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- How to do odds ratios with rxc groups on spss on mac how to#
- How to do odds ratios with rxc groups on spss on mac trial#
Since my knowledge in statistics is rather poor, my employer offered me to attend some seminars in Medical Biometry at the University of Heidelberg.
How to do odds ratios with rxc groups on spss on mac trial#
So while it’s not technically inaccurate, it can be unintentionally misleading.In June 2017 I've started working at the Clinical Trial Centre Leipzig at Leipzig University. *Notice I specifically used the word “odds” in that sentence and not “likely.” I’ve found that if you use the term “likely” most people will interpret that as meaning a 40% higher probability. So in comparison to every 100 people in that group without pain, the Runner’s group has 40% more in pain.
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But if you unpack it a bit, you can train yourself to think of it this way.īoth those odds: 49:100 for Runners and 35:100 for Non-Runners are comparisons to 100 people without joint pain. I realize it’s still a little hard to think in odds instead of probabilities, since that’s what most of us are used to. So the easiest way to think about it is the odds* of joint pain is about 40% higher for runners than for non-runners. 1.4 times as much is the same as 40% higher. It’s often easier to think of a ratio, though, as a percentage. One way to say it is that the odds of a Runner developing joint pain is 1.4 times that of a Non-Runner developing joint pain. So the odds ratio of a Runner developing joint pain compared to a Non-Runner is 1.4. It is the ratio of these two odds: Odds runners/Odds non-runners. Now that we have both odds, we can calculate the Odds Ratio. You can see it’s smaller than the odds for Runners, which was. Or we could say: for every 35 Non-Runners who have joint pain, 100 don’t. We calculate it the exact same way, but now we use the numbers from the Non-Runners’ row of data. Let’s call that P non-runners: the probability that a Non-Runner has joint pain. The odds that a non-runner has joint pain: The odds ratio we’re after is actually a ratio of two odds. Super confusing that the odds, which is a ratio of two probabilities, isn’t the odds ratio we’re after. But that is not the odds ratio we’re trying to compute. That’s right, the odds is itself a ratio. But those are both technically accurate, because it’s really about expressing the ratio. 49 runners who have joint pain, 1 doesn’t.” Or “for every 4.9 runners who have joint pain, 10 don’t”. I had to switch to 49 and 100 because it doesn’t make sense to say “for every. We interpret it like: for every 49 runners who have joint pain, 100 don’t. We say it like: the odds of a runner having joint pain is.
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The odds of joint pain for Runners is the probability of joint pain divided by the probability of not joint pain. Now that we have the probability of joint pain for Runners, we can calculate the odds of joint pain for Runners. Let’s call that P runners: the probability that a Runner has joint pain. Now a true probability is on a 0 to 1 scale, not the 0 to 100 scale that a percentage is. We estimate that by the percentage of non-runners who experience joint pain. The odds that a Runner has joint pain is based on its probability. (Yes, I’m calling people who run 24 km/week non-runners. We start by calculating two odds: the odds of experiencing joint pain for Runners and the odds of experiencing joint pain for Non-runners. There are different possible odds ratios we could get out of this table, but I’m going to choose the one that makes the most sense to me from a research perspective: the odds-ratio for runners experiencing joint pain compared to non-runners.
How to do odds ratios with rxc groups on spss on mac how to#
We will come back to how to interpret it, but first let’s talk about how to calculate the odds ratio. How do we get the odds ratio? How to Calculate the Odds Ratio 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.Īnd while that test gives you a p-value, it doesn’t give you a good effect size statistic.Īlong with association statistics, like phi, an odds ratio is a good standardized effect size statistic for a table like this.įor example, here is a simple data set with the cross-tabulation between two binary variables: Whether or not someone runs more than 25 km/week and whether or not they experienced joint pain. We usually analyze these tables with a categorical statistical test. One of the simplest ways to calculate an odds ratio is from a 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.