What I’m really inspired by is discovering different application areas of data analytics and statistics, understanding their epistemological differences, and adapting methodological knowledge of a field to other ones. Meanwhile, my aim is to support sociological knowledge discovery in empirical research.

At present, my main research interest is automated text analytics (text mining), since I’m ascertain about its untapped sociological opportunities. Topics we are working on: discursive framing of depression in online health communities, corruption in online editorial media, robustness studies in text analytics.

My previous researches concerned:

-        causality in social sciences. The axiom of “correlation does not imply causation” was put in a new light by some statistical results of the last decades. My main question was how these results can contribute to the social science research

-        marginal loglinear models, ie. to models which restrict certain marginal distributions in the contingency table. This research involved developing parameterisation of graphical models (eg. path models) for categorical data. I found this approach to be applicable in the area of social sciences, since graphically represented causal models for categorical data are widely used by sociologists

-        survey methodology, eg. desing-based variance estimation and survey sampling

-        empirical sociology: social mobility and social inequalities in health.