Panel Data Analysis New
Panel data consists of repeated observations measured at multiple time points, collected from multiple individuals, entities, or subjects over a period of time. For instance, child A’s numeracy test score in Year 1, Year 2, Year 3 and Year 4. Country B’s GDP per capita in year 2020, 2021, 2022 and 2023. Panel data analysis, as a subset of longitudinal data analysis, is particularly useful for addressing research questions that try to understand how variables change over time and how individual units differ in their responses to changes. An example research question could be: how do children's numeracy scores vary across different socioeconomic backgrounds, and how have these disparities changed over the years? Panel data analysis holds several advantages, such as (1) increased statistical efficiency, (2) more effective at controlling for unobserved individual or entity-specific effects, and (3) more capable to study the dynamics of relationships over time.
Over the course of this module, participants will learn how to work with panel data. Through hands-on exercises and practical examples, participants will gain proficiency in data manipulation, visualisation, and advanced statistical techniques tailored specifically for panel data. It is suitable for postgraduate students and researchers at any stages of their study and research. However, foundational Stata skills are required.
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 take the Introduction to Stata module.
Number of sessions: 2
| # | Date | Time | Venue | Trainer | |
|---|---|---|---|---|---|
| 1 | Mon 4 Mar 09:00 - 13:00 | 09:00 - 13:00 | Titan Teaching Room 1, New Museums Site | map | Yiran Zhao |
| 2 | Tue 5 Mar 09:00 - 13:00 | 09:00 - 13:00 | Titan Teaching Room 1, New Museums Site | map | Yiran Zhao |
Learning objectives
1. Understand panel data: Define and differentiate between cross-sectional, time series and panel data. Explain the advantages and limitations of using panel data in different research contexts
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 patterns and trends within longitudinal data
3. Fixed effects, random effects and mixed models: Understand the differences between fixed effects, random effects and mixed models in panel data analysis. Apply these models to account for individual-specific and/or time-specific effects
4. Diagnostics tests Use diagnostics tests to examine which model should be used for the panel data, and address issues of heteroskedasticity and endogeneity.
5. Interpretation and reporting. Communicate the results of panel data analyses.
The module consists of 8-hours spread over two half days.
Booking / availability