Comments on preparation for exam 1

The exam will be about 50% multiple choice and 50% open ended questions of various types. I’m trying to allocate points and time so that it’s 50/50 between multiple choice and open ended. About half of your time will be spent on multiple choice questions and about half on open-ended questions.

The questions range in difficult from quite easy to very challenging.

In all cases, I try to follow the notes and exercises closely.

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Think of these comments as a “study guide.” As I noted at the beginning of the semester, the exam is based on the notes and exercises, but this document offers a “big picture” overview of the important themes. See especially the open-ended questions at the bottom.

Oct. 12 Update:

• Define a sampling distribution. Describe how we can use a simulation to study a sampling distribution.

Oct. 11 Update:

• Be familiar with the various distributions you’ve seen throughout the exercises and notes. You should be able to recognize the key features of the plotted pmfs or pdfs, for example. (There’s no need to memorize their mathematical formulas.)
• Don’t forget the readings assigned in the exercises! You should know the key ideas from each reading, especially the portions the exercise mentions or focuses on.
• I’m now thinking the exam will be about 50% multiple choice (or similar, like fill in the blank) and 50% open ended.

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As I write the exam, I’m focused on the notes and the exercises.

• Make sure you understand the ideas in the notes and the examples.
• Make sure you understand each exercise.
• Make sure you are familiar with the readings (assigned in the exercise).

Don’t forget about the mathematical foundations we did in week one!

The exam will be organized by types of questions (e.g., short-answer questions, then long-answer questions). Within these sections, I’ll organize them roughly chronologically (i.e., starting math basics and ending with MCMC).

The exam will have a mix of long-answer and short-answer questions.

Short Questions

My goal with the short-answer questions is that you can see the correct answer immediately without any or much work. Many of these questions are multiple choice. These cover definitions, principles, and simple calculations.

Long Questions

Some long-answer questions require you to write several sentences. Others require a chain of math.

1. Descriptive v. causal claims
2. Find an ML estimator for a given model.
3. Find the posterior for a given model and conjugate prior.
4. The conceptual relationship between Bayesian and ML estimators. In competition? Interchangeable alternatives? What’s the unifying principle, if any?
5. First difference as a unifier.
6. How to use obtain point and interval estimates to obtain posterior simulations.
7. Comparing the key features of different distributions we’ve encountered.

Reflections

At least one of the following questions will appear on the exam. I have at least advocated for a particular position on each. It’s fine to disagree with my stance, but defend your position well.

Bayes v. ML

We have examined two general engines for producing point and interval estimates: maximum likelihood and Bayesian. How should we think about the relationship between the two? Are they best understood as competing, incompatible alternatives? Or simply as interchangeable tools? If they compete, which approach is right, and why? If they are interchangeable, what is the unifying principle?

Description

As quantitative social scientists, we have tools to test descriptive and causal claims.

• Define descriptive and causal claims.
• Assess the current state of quantitative political science with respect to descriptive and causal claims. Would you say that causal questions are over-emphasized in this moment? Or under-emphasized? Logically, must claims fall in one bin or the other?
• In practice, do authors clearly locate their claims in one bin or the other?

Defend your answers.