Designing, Implementing, and Analyzing Longitudinal Surveys

Longitudinal data are essential for understanding individual level change and are used in multiple social science fields such as sociology, psychology, economics, health and so on. Collecting such data brings with it specific challenges that are not present in cross-sectional surveys. The course will provide an overview of those aspects of survey design, implementation, and analysis that are unique to longitudinal surveys or that have distinct features in the longitudinal context. The course is unique in that it focuses on how to collect longitudinal data (4 days) and less on how to analyze data (1 day).

Course leader

Tarek Al Baghal is a Professor of Survey Methodology at the Institute of Social and Economic Research, University of Essex, UK. Alexandru Cernat is an associate professor in Social Statistics at the University of Manchester, UK.

Target group

You will find the course useful if:
- you are interested in the uses and importance of longitudinal surveys,
- you are involved in planning or conducting a longitudinal survey,
- you are interested aspects of longitudinal data collection, including data linkage,
- you are involved in the measurement of phenomena over time,
- you want to use appropriate methods to analyze longitudinal data.

Prerequisites:
- Basic knowledge of survey methodology from a cross-sectional perspective, in particular with respect to survey design, instrument development, and survey implementation.
- basic knowledge of statistics and statistical modelling (i.e., regression) and of a statistical software (Stata or R).

Course aim

By the end of the course, you will
- be familiar with the central design issues of longitudinal surveys,
- understand different strategies on how to collect longitudinal data,
- be able to design questions that meet research objectives for longitudinal surveys and minimize error,
- understand methods to link new sources of data to longitudinal surveys,
- be able to identify and implement features that should help to prevent panel attrition,
- be able to study the nature of non-response and attrition in a longitudinal survey,
- generally understand the methods used in weighting and imputation in a panel survey.