Logistic Regression- Enrollment
Logistic Regression Online Workshop for Binary, Ordinal, and Multinomial Outcomes
And no matter how many ways you transform the data, you just can’t force it into a linear regression or ANOVA.
But luckily, there is logistic regression, which is designed for categorical outcome variables.
Although logistic regression does contain a few complexities and new statistical concepts, it is within reach of anyone who can use linear models.
This workshop will guide you through those new concepts. You will learn the similarities and differences between linear and logistic regression for three kinds of categorical variables:
- binary: two categories
- multinomial: three or more unordered categories
- ordinal: three or more ordered categories
In short, after taking this workshop, you will be able to recognize the need for, conduct, evaluate, and interpret a logistic regression model and use it to predict outcome probabilities.
What You Will Learn:
Module 1: The Binary Logistic Regression Model
This module lays the foundation. We start with a review of linear models, then walk through differences and similarities to logistic models. We talk in detail about why the logistic model is necessary and what a logit link really means. We review probabilities and odds and what they really measure. Then we’ll end with a simple example, and walk through what the model tells us about the relationships between predictors and our categorical outcome.
- A brief review of the Linear regression model, why it does not work for categorical data, and why logistic regression does
- The similarities and differences between the linear and logistic models
- Link functions, the logit function, probability and odds, and how they are used in logistic regression
- Model assumptions
- Interpreting coefficients, odds ratios, relative risks, and confidence intervals, including odds ratios for categorical predictors and interactions
- Demonstrations in SAS and SPSS
Module 2: Fitting and Evaluating the Model
Now that we’ve got a solid understanding of the model, we learn ways of evaluating it. Is it any good? Does it fit the data? How well does the model predict outcomes? What happened to R-squared?
- Maximum Likelihood estimation: what it means and how it affects model fit statistics
- Evaluating the model as a whole using Likelihood Ratio Tests, Deviance, Information Criteria, and Pseudo R-Squared statistics
- Using the Model to Predict Probabilities and Classify Individuals with Classification Tables
- Evaluating the Predictive Ability of the Model using ROC Curves and Hosmer-Lemeshow Goodness of Fit tests
- Demonstrations in SAS and SPSS
Module 3: Using and Assessing the Model to Predict Outcomes
Now that we know that a model as a whole is working, we can now investigate in detail the effects of individual predictors. There are various ways to do that: graphing, calculating predicted probabilities and examining odds ratios. Odds ratios in particular are bizarre, but you can learn them. We’ll spend a lot of time on interpreting them, especially for interactions, which get even trickier.
- Evaluating each Predictor using Likelihood Ratios and Wald Statistics
- A step-by-step of how to use the estimated coefficients to calculate predicted probabilities and use them to interpret effects of predictors
- Interpreting Odds Ratios for Interaction Effects
- Model Assumptions and Dealing with Data Fitting Challenges, including complete separation, zero cell counts, and influential points
- Demonstrations in SAS and SPSS
Module 4: Ordinal and Multinomial Models
Everything we’ve done up to this point has been about binary models, as they’re the foundation. But everything we do in binary logistic models can be expanded to multinomial and ordinal categorical outcomes. So we do that here.
- How the multinomial model differs from the binomial model, and how it’s similar
- The different procedures in SAS, SPSS and Stata you need to run multinomial models
- The difference between ordinal models (PLUM) for 3 or more ordered categories and multinomial models for 3 or more unordered categories
- How to interpret the results of both and the big effect software defaults have
- The proportional odds assumption
But can I keep up?
This is an intermediate level workshop. It is for researchers, not statisticians.
But you should have solid experience running linear models. Experience with ANOVA or linear regression are both fine, as long as you have a basic understanding of least-squares estimation, dummy variables, and interactions.
We will be doing a bit of algebra and equation solving.
Concepts that are unique to logistic regression? I’ll explain those in detail–information criteria, maximum likelihood, deviance, logit links, odds ratios, ROC curves. All those terms that books seem to skim over, you’ll learn here.
