5 Unit 1 - Wrangling Network Data
Learning Elements
The first step of our workflow is to wrangle network data.
In this unit, we will cover how to prepare data for analysis. Before you jump into transforming network data, however, you need to buildup your understanding of how network data are structured. This involves building familiarity with the relational elements of networks and learning how to bring those data into an analytic environment like RStudio. Depending on how your network data are structured, this process is a little different.
Once your network data are in RStudio, we can then work with them. Network data aren’t always clean. Nor are they always representative of an entire group. As the researcher, it is up to you to learn best practices of cleaning your data. It is also important for you to know as much as possible about the group you are studying. For your research results to be generalisable to the group you are studying, your data need to represent the group you are studying (whether you have sample or population data). This process might include trimming or adding to your network. Or it might include making various subgraphs of your network.
With this in mind, in Unit 1 you will:
Grow understanding of how network data are structured
Bring network data into RStudio
Clean network data
Learn the difference between one and two mode networks
Project Milestones
| Milestone (assignments linked) | Explanation |
|---|---|
| My Ego Network | Students will draw their own ego network following an assignment in class. |
| Project Prospectus | Students discuss potential project ideas, possible sources of data, and research questions they could explore. |
Workforce Preparation
As you engage with Unit 1, be mindful of potential stakeholders. You will learn principles associated with how network data are structured and how to clean them. You will need to communicate the strategies you use in your studies (the cleaning you do etc.) and also the limitations of your study. With this in mind, I encourage you to think about data as information, not fact or truths. With data we can proximate the truth and make inferences about things that can inform decisions. The processes you take to arrive at the story you tell must be as clearly laid out for audiences as the story itself. Part of being a social network analyst is knowing how to communicate the strengths and weaknesses of your data and methods to make the most informed decisions.
Enjoy Unit 1!