Module 3 builds on the concepts surrounding data and measurement that we tackled last week by asking you to think critically about some fundamental questions: Where do your data come from? Who made them and why? What do they look like? And so on.
In addition, Module 3 asks you to grapple with how we can move toward making causal claims in the social sciences as well as analyze how scholars (i.e., Koch and Nicholson) have tried to overcome obstacles to causal inference.
- Explain the distinction between experimental and observational data; explain why random assignment to treatment can facilitate causal inference
- Lay out obstacles to causal inference when using observational data as well as some strategies for (potentially) addressing them; analyze scholarship in light of these obstacles and strategies.
- Develop additional tools to assist data exploration in R.
Reminder: Assignment 1 is due by 5pm CST on Monday 2/1. I suggest that you download and preview Assignment 1 early in the week so that you can plan your time accordingly.
Where do your data come from?
Watch the brief video “Where do your data come from?” Like last week, the video will sometimes ask you to press “pause” and answer some “class questions.”
Now watch the video “Where do your data come from? (Part 2)”.
I mentioned in the videos that our main aim as social scientists is to make valid causal claims. But, as the videos also noted, there are a host of obstacles in our path, including bad data.
Read Martin, Thinking through Statistics, Chapter 2 (“Know your data”). As you read, pay close attention to different data problems and, in particular, how those data problems have at times ruined prominent research. What can we do to keep ourselves from ruin?
Toward causal inference
Now watch the final video on “Toward causal inference.”
The video suggests that we can sometimes move toward causal inference by moving from the level of general laws to the level of mechanisms. How does this play out in scholarship?
Read Koch and Nicholson (2016, pp. 932–938). As you read, pay close attention to the authors’ argument and discussion of their empirical setup.
(1) What do the authors argue? (You might even take some time to diagram their argument). (2) How do thee authors try to move from establishing mere correlation to identifying causation? (3) Why does the structure of their argument potentially help them toward this end?
Now examine the regression table from the authors’ aggregate analysis. (4) Why do the authors control for the variables that they do? (5) How compelling is the aggregate analysis for helping the authors to make a causal claim? (6) Whether you think it is compelling or not, what work is the aggregate analysis doing for the authors?
In the spirit of really getting to know our data, the practice questions for this week will introduce some techniques that will ease the data cleaning and exploration process. In particular, we will practice using loops with
for() as well as the
dplyr package to summarize and reshape our data.
Download the “Module 3 practice questions” 1 and 2 RMD from Canvas. Note that there are two parts to the practice questions. Also note that the practice questions on loops have a couple of especially challenging questions (e.g., the de Montmort Problem). I suggest that you complete the simpler questions and then move on. You can return to the more challenging ones after you have completed Part 2 as well as Assignment 1.