In all honesty, not sure I completely followed all of Chris's comments but certainly agree with the tone. Not only do we have the ongoing problem in our media and among practicing psychologists who confuse correlation with cause-effect, but the size of correlations are often not reported. A very recent example of this was a report on NPR that men who become a father at age 55 or older are more likely to have children with mental disorders, with this particular study looking at the incidence of bi-polar illness. However, there was absolutely no mention of the size of the correlation. And more importantly to me, there also were no stated caveats about the various potential explanations for this correlation--for example, the potential differences in family dynamics when a father is 55+ versus 25 or 35. The assumption seemed to be that the age of the sperm was the issue but that's only one potential explanation. This tendency toward the biological explanation of behavior has become quite pervasive and concerns me. That aside, I do feel we psychologists need to continually communicate with our media about this ongoing misinterpretation of data.
Joan [EMAIL PROTECTED] > [EMAIL PROTECTED] wrote: >> A more technical set of questions >> >> (1) Is it proper to talk about independent and dependent variables in a >> correlational study? And to what extent? Isn't it *more* correct to call >> the >> variables predictor and criterion variables?What is the current status >> of this >> language? >> >> > Experimental psychologists tend to be rather "fastidious" about this > issue, but their usage is idiosyncratic. One doesn't see it ias much in > other natural sciences, and it is completely absent in mathematics > (where anything on the x axis is "independent" and anything on the y > axis is "dependent). >> (2) I have learned that a rule of thumb for evaluating the effect size >> of a >> significant correlation is to square r and this is a crude indicator of >> how much of >> the variability in the criterion variable comes from the predictor >> variable. I'd like >> to hear if this is too crude to be useable. Is there another, readily >> calculable >> effect size? I am very bothered by studies that make a big deal of a >> significant >> correlation of .2 or .3. >> > This is the standard interpretation of r. It is difficult for most > people not trained in stats to understand. As for low-but-significant > correlations, you should be bothered by this (though there are > situations where low- r0squared is misleading). On the other hand, these > are the kinds of effect psychologists typically discover. Should they go > unreported? > > In some circumstances (restricted range, nonlinearity, etc.), however, > it is better to use a different measure of correlation altogether. > Kendall's tau is best for ordinal data (much better than the "r-ish" > Spearman coefficient, but its interpretation is entirely different (not > based on variance, which makes no sense in the ordinal context). When > one of the two variables is dichotomous but can be viewed as being a > crude measurement of an underlying continuous variable, it is better to > use the biserial (rather than the "r-ish" point-biserial). Better still > is Lord's modification of the biserial, but it is rarely used and so > needs to be explained. In 2x2 situations, especially where the cells are > greatly unbalanced, it is often better to use the odds ratio rather than > the "r-ish" phi. Its interpretation (increased probability of X also > being a case of Y) is much more understandable for people not trained in > stats. One finds it used in prospective medical research quite commonly > (in which cells are often badly unbalanced because developing any > particular disease -- e.g., a heart attack -- in a given period of time > is a highly unlikely event). > > Check out David Howell's chapter on "Alternative Correlational > Techinques" for a good overview. > > Regards, > Chris > -- > > Christopher D. Green > Department of Psychology > York University > Toronto, ON M3J 1P3 > Canada > > > > 416-736-2100 ex. 66164 > [EMAIL PROTECTED] > http://www.yorku.ca/christo/ > > ========================== > > > --- > To make changes to your subscription contact: > > Bill Southerly ([EMAIL PROTECTED]) --- To make changes to your subscription contact: Bill Southerly ([EMAIL PROTECTED])
