Many research questions have a spatially explicit component, especially those dealing with natural resources, and how people respond to environmental changes. I enjoy transforming datasets, such as news articles, to spatially explicit datasets. See this article about drivers of conflict in Sudan and South Sudan.
People’s perception of the world around them influences their decisions and actions. Research here has focused how expertise might influence.
Whether using behavioral networks to understand relationships among strategies that people use, citation networks to understand thematic areas of academia, or semantic network to analyze texts, network analysis opens the door to systemstically study the structure of relationships.
Shared knowledge is the foundation for culture. This method can highlight areas or agreement and disagreement about a topic of interest,and when additional information is collected about the people, we can understand biases, how expertise may or may not influence perception. Best of all, the method provides the culturally-correct answer key.
Some research questions have only 20 cases to analyze. QCA uses Boolean logic to systematically analyze these cases to understand the conditions that are necessary and sufficient. Here we analyzed fishery patterns using QCA.
Today we are inundated with data--in the news, on Twitter, in interviews, and in documents. I enjoy making sense of these sources of data. My work has shown how we need to pay attention to fully automated appproaches of data synthesis, and be aware of these trade-offs in developing code books. I also have shown how when researchers don‘t take extra care in their words, research results can be misperceived, and we may think that native forests are growing when in fact plantations may be accounting for much of the change.