Stat503 / Soc650 is a course in in applied
statistics that assumes you have completed a class in linear regression, such as Soc 554. Categorical Data Analysis deals with
regression models in which the dependent variable is binary, nominal, ordinal, or count.
Models that are discussed include probit and logit for binary outcomes, ordered logit and ordered probit for ordinal outcomes, multinomial logit for nominal outcomes, and Poisson regression and zero inflated models for count. This web page serves as the syllabus for the class.
Assignment due dates · Workflow requirements · Grades and getting an A+ · Books
Computing and datasets · Getting Help · Enrolling, getting ready & time conflicts
News and policies
- Logistics: Class meets 1:00 - 2:15 TR in BH139. Please arrive on time and be ready to start at 1:00.
- Teaching Assistants: The AIs are Trent Mize (tdmize at indiana dot edu) and Bianca Manago (bmanago at umail iu edu).
- Computing labs: You have signed up for one of two labs sections held in TuTh 2:30PM - 4:30PM and TuTh 5:30PM - 7:30PM. Each lab section meets twice a week for two hours. The lab instructors might provide a short presentation or discuss the assignments at the start of each lab. Teaching assistants will be available for 90 minutes each day, but might not be available the last 30 minutes of lab.
- Office hours: Office hours are to be determined in Ballantine 842B Enter 842 (no need to knock). My office is at the end of the hall. If I am talking with someone, let me know you are waiting. Feel free to contact me by e-mail; during the week if you don't hear within 12 hours, try again.
- Course materials: Course materials including PDFs of the lecture notes are located at on the class LAN. Details on connecting to the LAN are here.
- Turning in assignments: Assignments are due by the beginning of lab on the day they are due. Pedagogically it is critical to complete assignments on time. You can turn in one assignment late without penalty; that assignment is due at the beginning of the next lab.Additional late assignments will be penalized 25% if they are turned in no later than the beginning of the next lab; assignments are not accepted more than one lab after they are due. Exceptions are made for special circumstances. You must get approval for a later assignment by e-mail (often confirming a conversation) from Professor Long. Turn in a copy of the e-mail along with your late assignment.
- Your work must be posted on the class LAN before assignments are turned in.
- IU students can run Stata for free over the internet. For details go here. Opinions vary on how well this works.
Assignments are due at the start of lab on the due date.
Assignments in the form of Word files will be available on the LAN. Add your answers to the assignment file and place the renamed, completed file in your LAN directory. Details are provided in lab. DATES ARE TENTATIVE.
- Assignment 1: Math review. Due Sep 1, 2015.
- Assignment 2: Data cleaning. Due Sep 8, 2015.
- Assignment 3: Picking your variables. Due Sep 15, 2015.
- Assignment 4: LRM. Due Sep 22, 2015.
- Assignment 5: BRM-1.
Due day 12, Oct 1.
- Assignment 6: BRM-2. Due day 16, Oct. 13.
- Assignment 7: Testing and Fit. Due tentatively day 19, Oct 28.
- Assignment 8: MNLM-1. Due tentatively day 21, Nov 4.
- Assignment 9: MNLM-2. Due tentatively day 24, Nov 13.
- Assignment 10: ORM. Due tentatively day 28, Dec 4.
- Assignment 11: Count models. Due Tuesday of exam week at 5PM in Scott Long's BH 744 mailbox.
Workflow requirements for assignments
An essential part of being an effective researcher is a workflow that allows you to organize your efforts and replicate your findings. Since this class is an applied course in data analysis, a portion of your grade is based on the workflow you use in completing your assignments. More general information and a detailed treatment of workflow is available at Long’s workflow page and his book The Workflow of Data Analysis Using Stata. For this class you are not required to implement the full workflow from the book, but you will be required to improve your workflow to allow reproducible results. In class, details on requirements for the workflow are explained.
- Overview: Grades are based on assignments, your research diary, and attendance. Each assignment is given a number of points with a total of about 1000. Your grade is based on your percent of the total points using A=100-94%; A-=93-91%; B+=90-88%=B+; B=87-84%=B; B-=83-81%; 80-71%=C; 70-61=D.
