Module 3 builds on Module 2 by examining theory, hypotheses, and arguments. We distinguish between these concepts but also reflect on how they work together within a final research report: In particular, we focus on the linkage between theory (our answer / logic) and hypotheses (the empirical implications of our theory). We examine some common pitfalls that crop up whenever we find ourselves working with imperfect data as well as some strategies for avoiding them.
- Differentiate theory, hypotheses, and arguments.
- Explain how these concepts work together within a research report.
- Identify common pitfalls in linking theory and hypotheses and explain how we can avoid them.
- Analyze the linkage between theory and evidence as it crops up in Lessing and Willis.
- Recall and practice the basics of cleaning and transforming data using dplyr.
- Reflect on how to give substantive and constructive peer feedback during the week’s team meeting.
Mon. @ 8pm CST. Write 1-2 paragraphs of feedback on each of your team members’ initial research designs. Post your comments as an attachment beneath your team’s sub-heading at Ed Discussion. If you are the first member of your team to post, please create a new thread.
Thu. / Fri. @ 8pm CST. Post your Q & A about the week’s reading at Ed Discussion.
Theory, hypotheses, and arguments
Before diving into the week’s concepts, let’s briefly review some of the concepts that we examined in Module 2 on “Research areas, topics, and questions.” Toward that end, watch the video on “Recap of Module 2.”
Now that we have examined research questions, let’s turn our attention to theories, hypotheses, and arguments. Before you watch the videos below on this topic, read the Chapter by Bryans et al. on “Explaining the Social World.” As your read, think about your answers to the following questions. Doing so will prepare you to better engage the video content and, hopefully, assist you as your progress in your research:
- What is theory? What are hypotheses? How do these concepts go hand-in-hand?
- Think about your own research proposal: Did it have a theory (or just hypotheses)? What is your theory?
- How did you develop your theory? Did you mainly use induction or deduction?
- What assumptions do you make in your theory?
*If your theory is correct, what do expect to observe in the world? In other words, what is the best evidence you could find that would tell you that your theory is correct?
Now watch the videos on “Theory, hypotheses, and arguments” and “Why theory?”
The next video aims to get you thinking critically about how we can effectively link theory to hypotheses: “If your theory is correct, what should we observe in the world?” Doing this well is not easy, in part because we rarely find perfect evidence in support of our theories. When this happens, it’s easy to step into some common pitfalls rather than take the more appropriate step of gathering additional evidence or modifying our theory to ensure it is useful despite empirical limitations. The next video examines some of these common pitfalls as well as some strategies for avoiding them. Be prepared to pause the video throughout, as this video is meant to be a sort of exercise in “spotting the fallacy.”
You will now read “Legitimacy in Criminal Governance” by Lessing and Willis. I selected the article not only because it is one of the most interesting academic articles you will ever read but also because it exemplifies the meme that I presented in the video on “Why theory?” above. That is, Lessing and Willis help us to interpret/connect/make sense of some rich data that I think are extremely puzzling.
Before you read, think about your intuitions to the following questions:
- How do you think criminal organizations (e.g., mafias and drug gangs) keep their members in line?
- If your answer (theory) is correct, what would you expect to find if you could examine the internal working of one of these criminal organizations?
Now read the article. When you have finished, watch the video on “Anatomy of an argument.”
R exercise on tidy data and
If you are completing the R exercises each week, be sure to download the exercise for Module 3 from Canvas. This week’s exercise is meant to help you brush up on your data cleaning and transforming skills using the
dplyr package. While this is less fun than the webscraping we did last week, it is probably more useful, as most of you will have to spend some time cleaning, transforming, and merging any data that you obtain for use in your final research report.