This course aims to provide PhD candidates with a solid background in standard and more advanced geostatistical methods, such that they can apply these in their own research. The course is a mix of theory and practice, with case studies that are analysed using R and contributed geostatistical packages.
The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions.
Programming can serve multiple purposes. Purposes like developing applications and working with data are also very useful for research. For dealing with these issues, Python offers many libraries. Getting the skills of working with some of these libraries will enable future learning. This can be for more advanced programming applications, but also for self-learning to apply different libraries.
Researchers trying to summarize the constantly growing body of published research are increasingly using meta-analysis. The focus of this 2-day course will be on concepts of linear models and mixed linear models in meta-analysis. The statistical software R will be used.
This course will empower attendees to maximize and defend their scientific interpretations based on their expert knowledge as informed by quantitative results. A new procedure, “causal knowledge analysis” will be introduced as a way to document expert knowledge and set the stage for SEM analyses. Once that is accomplished, structural equation modeling techniques will be presented as a methodology for representing and evaluating hypotheses involving networks of relationships.
