Workshop: Repeated Measures Enrollment – Base


“…This is arguably the best resource presently available for learning how to approach [repeated measures data] using traditional and modern analyses.”

Dr. Adrian Midgley, Analyzing Repeated Measures Data student
University of Hull, England

When it comes to analyzing repeated measures data, there are three main approaches:

  • Multivariate GLM (aka, repeated measures ANOVA)
  • Marginal Model
  • Mixed Model

Each has its own advantages and disadvantages. Depending on what repeated measures designs you’re dealing with, some approaches work better than others.

But do you know when to use which approach?

If you’re like me, you were trained on the GLM (Repeated Measures ANOVA) approach, but its limitations make it far from ideal:

  • 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, unfortunately, is often the whole point!).
  • You must have the same number of repeated observations for all subjects… or you have to toss out data.
  • You have to assume that all within-subject observations have equal variance/correlations (which isn’t always the case).
  • It can’t account for higher-level groupings of subjects into clusters (like kids in the same classroom or patients in the same hospital).

Mixed models solve these problems.

BUT… mixed models are often really hard to learn — especially on your own.

Even if you find a book that covers mixed models, the math is tricky, as they’re written for PhD students in statistics — not researchers.

I struggled with these new, more flexible models for quite a few years. What I needed was a guide that covered repeated measures from the beginning, and fit Linear Mixed Models into what I already understood about mixed ANOVAs.

There simply wasn’t one… so I decided to create a workshop that maps onto common designs that researchers can understand.

Introducing… One of The Analysis Factor’s Most Popular Workshops…

Analyzing Repeated Measures Data:
ANOVA and Mixed Model Approaches

An 8-Module Online, Live Interactive Workshop


“It’s 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, Analyzing Repeated Measures Data student
Resource Monitoring Technician, City of Boulder, Open Space and Mountain Parks



Karen Grace-Martin

I’m Karen Grace Martin, your statistics mentor and workshop instructor for Analyzing Repeated Measures Data.

My goal is that by the end of the workshop, students will be able to independently run repeated measures, marginal, and mixed models for repeated measures data… and have a solid basis of understanding which approach to choose.

In this eight-module live, online workshop, you’ll learn:

  • The difference between fixed, repeated, and random effects
  • How to set up your data, run the model, make model-building decisions, and check assumptions and model fit
  • The purpose of a covariance structure in different types of models, and which one to choose
  • How to interpret and decide when to use random intercepts and random slopes in your model

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


“…I’ve been stumbling through repeated measures analyses with incomplete understanding of the models, and I feel like I’ve had my blinders removed!”

Hilary Neckles, Analyzing Repeated Measures Data student
Research Ecologist, USGS Patuxent Wildlife Research Center

Who Is This Workshop For?

This workshop is for you if:

  • You’ve heard you should use Mixed Models for your repeated measures data but don’t know how
  • You’ve tried (unsuccessfully) to learn Mixed Models on your own
  • You’ve always run regressions and now are dealing with longitudinal or repeated measures data for the first time
  • You are facing a design where regressions won’t work, such as longitudinal or repeated measures data, lots of missing data, or unequal sample sizes

It’s not for you if:

  • You want to learn Bayesian approaches to Mixed Modeling
  • You’re new to statistics or regression and ANOVA (You’ll need a solid background in statistics. See below for more details on the course prerequisites)
  • You’ve only run regressions with continuous variables and have never run a repeated measures ANOVA
  • You’re in search of information on advanced topics like GLMM
  • You want a general linear mixed models course and don’t run repeated measures data (this workshop will focus on repeated measures data)



“I was very comfortable with the whole course. [The] descriptions for whom the workshop would be suited — or not — was very helpful…”

Klaus M. Frei, Analyzing Repeated Measures Data student


How Does It Work?

This course is an 8-module live, interactive online workshop from October 11, 2019 – December 19, 2019.

All sessions are conducted online via live webinar. You can log in via phone or internet. You’ll see the instructor’s screen to view the presentation… all from your own home or office.

During each webinar session, the instructor will present concepts and explain the meaning of the techniques in that module, then demonstrate how to implement those techniques in various software using different examples.

Each session, there will also be plenty of time to ask your own questions.

Have more questions? No problem!

In addition to the eight module sessions, you’ll also meet with your instructor for eight additional Q&A sessions where you can get additional assistance on workshop concepts, deepen your knowledge, and clarify any questions.


All 8 Workshop Modules and 8 Q&A Sessions Are Recorded for Your Convenience.


As a participant in the Analyzing Repeated Measures Data workshop, you’ll have access to a participant-only website, your workshop “hub.” That’s where you’ll access all workshop resources and material, including:

  • 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. 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 presentation slides.

    Made available ahead of each session so you can download, print out, and take notes as you follow along.

  • Video screen capture recordings of each workshop session.

    Made available within 48 hours after each session so you can review the material at your convenience.

  • Exercises.

    (yes, HOMEWORK!) You really need to practice this stuff and get your hands dirty, so we’re giving you the data to try it on your own with new models. 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.

    Got a question as you’re reviewing the video recording or your notes? Just submit a question in the membership site. 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 Q&A session.

