The research with which I am involved these days generally involves too many variables to report confidence intervals for all pairs of variables. I’m more likely to report a CI for multiple R-squared or semipartial eta-squared.
Cheers, [Karl L. Wuensch]<http://core.ecu.edu/psyc/wuenschk/klw.htm> From: Christopher Green [mailto:[email protected]] Sent: Wednesday, March 02, 2016 7:57 PM To: Teaching in the Psychological Sciences (TIPS) Subject: Re: [tips] Reporting CI for correlations I think the reason is simply that confidence intervals for r are rather large, and would undermine confidence (ha!) in the statistic itself. Chris ....... Christopher D Green Department of Psychology York University Toronto, ON M3J 1P3 43.773759, -79.503722 [email protected]<mailto:[email protected]> http://www.yorku.ca/christo On Mar 2, 2016, at 6:50 PM, Mike Palij <[email protected]<mailto:[email protected]>> wrote: On Wed, 02 Mar 2016 14:31:18 -0800, Lenore Frigo wrote: For a research methods class, I'm in search of some examples where results report a Pearson's r with a confidence interval (with or without a p-value/NHST). Finding such examples has been surprisingly difficult (searches hit articles about confidence intervals, not those that happen to report them). About 20 or so years ago I asked a senior researcher in public health with whom I was doing research the following: "Why do researchers report the odds-ratio with its confidence interval but they don't do the same for the Pearson r?" NOTE: this was for journal publishing research on HIV/AIDS and substance use. His answer was that was just the style of reporting people using though the confidence interval for the Pearson r should be reported (we didn't -- when in Rome....). I think that something similar has occurred in psychology. The Pearson r is one of the oldest statistics we have and pre-dates the concept of confidence interval by decades, so there is a history of not reporting the confidence interval. When Neyman came up with he confidence interval, using it implied that one was in Neyman's "camp" in contrast to Sir Ronald Fisher's "camp" where confidence intervals were considered to be as dumb as a bag of hammer. Fisher argued that the confidence interval was a ridiculous concept because it was based on the belief that one would replicate the study 100 times. Remember: the confidence interval does not provide the probability that the interval contains the population parameter of interest (it either contains it [p = 1.00] or it doesn't [p= 0.00]), rather it says that if this study/process that produced the confidence interval was repeated 100 times, 95% of these new intervals would contain the population parameter (that is if one uses a 95% confidence interval). Fisher argued that confidence intervals were appropriate for a manufacturing practice that puts out a large number of samples and not individual experiments. Fisher attempted to come up with something called fiducial intervals which would represent an interval with a 95% chance of containing the population parameter but this turns out to be much more difficult to do and Fisher didn't not come up with a useful solution. For the history of these ideas see the following book: Lehmann, E. L. (2011). Fisher, Neyman, and the creation of classical statistics. New York, NY: Springer. However, as Lehmann points out, most people interpret confidence intervals as though they are fidiucial intervals, something that distressed both Neyman and Fisher. The reason, I think is obvious, the Neyman definition doesn't really make much sense (who is going to replicate a study 100 times?) while the Fisherian definition does but does not apply to confidence intervals. So, I think that there is a basic argument about whether one should really report confidence intervals at all. For a single correlation it provides the same information as the t-test for the Pearson r, namely, does the Pearson r equal zero. If one is seriously interested in the variability of the Pearson r, that's why God created the standard error which, conceptually, may be easier to understand than a confidence interval. I'd greatly appreciate any leads on example that have r and confidence intervals reported. Or even any suggestions for how to search for that sort of thing? (Or much more broadly, any thoughts on teaching CIs and going beyond NHST?) Like I say above, it has not become standard practice for reporting confidence intervals for individual correlations, so I doubt that you'll find too many examples (especially in situations where the research cherrypicked the correlation from a correlation matrix and would have to calculate the CI by hand). It is easier to find confidence intervals for the intraclass coefficients, and other statistics where it has become standard practice to do so (that is, an agreed upon statistical ritual has been developed). On proponent of the use of CI and related statistics is Geoff Cumming and you might want to look at his book; here it is on Amazon: http://www.amazon.com/gp/product/041587968X?keywords=cumming%20%26%2334%3Bnew%20statistsics%26%2334%3B&qid=1456961421&ref_=sr_1_fkmr0_2&sr=8-2-fkmr0 For a contrary view, you can read my review of Cumming's book; see: https://www.researchgate.net/publication/236866116_New_statistical_rituals_for_old Ultimately, it comes down to doing "mindful statistics", that is, not relying on statistical rituals to guide one's statistical analysis. -Mike Palij New York University [email protected]<mailto:[email protected]> --- You are currently subscribed to tips as: [email protected]<mailto:[email protected]>. 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