Effect size statistics are extremely important for interpreting statistical results. The emphasis on reporting them has been a great development over the past decade. [Read more…] about Odds Ratio: Standardized or Unstandardized Effect Size?

# odds ratio

## Effect Size Statistics: How to Calculate the Odds Ratio from a Chi-Square Cross-tabulation Table

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.

## Member Training: Those Darn Ratios!

Ratios are everywhere in statistics—coefficient of variation, hazard ratio, odds ratio, the list goes on. You see them reported in the literature and in your output.

You comment on them in your reports. You even (kinda) understand them. Or, maybe, not quite?

Please join Elaine Eisenbeisz as she presents an overview of the how and why of various ratios we use often in statistical practice.

## The Difference Between Relative Risk and Odds Ratios

*by Audrey Schnell*

Odds Ratios and Relative Risks are often confused despite being unique concepts. Why?

Well, both measure association between a binary outcome variable and a continuous or binary predictor variable. [Read more…] about The Difference Between Relative Risk and Odds Ratios

## Effect Size Statistics in Logistic Regression

Effect size statistics are expected by many journal editors these days.

If you’re running an ANOVA, t-test, or linear regression model, it’s pretty straightforward which ones to report.

Things get trickier, though, once you venture into other types of models.

## Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery

A number of years ago when I was still working in the consulting office at Cornell, someone came in asking for help interpreting their ordinal logistic regression results.

The client was surprised because all the coefficients were backwards from what they expected, and they wanted to make sure they were interpreting them correctly.

It looked like the researcher had done everything correctly, but the results were definitely bizarre. They were using SPSS and the manual wasn’t clarifying anything for me, so I did the logical thing: I ran it in another software program. I wanted to make sure the problem was with interpretation, and not in some strange default or [Read more…] about Opposite Results in Ordinal Logistic Regression—Solving a Statistical Mystery