Categorical Data Analysis - Online Course
A 4-Day Livestream Seminar Taught by
Trenton Mize10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
Many—perhaps even most—behavioral, health, and social science questions include outcome variables that are categorical. E.g., Which political candidate will win the next election? How does a parent’s social class influence children’s educational attainment? How many publications does it take to receive tenure? Do men or women drink more alcoholic drinks? Is a vaccine effective at preventing disease? Answering these—and countless other—questions cannot be adequately accomplished via the linear regression model and instead require the more advanced techniques covered extensively in this seminar.
Categorical Data Analysis is a seminar in applied statistics that primarily deals with regression models in which the dependent variable is binary, nominal, ordinal, or count. Many common statistical issues including interpretation of coefficients, calculation of predictions, testing of interaction effects, testing for mediation or other cross-model comparisons, and assessing model fit, require a different approach for models with categorical dependent variables. The focus of the course is on interpretation and learning to deal with the complications introduced by the nonlinearity of the models.
Specific models considered include: probit and logit for binary outcomes; ordered logit/probit and the generalized ordered logit model for ordinal outcomes; multinomial logit for nominal outcomes; and Poisson, negative binomial, and zero inflated models for counts.
Starting July 29, this seminar will be presented as a 4-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
Computing
The vast majority of what you will learn in this course can be applied in any software package, but course examples will use Stata and R. Template code, examples, and optional assignments will be provided to all participants in both Stata and R. Some limited resources for other software packages (e.g., SAS, SPSS, and Mplus) are also available.
Stata
For those who wish to follow along with the course in Stata, you should have Stata already installed on your computer when the course begins. Stata version 14 or above can be used for all course examples and assignments.
To follow along with the course exercises, you should be able to perform basic data manipulation and analyses in Stata. For users new to Stata, we recommend following along with a “getting started” video like the one here. In addition, this “Introduction to Stata” guide covers the basics of using Stata.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
R
For those who wish to follow along with the course in R, you should have the most recent version of R installed. We also encourage you to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills. You may want to consider taking a short introductory seminar on R (such as Introduction to R for Data Analysis, R for SPSS Users, or R for Stata Users).
The vast majority of what you will learn in this course can be applied in any software package, but course examples will use Stata and R. Template code, examples, and optional assignments will be provided to all participants in both Stata and R. Some limited resources for other software packages (e.g., SAS, SPSS, and Mplus) are also available.
Stata
For those who wish to follow along with the course in Stata, you should have Stata already installed on your computer when the course begins. Stata version 14 or above can be used for all course examples and assignments.
To follow along with the course exercises, you should be able to perform basic data manipulation and analyses in Stata. For users new to Stata, we recommend following along with a “getting started” video like the one here. In addition, this “Introduction to Stata” guide covers the basics of using Stata.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy.
R
For those who wish to follow along with the course in R, you should have the most recent version of R installed. We also encourage you to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills. You may want to consider taking a short introductory seminar on R (such as Introduction to R for Data Analysis, R for SPSS Users, or R for Stata Users).
Who should register?
If you need to analyze categorical outcome data (i.e. binary, ordinal, nominal, or count dependent variables) and have a basic statistical background and familiarity with regression, this seminar is for you. The seminar is helpful for graduate students, applied researchers, faculty, and others who want to learn these methods for the first time—but also for researchers who have some familiarity with the methods but want to learn the contemporary techniques now widely available for analyzing categorical data.
If you have a good working knowledge of linear regression, you are well-prepared for this seminar. The seminar assumes knowledge of linear regression at the level of Lewis-Beck’s Applied Regression. Those wanting a refresher in regression before beginning the seminar are encouraged to read Applied Regression, a short (120 page) and accessible overview of regression modeling.
If you need to analyze categorical outcome data (i.e. binary, ordinal, nominal, or count dependent variables) and have a basic statistical background and familiarity with regression, this seminar is for you. The seminar is helpful for graduate students, applied researchers, faculty, and others who want to learn these methods for the first time—but also for researchers who have some familiarity with the methods but want to learn the contemporary techniques now widely available for analyzing categorical data.
If you have a good working knowledge of linear regression, you are well-prepared for this seminar. The seminar assumes knowledge of linear regression at the level of Lewis-Beck’s Applied Regression. Those wanting a refresher in regression before beginning the seminar are encouraged to read Applied Regression, a short (120 page) and accessible overview of regression modeling.
Seminar outline
Day 1
-
- Why can’t I use OLS for all dependent variables?
- Nonlinear effects, interaction effects, and nonlinear interaction effects
- Binary dependent variables: logit and probit models
- Take-home data analysis assignment #1 (optional): binary DV models
Day 2
-
- Interpreting categorical dependent variable models: coefficients, multiplicative effects, predictions, marginal effects, and visualizations
- Count dependent variables: Poisson and negative binomial models
- Zero-inflated count models
Day 3
-
- Nominal dependent variables: multinomial logit models
- Ordinal models: ordinal logit and probit, generalized ordered logit models
- Take-home data analysis assignment #2 (optional): nominal and ordinal DV models
Day 4
-
- Interaction / moderation for categorical models
- Comparing predictions and effects across categorical models (e.g. mediation)
- Absolute and comparative model fit for categorical models
- Model diagnostics for categorical models
Day 1
-
- Why can’t I use OLS for all dependent variables?
- Nonlinear effects, interaction effects, and nonlinear interaction effects
- Binary dependent variables: logit and probit models
- Take-home data analysis assignment #1 (optional): binary DV models
Day 2
-
- Interpreting categorical dependent variable models: coefficients, multiplicative effects, predictions, marginal effects, and visualizations
- Count dependent variables: Poisson and negative binomial models
- Zero-inflated count models
Day 3
-
- Nominal dependent variables: multinomial logit models
- Ordinal models: ordinal logit and probit, generalized ordered logit models
- Take-home data analysis assignment #2 (optional): nominal and ordinal DV models
Day 4
-
- Interaction / moderation for categorical models
- Comparing predictions and effects across categorical models (e.g. mediation)
- Absolute and comparative model fit for categorical models
- Model diagnostics for categorical models
Payment instructions
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.