Count Models – Enrollment

analyzing-count-data-1

Have you ever worked with a dependent variable that is a discrete count of some quantity?

Number of days, number of symptoms, number of incidents, number of arrests — count variables show up across many fields.

They’re truly numerical, so parameters like the mean make sense, but they’re not continuous. They can look normally distributed, but they aren’t.

They have limitations that normally distributed variables don’t:

  • They can’t have negative values.
  • They can only have values with whole numbers.
  • They often have a mode at the end of the data set (usually at 0).

There is a whole family of models made just for count variables. These models can account for the limitations, common distributions, and unique issues that arise with count variables.

This workshop will guide you through them.

You’ll learn why linear regression doesn’t work well (even with a transformation), and what does. You’ll learn 6 different types of count models (plus all their varieties) and how to decide which one works best in your specific data set.

After taking this workshop, you’ll be able to recognize the need for, conduct, evaluate, choose, and interpret a count model — and use it to predict outcome probabilities.

Quick Facts

  • Begins: January 12
  • 12 hours of live instruction, plus 6 Q&As
  • 1-year access to workshop website
  • Instructor: Jeff Meyer
  • Stat Software: Demonstrations in Stata, SAS, and SPSS
  • Level: Advanced (Solid experience running linear models is required)
  • Investment: $497 / $297 (Student)

enrollment-options

Our Workshop Approach

Practical, Not Theoretical

You’re not interested in statistics as a field of inquiry: You need to develop skills and get your data analyzed.

We explain concepts in practical, intuitive terms and show you step-by-step how to run the data analysis, how to make choices along the way, and how to interpret results.

Our instructors are applied statisticians and consultants with excellent teaching backgrounds.

They are personally acquainted with the challenges of data analysis and show you how to deal with them.

Examples Are Vital

Data analysis is all about the context of the design, the research question, and the variables.

It’s really hard to learn statistical skills without seeing how the same concept applies in different data situations.

So we always show you multiple examples, pulled from multiple fields.

Real Data Sets Show Real Challenges

The hard part of data analysis is figuring out how to handle imperfect data. You can’t learn how to do real data analysis on textbook-perfect data sets.

We always use real data sets.

Demonstrations in the Software You Use

It doesn’t matter how well you understand an analysis if you can’t get your software to do it, or if you don’t understand what their options mean or their defaults are.

We always give you software demonstrations in multiple packages so you learn how the statistics apply to your software.

Having Syntax Saves You Time Later

Writing syntax from scratch is like reinventing the wheel — and neither is a good use of your time.

We always share the code we use in our examples and exercises so not only can you try these examples yourself, but you can save it for your own analysis.

✅ Many Opportunities to Ask Questions

You learn when you ask–creating a safe space to ask any question (no matter how silly the question feels) is our core value.

You are encouraged to ask questions during webinars, in Q&As, and on the forum.

Access to Instructors During and After the Workshop

We know that even if everything makes sense as you’re learning it, when you encounter a new situation 6 months later in your own data, you can get stuck.

We’re not going to leave you out in the cold.

So in addition to all the opportunities to ask questions during the workshop, our instructors hang around to answer questions on the workshop website forum for a whole year.

And if you still need help after that, you can extend that access each year for just $25.

That includes any live webinars or Q&As if we run the workshop again during your yearlong access.

You Learn When You Practice…

Because statistical analysis is a skill, you need experience.

We give you everything you need — data, code, exercises — to practice.

✅ And When You Ask Questions about Applying What You Learn to Your Own Data.

Practicing on your own data is crucial.

So we encourage you not only to try out what you’re learning on your own data, but to ask questions when you get stuck. That’s when the real insights occur.

Having Multiple Resources Is Important

There are great resources out there on many statistical topics.

We share as many as we find. Sometimes seeing the same issue explained in different ways is just what you need.

About the Instructor

jeff-meyer-150Jeff Meyer is a statistical consultant, instructor, and writer for The Analysis Factor.

As your workshop instructor, Jeff will be teaching the workshop via live webinar sessions. He’ll be writing the code for Stata to use in the examples and exercises. He’ll also be at the live Q&A sessions to answer any questions you have.

For Jeff, being an effective instructor is about more than just knowing the subject matter and applying a logical approach to analyzing data: He also enjoys working with people and, most importantly, he cares about your success.

He has an MBA from the Thunderbird School of Global Management at Arizona State University and an MPA with a focus on policy from the Wagner School of Public Service at New York University.

 

About the Software Specialist

audreyAudrey Schnell is a statistical consultant at The Analysis Factor. She has particular expertise in biostatistics, including Inter Rater Reliability, Case control studies, and linear models.

As your software specialist for this workshop, Audrey will be creating the SAS demonstrations and code for examples and exercises. She’ll be at the live Q&A sessions to answer your SAS questions.

Audrey has a Master’s Degree in Clinical Psychology and a PhD in Epidemiology and Biostatistics.

 

Comments from Past Participants in Jeff’s Workshops:

Having Jeff able to respond to queries on the spot – via the question box – was a fantastic resource. For off-topic questions, which he obviously didn’t have time to address within the lecture, he then separately uploaded ‘answers’ onto the course website. That real-time interaction and feedback was truly invaluable. It honestly felt like we were in a physical classroom, which is the biggest compliment I can give.
– Ryan J. Kee, Queen’s University Belfast


 

Jeff Meyer clearly explained how to apply statistical concepts to real-life data sets using Stata, my primary programming language. Jeff is an excellent instructor. He explained when and how to use the commands in such a clear and understandable manner.
– Anonymous



Mr. Meyer is a very knowledgeable Stata user and works very hard to pass along as much information as he can.
– Collin Stewart

 

 

The Format

 

6 Live, Interactive Webinar Sessions with Jeff Meyer

Sessions start at 12 pm (US EST) and last 1.5 – 2 hours, depending on the number of questions.