You do need to spend some time each week. You will learn concepts and get some clarity if you don’t do the exercises and try to rerun my examples on your own. But you won’t entirely get it.
This is a workshop where you want to get your hands dirty with some data. Please expect to spend 4-6 hours per module just doing the exercises.
This workshop is for you if you:
- Have tried to do a logistic regression before, but found it confusing or difficult, and don’t really understand it.
- Have used linear regression or ANOVA, and want to expand your knowledge.
- Know you will soon need to implement a logistic regression
- Want to expand your statistical capacity
- Are familiar with any statistical software. I only offer demonstrations in SAS and SPSS, and it’s ideal if you know one of those, but the focus of the workshop is on the meaning, steps, and interpretation of logistic regression models, NOT on the software. So if you’re familiar with another software package and are comfortable reading the manual to understand the defaults in its logistic regression procedure, you’ll be fine.
This workshop is not for you if you:
- Are a statistics beginner and have never done a linear regression or ANOVA. You should be familiar with interpreting regression coefficients, p-values, R-squared, dummy coding, and interactions.
- Don’t have enough time to keep up. Each week will require about 5-10 hours for full participation in workshop sessions, Q&A, and exercises. You do have access to all materials for a full year, but if you want to take advantage of the interactive webinars and the Q&As, it’s ideal to put the time in now.
- You will get the most out of the workshop if you have had at least two statistics classes, and some experience in data analysis, particularly linear modeling.
- You need to be familiar with using a statistical software package. I will present examples and provide data sets and code for SPSS, SAS and Stata. I am familiar with S-Plus, Stata, Minitab, and JMP, and all are reasonable options for implementing logistic regression. This workshop focuses on the concepts, steps, and interpretation of logistic regression–it is not about the software. I will do my best to answer software questions, but I’m not familiar with the intricacies of defaults in these other packages.So if you’re familiar with another software package and are comfortable reading the manual to understand the defaults in its logistic regression procedure, you’ll be fine.
The workshop is offered as an interactive, live online workshop + Membership Site
I will be conducting the workshop live via webinar. You attend online and listen and speak either by phone or over the internet as you see what is happening on my computer screen. Webinars are highly interactive–you can see my screen and ask questions during the session, yet you never have to leave your house or office.
During the webinar sessions, I will present concepts and explain the meaning of the techniques in that module, demonstrate how to implement those techniques in on different examples, and answer your questions.
We’ve also created a workshop site that you have access to. For a year.
The site is our home-base for the workshop. It’s where you’ll find everything you need to support your learning of each module:
- Video Screenshot recordings of each workshop session made available within 48 hours. Review the material right away, a year later, or while doing the exercises. Or if you need to miss one (or all) of the live sessions, you can still participate on your own schedule.
- Video Recordings of all Q&A sessions. So if you have to miss one, just submit your questions ahead of time and I’ll answer in the Q&A. Just watch it later.
- Data files. These are real, true, not-textbook-perfect data files from real research projects I’ve worked on with clients, who have graciously allowed me to share them with you. You get full access to use the data to try everything I demonstrate in the workshop and try things on your own.
- SPSS, SAS and Stata syntax to run and explore all of the examples yourself. You won’t learn it unless you try it. So I’m giving you my syntax so you can see exactly how I got all the output I’m showing you.
- PDF handouts of the presentation slides, which will be available ahead of each session, on which you can take notes.
- Exercises. (Yes, homework!). You really need to practice this stuff. Get your hands dirty. So we’re giving you the data to try it on your own with new models to try out. But don’t worry–you won’t be on your own stuck on some coding error that won’t work. I’m also giving you the syntax I used to do the exercises and the answers.
- A place to submit written questions between sessions. So as you’re reviewing videos afterward (or if you missed a live session), just submit a question. I’ll answer it there if I can, or if it’s something I need to show you, I’ll answer it in the next session.
