Analyzing Repeated Measures Data: ANOVA and Mixed Model Approaches

There are three main approaches to analyzing repeated measures data–the multivariate GLM approach, the marginal model approach, and the mixed model approach.

You’ve probably heard of at least two of these, and actually used one or two. Maybe all three.

Each one has advantages and disadvantages. Each one has certain repeated measures designs that it works well for, and certain designs it does not.

The trick is figuring out which approach is best for your design and your research questions.

The GLM (Repeated Measures ANOVA) approach is easy to run, but the output is not easy to understand. And it’s limited to certain designs.

Most people (me included) were trained on the GLM approach.

But it has severe limitations and pretty strong assumptions that are hard to meet.

• If even one observation is missing, you lose the whole subject’s data.
• You can’t run post-hoc tests on within-subjects factors (which is usually the whole point!).
• You must have the same number of repeated observations for all subjects. Or you have to throw away data again.
• And you have to assume that all observations within a subject have equal variance and equal correlations (that’s sphericity, if you’ve ever wondered). It works sometimes, but often not.

You may have realized some of these limitations and tried to learn mixed models on your own.

But that’s hard–really hard.

The math gets tricky, and few books spell it out for you. And every book on mixed models I’ve ever seen puts one chapter in the back about repeated measures data. You need to master the material before you get there or it doesn’t make sense. But all of that material uses examples that don’t make sense, given your design.

My goal in this workshop is that you will be able, on your own, to run repeated measures, marginal, and mixed models for repeated measures data.

And have a solid basis for which approach to choose.

You’ll learn the difference between the repeated and the random statement. You’ll know what to check for, the steps to take, and how to set up your data. You will finally understand what a covariance matrix is for, and how to choose which one to use. You’ll know the difference between a fixed and a random factor and how to choose.

And when you do need to look something up in a book on Mixed Modeling, you’ll understand it.

Every Live Workshop at The Analysis Factor includes:

✅ All the data, programming code, exercises and handouts you’ll need
✅ Live webinars and question & answer sessions
✅ Video recordings of all live content for later or repeated review
✅ The opportunity to ask questions live and on workshop web pages

In the Workshop, you will learn:

Module 1: An Overview of Repeated Measures Data

One of the things I’ve found troublesome over the years is that when people say repeated measures, they often mean very different things. There are actually a lot of different designs that can be considered repeated measures. There are a few I’ve seen people attempt to analyze as repeated measures ANOVAs over the past few years:

• Experiments with one or two within-subjects factors, with a possible between subjects design
• Pre-post designs
• Longitudinal studies with pre-, post-, and followup measures taken months apart, with some covariates measured only at baseline and some measured at each measurement
• Studies that measured patients five times over two years on an outcome variable only, where only 10% of participants had all five measurements
• Experimental studies where repeated trials were pure replicates

Consequently, we’re going to start by taking away the names which may or may not communicate what we need and examine the structure of the variables and the research questions to think about the real issues:

• What is the role of time?
• Should time be considered continuous or categorical?
• What is the role of baseline measurements?
• Are correlations among a subjects measurements something of interest or a nuisance?
• Is time confounded with any predictors? Is Subject?

We’ll then go through an overview of the three main approaches so you have the big picture before we delve deeply into each one.

Module 2: The Repeated Measures ANOVA (GLM) Model

This is the “usual” method for running repeated measures data, and probably the one with which you’re most familiar. It does work reasonably well in some situations, and although it doesn’t look like it, there are many similarities between this model and the mixed model.

We’ll discuss the similarities, the differences, and the assumptions this model has that we are able to relax in mixed models.

We’ll go through examples of running a model with this method (using SPSS’s GLM Repeated Measures procedure, though the issues are the same in whatever software you use) and interpreting the output so that you have a new understanding of the model you’ve been running for years and its assumptions.

We will then be able to compare two new approaches to this one, so you see where they differ and where they’re really the same.

Module 3: Transitioning to the Mixed Approach

There are many advantages to running repeated measures data using mixed models. But they do require you set up your data differently (in the long, or observation-level format, as opposed to the wide, or person-level format you’re used to).

In this module I’ll give a tutorial on how to restructure the data set from the Wide Format required by GLM Repeated Measures (with one row per subject) to the Long Format, (with one row per observation), as required by mixed model procedures.

