Module 2 examines research questions: in particular, the attributes of a good research question, and a general process we can follow to move from a broad research area to a more specific topic and, finally, a focused question. The module will ask you to reflect on research questions as they crop up in real social science research. Finally, the week’s R exercise will guide you as you learn about and practice web scraping.
- Grasp the attributes of a good research question
- Grasp the process for developing a good research question and begin to implement it
- Examine the development of a research question by engaging a real-world example
- Examine and practice web scraping in R
Due this week
- Thu and Fri @ 8pm CST: Ed discussion Q & A.
- Fri @ 8pm CST: Proposed research design. Submit via the Assignments section at Canvas. Also post as an attachment beneath your team’s heading at Ed Discussion. NOTE: You should read each others’ proposals and prepare 1-2 paragraphs of feedback for each one. You will be required to post your written feedback to Ed Discussion by Mon. 4/12 @ 8pm (Week 3).
Some guidance on research designs
Your proposed research design should be a roughly 1-page, single-spaced document. It should do three things: (1) lay out a research question and justify it; (2) delineate a brief answer to the question; and (3) discuss the sort of data you will need in order to answer the question and why.
The readings and videos for this week are meant to help you to develop your research question and design. So, if you can, try to complete the reading and watch the videos relatively early in the week.
I want to preview a couple of key points from the reading and videos here as means of reassuring and guiding you. In particular:
- Your research question and design will evolve. In fact, it will likely continue to evolve even as you conduct your analysis and write up your results toward the end of the quarter. (I am still constantly revising the design document for my own dissertation as it becomes a book!) This can be frustrating, but it is inevitable. You will be moving in and out of the literature and data surrounding your question in upcoming weeks, and in doing so, you will get a much better idea about what makes a “good” question as well as the sorts of questions you can examine given the available data.
- For part 3, it is not necessary that you have specific data / datasets in mind. You can instead approach this as a thought experiment: Given your question and tentative answer, what are your ideal data and why? What sort of measures would you obtain? What would the units of analysis be? Would the data be cross-sectional, panel / time-series, or something else? Are the data likely to derive from an experiment, survey, or machine-learning algorithm, or are they likely to have been hand-coded by scholars? The more you think through these and related questions, the better. Thinking carefully about your ideal data now will help you to identify workable data later.
Attributes of good questions and developing your question
First read the chapter on “Beginning the research process” by Buttolph Johnson. As you read, think about your answers to the following questions. You don’t need to write your answers down, but thinking about them will help you to develop your own research question:
- What are some attributes of a good research question?
- How does one actually go about developing a good research question?
- How does the literature review go hand in hand with the development of a good question?
Once you have completed the Buttolph Johnson chapter, watch the three videos below on “Research areas, topics, and questions”. Note that you will need to enlarge the videos in order to view them.
Anatomy of a research question
Now read the article by Albertus and Deming on “Branching out.” You should read the entire article. In your reading, you might try to implement some of the advice laid out by Dane’s chapter on “Reading and structuring research” in Module 1. In addition, in line with this week’s overarching topic, here are some questions to think about as you read. To the degree that there is time, we may discuss some of these questions during our team meetings this week:
- What is the central question that the authors ask?
- What have other scholars found in terms of answers to this question (or very similar questions)?
- Given that the question is not really new, what contribution – if any – do you think that the authors make?
- What sort of data would you want – in theory – in order to answer the question posed by the authors?
- What data do the authors actually use and how does it differ from the ideal data that you described above?
- How do the authors justify the data that they use?
Once you have read the article, watch the video on “Anatomy of a research question.”
R exercise on web scraping
Remember that you are not required to complete and submit the weekly R exercise for a grade. They are a voluntary tool for introducing you to some operations in R that you may find useful this quarter and/or in the future. This week’s exercise introduces you to one procedure for scraping data from the web pages written using HTML. I am not an expert on this topic, so any data scientists with experience in web scraping should feel free to post additional resources and advice at Ed Discussion.
Note that for this exercise, you will need to add SelectorGadget to your web browser. I use Chrome and have installed the SelectorGadget extension from Chrome’s Web Store (https://tinyurl.com/298y44yt). For other browsers, you can simply drag the SelectorGadget bookmarklet to your bookmarks toolbar. You can find the bookmarklet at selectorgadget.com.
I have included a very brief video below that introduces the selector tool as it applies to this week’s R exercise.