Most researchers can relate!
Let’s face it: The statistics classes you’ve had thus far have probably focused on straightforward models with only continuous predictors.
But observational and experimental research, unfortunately, isn’t usually that simple.
At some point, you’re going to need to build a more complex model, one that includes multiple types of predictors with different types of scaling to test all sorts of research questions:
There are ways to set up the model so the interpretation is much easier.
When you understand linear regression coefficients for ALL types of predictor variables, you can build more sophisticated and accurate models. AND you can easily translate your findings into meaningful results.
Bridging the gap to a deeper understanding of regression coefficients is within your reach.
Our solution: This FOUNDATIONAL on-demand tutorial that’s essential for anyone undertaking data analysis for publication or a thesis:
- Ursula S., Research Consultant
My goal is that by the end of the tutorial, you will understand linear regression coefficients for all types of predictor variables. You’ll know how to set up your models so that later interpretation is much easier. And when you do go on to learn multilevel models, logistic, Poisson, or any other kind of regression, you can focus on the unique challenges of that type of modeling rather than getting stuck on the fundamentals.
In this five-hour online tutorial, we’ll start with a review of regression models with continuous predictors. We’ll then have individual lessons on how to interpret each type of tricky coefficient. Then we’ll see how all the terms fit together in one big model and how to break the model apart to understand each term.
This tutorial is for you if:
The tutorial and accompanying materials are already available for you to access on your private tutorial website. Log in at any time from any internet-enabled computer, phone, or tablet… all from your own home or office.
Watch (and re-watch) these videos on YOUR schedule, as many times as you like, at the speed you desire.
These videos explain the concepts and steps as well as step-by-step demonstrations in SPSS.
You’ll have access to the presentation slides, handy checklists, and worksheets to help you set up your next power calculation. You can download, print out, and take notes as you follow along.
So you can see how to deal with the challenges inherent in real data.
With answers to check your work.
Got a question as you’re reviewing the video recording or your notes? Just submit a question in the tutorial website. We’ll answer it right there.
To enhance your understanding of where power calculations fit into the data analysis process, we’ve included the following bonus video webinars:
— The 13 Steps to Performing Any Statistical Model (60-minute video presentation)
— The First 3 Steps in Running Any Statistical Model: Define and Design (60-minute video presentation)
— Dummy and Effect Coding (60-minute video presentation)
— A Review of Logarithms for the Data Analyst (60-minute video presentation)
You’ll have access to this site and all the related materials and resources for ONE FULL YEAR. That means you can re-watch sessions and ask additional questions any time during that 12-month period.
Often, our students report they understand the material at a deeper level on a second or third pass. This stuff is not always easy. You’ll learn more every time, so take advantage of it!
- Sheryl Kopel
Our tutorial goal is for you to understand linear regression coefficients for all types of predictor variables, so you can build more sophisticated and accurate models and generate meaningful results.
1:
- A Review of the Simple Linear Regression Model
- A Review of the Multiple Linear Regression Model with continuous predictors
- Interpreting Regression Coefficients when Predictors are Correlated
2:
- Understanding Unstandardized and Standardized Coefficients
- How and when to center predictors to improve interpretability of coefficients
- How transforming Y or X to meet assumptions affects the interpretation of Coefficients
- When centering affects the meanings of other coefficients in the model
3:
- What quadratic terms tell you about the curved relationship between X and Y
- How to break apart the linear trend from the curvature to understand both coefficients
- An introduction to interactions
- Interpreting interactions between two continuous variables
- Interpreting interactions with quadratic terms–changing the curve
4:
- The basic difference between dummy and effect coding
- How to dummy code binary predictors, what the coefficients mean, and when including dummy variables changes other coefficients
- How to interpret interactions for dummy-coded predictors
- How including interactions changes coefficients of main effects (this is different from ANOVA!)