  • Video recordings of all Q&A sessions

    Review at your convenience.

  • A list of helpful resources and suggestions for further reading.

    There’s no required textbook, but there are some good books and articles on this topic, and you’ll learn the techniques better with some background reading. The list is available when you first register, so you have time to request them through inter-library loan if necessary.

  • 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
    – Running Repeated Measures as a Mixed Model
    – What Happened to R-squared?
    – Random Intercept and Random Slope Models


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, ask additional questions, and attend the course again 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 easy. You’ll learn more every time, so take advantage of it!




“The instructor was delightfully pleasant, knowledgeable, slow-paced, non-judgmental, and responsive… Thank you for letting those of us overwhelmed by other time constraints time to absorb it all.”

Analyzing Repeated Measures Data student


What’s Covered in the Workshop?

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 follow-up 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 subject’s 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 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 for repeated measures ANOVA (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 its 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 usually run using mixed procedures. (The exception is R, which requires a different command.)

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 Intraclass Correlation to understand the variation in responses over time, conditions, or individuals.

This module introduces the simplest 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– 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 two very different 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.




The Details:

There are 8 Workshop Webinar Sessions. In order to keep momentum going while giving you enough time to keep up, we’ve devised this schedule based on feedback from previous participants:

We’ll meet for three weeks in a row – beginning October 11, 2019 – then we’ll take a week off. Then three more weeks and another week off. Finally, there will be two more sessions to finish up. We will meet on Fridays at 1 pm, U.S. Eastern. The workshop sessions are approximately 2 hours long, including time for questions.



















We will also meet for eight Q&A sessions throughout this time. We’ll meet on Thursdays at 1 pm, U.S. Eastern, on the following dates:

















Remember, everything is recorded and available for you to watch at your convenience, should you be unable to attend a live session.

Also, you have access to all the class materials for a full 12 months from your enrollment date.

Sorry, enrollment for this workshop has closed.

Please contact us if you have any questions
or get on the waiting list to be notified when it's offered again.


Every Live Workshop from The Analysis Factor includes:

  • 1 year of access to the workshop website
  • 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 to submit questions to be answered between sessions



“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 resources I read. Plus Karen is super helpful and nice — I always felt like I could ask questions without worrying about feeling ‘stupid.’”

Lisa, Analyzing Repeated Measures Data student



About Your Instructor

Karen Grace-MartinI’m Karen Grace-Martin, your statistics mentor.

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 “stats speak” 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.


Our Software Teaching Assistants

Kim Love is the owner of and lead consultant at K. R. Love Quantitative Consulting and Collaboration.

As a software specialist for this workshop, Kim will create the R demonstrations and code to use in the examples and exercises. She will attend the live Q&A sessions and monitor the workshop website to answer your R questions.

She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center. Kim has a B.A. in mathematics from the University of Virginia, and an M.S. and Ph.D. in statistics from Virginia Tech.


Jeff Meyer is a statistical consultant, instructor, and writer for The Analysis Factor, specializing in Stata.

As a software specialist for this workshop, Jeff will write the Stata code to use in the examples and exercises and making Stata demonstrations.

He’ll also attend the live Q&A sessions and monitor the workshop website to answer any Stata questions you have.

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.



“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, Analyzing Repeated Measures Data student
Director, The National Institute for STEM Evaluation and Research




So what kind of background in statistics do you need?

This course starts out at a moderate level, but we do move into more advanced material. No, you don’t need a PhD in statistics… we know you’re a researcher, not a statistician.

We’re assuming:

  • You haven’t taken linear algebra
  • You don’t want to see things derived
  • You’re here because you want to see these concepts explained in English, not formulas

That being said, you should be fairly statistics savvy. In other words, this shouldn’t be your first exposure to running linear models. You should be familiar with:

  • Centering
  • Least-squares estimation
  • Dummy variables
  • Interactions
  • Residuals
  • Variance
  • Regression coefficients

You’ll get the most from this workshop if you have:

  • MINIMUM two statistics classes, one of which MUST include linear models — multiple regression or Analysis of Variance (ANOVA)
  • Real experience doing some sort of linear modeling (familiarity with the GLM procedure will be very beneficial)
  • SPSS users: It will help if you’re familiar with SPSS Syntax. (Some parts using menus will be demonstrated in class, but at this level of modeling, you must have control of the details at the level that syntax allows.)

If you have questions about whether you’re ready for this class, just email us. We’ll give you our honest opinion. We want you to succeed!



What Others Have to Say about Analyzing Repeated Measures Data:


“I found the content and overall approach truly exceptional…”

“I found the content and overall approach truly exceptional. The individual modules covered reasonable chunks of information and were very well integrated, so that learning was sequential and comprehensive. Karen’s lectures were well-organized and clear, and I really appreciated having both the current lecture videos and the transcripts from a previous iteration available to review.

Karen was very engaging as a teacher, eminently respectful of students’ varying degrees of understanding, and always responsive to questions in a very helpful way. I found her willingness to assist with students’ own repeated measures studies and data sets particularly helpful.

I found the exercises and answer sets useful to test my understanding, and having the code available for both the class examples and the exercises provided a big step up.

