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Running Regressions and ANCOVAs in SPSS GLM

Overview:

Learn SPSS GLM Inside Out so you can run Linear Regressions and ANCOVAs and Read Results Accurately, Efficiently, and With Confidence

Some of my clients are surprised when I tell them I run multiple regression models, as well as ANOVAs and ANCOVAs, in GLM instead of Regression.

But every model you can run in regression can be run in GLM.

And for the most part, GLM output is much more informative.

But even better, it does most of the work for you. If you use GLM, you don't have to dummy code categorical variables--GLM does it for you.

And you don't have to create new variables every time you want to try an interaction term. Just specify the interaction you want in the Design statement or button.

Now, it's true, there are some options that are available only in Regression. And when I need those, I'll run Regression. But for the most part, my default is to use GLM.

But GLM's greatest asset is also its downside: there are so many options.

That makes it hard to know which apply to your analysis. The good thing though, is that by using GLM, you don't have to make arbitrary decisions like whether the model you're running is a multiple regression with dummy variables or an ANCOVA.

GLM doesn't care what you call it. It runs them both the same. You just need to figure out whether each predictor is continuous or categorical, whether to include any interactions, and which output will help you interpret those variables' effects.

Which of course, can also be tricky. Which options should you choose, and when?

And what does that output mean?

You'll learn all the ins and outs of SPSS's GLM, and how it compares to Regression.

After an extensive overview, we work through two examples, and as we go, we discuss the relevancy, meaning, and how to use each of the buttons and options in SPSS GLM.

We'll talk about how each of these relate to the syntax, and when that's easier to use.

We will then go over the output and see how making different analysis decisions affected the output.

You will complete the workshop with a much better understanding of how SPSS handles linear models, what the defaults are and when to change them, and which options are appropriate for which analyses.

And although the focus of this workshop is on the software, you will also improve your understanding of the General Linear Model, and how regression and ANOVA fit into that overall framework.

What You Will Learn:

 

Here are the details:

Module 1: Univariate GLM overview:

The 5 boxes: Dependent Variable, Fixed Factors, Random Factors, Covariates, and WLS Weight

What you put into each one, when, and the defaults and language in SPSS.

The Options Button: Which of the 10 Display options are relevant to most GLM analyses. The definition, meaning and situations to use each.

The Model Button:

  • The defaults in SPSS for interaction terms, how and when to override them, and how the order in which you build terms affects the meaning of the output.
  • Type I and Type III Sums of Squares--what they are, what they mean, and when to use each one.

The Save Button: Saving Predicted Values, Residuals, and Diagnostics to test assumptions

Syntax: Using Syntax is usually more efficent than the menus, has more options, and gives you an audit trail of what you've done. We will talk about how to use the Paste button to create syntax, then how to modify it, and the basic logical structure of the Univariate GLM syntax.

Module 2 : Linear Regression Demonstration

In Session 2, I'll demonstrate a Multiple Regression Analysis with a dummy-coded categorical predictor first without, then with an interaction. You'll see how the choices you need to make and the output changes by including the dummy variable and the interaction.

We will go through the 5 boxes, the Options, Model, and Save in the specific context of a Linear Regression model.

We'll specifically run this example in both Regression and GLM to see the differences in options, default, and possible output. We'll also:

Comparison to Regression: The differences between SPSS GLM and SPSS Regression

Module 3: Analysis of Variance and Analysis of Covariance Demonstration.

We'll run an ANOVA with two categorical factors and a continuous covariate. You'll see how adding the covariate requires different options and output to interpret, and the similarities and differences between this analysis and the multiple regression.

This module will specifically cover sections of the SPSS GLM that are only relevant for ANOVAs:

Estimated Marginal Means--what they are, what they mean, and when you need them

The Plots Button: Profile Plots--how to do them, why to do them, and what they mean

The Post-Hoc Button:

  • How and when to perform a post-hoc test
  • How to read the results and what they mean
  • Which Post-hoc tests you should never use, and when to use Tukey, Bonferroni, and Dunnett

The Contrasts Button: Definitions for each of the Contrasts, changing the reference group, and seeing how to do and interpret a Simple Contrast.

Format:

Web based online workshop: Each week, we'll release one module, so you don't get overwhelmed with too much information all at once. For each module, you'll get:

1. Training Materials

  • training videos to download and watch on your own time (and keep forever). The videos are screen shots of what is happening on my computer screen. They include both slide presentations as I go over the concepts and software demonstrations.
  • pdf handouts of the presentation slides for note-taking
  • exercises to practice what you've learned, with answers to check your work.

2. Live Q&A Webinars

After you've had about a week to work through all the training materials, we'll meet in a live Question & Answer webinar.

You attend over the internet. Audio is through either your computer speakers/microphone or by telephone. Ask questions out loud or write it into the chat.  You'll be able to see my screen, so I can show you how to do something in SPSS, draw you a graph, or we can review the output.

