June 1st, 2013 by Karen Grace-Martin
MANOVA is the multivariate (meaning multiple dependent variables) version of ANOVA, but there are many misconceptions about it.
In this webinar, you’ll learn:
- When to use MANOVA and when you’d be better off using individual ANOVAs
- How to follow up the overall MANOVA results to interpret
- What those strange statistics mean — Wilk’s lambda, Roy’s Greatest Root (hint — it’s not a carrot)
- Its relationship to discriminant analysis
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Karen Grace-Martin 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 master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
May 1st, 2013 by Karen Grace-Martin
Graphing predicted values from a regression model or means from an ANOVA makes interpretation of results much easier.
Every statistical software will graph predicted values for you. But the more complicated your model, the harder it can be to get the graph you want in the format you want.
Excel isn’t all that useful for estimating the statistics, but it has some very nice features that are useful for doing data analysis, one of which is graphing.
In this webinar, I will demonstrate how to calculate predicted means from a linear and a logistic regression model, then graph them. It will be particularly useful to you if you don’t have a very clear sense of where those predicted values come from.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Karen Grace-Martin 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 master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
April 1st, 2013 by Karen Grace-Martin
Hierarchical regression is a very common approach to model building that allows you to see the incremental contribution to a model of sets of predictor variables.
Popular for linear regression in many fields, the approach can be used in any type of regression model — logistic regression, linear mixed models, or even ANOVA.
In this webinar, we’ll go over the concepts and steps, and we’ll look at how it can be useful in different contexts.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Karen Grace-Martin 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 master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Just head over and sign up for Statistically Speaking.
You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.
May 21st, 2012 by Karen Grace-Martin
I am reviewing your notes from your workshop on assumptions. You have made it very clear how to analyze normality for regressions, but I could not find how to determine normality for ANOVAs. Do I check for normality for each independent variable separately? Where do I get the residuals? What plots do I run? Thank you!
I received this great question this morning from a past participant in my Assumptions of Linear Models workshop.
It’s one of those quick questions without a quick answer. Or rather, without a quick and useful answer. The quick answer is:
Do it exactly the same way. All of it.
The longer, useful answer is this: (more…)
April 27th, 2012 by Karen Grace-Martin
Factor is confusing much in the same way as hierarchical and beta, because it too has different meanings in different contexts. Factor might be a little worse, though, because its meanings are related.
In both meanings, a factor is a variable. But a factor has a completely different meaning and implications for use in two different contexts. (more…)
June 24th, 2011 by Karen Grace-Martin
Model Building–choosing predictors–is one of those skills in statistics that is difficult to teach. It’s hard to lay out the steps, because at each step, you have to evaluate the situation and make decisions on the next step.
If you’re running purely predictive models, and the relationships among the variables aren’t the focus, it’s much easier. Go ahead and run a stepwise regression model. Let the data give you the best prediction.
But if the point is to answer a research question that describes relationships, you’re going to have to get your hands dirty.
It’s easy to say “use theory” or “test your research question” but that ignores a lot of practical issues. Like the fact that you may have 10 different variables that all measure the same theoretical construct, and it’s not clear which one to use. (more…)
