Read the 2020 Syllabus

Sociology 500 is the first class in a two-semester statistics sequence for graduate students in Sociology. We also welcome advanced undergraduates and graduate students from other departments. The course assumes some basic mathematical background (e.g. very basic calculus and matrix operations) as well as a basic working knowledge of R. These can both be obtained through the Princeton Sociology Summer Methods Camp. Soc500 covers probability, regression and basic causal inference. My version of the second course in the sequence, Soc504, covers maximum likelihood, generalized linear models and assorted topics.

**Two Important Notes**:

1) Credit

My personal philosophy on teaching preparation is that it is best to stand on the shoulders of giants; that is, I would rather spend several hours improving/tweaking/remixing a set of already strong slides than recreating some from scratch just so they are completely unique. Thankfully, I have access to a network of generous scholars who have been willing to share their materials. Many of the slides linked below are either taken directly from others or are adapted from their original design- I, of course, take responsibility for any errors that remain.

The Soc500 course design is in many ways a reinterpretation/combination of courses by Matt Blackwell, Adam Glynn and Jens Hainmueller. I have also drawn material from Joe Blitzstein, Justin Grimmer, Erin Hartman, Chad Hazlett, Kosuke Imai, Gary King, Kevin Quinn, Matt Salganik, Teppei Yamamoto and many more. All of these scholars have kindly allowed me to post here. Whenever material is drawn from someone they are credited at the bottom of the title slide or as a one-off on the individual slide where their material is used. If you believe your material was used here without attribution, please reach out to me and let me know so I can correct it.

This class is not sustainable without great teaching assistants. I have posted materials from precepts (sections run by the teaching assistants or preceptors as we call them here). These materials have been developed by previous teaching assistants of mine. I also initialized these materials using material I developed while a teaching assistant at Harvard which in turn built on previous generations of teaching assistants at Harvard's Department of Government and Harvard's Statistics Department. It is often difficult to find the original source of these materials, but if you developed some of the materials you see here- please reach out and let me know.

My amazing prior preceptors:

- 2015: Clark Bernier and Elisha Cohen
- 2016: Ian Lundberg and Simone Zhang
- 2018: Alex Kindel, Shay O'Brien, Ziyao Tian
- 2020: Emily Cantrell and Alejandro Schugurensky

2) Style and Form

This course was taught twice a week for an hour and a half. Each lecture is a week's worth of material except Lecture 1 (one class) and another lecture (three classes) due to the nature of the schedule. I talk very quickly which is why we cover so much ground. Stylistically I see class as an opportunity to expose people to new ideas and it is through the weekly problem sets and precepts that the material is really solidified. So if the pace seems almost inconceivably fast, that's why.

**Materials:**

I have included both slide and handout forms of the lectures. They are intended to be viewed in slide form and while I have tried my best, the handouts do not always do justice to what is intended on the slides. For precept materials there are typically slides and occassionally additional materials. Materials from older versions of the class are below the most recent iteration.

If you see a typo or other error- please email me!

**2020:**

NB: materials this year are partitioned into much small sections because they were filmed for a flipped classroom. Precepts this year covered exclusively coding material and were recorded as videos.

Lecture 1: Introduction and Probability - August 31

slides, handout

Lecture 2: Random Variables - September 7

slides, handout

Lecture 3: Learning from Random Samples - September 14

slides, handout

Lecture 4: Hypothesis Tests and What is Regression? - September 21

slides, handout

Lecture 5: Simple Linear Regression - September 28

slides, handout

**2018:**

Lecture 1: Introduction and Probability - September 12

slides, handout

Precept 1: Probability, Simulations, Working With Data - September 13

(Shay O'Brien)

slides, handout, simulation example, data manipulation

Lecture 2: Random Variables - September 17-19

slides, handout

Precept 2: Random Variables - September 20

(Alex Kindel)

slides

Lecture 3: Learning from Random Samples - September 24-26

slides, handout

Precept 3: Random Samples - September 27

(Ziyao Tian)

slides

Lecture 4: Testing and Regression - October 1-3

slides, handout

Precept 4: Hypothesis Testing - October 4

(Alex Kindel)

slides

Lecture 5: Simple Linear Regression in Scalar Form - October 8-10

slides, handout

Precept 5: Simple OLS - October 11

(Shay O'Brien)

slides, additional files

Lecture 6: Linear Regression with Two Regressors - October 15-17

slides, handout

Precept 6: Regression - October 18

(Alex Kindel)

slides

Lecture 7: Multiple Linear Regression - October 22-24

slides, handout, matrix review

Precept 7: Multiple Regression - October 25

(Ziyao Tian)

slides, additional files

Fall Break

Lecture 8: What Can Go Wrong and How to Fix It - November 5,7,12

slides, handout

Precept 8: Diagnostics - November 8

(Ziyao Tian)

slides, additional files

Lecture 9: Regression in Social Science - November 14, 19

slides, handout

Precept 9: Some Review, Heteroskedasticity, and Causal Inference - November 15

(Alex Kindel)

slides

Lecture 10: Causality With Measured Confounding - November 26-28

slides, handout

Precept 10: Identification - November 29

(Alex Kindel)

slides

Lecture 11: Unmeasured Confounding and Instrumental Variables - December 3-5

slides, handout

Precept 11: Unmeasured Confounding - December 6

(Ziyao Tian)

slides

Lecture 12: Repeated Observations and Panel Data - December 10-12

slides, handout

Precept 12: Causality with Repeated Measurements - December 13

(Alex Kindel)

slides

Review Session

(Shay O'Brien)

slides

**2016:**

Lecture 1: Introduction and Probability - September 14

slides, handout

Precept 1: Probability, Simulations, Working With Data - September 15

(Simone Zhang)

slides, additional files

Lecture 2: Random Variables - September 19-21

slides, handout

Precept 2: Random Variables - September 22

(Ian Lundberg)

slides

Lecture 3: Learning from Random Samples - September 26-28

slides, handout

Precept 3: Random Samples - September 29

(Simone Zhang)

slides, additional files

Lecture 4: Testing and Regression - October 3-5

slides, handout

Precept 4: Hypothesis Testing - October 6

(Ian Lundberg)

slides, additional files

Lecture 5: Simple Linear Regression in Scalar Form - October 10-12

slides, handout

Precept 5: Simple OLS - October 13

(Simone Zhang)

slides, additional files

Lecture 6: Linear Regression with Two Regressors - October 17-19

slides, handout

Precept 6: Regression - October 20

(Ian Lundberg)

slides, additional files

Lecture 7: Multiple Linear Regression - October 24-26

slides, handout

Precept 7: Multiple Regression - October 27

(Simone Zhang)

slides, additional files

Fall Break

Lecture 8: Regression in Social Science - November 7-9

slides, handout

Precept 8: Diagnostics, Presentation and Causal Inference - November 10

(Ian Lundberg)

slides, additional files

Lecture 9: What Can Go Wrong and How to Fix It - November 14-21

slides, handout

Precept 9: Diagnostics - November 17

(Simone Zhang)

slides, additional files

Lecture 10: Causality With Measured Confounding - November 28-30

slides, handout

Precept 10: Causal Identification/Estimation - December 1

(Ian Lundberg)

slides

Lecture 11: Unmeasured Confounding and Instrumental Variables - December 5-7

slides, handout

Precept 11: Unmeasured Confounding - December 8

(Simone Zhang)

slides

Lecture 12: Repeated Observations and Panel Data - December 12-14

slides, handout