Tools and models: Developing methods for complex data
Many important research problems addressed in our graduate school can only be answered adequately with the appropriate statistical models at hand. Inspired by such research problems, a group of mathematicians, statisticians and methodologists collaborate in this research cluster.
The general aim is to develop and elaborate statistical methods and software that allow social scientists to deal with complex data that require special methods, models, and software to analyze. Examples are large cross-national data sets, cross-sectional and longitudinal social network data collected in classrooms or in experiments, or event-history data. Within the cluster, fundamental and applied research is done to build tailor-made software and to find good ways of applying existing methods. Areas of expertise include mathematical sociology, missing data imputation methods, categorical data analysis, multilevel analysis, and statistical models for social network analysis.
- Aksoy, O., & Weesie, J. (2014). Hierarchical Bayesian analysis of outcome-and process-based social preferences and beliefs in Dictator Games and sequential Prisoner’s Dilemmas. Social Science Research, 45, 98-116.
- Besamusca, J., Tijdens, K., Keune, M., Steinmetz, S. (2015). Working women worldwide. Age effects in female labor force participation in 117 countries. World Development, 74, 123-141.
- Eisinga, R.N., Grotenhuis, H.F. te & Pelzer, B.J. (2013).The reliability of a two-item scale: Pearson, Cronbach or Spearman-Brown? International Journal of Public Health, 58, 637-642.
- Grotenhuis, H.F. te, Scholte, M., Graaf, N.D. de & Pelzer, B.J. (2015). The between and within effects of social security on church attendance in Europe 1980-1998: The danger of testing hypotheses cross-nationally. European Sociological Review, 31, 643-654.
- Huitsing, G., Van Duijn, M.A.J., Snijders, T.A.B., & Wang, P. (2012). Univariate and multivariate models of positive and negative networks: Liking, disliking, and bully-victim relationship. Social Networks, 34, 379-386.
- Steglich, C., Snijders, T.A.B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329-393.
Coordinators: Peer Scheepers, Vincent Buskens, Marijtje van Duijn