
Aarhus, Denmark
Animal Matters: Transforming Perspectives through Human-Animals Studies
When:
23 July - 08 August 2025
Credits:
10 EC
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Social Sciences
When:
24 March - 28 March 2025
School:
Institution:
European Consortium for Political Research (ECPR)
City:
Country:
Language:
English
Credits:
4.0 EC
Fee:
492 (member fee) 985 (non-member fee) GBP
Learn key statistical techniques for cross-national data analysis, including 'country effect' in multivariate methods, comparing regression estimates, and exploring factorial analysis (PCA) with a focus on political science methodology intricacies.
Need to Know
You must have some introductory familiarity with basic descriptive statistics, statistical inference and some basics about regression models. If you are not, take the course Introduction to Inferential Statistics before you sign up for this one.
Learning commitment
As a participant in this course, you will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.
Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings. The time commitment required to familiarise yourself with the content and complete any pre-course tasks is estimated to be approximately 20 hours per week leading up to the start date.
During the course week, you are expected to dedicate approximately two-three hours per day to prepare and work on assignments.
Each course offers the opportunity to be awarded three ECTS credits. Should you wish to earn a 4th credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.
This comprehensive approach ensures that you not only attend the live sessions but also engage deeply with the course material, participate actively, and complete assessments to solidify your learning.
In Depth - Key topics covered
The course consists of five sessions, organised into four main topics.
Topic 1: What is a âcountry effectâ and how is it translated in multivariate statistical methods?
In this topic, you will review substantial and methodological issues in multivariate statistical analysis for comparing countries, particularly what is a 'country effect' and how this translates in statistical terms. You will also engage in very simple recalls using descriptive and bivariate techniques applied to 'country effects' problems.
Topic 2: Comparing regression estimates (linear, logistic, log-linear regression) between countries
In this topic, you will learn how regression methods respond to a much more complex problem than commonly acknowledged: How can we compare the values of estimated parameters, goodness of fit statistics, and statistical tests between countries? Notably, this varies significantly for linear regression and logistic regression. Thereâs even a debate over whether we can compare logit coefficients between countries directly. What strategies can we employ to navigate this challenge?
Topic 3: Comparing factorial analysis results (PCA analysis dimensions, factor loadings)between countries
In this topic, you will learn how data reduction techniques such as principal components (and more generally scaling techniques) respond to the main question of the course, a seemingly simple question but in fact much more complex: Can we directly compare the values of the coefficients of a PCA type factor analysis obtained between different countries?You will discover that researchers have proposed interesting technical solutions to compare factor loadings (Procruste rotations, Tucker coefficient for example). But what are the limits of these methods, and can we go further in the comparison between countries? What steps should we take?
Topic 4: Comparing countries' variability with multilevel regression models
In this topic, you will learn that regression analysis of harmonised data from multiple countries (like the ESS, Eurobarometers, Share) in which individual-level responses are modeled as a function of both individual-level and country-level characteristics, the so-called âmultilevel modelsâ or âhierarchical modelsâ are among the most popular of quantitative approaches. Is this the best way to approach cross-national comparisons and how do these methods deal with comparing estimated coefficients? What are the limitations? Can we apply these techniques to a small number of countries?
Bruno Cautrès is attached to Centre de recherches politiques de Sciences Po (Paris. He is a senior CNRS research fellow with interests in voting behaviour, political attitudes and behaviours, comparative survey research and quantitative techniques.
Researchers, professional analysts, advanced students
The course offers an introduction to the main statistical techniques used to analyse cross-national comparative data analysis, particularly survey data. This course highlights the most important methods of quantitative analysis in the social sciences but revisits them from the perspective of comparing the statistical estimates between countries.
The central enquiry revolves around how different statistical methods treat the âcountry effectâ and identify it: how statistical models (linear regression, logit models, multilevel regression models), scaling techniques of data reduction methods (PCA analysis for instance) test for the âinvarianceâ of the relationship between variables across countries.
The course seeks to address a couple of seemingly straightforward questions , that unearth methodological intricacies far beyond initial expectations: Can we feasibly compare coefficient values derived from primary statistical methods in political science across different countries? What are the primary pitfalls and viable resolutions within this pursuit?
The objective of a statistical analysis between countries is, in fact, to determine whether the variance between countries is greater than the variance within each country. To do this, most researchers compare the values of the coefficients (for example the coefficients of regression models, indicators of goodness of fit). However, they often do so empirically without taking a certain number of methodological precautions (for example, the fact that the sample size is not the same, or that the residuals are not distributed the same). The objective of the course is thus to teach simple but efficient methods to avoid falling into certain traps of comparative analysis using quantitative techniques
The course has also two pedagogical focal points:
1. To teach with a reasonable level of formalisation, as much as required to understand the methods.
2. To highlight the links among the different methods and facilitate understanding of how they complement each other.
If you're interested in this course, registration opens on 01/10/24 and you can register here: https://ecpr.eu/Events/Event/PanelDetails/15613
Fee
492 (member fee) 985 (non-member fee) GBP, ECPR member - check whether your institution is a member here: https://ecpr.eu/Membership/CurrentMembers
Fee
985 GBP, ECPR non-member
When:
24 March - 28 March 2025
School:
Institution:
European Consortium for Political Research (ECPR)
Language:
English
Credits:
4.0 EC
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When:
23 July - 08 August 2025
Credits:
10 EC
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31 March - 04 April 2025
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