can access learning modules, teaching datasets, and research case studies, which explore how longitudinal studies can be used to investigate topics such as obesity, social mobility, and childhood bullying.
Explore the CLOSER Learning Hub