Mediation indicates that a mediator M is the causal link in the effect between an independent variable X and a dependent variable Y.
How to test mediation has become a very hot topic of statistical research in the past 10 years, and there are many new advances.
In this webinar, we’ll discuss a number of issues, new and old, in mediation, including the Baron & Kenny 4-step approach, the difference between mediation and moderation, Sobel and bootstrap tests for indirect effects. (And of course, what all these terms mean.)
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.
Outliers. There are as many opinions on what to do about them as there are causes for them.
In this webinar, we’ll explore the different types of outliers, methods for figuring out which type you have, whether they’re influential, and what to do about them.
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.
There are dozens of measures of association. Even just correlations come in many flavors: Pearson, Spearman, biserial, tetrachoric, squared multiple, to name a few.
And there are many measures beyond correlation.
You probably learned many of these way back in intro stat, then promptly forgot about them. That may be reasonable, but they do pop up as important within the context of other, more complicated statistical methods. A strong foundation in the measures of association makes those other methods much easier to understand.
In this webinar, we’re going to re-examine many of these measures, see how they fit together (or don’t), and talk about when each one is useful.
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.
Principal Component Analysis (PCA) is a handy statistical tool to always have available in your data analysis tool belt.
It’s a data reduction technique, which means it’s a way of capturing the variance in many variables in a smaller, easier-to-work-with set of variables.
There are many, many details involved, though, so here are a few things to remember as you run your PCA.
1. The goal of PCA is to summarize the correlations among a set of observed variables with a smaller set of linear (more…)
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.
One important consideration in choosing a missing data approach is the missing data mechanism—different approaches have different assumptions about the mechanism.
Each of the three mechanisms describes one possible relationship between the propensity of data to be missing and values of the data, both missing and observed.
The Missing Data Mechanisms
Missing Completely at Random, MCAR, means there is no relationship between (more…)
