The Analysis Factor
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Lest you believe that odds ratios are merely the domain of logistic regression, I’m here to tell you it’s not true.
One of the simplest ways to calculate an odds ratio is from a cross tabulation table.
We usually analyze these tables with a categorical statistical test. There are a few options, depending on the sample size and the design, but common ones are Chi-Square test of independence or homogeneity, or a Fisher’s exact test.
Odds is confusing in a different way than some of the other terms in this series.
First, it’s a bit of an abstract concept, which I’ll explain below.
But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term.
I found this incorrect definition recently in a (non-statistics) book: [Read more…] about Confusing Statistical Term #8: Odds
When a model has a binary outcome, one common effect size is a risk ratio. As a reminder, a risk ratio is simply a ratio of two probabilities. (The risk ratio is also called relative risk.)
Risk ratios are a bit trickier to interpret when they are less than one.
A predictor variable with a risk ratio of less than one is often labeled a “protective factor” (at least in Epidemiology). This can be confusing because in our typical understanding of those terms, it makes no sense that a risk be protective.
by Jeff Meyer, MBA, MPA
One of the most important concepts in data analysis is that the analysis needs to be appropriate for the scale of measurement of the variable. The focus of these decisions about scale tends to focus on levels of measurement: nominal, ordinal, interval, ratio.
These levels of measurement tell you about the amount of information in the variable. But there are other ways of distinguishing the scales that are also important and often overlooked.
[Read more…] about Count vs. Continuous Variables: Differences Under the Hood
After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times for each test. When you figure out the probability of Type I error across all the tests, that probability skyrockets.
[Read more…] about Member Training: Adjustments for Multiple Testing: When and How to Handle Multiplicity
Do you remember all those probability rules you learned (or didn’t) in intro stats? You know, things like the P(A|B)?While you may have thought that these rules were
only about balls and urns (who pulls balls from urns anyway?), it’s actually not true.
It turns out that having a good understanding of these rules (as well as actually remembering them) does come in handy when you’re doing data analysis.
There are so many situations and methods in statistics that draw directly from those rules. Everything from p-values to logistic regression to maximum likelihood estimation are all direct applications of these rules. In this webinar, we’re going to review those rules, with examples of when they come up in statistical methods that you use and are learning.
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.
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.
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