This blog has been created to feature the research efforts of students pursuing the Letter of Specialization for Data Analysis in Politics, Policy and Legal Studies (DAPPLS), the Master’s of Science Degree in Data Analytics and Computational Social Science (DACSS), or the related graduate certificate in Data Analytics (C-DACSS), but all UMass students and faculty who are involved in empirical research involving systematic data analysis are welcome to contribute. Please contact the administrator at firstname.lastname@example.org to submit your blog idea and request access to contribute to the blog. See below for complete instructions on how to become a blog contributor. For students who are involved in courses where such blog posts are suggested or required, the links below provide suggested ideas for blog posts and instructions for creating a post once you have been added to the author list.
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In an earlier post, we learned a command for one type of network homophily in R: the proportion of ties in a social network connecting two actors matching on a given characteristic. Today, we’ll build a command for calculating a second conception of network homophily. Specifically, this is the average proportion of characteristic matching within ego-networks, or the neighborhoods of each individual node. First, let’s load igraph as we always do for these commands, and install it first if
A student in the Networks course recently asked for help calculating homophily scores for the network data she had collected, and I was surprised to find that no command exists in R to calculate network homophily, or the proportion of shared ties among nodes with shared attributes. After doing a little background digging, I now suspect that this is in part due to some disagreement on what constitutes homophily in a social network at all!
I offered PS328, Research Methods for Political and Social Science, in the fall of 2014 when I first arrived at UMass. Four students were initially enrolled, and only three finished: Nelson Roland, Stephanie Chan and Tim Marple. Nelson graduated that spring, but Stephanie and Tim became part of the driving force behind the creation of a letter of specialization in Data Analysis in Politics, Policy and Legal Studies (DAPPLS) by the Political Science Department in spring