August 1st, 2014 by Karen Grace-Martin
Why does ANOVA give main effects in the presence of interactions, but Regression gives marginal effects?
What are the advantages and disadvantages of dummy coding and effect coding? When does it make sense to use one or the other?
How does each one work, really?
In this webinar, we’re going to go step-by-step through a few examples of how dummy and effect coding each tell you different information about the effects of categorical variables, and therefore which one you want in each situation.
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.
June 1st, 2014 by Karen Grace-Martin
Whenever you run multiple statistical tests on the same set of data, you run into the problem of the Familywise Error Rate. What this means is that the true probability
of a type 1 error somewhere in the family of tests you’re running is actually higher than the alpha=.05 you’re using for any given test.
This is a complicated and controversial issue in statistics — even statisticians argue about whether it’s a problem, when it’s a problem, and what to do about it.
In this webinar, we’ll talk about the meaning and consequences of these issues so you can make informed decisions in your data analysis.
We’ll also go through possible solutions, including post-hoc tests and the false discovery rate.
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.
March 1st, 2014 by Karen Grace-Martin
Multicollinearity isn’t an assumption of regression models; it’s a data issue.
And while it can be seriously problematic, more often it’s just a nuisance.
In this webinar, we’ll discuss:
- What multicollinearity is and isn’t
- What it does to your model and estimates
- How to detect it
- What to do about it, depending on how serious it is
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.
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January 1st, 2014 by Karen Grace-Martin
In this follow-up to December’s webinar, we’ll finish up our discussion of interactions.
There is something about interactions that is incredibly confusing.
An interaction between two predictor variables means that one predictor variable affects a third variable differently at different values of the other predictor.
How you understand that interaction depends on many things, including:
- Whether one, or both, of the predictor variables is categorical or numerical
- How each of those variables is coded (specifically, whether each categorical variable is dummy or effect coded and whether numerical variables are centered)
- Whether it’s a two-way or three-way interaction
- Whether there is a directionality to the interaction (moderation) or not
Sometimes you need to get pretty sophisticated in your coding, in the output you ask for, and in writing out regression equations.
In this webinar, we’ll examine how to put together and break apart output to understand what your interaction is telling you.
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.
December 1st, 2013 by Karen Grace-Martin
There is something about interactions that is incredibly confusing.
An interaction between two predictor variables means that one predictor variable affects a third variable differently at different values of the other predictor.
How you understand that interaction depends on many things, including:
- Whether one, or both, of the predictor variables is categorical or numerical
- How each of those variables is coded (specifically, whether each categorical variable is dummy or effect coded and whether numerical variables are centered)
- Whether it’s a two-way or three-way interaction
- Whether there is a directionality to the interaction (moderation) or not
Sometimes you need to get pretty sophisticated in your coding, in the output you ask for, and in writing out regression equations.
In this webinar, we’ll examine how to put together and break apart output to understand what your interaction is telling you.
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.
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.
