In this nearly 6-hour tutorial you will learn menu-based R libraries so you can use R without having to fuss with R code. These libraries don’t cover everything R can do, but they do quite a bit and can set you up to make running R much easier.
Stage 1
Member Training: R for Menu Users Software Tutorial
December 30th, 2021 by TAF Support
Guidelines for writing up three types of odds ratios
December 6th, 2021 by Karen Grace-Martin
Odds ratios have a unique part to play in describing the effects of logistic regression models. But that doesn’t mean they’re easy to communicate to an audience who is likely to misinterpret them. So writing up your odds ratios has to be done with care. (more…)
Six terms that mean something different statistically and colloquially
November 8th, 2021 by guest contributer
by Kim Love and Karen Grace-Martin
Statistics terminology is confusing.
Sometimes different terms are used to mean the same thing, often in different fields of application. Sometimes the same term is used to mean different things. And sometimes very similar terms are used to describe related but distinct statistical concepts.
Best Practices for Data Preparation
October 4th, 2021 by Audrey Schnell
If you’ve been doing data analysis for long, you’ve probably had the ‘AHA’ moment where you realized statistical practice is a craft and not just a science. As with any craft, there are best practices that will save you a lot of pain and suffering and elevate the quality of your work. And yet, it’s likely that no one may have taught you these. I know I never had a class on this. (more…)
Member Training: Moving to a World Beyond p<0.05
August 2nd, 2021 by TAF Support
For nearly a hundred years the concept of “statistical significance” has been fundamental to statistics and to science. And for nearly that long, it has been controversial and misused as well. (more…)
Six Easy Ways to Complicate Your Analysis
July 13th, 2021 by Karen Grace-Martin
It’s easy to make things complex without meaning to. Especially in statistical analysis.
Sometimes that complexity is unavoidable. You have ethical and practical constraints on your study design and variable measurement. Or the data just don’t behave as you expected. Or the only research question of interest is one that demands many variables.
But sometimes it isn’t. Seemingly innocuous decisions lead to complicated analyses. These decisions occur early in the design, research questions, or variable choice.