I highly encourage you to attend the Q&A Sessions live, but if you have to miss one (it happens) they will be recorded and made available within 24 hours on our workshop website. You can still get your questions answered by submitting a question before a Q&A and I'll cover it in the session.

3. One year membership in our private workshop website, where you can submit questions and get all the workshop materials.

This private website contains everything you need: Workshop materials to download, bonus videos, the exercises, Q&A information and recordings, a contact form to get some help if you get stuck with anything.

But best of all, on each Module page, you can submit questions. So if something isn't clear while you're watching the training, pause your video and type in your question. We'll answer quick questions right there on the website, and things that are more in-depth, we'll cover in the next Q&A.

It's all the advantages of a live workshop without the disadvantages.

You don't have to drop a few thousand travelling to a workshop and take a few days off work. And best of all, because you’re not travelling, we don’t have to concentrate an overwhelming amount of information into a marathon session. We can spread it out in digestible amounts.

So you really learn it. Inside out.

And best of all, unlike live workshops, you keep the workshop recordings forever. So if you need to review the material in a few months, or years, or find yourself needing to run a new type of model, you can watch it again.

Who Is It For:
This workshop is for you if you:
  • Have worked with SPSS before. You should have a basic understanding of how variables and cases are set up in SPSS, and how to conduct basic analyses.
  • Have used SPSS GLM and are confused by all the options. You wish you really understood what they mean, whether or not you should use them, and why.
  • Run all your regression models in the Regression procedure, but spend hours creating dummy variables or interaction terms. You've heard that GLM can do this for you, but you don't understand the difference.
  • Are working on, or will soon begin analyses for a research project that involves regression or ANOVA. Learn how to do it before you even start, and save hours, or even days of time.
  • Are an experienced SPSS user, but would love to go back and really brush up on what all this means.
  • Are pretty comfortable with SPSS menus, but haven't used Syntax much, and would love to learn how the menus correspond to the Syntax.

Prerequisites:

  • You will get the most out of the workshop if you have had at least two statistics classes, some experience in data analysis using SPSS. This is not for total SPSS or statistics beginners.
What's Included:
  • 3 Modules of training videos.
  • PDF handouts of the presentation slides on which you can print out and take notes.

  • SPSS and SAS syntax code to run, explore, and practice all of the workshop examples yourself.

  • Data sets for the workshop examples and exercises in SPSS, SAS, and excel versions, so you can choose the one that works best for you.

  • One year membership in our private workshop website, where you can download videos, handouts, and bonus materials, get resource recommendations, or ask for customer support.  We keep everything web based because statistical software is changing fast.  Any time we update our materials, you get access to the new version.

  • Exercises and answers.  (Yes, homework!). You’ll get more out of the workshop if you participate fully by doing the exercises.  We suggest you use one of the data sets provided, so you can check answers, then redo them on your own. It's when you do statistics on your own data that it really starts to make sense.
The Instructor:

Karen Grace-Martin is a statistical trainer and consultant and an expert on linear models, data analysis, and SPSS.

She has guided and trained researchers through their statistical analysis for over 15 years. Her focus is on helping statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

 

What past Students Say About This Workshop:

“The overview of the GLM features was excellent!!!  I’ve already recommended the workshop to colleagues.”

- Shani Shenhar-Tsarfaty

“The ability to hang on to all of these materials, including the bonus materials is a really great feature. I feel llike I have this whole library of references that I can go back to if I need it.”

- Noelle Chesley

“The materials and videos are so clear. The homework followed by live Q&A were were an excellent supplement to the videos.  Knowing I had to do the homework before the Q&A session made me make time to watch the videos and do the exercises every week instead of putting it off!”

- Anonymous

“It is one of the easiest and most friendly ways of explaining regression to non-statisticians that I have ever followed. Very useful.”

- David Arteta

Dates & Times:

The modules will be released on:

  • Thursday, March 1
  • Thursday, March 8
  • Thursday, March 15

Question & Answer series:

11am Eastern (GMT-5)  

  • Thursday, 3/8/12
  • Thursday, 3/15/12
  • Thursday, 3/22/12

7pm Eastern (GMT-5)   

  • Tuesday, 3/6/12
  • Tuesday, 3/13/12
  • Tuesday, 3/20/12

 

Registration:

Registration for this workshop is closed. If you'd like a notice when we offer this workshop again, join our Advance Discount List by filling in your name and email address below. 

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Bonuses:

To fill in your understanding about General Linear Models, you'll get videos of:

  • Dummy Coding and Effect Coding in Regression Models
  • Getting Started with SPSS Syntax
  • Correlated Predictors in Regression Models: Detecting Multicollinearity
Guarantee:

Your registration fee is fully refundable up to 72 hours in advance minus a $35 administration fee. Because you will still receive the recorded video, no refunds will be granted after the program begins.

That said, your satisfaction is guaranteed. If you participate in the full workshop and find you are not satisfied for any reason, we will give you a full refund. Just notify us within 90 days of the conclusion of the program.