I’ve been stumbling through repeated measures analyses with incomplete understanding of the models, and I feel like I’ve had my blinders removed!”

— Hilary Neckles, Analyzing Repeated Measures Data student


“…I would not have made it through the statistical analysis for this project without your Repeated Measures class.”

“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, Plant Ecology Lead Technician, Joseph W. Jones Ecological Research Center



“…Amazing value…”

“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


“…Absolutely worth my time and money.”

“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


“…One of the best things that I have come across in my professional career.”

“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



“…I’ve gone from just trying to wrap my head around what mixed models are for and what they do, to feeling confident…”

“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 (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


“…Karen is easily the best teacher I have ever had.”

“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



“Karen explains things so clearly and thoroughly…”

“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


“…Thanks for your style of teaching and the content you cover.”

“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…The point at which the webinar was most helpful was the covariance structure explanations. Who knew that those assumptions were so critical?”

–Laurel M. Fisher, House Research Institute


“…Absolutely and exactly what I had been searching for…”

“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, Graduate Student, University of Newcastle


“…You have really thought through the approach to teaching these materials in an effective way.”

“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 interpreting 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. It’s 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


“This workshop was a godsend!”

“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


“…Karen is a very talented and knowledgeable data analyst, and could communicate complex ideas in an accessible way.”

“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.”

–Joshua Madsen, Ph.D.



Your Satisfaction Is Guaranteed

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.




Q: Will I be able to keep up?

A: Concepts unique to mixed modeling that textbooks typically just gloss over? We’ll cover — and explain them, including:

  • Information criteria
  • Maximum likelihood
  • Deviance
  • Covariance matrices
  • Variance parameters
  • Sphericity
  • Growth models
  • Random slopes

We’ll also be covering matrices (they’re critical) but we won’t be manipulating them, so don’t worry about Kroniker products for this workshop.

We’ll also be looking at model equations (also critical), which means you’re going to see some Greek letters so we can stay consistent with books and articles on the topic. But don’t stress out. We’ll walk you through them step-by-step to make sure you understand what everything means and why it’s important.

You WILL need to spend some time each week, both going over the modules, and also “getting your hands dirty” with some data. Expect to spend 6-10 hours per week reviewing the concepts and doing the exercises, and re-running class examples on your own.

You know how this goes:

The more time you put in, the deeper your understanding.


Q: What’s your refund policy?

A: Your registration fee is fully refundable up to 72 hours before your first class session. No refunds will be granted after the program begins.

Q: Do you offer student discounts?

A: Yes, we do! Current students with a valid ID can register for 40% off the standard rate. Click here to receive our student discount on this and future workshops:

Q: Will you offer SAS support in this workshop?

A: Yes, 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 some experience using 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’ll be okay?

A: If you want to learn mixed models 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.

Each workshop instruction session is about two hours, whether you attend live or listen to the recording. We know it’s hard to attend 8 weeks in a row at the same time, so we split up the eight core modules over a nine-week session (four weeks on, one week off, four weeks on).

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. The exercises aren’t required—you’re not graded. But we’ve gotten feedback that they’re very helpful, as it allows you to practice and figure out what you did and didn’t understand.

In other words, 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. But If you have a crazy hectic month, you may be better off waiting to take the class as you won’t really have a chance to catch up.

One reminder: You have access to the workshop 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.

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

A: Yes! We have participants from many time zones with many different work schedules. In order to support our diverse student base, all workshop sessions – including the modules and Q&A’s — are recorded and made available to students within 48 hours. They’re screenshot video files, so you’ll hear me talking and see my screen, just as live participants do. You can even submit questions for written answers any time between sessions. Many students take the entire workshop in this manner, never attending a session live.

Q: I’m outside the U.S. Can I still participate?

A: Yes. We have participants in our workshops from many different countries. You will want a fast internet connection and either a computer speaker or a telephone if you plan to attend live.

Q: Can I pay with PayPal?

A: Yes. When you check out, you can pay directly using our system or paying with Paypal. However, the three-payment option is not available using PayPal.

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

A: Yes. Thanks to a new upgrade to GoToWebinar, attendees now have the option of registering and joining the workshop Q&A sessions from your Apple or Android device 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 this workshop twice per year. Remember, though, that you can sign up now and get access to all the course materials ( live trainings, support, exercises, the option to ask questions, and live Q&A sessions, as well as any additional bonuses) I would suggest registering now. You’ll get all the materials to review at your convenience, and you can attend again for free any time in the next 12 months.

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.

Q: What software do you support for this class?

A: SPSS, SAS, Stata, and R. Instruction sections will focus on concepts, steps to run a model, and interpretation of output regardless of software. For each software package, we will provide:

  • Pre-recorded software how-to video demonstrations of all the examples from that module’s instruction webinar
  • Syntax to run those examples
  • Syntax to run the exercises
  • Answers to the exercises based on that software

Furthermore, I will show compare and contrast all four software packages throughout 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 Kim Love and Stata examples and exercises by Jeff Meyer. Jeff and Kim will join us in the Q&A sessions and on the workshop website to answer your Stata and R questions.


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