5:
- How to dummy code multicategory predictors and interpret their coefficients
- How to interpret interactions for multicategory dummy predictors
- How to interpret interactions between two dummy-coded variables
Now that you understand all the pieces, we’ll see how all the terms fit together in one big model, and how to break the model apart to understand each term. (This is often the trickiest part).
- Anna Nadirova, Educational Researcher (Edmonton, Alberta, Canada)
I’m Karen Grace-Martin, your instructor for Interpreting (Even Tricky) Regression Coefficients in SPSS.
As president and founder of The Analysis Factor, I’ve been supporting researchers like you through their statistical planning, analysis, and interpretation since 1997.
With masters degrees in both applied statistics and social psychology, I’ve been honored over the past 15 years to work with everyone from undergrad honors students to Cornell professors, and from non-profit evaluators to corporate data analysts.
After seeing so many smart people get nervous, uncertain, and downright phobic about analyzing their data, I made it my mission to remove the barrier between research and statistical analysis.
I want to banish the confusing jargon that makes eyes glaze over, and instead explain statistical terminology in plain English.
My goal is to help you improve your statistical literacy so you can bring your important research results into the light with confidence.
“Karen Grace-Martin… has a gift for explaining and teaching applied statistics. I really appreciate her patient, pleasant, down-to-earth manner. My previous statistics professors were knowledgeable, but un-engaging, and not easy to understand. I wish I would have had a teacher like Karen in my grad program.”
So what kind of background in statistics do you need?
Our tutorials and workshops are for researchers, not statisticians.
That being said, you’ll get the most from this tutorial if you have a MINIMUM of at least two statistics classes—an intro class, and a class that contains multiple regression or Analysis of Variance (ANOVA).
If you are a “beginner,” you may have trouble keeping up. However, if you’ve had one statistics class, but are working on regression and are confused, this tutorial will be helpful.
An additional word about software packages:
You should be familiar with SPSS. If you prefer another program you can still benefit, since really, the emphasis here is on what the output means. As long as you can replicate the results in exercises, it doesn’t really matter which software program you use. I know SAS well and I have used, but am less familiar with JMP, Stata, and R, and I will answer questions about them if I can.
If you have questions about whether you’re ready for this tutorial, just email us. We’ll give you our honest opinion. We want you to succeed!
- Raul
As with all our programs, your satisfaction is guaranteed. If you participate fully in this tutorial – 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.
If you meet the prerequisites listed above, you’ll be prepared to understand the tutorial materials.
Each module includes about an hour of instructional videos, plus exercises and supplemental materials. We also suggest aside some time (2-3 hours) to re-run class examples attempt the exercises on your own, and watch the bonus videos.
Remember — you also have 12 months to revisit the material! It’s completely on YOUR schedule.
You know how this goes: The more time you put in, the deeper your understanding.
We’ve worked really hard to make sure people can keep up with the tutorial in the midst of their normal work life. And with on-demand access, you’re all set to deal with unexpected deadlines or “life” emergencies.
The core videos total approximately 5.5 hours. If you have a crazy hectic month ahead, you can fit watching the materials into your schedule. You can also submit your questions in writing through the tutorial website.
One reminder: You have access to the tutorial website, all materials, and a place to ask questions for a year. So if you get behind, you aren’t missing out. You can catch up on your schedule.
Yes! That’s the beauty of our on-demand tutorials. We have participants from many time zones with many different work schedules. In order to support our diverse student base, all tutorial sessions are pre-recorded and available immediately. You can also download and keep these videos forever, so if you need to refresh your memory at any point in the future, all the material is yours to review.
Yes. We have participants in our tutorials and workshops from many different countries.
Yes. When you check out, you can pay directly using our system or paying with PayPal.
This tutorial (and several others!) is available year-round. You can sign up now and get access to all the course materials (trainings, support, exercises, the option to ask questions, and any additional bonuses). You’ll get all the materials to download and review at your convenience, and you can access them for free any time in the next 12 months.
Plus, ongoing support is included through access to the tutorial website for a full year.
You do NOT need prior experience with any specific statistical software. During the tutorial, we will be using SPSS.