I’ll also show you how to graph your data, per person, so you can see what is going on. I have always found this absolutely indispensable in choosing an approach. But you can’t just use a regular means plot or scatter plot, because of the clustering of the data, so once again, I’ll show you just how to do it.

Both the marginal and the mixed model use Maximum Likelihood Estimation, so we’ll do a quick review, then talk at length about measuring model fit.

We’ll still use F tests, but our old friend R-squared disappears as a meaningful measure. In it’s place comes Deviance, and its Information Criteria buddies.

They’re not so bad once you get used to them, and you’ll see how necessary they become when you are choosing a model later on. Not just which predictors to include, but whether to include various random effects.

Module 4: The Marginal Model and the Repeated Statement

The Marginal Model is not actually a mixed model–there are no random effects, although it is still run using the Repeated statement in the Mixed procedure.

In this session we will explore the Marginal Model (also known as population averaged models) both conceptually and for Repeated Measures Data.

In many repeated measures situations, the marginal model fits the data better than a Mixed Model. It has many of the same advantages as Mixed Models, and is in many ways more intuitive.

It’s a nice bridge because conceptually, it’s very similar to the Repeated Measures ANOVA you’re used to. It too takes into account the correlation among each subject’s observations. But it’s more flexible than the Repeated Measures ANOVA because it allows those correlations to be unique (goodbye, Sphericity assumption! Hello, post-hoc tests on within subject factors!).

The result is more choices, and often better model fit.

Module 5: The Mixed Model: Random Intercept Models

If we stopped before this module, you would already have a whole new set of tools that greatly expand the types of analyses you can do, and do well.

But being able to run mixed models will exponentially expand the types of research questions you can answer.

For repeated measures data, there are two types of mixed models you’ll use–random intercept models and individual growth curve models.

Random intercept models take care of the correlation among a subject’s responses as do marginal models, but they do it in a different way. Instead of measuring correlations, they actually measure how much responses vary across individuals. If one individual generally has high responses, and another always low, the random intercept measures and accounts for that. It literally redefines the residuals.

We can then measure how much of the variation in the outcome is due to the covariates, the treatment conditions, or the subjects themselves. So here you will learn how to use statistics like variance components and Interclass Correlation to understand the variation in responses over time, conditions, or individuals.

This module introduces the linear mixed model in depth before going on to random slope models.

Module 6: The Mixed Model: Individual Growth Curve (Random Slope) Model

Individual growth curve models go a step further–they allow each subject to have a different growth trajectory over time–or over any other covariate that changes over time.

In many, many designs, the random intercept model is enough to take care of similarities in individuals’ responses.

But when the real research questions are how factors affect growth (or decline) over time, growth models answer the question directly.

We’ll talk about what individual growth curve models are, how to run them, when to use them, and when not to.

We’ll also explore issues like improving interpretation through centering, when missing data causes no problems at all, and and how the models differ when time is measured as continuous or categorical.

We’ll also introduce covariance structures for random effects.

Module 7: Model Refinements, Extensions, and Assumptions

Now that you’ve got a good understanding of the model, what it measures, and what the various statistics mean, we’re going to talk about ways to improve it.

We’ll start by checking whether or not the newly-defined residuals meet assumptions of normality and independence. If they don’t, we can account for it by refining the residual covariance structure with a repeated statement. Nice, eh?

Then we’ll go over a few common extensions of the model

• three-level models for when individuals are clustered into classrooms or companies or some other grouping
• and Random Curvature models, which allow growth to vary from a linear trajectory

Module 8: Building and Choosing a Model

Okay, now that you’ve learned how to create and refine linear mixed models, how do you choose?

There are many, many choices along the way–the type of model, which random effects to include, which covariance structures–before you even get to choosing which predictors to put into the model.

There are two main approaches to take to make all these decisions–you either start with the most complex model and simplify, or start with the simplest and add complexity.

Here we go through examples start to finish to bring together everything you’ve learned. You will learn the exact steps to take to build the model and choose the best one.

But can I keep up?

This starts out at a moderate level, but moves into an advanced level workshop. Not need-a-Phd-in-statistics advanced. It is for researchers, not statisticians.

But you should be pretty statistics savvy and have solid experience running linear models. Centering, least-squares estimation, dummy variables, interactions, residuals, and variance should not be new concepts.

