Background Overview
Social scientists often grapple with phenomena that change over time. Criminologists, for instance, analyse crime trends and examine how criminogenic factors change over months or years. Psychologists explore the development of attitudes during childhood and adolescence, investigating how early experiences shape adult outcomes. Political scientists evaluate public policies, assessing whether they achieved their intended goals and how they affected different populations.
What unites these diverse examples is the central role of time. When data are collected over both units (e.g., survey respondents, cities, pupils) and time (e.g., waves of data collection, months, years) — i.e., longitudinal data — they provide an opportunity to address fundamental questions in the social sciences. Panel data allow researchers to investigate temporal ordering, changes over time, developmental trajectories, reciprocal relationships, and sometimes even causal effects.
The course
This course offers a comprehensive introduction to the analysis of longitudinal data. Different disciplines within the social sciences have developed distinct analytical frameworks to analyse panel data, and this course will provide an overview of some of these strategies.
The focus will be on three core areas:
- Estimating latent trajectories to analyse change over time
- Addressing ‘reverse causality’ and modelling reciprocal relationships
- Leveraging panel data for causal inference within the potential outcomes framework.
Specific topics include an introduction to longitudinal data, multilevel models for change, latent growth curve models, cross-lagged panel models, and difference-in-differences. For each topic, participants will learn the foundational principles and explore recent advancements in the field.
The course adopts a hands-on approach, enabling participants to analyse panel data using R. Hands-on exercises and real-world applications will help solidify understanding of these methods. By the end of the course, participants will be well-equipped to analyse and interpret models using longitudinal data in their own research projects.
Meet your course lead
Thiago R. Oliveira is a lecturer in quantitative criminology in the criminology department at the University of Manchester. He has a PhD in Social Research Methods from the London School of Economics and Political Science and has previously held positions at the University of Oxford and the University of Surrey.
As a quantitative social scientist, he uses methods from social statistics, data science, and econometrics to investigate policing's sometimes conflicting objectives of crime deterrence and public legitimacy. His research mostly draws on survey data and spatial data, and he is particularly interested in longitudinal data analysis and causal inference.
Course aims
During this this course attendees will:
- Acquire understanding of concepts, designs, and methods for longitudinal data analysis
- Use R to analyse longitudinal data and address a range of research questions that involve change over time
- Critically reflect upon the role of time in social science research
- Evaluate and fit models estimating latent trajectories under a multilevel and an SEM frameworks
- Understand the potentials and limitations of cross-lagged panel models in handling reverse causality
- Engage critically with the challenges and possibilities of leveraging panel data to address causal questions under the potential outcomes framework
Who is likely to most benefit from attending this course?
While PGRs and Early Career Researchers from across all disciplines are likely to be the main audience, the course may also be of interest to senior scholars, practice-based researchers, independent researchers, and scholars who are seeking to refresh and further develop their data analysis skills.
Any preparatory work or pre-requisites?
No prior knowledge of longitudinal, multilevel, or causal inference methods is assumed for this course, but participants should have a solid understanding of linear regression models as a foundation for the topics covered. While prior experience with R is not strictly required, it is highly recommended to facilitate engagement with the course material. For those less familiar with R, please see the linked supplementary materials which introduce the basics of R and will help you to prepare for course attendance.
Course timetable
Costs
- Full price: £900
- PGR/reduced rate: £600
As well as PGRs, reduced fees are also available to those working within the voluntary, charity and community sector. We also have two bursary options available for those entitled to reduced fees. Please view more information on our page here to find out your eligibility and how to apply.
Book Your Place
Please purchase via our online store below before 20 June (payment by card only). If you any questions, or will have trouble purchasing by this date please get in touch with methods@manchester.ac.uk.
Any questions, please do not hesitate to contact us on methods@manchester.ac.uk
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