• name and understand the most important inductive statistical techniques to analyse coherence;
• identify the most frequently used techniques for statistical analyses of correlation and causal relations in human geography, planning and environmental studies (linear regression, discrete choice models, time-series analysis, spatial analysis techniques, structural equation modelling and latent class analysis)
• explain the meaning of the formal concepts of these different models
• identify the general principles of estimating parameters and calibrating models and the various applications and limitations of causal modelling techniques
• assess the applicability of quantitative data to do empirical research into particular questions
• under supervision, how to build a simple formalised model, departing from a specific theory and the estimation and calibration of a model
• under supervision, how to estimate and assess the effects of change and policy interventions based on calibrated models and to draw theoretical and policy-related conclusions based on the results;
• independently assess the quality of quantitative analyses in existing research-projects.

To explain the spatial behaviour of individuals and organisations and test the effectiveness of related policies, thorough knowledge of quantitative research methods and the ability to apply these methods are necessary. This course will familiarise you with the most important quantitative methods that are available. You will learn how to use these methods appropriately and how to support your methodological choices. Furthermore you will learn how to critically assess the results of quantitative research done by others. As a preparation for the thesis (both in the pre-master’s programme and later on in the Master’s), this course will train you especially in skills to apply these kinds of methods. To this end you will work on weekly assignments, read and assess scientific articles and complete an independent final project

The course is built on several weekly themes. The first block deals with the often used method of multiple regression and the construction of simple statistical models. Next, categorical data analysis is covered. Although most of the data gathered through questionnaires is categorical, spatial decisions are often discrete choices. The next two blocks deal with the temporal and spatial dimensions of quantitative analyses. Time-series, forecasting and spatio-statistical methods are introduced here. Next, more complex causal structural models are dealt with using AMOS (SPSS) software specifically targeting structural equational modelling. This is linked to the final theme of latent class analysis.