Concepts that are unique to mixed modeling? I’ll explain those–information criteria, maximum likelihood, deviance, covariance matrices, variance parameters, sphericity, growth models, random slopes. All those terms that books on mixed models seem to skim over, you’ll learn here.

I’m assuming you’ve never had linear algebra, don’t want to see things derived, and you’re here because you want to see it explained in English.

We’ll look at matrices (we have to), but we won’t manipulate them–no Kroniker products here.

We will look at model equations (we have to do that, too). To stay consistent with books and articles on the topic, they will include some Greek letters. But I’ll walk you through them–I won’t assume you automatically know what everything means or why it’s important.

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 6-10 hours per module just doing the exercises.

This workshop is for you if you:

• Have always run regressions and now you’re facing longitudinal or repeated measures data for the first time
• Have heard you should use Mixed Models for your repeated measures data, and want to learn how
• Have used Mixed Models before, but never quite got the difference between the random and repeated statement, or what those covariance structures really are
• Have tried to learn mixed models on your own, and have found that’s really, really hard to do
• Have always run repeated measures using an ANOVA or GLM Repeated Measures, but now you’re facing a design where it doesn’t work–unequal sample sizes per person, time as continuous, or lots of missing data
• Use SPSS, SAS, Stata, or R. I will demonstrate using SPSS and SAS, and am providing all the examples I present and answers to exercises using Stata and R.I will show how all four software packages are similar and different throughtout the workshop.
• The workshop is not all about software, though. But I have found that for mixed models, even more than other methods, knowing the software inside and out is crucial. The techniques are so flexible, and it’s really easy to mis-specify a model without realizing it, and get different results. You are welcome to try it on another software package. Many past participants have.  Stata and R users: Note that I don’t use either, although I am familiar with both. All the R examples and exercises were provided by statistical consultant Dan Neal and Stata examples and exercises by Jeff Meyer. Jeff will be joining us in some of the Q&A sessions to answer your Stata questions.
• Have the time over the next few months to really invest in learning. It will require about 6-10 hours per week.

It is not for you if you:

• Want to learn Bayesian approaches to Mixed Modeling
• Are new to statistics or to regression and ANOVA. You need a solid background.
• Have only run regressions with continuous variables and have never run a repeated measures ANOVA
• Already understand linear mixed models and want to learn advanced topics like GLMM
• Want a general linear mixed models course and don’t run repeated measures data. You’ll still learn a lot of relevant info, but our focus is the specific application to repeated measures data.

Prerequisites:

• You will get the most out of the workshop if you have had at a bare-bones minimum two statistics classes, and one must include a class on linear models–either multiple regression or Analysis of Variance (ANOVA). Four or more would be better.
• Real experience doing some sort of linear modeling. Familiarity with the GLM procedure will be very beneficial.
• SPSS users: It will help if you are familiar with SPSS Syntax. I will demonstrate some parts using the menus, but once you get to this level of modeling, you need to have control of the details that syntax allows. There are just too many parts that the menus can’t do. And honestly, I have found the SPSS Mixed dialog windows to be the most unintuitive, confusing ones I’ve come across.

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 power point 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 various software 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:

• SPSS, SAS, Stata, and csv 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, Stata, and R syntax code 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.
• Video Screenshot recordings of each workshop session made available within 48 hours. So you can 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.
• 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 we 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.
• 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.
• 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:
• Getting Started with SPSS Syntax
• Dummy and Effect Coding
• What Happened to R-squared?
• Fixed and Random Effects
• Random Intercept and Random Slope Models

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. This stuff is hard, and there is a lot to it. You’ll learn more every time.

The Instructor:

Karen Grace-Martin is the workshop instructor. Karen helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has masters degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Comments from past participants in this workshop:

“I wanted to share this recent publication with you. I would not have made it through the statistical analysis for this project without your Repeated Measures class. I am continually pulling out my class notes for other analyses. Thanks again!”

Lisa Giencke
Joseph W. Jones Ecological Research Center

“This course is an amazing value for all the extra resources provided and the expert instruction. Karen’s pace of instruction and teaching style are really a strength.”

Kelsey Simons

“It’s all ready to be followed. No fear of being too uneducated. I was very comfortable with the whole course. The statements, at the outset and in the course descriptions, for whom the workshop would be suitable–or not–was very helpful, and it was the simple truth.”

