Longitudinal Analysis

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Longitudinal data analysis is a statistical method used to examine data collected from the same subjects or entities over multiple time points. This type of data analysis is particularly valuable for understanding how variables change over time and for investigating trends, patterns, and relationships within a dynamic context. For instance, how does children’s early home environment affect their future mathematical development?

Longitudinal data analysis holds several advantages, such as (1) understanding individual-level trajectories, enabling a deeper understanding of how different subjects respond to interventions or external factors over time, (2) supporting stronger causal inference by tracking changes before and after an intervention and (3) accounting for heterogeneity since it recognises that not all subjects respond uniformly to changes over time.

Over the course of this module, participants will learn how to work with longitudinal data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for longitudinal data. From understanding growth trajectories to uncovering causal relationships, this module will empower participants to navigate the complexities of longitudinal data with confidence. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required.

• Postgraduate students and staff
• Further details regarding eligibility criteria are available here
• The module is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required.

If you are not already an experienced user of Stata software then we recommend you first take the Introduction to Stata module.

Number of sessions: 2

Learning objectives:

1. Foundational concepts:

Define and differentiate between cross-sectional and longitudinal data.

2. Prepare panel data and exploratory data analysis:

Collect, clean and prepare panel datasets for analyses. Conduct descriptive analyses, including summary statistics and data visualisation. Identify trends in the data.

3. Linear mixed models:

Apply mixed models to account for within-subject correlations and individual-specfici effects. Interpret fixed and random effects.

4. Non-linear growth modelling:

Explore non-linear growth modelling techniques, such as growth curve modelling. Fit and interpret growth models to describe changes over time.

5. Interpretation and reporting

Communicate the results of longitudinal data analyses.

This is an 8-hour in-person module with practical session spread over two half days.

For the practical lab session you will need to bring a fully charged laptop with Stata software already downloaded. A free copy of Stata MP4 can be downloaded for free to students of Cambridge University with a valid CRSID who book a place on this module.

Click the "Booking" button panel on the left-hand sidebar (on a phone, this will be via a link called Booking/Availability near the top of the page).

Moodle is the 'Virtual Learning Environment' (VLE) that the SSRMP uses to deliver online courses.

SSRMP lecturers use Moodle to make teaching resources available before, during, and/or after classes, and to make announcements and answer questions.

For this reason, it is vital that all SSRMP students enrol onto and explore their course Moodle pages once booking their SSRMP modules via the UTBS, and that they do so before their module begins. Moodle pages for modules should go live around a week before the module commences, but some may be made visible to students, earlier.

For more information, and links to specific Moodle module pages, please visit our website