Data Science Forum | Michael Windzio › view all

Global cultures and the world-wide gender gap in education  – Fuzzy clusters and multilevel data structures

December 16, 2021 - 12:00-13:00
Online
Event type: public

Michael Windzio talks at the Data Science Forum about "Global cultures and the world-wide gender gap in education – Fuzzy clusters and multilevel data structures"

The talk will be held online. Find the link to teh Zoom Meeting here.

 

About the talk

The world-wide gender gap in education depends not just on countries' economic performance, but also on cultural factors. However, world cultures are not fixed entities. Rather, culture is a characteristic of groups as well as of (world-)regions. How do global cultures moderate women's low education? Based on data of the World Value Survey, this study applies Latent Profile Analysis to generate a fuzzy-set typology of cultures in the world, but based on individuals instead of nation states. Individuals do not belong exclusively to one culture, but to several cultures simultaneously, with varying probabilities. In the second step, cross-classified logistic multilevel models test the country-time specific effects of 'female' on the risk of getting (at best) low education, controlling for various individual and country-specific factors. Cross-level interactions show that the 'female' effect on low education is indeed moderated by world cultures, but neither world cultures, economic factors nor individual characteristics completely explain the strength of the female effects.

 

About the Data Sceince Forum

The Data Science Forum offers scientists from all disciplines and faculties the opportunity to present and discuss their research, interests, and challenges related to data science in front of an interdisciplinary audience. All topics in the field of data science can be addressed including general aspects of big data and data-intensive research, the development of state-of-the-art data science methods such as artificial intelligence, the application of data science methods in various research areas as well as the investigation of legal, ethical, and social aspects.