Klaus M. Frei

“For the cost it delivered a LOT of material, but that material was thoughtfully prepared and organized – plus to have access to so many resources, both those listed in materials and Karen’s expertise in the Q&A, absolutely worth my time and money.”

Carol Skay

“Karen’s workshops and consultations provide a highly needed service for researchers who are not statisticians but have a basic understanding of statistics. She is an excellent teacher. Her explanations are clear with just the right amount of technical detail. She also is extremely patient, friendly, and has a great sense of humor. I have been looking for someone who provides this kind of service/teaching for years. Finding Karen’s website and seeing what she offers was one of the best things that I have come across in my professional career.”

Julie Staples, Ph.D. Awareness Technologies, Inc.; Georgetown University School of Medicine

“Karen explained all topics clearly, with the right balance of concepts and applications. i.e., enough conceptual discussion so that I understand the underlying concepts enough to know why I am doing what I am doing, but then plenty of time devoted to ‘how to’ so that I can actually go and run the analyses myself. Resources provided (videos for download, transcript of each webinar, syntax for all the examples, answers to the homework) are all excellent resources that I know I’ll keep going back to. Also now I can read all the other resources I have on mixed/multilevel models and actually understand them! I’ve gone from just trying to wrap my head around what mixed models are for and what they do, to feeling confident that I can start to tackle a few different datasets that are waiting for me. Thank you!”

Catherine Ortner

“If you analyse repeated measures data this is arguably the best resource presently available for learning how to approach this using traditional and modern analyses.”

Dr Adrian Midgley, University of Hull, Hull, England

“Karen’s patience and thoroughness make her classes sensational. She is an incredibly talented teacher who never glosses over details and will not rest until everyone understands the material. I have taken dozens of math and stats classes, some from very famous people, and Karen is easily the best teacher I have ever had. She is a tremendous resource for applied statisticians and researchers. Also, the class was clearly very well thought out. The structure was clear and sensible, and the examples were varied and spot-on for the topic.”

Dan Neal
Biostatistics Consultant
Department of Neurosurgery, University of Florida

“I appreciated the supporting materials, information on mixed models, marginal models, random slopes and intercepts; all of it.”

Debbie Humphries

“The recordings and handouts will be a great resource in the future. I will review the material before my defense in a couple of months. I always learn a lot from Karen’s workshops!”

Glenys Webster
PhD Candidate, School of Environmental Health

University of British Columbia

“Thank you so much for offering this workshop! I feel that for the amount of live instruction time given and the exercises / blog format feedback on those, the rate was an excellent value. I think this covered nearly as much as a full quarter-length college course but cost lots less and was so much more convenient! Compared to the cost of sending someone to a training in a hotel, etc. this was very good value.”

Cassandra Vaughn

“The workshop did a great job of understanding the practical and theoretical nature of this analysis. People talk about this stuff in every which way so this shed light on this, as I have been working on this topic for a year or so.”

Becca Lewis

“The Workshop is absolutely and exactly what I had been searching for a long time. I am glad I finally found your website.”

Abdul Aziz Farooq
University of Newcastle

“Before taking the workshop I spent a ton of time trying to learn mixed models in SPSS on my own but the workshop covered this material in much less time and in a more easy to understand way than the books/online sources I read. Plus Karen is super helpful and nice – I always felt like I could ask questions without worrying about feeling ‘stupid’.”

Lisa

“Karen is a wonderful teacher and she explains things so clearly and thoroughly that anyone with a background in statistics can keep up.”

Pam Bishop

“I am just taking a moment to express my thanks for your style of teaching and the content you cover. I analyzed a dataset with which I have been struggling for some time. It is a dataset of hearing measures, 8 tones per ear, both ears per person (n=687 people). I passed the data through successively more restrictive models (more estimated parameters) and the drop in 2LL was amazing–of course, because the variance was [finally] appropriately modeled. I have at least one more set of analyses to do, to add a quadratic parameter because the relationship is clearly non-linear. The point at which the webinar was most helpful was the covariance structure explanations. Who knew that those assumptions were so critical? And, the point that the RANDOM part of the analysis is not our goal, better estimations of the FIXED effects are the point. One uses the RANDOM estimations to appropriately estimate the FIXED effects.”

