Multilevel – Analyzing Comparative Longitudinal Survey Data Using Multilevel Models

About

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, EU Statistics on Income and Living Conditions, and (across states) the U.S. and German General Social Surveys. The range of topics that can be studied using data from these surveys is extremely broad: from health to religiosity to social and political attitudes and behaviors. This course will show students how to analyze 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.
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 characterized by clustering. We will see how CLSD can be understood as doubly hierarchical (or clustered), and therefore how we can analyze 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 emphasize the use of graphical analysis throughout, and note some risks that analysts of CLSD need to avoid.

Course leader

Alexander Schmidt-Catran - Goethe University Frankfurt

Target group

Professionals, researchers and students who wish to develop high-quality surveys and employ up-to-date statistical methods for survey and quantitative data analyses. Potential attendees include survey practitioners, marketing professionals, social science students, and researchers.

Fee info

Fee 700 EUR, for professionals