This course comprises a series of introductory workshops for students facing the challenges of using ‘real-world’ datasets.
The course commences the week beginning 26 February 2024 and includes three 60-minute webinars on consecutive weeks: Wednesday 28 February; Wednesday 6 March and Wednesday 13 March 2024 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 is 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 uses 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 is aimed at undergraduates or recent post-graduates with some knowledge of Stata and a basic understanding of statistical techniques. Participants will require access to a computer with Stata version 13 or newer installed (including during the webinar sessions).
Timing and access
The course commences the week beginning 26 February 2024 and includes three 60-minute webinars on consecutive weeks: Wednesday 28 February; Wednesday 6 March and Wednesday 13 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:
Understanding and Accessing Longitudinal Study Data
Preparing a Dataset for Analysis; and
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).
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
If you have any questions or require further information about this course, please contact CLOSER Digital Communications and Events Manager, Jennie Blows at firstname.lastname@example.org
CLOSER Learning Hub
Interested in finding out more about longitudinal population studies and research in general?
Take a look at our Learning Hub – a free educational resource designed to introduce longitudinal population studies to beginners.
The Learning Hub includes information and resources covering the basics of longitudinal population studies, study design, and analysis techniques. Users can access learning modules, teaching datasets, and a suite of research case studies, which explore how longitudinal population studies can be used to investigate topics such as obesity, social mobility, and childhood bullying.
Explore the CLOSER Learning Hub