13.12.2021
| Inside Data Science |
Michael Windzio über seine datenbasierte Forschung in der Soziologie
Michael Windzio geht auf die Bedeutung von Daten und deren Analysen in seiner Forschung ein und beleuchtet den Begriff “Data Science”.
What topics are you currently working on in your research?
I am working on social networks of adolescents in school classes, in particular on the integration of immigrants into these networks, and on social cohesion and social milieus. Another focus is on global networks of migration and student mobility, and diffusion of education policy. Moreover, I work on juvenile delinquency, adolescents’ gender role orientations, residential relocations of families and residential segregation, and on global educational inequalities.
How important is data to your research?
Data is crucial for my research.
What role does data science play in your research? Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
I am still wondering what “Data Science” actually means. Is it limited to Bayesian approaches and machine learning? Or does it also include the standard approach to quantitative analysis in the social sciences? Does it include generalised regression, cross-sectional and longitudinal, as well as econometrics? If so, then data science is at the core of my work.
Anyway, I am definitely a user.
Which data science methods and technologies are in the focus of your research or could also become interesting in the future?
I use longitudinal methods for network analysis, event history analysis (= hazard models), panel regression, multilevel analysis (= mixed models), but also models for causal inference for observational data, such as propensity score matching. In my field, sociology, “computational social science” is rapidly growing, but again, this label is rather vague. But methods of text analysis, such as topic modelling, will certainly become more important also in my work.
What are your main challenges in dealing with data?
I currently do some time-consuming analyses, e.g. a Bayesian model for network evolution over time. Maybe the Data Science Center can provide a better hardware infrastructure to reduce computation time.
And finally, what is your personal motivation for joining the Data Science Center?
It is interesting to see which methods people from other disciplines apply. Hopefully, there will be some dissemination of knowledge, e.g. by workshops on methods we are interested in.
Mehr zu Michaels Tätigkeiten erfahren Sie am 16.12.2021 in seinem Beitrag „Global cultures and the world-wide gender gap in education – Fuzzy clusters and multilevel data structures“ im Data Science Forum.
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13.12.2021 | Inside Data Science
Michael Windzio über seine datenbasierte Forschung in der Soziologie
Michael Windzio geht auf die Bedeutung von Daten und deren Analysen in seiner Forschung ein und beleuchtet den Begriff “Data Science”.
What topics are you currently working on in your research?
I am working on social networks of adolescents in school classes, in particular on the integration of immigrants into these networks, and on social cohesion and social milieus. Another focus is on global networks of migration and student mobility, and diffusion of education policy. Moreover, I work on juvenile delinquency, adolescents’ gender role orientations, residential relocations of families and residential segregation, and on global educational inequalities.
How important is data to your research?
Data is crucial for my research.
What role does data science play in your research? Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
I am still wondering what “Data Science” actually means. Is it limited to Bayesian approaches and machine learning? Or does it also include the standard approach to quantitative analysis in the social sciences? Does it include generalised regression, cross-sectional and longitudinal, as well as econometrics? If so, then data science is at the core of my work.
Anyway, I am definitely a user.
Which data science methods and technologies are in the focus of your research or could also become interesting in the future?
I use longitudinal methods for network analysis, event history analysis (= hazard models), panel regression, multilevel analysis (= mixed models), but also models for causal inference for observational data, such as propensity score matching. In my field, sociology, “computational social science” is rapidly growing, but again, this label is rather vague. But methods of text analysis, such as topic modelling, will certainly become more important also in my work.
What are your main challenges in dealing with data?
I currently do some time-consuming analyses, e.g. a Bayesian model for network evolution over time. Maybe the Data Science Center can provide a better hardware infrastructure to reduce computation time.
And finally, what is your personal motivation for joining the Data Science Center?
It is interesting to see which methods people from other disciplines apply. Hopefully, there will be some dissemination of knowledge, e.g. by workshops on methods we are interested in.
Mehr zu Michaels Tätigkeiten erfahren Sie am 16.12.2021 in seinem Beitrag „Global cultures and the world-wide gender gap in education – Fuzzy clusters and multilevel data structures“ im Data Science Forum.
Interviewpartner:
Prof. Dr. Michael Windzio
Professor für Soziologie, Schwerpunkt Migration und Stadtforschung
FB 08 – Soziologie
Leiter der Abteilung „Methodenforschung“ am SOCIUM
mwindzio@uni-bremen.de
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