SOSC 13200-2 (WIN22)
Module 1 has three main components: (1) We begin by recapitulating some key points from Tuesday’s course overview. (2) We then get to know R, RStudio, and Markdown via a couple of brief videos and a practice exercise. (3) Finally, we start thinking about the nature of research questions in the social sciences: What are the attributes of a good research question? How do we go about formulating one?
- Become familiar with the RStudio interface and R’s basic functionality.
- Create a class directory on your personal laptop (using an R Project, if you are up for it).
- Install and load external packages such as the
- Load excel-style datasets into R using functions such as
- Begin to explore and manipulate datasets.
- Lay out some attributes of good research questions in the social sciences, and how we can begin to assess them.
- Explain the distinction between correlation and causation, and some of the assumptions that get us from the former to the latter.
Tasks to complete in Week 1
- Send an email to Professor Deming with your preferred meeting times for Week 2.
- Download and install R, RStudio, and LaTex. Installation instructions are located at the “Files” section of Canvas.
- Complete this Module.
- Download, complete, knit, and submit Assignment 1 as a PDF to Canvas by 11:59pm on Monday 1/17.
And now, Module 1…
If you were not able to attend our first meeting on 1/11, you should first go to the course Canvas page to download the course Syllabus. Read it. This will ensure that you do not miss any important logistical points.
Introduction to R, RStudio, and Markdown
If you have not already done so, download and install R, RStudio, and LaTex. I have posted detailed installation instructions to the “Files” section of Canvas. If you are still having trouble with installation after carefully following the instructions, please meet with me during our regularly scheduled meeting time on Thursday 1/13. Together, we will get you up and running.
Once you are up and running in R, watch the “Introduction to R” video below.
Let’s now get to know the RStudio interface. Start by watching the “R Demonstration” video below.
We will work in R and RStudio extensively this quarter. In class, my lecture slides will regularly display R code, and I will often ask you to work in small teams to complete practice exercises in R. The aim of these exercises is to boost your programming skills. But more importantly, they will help you apply key statistical concepts and more deeply engage the research that we examine by allowing you to “get under the hood” of the data used by different authors.
Let’s start building our programming skills via a short exercise. Go to the “Files” section of Canvas. You will see a folder entitled “Practice Exercises.” Open it. Then, download the file entitled “module1_practice_exercise.” It is an R Markdown (RMD) file. Save it to your personal computer, open it in RStudio, and work through it. When you are finished, knit it to PDF format. You do not have to submit it to me.
When you get stuck on a practice exercise or assignment: First, breathe! R programming can be frustrating, especially at first. Then, do some googling to see if you can find a solution to the problem. After that, post your question to the class Ed Discussion site. (Be sure to review older posts to see if your question has already been answered.) Finally, note that you are welcome to complete exercises and assignments in teams of 3-4 students (in fact, I encourage it!). Just be sure to follow the guidelines that I have laid out in the Syllabus.
At the end of this course, you will submit a brief report in which you lay out a research question and begin to assess via analysis of some quantitative data. With this in mind, what makes a good research question? How do we go about formulating and, ultimately, assessing one?
The readings by King, Keohane, and Verba (1994) and Holland (1986) will help us begin answering these questions. We will discuss the readings when we meet in our small teams during Week 2, so be sure to jot down key points.
Let’s start with KKV (1994). This comes from what is perhaps the most famous (for some scholars, infamous) contemporary texts on the nature of social science research. Before you read, think about your own answers to the two questions that I raised above: What makes a good research question? How would you go about formulating one?
Once you have laid out your own preliminary answers to these questions, read the chapter. Here are some discussion questions that you should try to answer. We will circle back to them during our small-team meetings in Week 2:
- The authors say that the aim of social science is descriptive and causal inference. What is inference? What is causal inference?
- What are some attributes of a good research question?
- Based on your reading, how might the authors answer the second question above? How does this coincide with and/or differ from your own preliminary answer?
Once you have read KKV (1994) and thought about your answers to the questions above, we can move to Holland (1986). This is not an easy read, but it is a good one. In particular, it will help us think carefully about an important distinction between simple correlation and causation. Before you read, consider how you think about this distinction: What is the difference between correlation and causation?
You should now read Holland (1986). As with KKV (1994), here are some discussion questions that you should try to answer:
- What does Holland mean when he refers to causation? How does this differ from the way other authors (and philosophers) might use the term?
- What is the Fundamental Problem of Causal Inference (FPCI)?
- How can we overcome the FPCI, according to the author?
- Do you see any problems with the solutions in practice?
- How do correlation and causation differ? What might Holland say here?
Download “assignment1” from Canvas. It is located at at “Files/Assignments and Final Project.” It is in RMD format. Open Assignment 1 in RStudio and read the instructions carefully. Complete it, knit it to PDF, and submit it to Canvas. It is due at 11:59pm on Monday 1/17.
Recall from the video above that, although practice exercises will guide you through some of the coding procedures that you will later use to complete assignments, it will not guide you through all of the required procedures. That is, completing the assignments will require some additional, independent learning of R. With this in mind, be sure to see my earlier note about how to go about learning R (i.e., googling, review of coding fora, Ed Discussion, and teaching and learning from each other).