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] http://www.yorku.ca/christo > On Mar 2, 2016, at 6:50 PM, Mike Palij <[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] > > > > --- > You are currently subscribed to tips as: [email protected]. > To unsubscribe click here: > http://fsulist.frostburg.edu/u?id=430248.781165b5ef80a3cd2b14721caf62bd92&n=T&l=tips&o=48233 > or send a blank email to > leave-48233-430248.781165b5ef80a3cd2b14721caf62b...@fsulist.frostburg.edu --- You are currently subscribed to tips as: [email protected]. 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