Ensuring Comparability in Cross-National Surveys: Methods and Key Topics - Online

How does ideological positioning, such as identifying as right-wing in the UK, compare across countries like Germany or Sweden? How can a single question in a cross-national survey account for contextual differences (where they exist)? Who should we ask to ensure nationally representative results, and how should we analyse and interpret the data across different countries?

This two-day online workshop equips participants with the tools to design, analyse, and interpret cross-national surveys, tackling key challenges such as measurement equivalence, sampling techniques, data harmonization, and weighting adjustments. Participants will explore how the Total Survey Error (TSE) Framework can help identify and address measurement error, nonresponse error, and sampling error—key factors that influence data quality and comparability.

The course will focus on practical issues like the impact of question wording on survey results, the influence of social desirability bias across cultures, and the role of cultural and demographic variability in shaping responses. Participants will be introduced to Multi-group Confirmatory Factor Analysis (MG-CFA) as a method for assessing measurement equivalence in diverse contexts. We will also cover essential sampling methods in cross-cultural survey research, as well as weighting techniques designed to make survey results nationally representative, while addressing the challenges of weighting in cross-national surveys.

The course covers: 

• Introduction to Measurement in Comparative Surveys

• Testing for Equivalence in Comparative Data

• Data harmonisation in Comparative Surveys

• Comparative Sampling Fundamentals

• Weighting Adjustments in Comparative Surveys

• Advanced sampling techniques and Ethical Considerations

By the end of the course participants will:

• Learn how to identify and apply the right strategies to ensure survey data comparability.
• Have an introductory understanding of key statistical tools such as Multi Group Confirmatory Factor Analysis to test for measurement equivalence in cross-national survey data.
• Understand the principles and implications of using different sampling methods.
• Learn about types of weights and the importance of weighting in comparative research.
• Learn to autonomously address common data quality issues (eg. nonresponse bias, social desirability bias).
• Enhance the ability to analyse and interpret cross-national survey data. 

This course is aimed at all researchers interested in using cross-national survey data, mainly from social sciences and related disciplines would benefit from the course. 

Participants should have a basic understanding of linear regression analysis.