The course considers how networks, including personal networks, can hamper or facilitate innovation and technological change, focusing primarily at the meso- and micro level. Not only the characteristics of the actors themselves drive their behavior, but also the way in which the ties between these actors are shaped. The course covers the theories explaining the general effects of networks and applies the theories to several empirical examples.
Methods and models in behavioral research This course provides a general introduction to the defining properties of scientific research and the empirical research cycle. The first objective is to teach the principles of sound scientific argument. The course also provides hands on training of these skills in empirical research through assignments that cover the separate parts and their integration. Several standard statistical models are included in the course, such as multiple regression analysis, AN[c]OVA, and Factor Analysis.
Advanced models and methods in behavioral research The course treats two methods that were not tackled in the Models and Methods in Behavior Research course: Logistic regression and Multi-level analysis (in our case we need to do this because of the Conjoint analysis method that we also introduce in this course). You have to understand both methods and should be able to carry out these analyses on actual (our own) data. And this time we run Stata, not SPSS.
The increasing availability of data on practically anything makes the issue of human versus computer decision making all the more relevant. When do models outperform humans? Which kinds of models could you consider? Where are models being used? Why aren't they used (sometimes) even when it can be shown that they are better decision makers? What is the impact of having so much information around? We cover such matters in this course, and more.