- A list of helpful resources and suggestions for further reading. I’m not requiring a text book. But there are definitely some good books and articles out there on this topic, and you will learn the techniques better with some background reading. I’m selecting the chapters from each book that best explains each topic, and I’ll make the list available when you first register, so you have time to inter-library loan them.
- Bonus videos. I’ve included a number of videos from some webinars on relevant topics to help jog your memory or clear up misunderstandings. Included are:
- A Review of Logarithms for the Data Analyst
- What Happened to R squared?: Assessing Model Fit for Logistic, Multilevel, and Other Models that use Maximum Likelihood
- Understanding Probability, Odds, and Odds Ratios in Logistic Regression Models
- ROC Curves
You’ll have access for a year to this workshop membership site. So as we add resources, re-record sessions, and answer more questions each time we offer the workshop, you have access to the updated material.
You may also find that you learn a LOT more the second time through. Or third. You’ll learn more every time.
Karen Grace-Martin is a statistical trainer and consultant and an expert on implementing linear and logistic regression models, SPSS, and SAS.She has guided and trained researchers through their statistical analysis for over 15 years.Her focus is on helping researchers gain an intuitive understanding of how to apply statistics to real data and understand the results.
Comments from Past Participants in This Workshop:
|“Karen is a brilliant presenter. She has a very good command of the subject matter and is able to present complicated material in a way that is easy to understand, while at the same time conducting the workshops in a professional manner..”
“First, Karen has a gift for teaching this material in a way that is clear and accessible to everyone. In addition, the medium allows for real-time student-teacher interaction (e.g., allows Karen to gauge where students are at, and students to ask questions).
That this workshop is recorded, can be downloaded and used as a self-paced self-study course is invaluable to me. Given my work, I’m unable to regularly “attend” the live presentations or Q&A sessions, or complete the course within the scheduled time span. I’m also comforted to know I can revisit the materials when the needed (e.g., may be months later before I need to perform the particular analysis in my work).”
Jason Hurwitz, PhD
“If a colleague wanted to learn about logistic regression, this workshop would be a good start. For me, [the most helpful part is] having the downloads of the lectures so I can revisit the topics. It helps me when I can replay the same material.”
“Karen did an amazing job presenting, teaching, allowing participants to ask questions, answering questions, and keeping the workshop on pace. She explained the theory well in laymen’s terms and made me feel comfortable at my level of understanding. The prep work and homework were also on point, guiding one through necessary steps and thinking processes for the analyses learned. The live Q&A sessions were very helpful as well!!
I’m so glad I came across her site and registered for this workshop. I’m sure I’ll be registering for more workshops in the future! The cost-benefit for this workshop is incredible! You just can’t beat it!”
Paula Fleshman, Ph.D.
“The Analysis Factor does a great job and i really like the applied approach. For me it connects the theory with practice and reinforces the concepts.”
“Your explanations are clear and precise, you always answered all questions that were asked during the sessions and I never felt belittled or stupid asking a question. You are an exemplary teacher.”
The workshop sessions will be from 1:00-3:00pm U.S. Eastern Daylight Time on:
- Thursday, July 14
- Tuesday, July 19
- Thursday, July 21
- Tuesday, July 26
The live Question & Answer sessions will be on the following days and times:
- Monday, July 18 @ 1pm EDT
- Monday, July 25 @ 3pm EDT
- Thursday, August 11 @ 1pm EDT
Sorry, this workshop is full, and registration has closed.To get first notice when it opens again, please join our Advance Discount List. Check out some of our other online workshops too!
Refund Policy: Your registration fee is fully refundable up to 72 hours in advance. Because enrollment is limited, no refunds will be granted after the program begins.
As with all our programs, your satisfaction is guaranteed. If you participate fully in this workshop–watch, read, and try out everything included–and find you are not satisfied for any reason, we will give you a full refund, no questions asked.
Just notify us within 90 days of purchasing the program.