- Mistakes and inconsistencies: I apologize if a mistake is made in grading. Return the assignment to me along with a cover page explaining the error. If I do not return the assignment documenting the change within two class periods, remind me by e-mail. Multiple people are doing the grading and we try very hard to be consistent.
- Getting an A+: To get an A+ you must do a project as well as receive an A on the assignments. You must get Professor Long's approval for your project and meet with him periodically. The final project is due the first day of finals. A careful write-up of your results along with the supporting Stata files and research log are required.
Computing and datasets
- Getting Started with Stata and CDA: Lab Guide for Stata are available on the LAN. I recommend that you work through the section of the guide corresponding to the current assignment before you start the assignment, even if you are sure you don't need to!
- Datasets: Datasets for the the class are located on the LAN and can also be accessed with the SPost spex command. Codebooks are in the Lab Guide. These datasets are not fully "cleaned" and sometimes the codebook doesn't clearly describe what a variable is, just like real-world data.
- Stata: While you may freely use my ado files, they require Stata to run. Stata is installed in campus computing labs. Personal copies can be purchased from the IU Stat/Math Center.
If you need help debugging a program, the best
thing is to place relevant files in your directory on the LAN in a subdirectory called \helpme (e.g., \jslong\helpme). Include the do-file, the dataset, and log file in
text format, not smcl. Follow the guidelines below; for further details on getting help, check here.
1) The do-file must be self-contained. It must load the data, create needed variables (if any), generate the
problem, and save a log file in text format. The do-file must have comments explaining what you are
doing and what the problem is.
2) If a SPost command is causing a problem,
include the command which command-name for the command causing the problem. This tells me which
version of the command you are using.
3) Do not refer to specific directories (e.g., do not: use d:\mydata\science3.dta).
Assume that your data is located in your Stata working directory.
Here is an example of what the do-file might look:
capture log close
log using jslong_assgn1_problem, text replace
// Scott Long - 2011-08-31
// Assignment 4: binary regression
// ERROR: see #3 below.
// #1: load data and check data
spex science2, clear
sum x1 x2
// #2: estimate logit
logit y xl x2, nolog
// #3: compute discrete change
// ERROR: variable xl not found
mchange, at(x1=1 x2=3)
- The lecture notes are available as PDF files on the FTP site.
- Long, J. Scott and Freese, Jeremy. 2014.
Regression Models for Categorical Dependent Variables Using Stata, 3rd
Edition. Stata Press: College Stata, TX is required.
- Long, J.S. 2008, The Workflow of Data Analysis Using Stata. Stata Press: College Station, TX. If you plan to do a lot of data analysis, this book will save you a lot of time and make your work replicable. Recommended but not required.
- Long, J. Scott. 1997. Regression Models for
Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage.
Required and especially useful for those who are interested in mathematical details. Recommended but not required.
Enrolling in Soc 650/Stat 503 and Time Conflicts
Sometimes there are more students who want to take the class than there are
seats in the class. First priority is given to graduate students for which this is required for their degree program. Otherwise, authorizations are given on a first-come-first-serve basis. If you are interested in
taking the class, contact Scott Long for authorization
(email@example.com). If you are given an authorization, you need to sign
up for the class during the normal enrollment period; if you do not, your authorization will be given to a student on the wait
Time conflicts: If you have another class that
overlaps with the lecture time, you will need to take the class another semester. If you have a time conflict with all of the lab
times, you should take the class some other semester. If you can attend some of the
labs each week and you are already familiar with Stata (or can learn it on your
own), you should do fine, but might have to work harder. While most of the lab time is used for students doing
independent work, the teaching assistant provide short lectures related
to the assignments. For example, he/she might provide additional information
about keeping a research log or how to format tables using Word.
Getting ready for Soc650/Stat503
There are several things you can do to get ready for the class.
- Review a book on the linear regression model.
- If you are rusty on mathematics, you can review the materials in this file.
- Feel free to start reading the books listed above.