EH158-7-SL-CO:
Longitudinal Data Analysis
2022/23
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer & Long Vacation
Postgraduate: Level 7
Current
Monday 24 April 2023
Wednesday 04 October 2023
15
03 February 2023
Requisites for this module
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Longitudinal data are an essential tool for researchers as they can help answer questions about change in time, causal relationships and the timing of events. They come in many shapes, from traditional panel surveys to social media and sensor data. Because of their additional complexity, specialized statistical models are needed to analyse them.
In this course you will learn how to analyse longitudinal data using R. The course is developed to include statistical models from a number of different fields, giving students a comprehensive knowledge of models such as: multilevel models for change, latent growth models, cross-lagged models and survival models. The course is also hands, each topic being accompanied by real world applications using R and practical exercises. In addition to learning about statistical models the students will also learn how to prepare and visualize longitudinal data. They will also have the opportunity to discuss about their own research projects and get guidance on how they can use the methods covered in the course in their own work.
No information available.
To gain competence in the concepts, designs and terms of longitudinal research;
To be able to apply a range of different methods for longitudinal data analysis;
To have a general understanding of how each method represents different kinds of longitudinal processes;
To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.
Course Prerequisites:
The course will be based on the free and open source software R. As such, a basic working knowledge of R is needed at the start of the course. Also, the course assumes a working knowledge of linear and logistic/probit regression.
Representative Background Reading – book will be provided by ESS:
Cernat, A. (2023). Longitudinal Data Analysis using R. LeanPub.
Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press.
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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