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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the July/August 2023 or February 2024.
Monday 6 – Friday 10 February 2023
Minimum 2 hours of live teaching per day
09:30 – 12:00 CET
This course provides a highly interactive blended learning environment, using state-of-the-art online and in-person pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.
This course offers an introduction to the main statistical techniques used to analyse cross-national comparative surveys data.
By the end, you will gain an understanding of how different statistical methods treat the ‘country effect’: how statistical models (linear regression, logit models, loglinear models, multilevel regression models), scaling techniques (from simple methods to complex factorial techniques) or data reduction methods (factor and PCA analysis) test for the ‘invariance’ of the relationship between variables across countries.
During the course you will use R and/or Stata.
The course has two key points:
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
Bruno Cautrès is attached to CEVIPOF – Centre de recherches politiques de Sciences Po (Paris), at the Fondation Nationale des Sciences Politiques in Paris.
He is a senior CNRS research fellow with interests in voting behaviour, political attitudes and behaviours, comparative survey research and quantitative techniques.
Bruno is involved in a variety of projects, including the European Social Survey, European Values Studies, International Social Survey Programme and European elections studies; and he participates in the development of elections studies in France. His current research programme concerns political trust and attitudes to democracy in France.
We begin with a review of the substantial and methodological problems the multivariate statistical analysis tries to solve in the comparison of countries, particularly what is a ‘country effect’ and how this translates in statistical terms.
On Monday, you will undertake activities that show very simple recalls using descriptive and bivariate techniques applied to ‘country effects’ problems.
Following this, you will work to understand the two main types of multivariate techniques, still applied to the same question of ‘country effects’:
On Friday, we will also extend the perspective to multilevel analysis applied to cross-national data analysis. Examples will be given and replicated through R programmes.
The course consists of five sessions, organised in three main topics, detailed below.
The substantial and main methodological issues of comparing countries through statistical techniques
How the reasonings of statistical models do this ‘country effects’ analysis, through classical techniques like linear regression models, logit and loglinear models. We will pay attention to the fundamental problem of comparing regression estimates from one country to another, and the sometimes tricky issues.
How data reduction techniques are used, and can be used, in cross-national analysis, through classical techniques like PCA. We tackle another fundamental problem of comparative analysis: can we use the same instruments (survey questionnaire items) across countries, and what does it mean to run scaling analysis across it? Friday’s session will provide some short but important extensions to multilevel analysis and structural equations models, preparing you to attend other more specialised courses in these techniques.
The course will use interactive online technology, combining annotated readings, short pre-recorded lectures, and live group work. It combines pre-class activities and live interaction.
You should complete the readings and watch the pre-recorded lectures on key topics ahead of the course start date.
The lectures take two forms: a methodological presentation and an illustration, through a published text or a data analysis, explained in plain English. The main tables or graphics will help you understand the substantive issues of the methods.
There are two hours of classroom teaching each day, during which you will review the main methodological issues and concepts related to the pre-recorded presentations and annotated readings. Following this, the TA will give one hour of practical examples.
In the afternoons, we will discuss course-related matters in an online Slack community (one hour of office hours). The Instructor will provide R scripts for running analysis and you will develop and complete them as a project. During office hours, you’ll also be able to sign up for a quick one-to-one consultation with the Instructor or TA.
This course requires an understanding of basic statistical inference and regression models. If you don't have this level of understanding, consider taking the Introduction to Inferential Statistics course before this one.