Laurel M. Fisher
House Research Institute

“The recorded files after the sessions extremely helpful in reviewing the materials I was challenged by; I greatly appreciated the time you took in the early sessions to step us through the models and the elements of the equations and how they fit with the matrices. Also thought the use of a wide variety of datasets that we revisited worked really well. The session on graphing and interpretting graphs was really useful, particularly when you provided your thoughts on what was useful to notice about the data. Explanations of marginal model, evaluating models, and how you built up to the full mixed model all clarified much fogginess on my understanding in the past. Its clear that you have really thought through the approach to teaching these materials in an effective way. This is by far the best and most useful statistics course I have taken.”

Ann Lezberg

“I had a very hard time finding high-quality, comprehensible resources on repeated measures and mixed designs. It was difficult to find any of the syntax I needed to use in SPSS. Unfortunately, before this webinar, the syntax that I did happen to find, I didn’t have the tools to understand. This workshop was a godsend! I really benefited from the video recordings and the transcripts, which allowed me to review the material at my own pace.”

Colleen Krause

“MANY features of the workshop were helpful. I really appreciated the range of materials that were available to us, so that we could review as needed (e.g., session notes, videos, recommended readings, syntax). Sample data sets, homework assignments, and answers by module were very helpful. Finally, and most importantly, Karen is a very talented and knowledgeable data analyst, and could communicate complex ideas in an accessible way. She was also practical in her recommendations for analysis, and fielded questions in a concrete, comprehensible, and useful fashion.”

The Details:

The Workshop Webinar Sessions will meet 8 times. You may have noticed you’re going to learn a lot. But we know you have other things going on in your life. In order to balance keeping momentum going and giving you enough time so you don’t get behind, we’ve devised this schedule based on feedback from previous participants.

We’ll meet for four weeks in a row–beginning March 24, 2017–then we’ll take a week off. We will meet on Fridays at noon, U.S. Eastern. The workshop sessions are approximately 2 hours long, including time for questions.

• Module 1 – March 24
• Module 2 – March 31
• Module 3 – April 7
• Module 4 – April 14
BREAK WEEK
• Module 5 – April 28
• Module 6 – May 5
• Module 7 – May 12
• Module 8 – May 19

We will also meet for eight Q&A sessions throughout this time. We will meet on Thursdays at noon, U.S. Eastern, on the following dates.

• March 30
• April 6
• April 13
• April 27
• May 4
• May 11
• May 18
• June 1

Full Workshop Investment: \$697

(student discount available)

Pay in Full or in 3 installments of \$233 (Your first installment will be billed today and your credit card will automatically be billed every 30 days for the remaining 2 payments)

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, 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.

FAQ

Q: Do you offer student discounts and how do I get one?

A: Yes. 40% off. Click here to receive our student discount on this and future workshops: http://theanalysisinstitute.com/student-discount/

Q: You said you may offer SAS support in this workshop, if there was enough interest. Will you?

A. Yes. We got quite a positive response. So we’ll have support for SPSS, R, Stata and SAS.

Q. I don’t use any of those packages. What about the rest of us?

A. You may be fine, even if you use another stat package. Yes, we’re supplying code for SPSS, R, Stata and SAS, but the focus of the workshop is on the steps to take and what it all means. Those, as well as the logic, are the same across all software packages.

You may have to work a little harder to implement the exercises and to translate it into the software you use.

I have a little experience using Stata and HLM for mixed models, so I can answer basic questions, but I don’t know any defaults or details and wouldn’t be able to guide you much.

So if you’re using another program and are comfortable working in that program and figuring out a new procedure, the workshop will fill in the concepts, vocabulary, and steps.

If you’re still learning that other program, this might be too hard, unless you have a lot of time and willingness to figure things out.

Q: I use SPSS, and I am “familiar” with SPSS syntax, but I tend to use the menus. However, I do save the syntax and read it, and may alter it as appropriate.

Based on that do you think I will do okay?

A: If you want to learn this in SPSS, you need to be familiar with SPSS syntax. In the repeated measures GLM, the data restructuring and the graphing, I show both menus and syntax. But once we get to Mixed, the menus are so unintuitive and the options so numerous, I find the syntax easier. So at that point, you’ll need to do syntax. If you have never used syntax, this is going to be a lot of work. But as long as you can basically tell what’s going on in syntax, you should be fine.

