Data and Methods Unit

The Data and Methods Unit (DMU) is part of the MZES research infrastructure and provides  data and methods support for planned and ongoing projects at the MZES. 

As part of the project-specific support, the DMU accompanies project teams from the initial project design through the research process to project completion.

In addition, the DMU offers generic support to researchers at the MZES. These services include support in obtaining restricted-access data, help with data management and documentation, and additional training in various domains of social science data and methods. The training is offered in various formats:

  • the Social Science Data Lab, an event series that highlights cutting-edge methods for the collection, management, analysis and visualisation of data in the social sciences
  • Methods Bites, a blog with methods tutorials, code, and applied examples
  • the MZES-CDSS Workshop Series in Data and Methods, with courses on advanced research methods in the social sciences.

The DMU is a team of four researchers with complementary expertise in social science data and methods, who also conduct their own research at the MZES. The profile texts below summarise in which areas each member supports the research landscape at the MZES.

Dr. Ruben Bach: Computational social science
Text analysis, machine learning, and big data
Ruben can help with topics related to computational social science, such as collection of information from novel data sources (e.g., social media, web scraping, online behavioural data). Likewise, he provides support in analysing data in unstructured formats, using, e.g., machine learning and natural language processing techniques. He offers software support mostly for R, SQL, Stata, a little python and he can assist researchers in setting up and running cloud computing machines on BWcloud/AWS/Google Cloud.

Dr. Denis Cohen: Methods of data analysis
Statistical modelling, causal inference, and data visualisation
Denis offers methodological support in the areas of statistical modelling, Bayesian statistics, causal inference, and data visualisation. He also offers software-specific support in R, Stata, Stan, BUGS/JAGS, and Markdown, and can assist researchers in using remote computing services via bwHPC. His research focuses on context-dependent explanations of political preferences and voting behaviour, the drivers and outcomes of party competition in multiparty democracies, and the causes and effects of strategic elite behaviour. 

Dr. Nadia Granato: Secondary data
Micro, macro, and geospatial data
Nadia provides information on data for secondary analyses with a focus on research data and microdata from official statistics. In addition, she offers support on issues of data management and documentation. Her research interests include ethnic inequalities in occupational and educational outcomes as well as labour market research. 

Dr. Alexander Wenz: Survey methodology and data protection
Survey design, survey data collection, survey data analysis, research ethics, and data protection
Alexander offers methodological support in survey design, survey data collection, and survey data analysis. He also provides advice on research ethics and data protection for the surveys conducted at the MZES. He offers software support in R, Stata, Qualtrics, Unipark and LaTeX. His research examines the quality of novel methods of data collection, with a focus on mobile web surveys, passive measurement with smartphone apps and wearable sensors, and linkage of survey and social media data.