January

12
Thursday

January

19
Thursday

January

26
Thursday

February

2
Thursday

February

9
Thursday

February

16
Thursday

6 Live Q&A Sessions with Jeff Meyer and Karen Grace-Martin

Q&As are from 12 – 1 pm (US EST).

January

18
Wednesday

January

25
Wednesday

February

1
Wednesday

February

8
Wednesday

February

15
Wednesday

March

1
Wednesday

Exclusive Access to a Participants-Only Website, Your Home Base for the Workshop

 You’ll find everything you need here:

 

✅ Video recordings of each webinar session

✅ Video recordings of each Q&A session

✅ Data files from real research projects

✅ Video software demos

✅ Syntax for SPSS, SAS, and Stata to run the examples

✅ PDF handouts of the presentation slides

✅ Exercises to practice what you learn

✅ A forum to submit questions between webinar sessions

✅ A list of helpful resources and suggestions for further reading

✅ Bonus videos on relevant topics

enrollment-options


 

The Curriculum

Each topic we cover will include demonstrations in Stata, SAS, and SPSS.

 

✅ Understanding Count Models

We’ll start with an understanding of what counts are and why they don’t work well in linear models. We’ll talk in detail about why a count model is necessary and what a log link means.

Then we’ll go through a brief overview of the most important concepts and steps so you have a big-picture understanding. This lays a strong foundation for the rest of the workshop.

We’ll cover:

  • Understanding what counts are
  • Why OLS linear regression doesn’t work
  • Model distribution curves
  • Model assumptions
  • The modeling process
    • Maximum likelihood estimation
    • Generalized linear model (IRLS algorithm)
  • The basic Poisson model and the log link function
  • Exposure variables: rates vs. counts
  • Understanding and testing for overdispersion

 

✅ Understanding the Various Types of Count Data

The tricky thing with distributions of count data is they’re not all the same. Some have more zeros than we’d expect; others none. As we explore these patterns, you’ll learn how to recognize which one you’ve got, like:

  • Excessive zeros (zero inflated)
  • Excluded observations (truncated)
  • Aggregate observations (censored)
  • Bounded and unbounded (hurdle)

 

✅ Reviewing Specific Count Models

As we go through the workshop, we’ll discuss in detail quite a few distinct models. For each one, you’ll learn when a model is appropriate (and how to tell), how to interpret output, and how to build and assess a well-fitting model that tests the hypotheses you’re interested in.  These model types include:

  • Poisson
  • Negative Binomial
  • Zero inflation
  • Hurdle
  • Truncated
  • Censored

 

✅ Understanding the Model Output

There are various ways to investigate in detail the effects of individual predictors: graphing, calculating predicted counts and examining rate ratios.

We’ll spend a lot of time on interpreting coefficients, especially for interactions, which get even trickier. We’ll cover:

  • Interpreting coefficients and rate ratios (details including interpreting interactions)
  • Marginal effects for predicted counts
  • Partial effects

 

✅ Evaluating Model Fit

Now that we’ve got a solid understanding of each model, we’ll 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?

You’ll learn how to evaluate the model with:

  • Deviance
  • Likelihood ratio tests
  • Information criteria: AIC, BIC
  • Analysis of residuals

 


 

Is This Workshop Right for Me?

 

This workshop is for you if:

  • You have tried to do a Poisson or negative binomial regression before, but found it confusing or difficult, and don’t really understand it.
  • You have used linear regression or ANOVA, and you want to expand your knowledge.
  • You know you will need to implement a count model soon.
  • You want to expand your statistical capacity.

We’ll do demonstrations in Stata, SAS, and SPSS. It’s ideal if you know one of those, but the focus of the workshop is on the meaning, steps, and interpretation of count models 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.

A special note for SPSS users: While SPSS will do most of the models we cover, it can’t do all of them. So at some point you may need to use another package.

 

This workshop is not for you if:

  • You are a statistics beginner and have never done a linear regression. You should be familiar with interpreting regression coefficients, p-values, R-squared, dummy coding, and interactions.
  • You have an advanced degree in statistics and are looking for a theoretical course on the topic.  You’ll be disappointed in the lack of proofs, calculus, and equations.

 

This is an advanced level workshop.

But it’s designed for researchers, not PhD statisticians.

You should have solid experience running linear models. Experience with ANOVA or linear regression is fine — as long as you have a basic understanding of least-squares estimation, interpreting coefficients, dummy variables, and interactions.

Familiarity with logistic regression will be helpful, but not necessary. Many of the concepts you’ll learn here are similar (though not all identical) to those in logistic regression.

 

Plan to set aside 5-8 hours each week.

Aim for full participation in webinar sessions, Q&As, and exercises.

You’ll learn the basic concepts and get some clarity even if you don’t do the exercises and run the examples on your own.

But you won’t entirely get it unless you get your hands dirty with some data. That’s what this workshop is for.


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.

Guarantee

As with all our programs, your satisfaction is guaranteed. If you participate fully in this workshop — watch the videos, read the materials, complete the homework, 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 workshop.