7 July 2017
Analysing Comparative Longitudinal Survey Data Using Multilevel Models
We will begin by considering the structure of CLSD, and then what fixed effects and random effects (multilevel) models each reveal about the variation between and within groups in data characterised by clustering. We will see how CLSD can be understood as doubly hierarchical (or clustered), and therefore how we can analyse them with models partitioning between and within effects. We will also consider the capabilities of societal growth curves, and the insights that can be gained from models with random (country-specific) slopes. The course will emphasise the use of graphical analysis throughout, and note some risks that analysts of CLSD need to avoid. In the lab sessions will use the open-source R software and environment for statistical computing, including some easily installable add-on packages.
Students, researchers, business professionals who need to develop good quality surveys and/or need to apply appropriate and up-to-date statistical methods. At the conclusion of the Summer School, participants will receive a certificate for each course and with the number of hours attended.
Many surveys spanning multiple countries--or many regions within a single country--are now being fielded multiple times over the course of years or even decades. Examples include the European Social Survey, International Social Survey Programme, and (across states) the U.S. General Social Survey. The range of topics that can be studied using data from these surveys is extremely broad: from health to religiosity to political attitudes and behaviours. This course will show students how to analyse these comparative longitudinal survey data (CLSD) using multilevel models that exploit any or all of three different kinds of variation: differences between countries, change within countries over time, and variation across individuals.
EUR 0: Several Rates (check website)