Q: I have just met with the other co-authors of the article I am about to write, and the “expert” among us on statistics only knows how to use SEM with longitudinal data. Are some of the principles the same as in mixed models? I was wondering if there is any point in taking the course, if it will help me with learning SEM?

A: This workshop won’t help at all with SEM. You can do the growth curve models we’re talking about in the workshop with SEM, but I’ve never done it personally.

There is definitely an overlap in concepts and principles. It would be a good idea from a long-term learning perspective, but it won’t help you with this project.

Q: Things are very busy at work right now, and I’m afraid I won’t have time to keep up. How much time per week does this really take?

A: We’ve worked really hard to make sure people can do the workshop in the midst of their normal worklife. This is what we’ve done.

Each workshop instruction session is about two hours. You can attend live or listen to the recording, but in either case, it’s two hours. There are eight of these, but we’ve split them up over about 9-10 weeks. So the schedule is four weeks on, one week off, to give you a chance to “catch up” and fit it into your schedule. We get it that it’s hard to put aside 8 weeks in a row at the same time.

After you’ve had a week to digest the material from the live session, we have a one-hour Q&A session. You don’t have to attend, we just make those available so you can have any questions answered or listen in on others’ questions.

We provide exercises, answers and data sets, so you can practice what you’ve learned each week and get help in the Q&As. These will take you a few hours each week, maybe up to 5 or so. This aren’t required—you’re not graded. But we’ve gotten feedback that they’re very helpful. It’s when you practice that you figure out what you did and didn’t understand.

So, one way to think of it is in each 4 week period, there are 6 hours of instruction, three hours of Q&A, and about 10-15 hours of practice time. So if you have a crazy hectic week during the workshop, you have catch up time. If you have a crazy hectic month, you may be better off waiting.

The other thing to remember is you get access to the workshop website, all materials, and a place to ask questions for a year. So it’s ideal if you keep up, but you have not missed out completely if you get behind.

Q: I teach/work during this time/live outside the U.S and cannot attend live. Is there any way to register and access the full webinar later?

A: Yes, actually. I record all of the webinar sessions – even the Q&A’s — and make them available to all workshop participants within 24-48 hours. They’re screenshot video files, so you’ll hear me talking and see my screen. Many of our participants follow along in this way without ever attending a session live.

We have participants from many time zones with all kinds of work schedules, so this is why we set them up this way. The other advantage is you can download and keep these videos forever, so if you need to refresh your memory in a year, it’s yours to review.

And if none of the live webinar and Q&A times works for your schedule, there is still a way to ask questions. Just submit any questions on the workshop website before a Q&A session and then download the session recording, which is yours to keep. Plus the workshop website has a place for you to submit questions for written answers at any time in between sessions or for up to a year.

Q: I’m not located in the U.S. Can I still participate?

A: Yes. We have participants in our workshops from many different countries.

For our live webinars, all you need is a fast internet connection and either a computer speaker or a telephone. If you can hear music through your computer, this will work. If not, the system has local phone numbers for dozens of countries, so even if the speaker option doesn’t work, you can still call in to listen while you watch what’s happening on the screen. It might be a long-distance call for you, but it won’t be international.

If you have a computer headset with a microphone, it’s even better.

Q: Can I pay with Paypal?

A: Yes. When you check out, there is a choice of either paying directly using our system or paying with Paypal. However, splitting the workshop price into 3 payments is not available using PayPal.

Q: Can I join the Q&A webinars from my iPad?

A: Yes. Thanks to a new upgrade to GoToWebinar, attendees now have the option of registering and joining the workshop Q&A sessions from an iPad by downloading a free GoToMeeting app.

Q: This really isn’t a good time for me. Will you be offering this workshop again soon?

A. Yes, we’ll offer it again, but it’s not scheduled yet. We tend to run our workshops once every 12-18 months.

So if you want the live training, the bonuses, the support, the exercises, the option to ask questions, and the live Q&A sessions, as well as any discounts we are currently offering within the year, I would suggest registering now. You’ll get all the materials to download and review at your convenience.

If the workshop dates are just bad for you, it’s okay. Registering now will give you complete access to the website and all materials for one year. Plus, ongoing support is included through access to the workshop website for a full year.