At 07:36 AM 12/5/01 -0500, Karl L. Wuensch wrote:
> Accordingly, I argue that correlation is a necessary but not a > sufficient condition to make causal inferences with reasonable > confidence. Also necessary is an appropriate method of data > collection. To make such causal inferences one must gather the data by > experimental means, controlling extraneous variables which might confound > the results. Having gathered the data in this fashion, if one can > establish that the experimentally manipulated variable is correlated with > the dependent variable (and that correlation does not need to be linear), > then one should be (somewhat) comfortable in making a causal > inference. That is, when the data have been gathered by experimental > means and confounds have been eliminated, correlation does imply causation. the problem with this is ... does higher correlation mean MORE cause? lower r mean LESS cause? in what sense can think of cause being more or less? you HAVE to think that way IF you want to use the r value AS an INDEX MEASURE of cause ... personally, i think it is dangerous in ANY case to say that r = cause ... if you can establish that as A goes up ... so does B ... where you manipulated A and measured B ... (or vice versa) ... then it is fair to say that the causal connection THAT IS IMPLIED BECAUSE OF THE WAY THE DATA WERE MANIPULATED/COLLECTED also has a concomitant r ... BUT, i think one still needs to be cautious when then claiming that the r value itself is an indicant OF cause > > > So why is it that many persons believe that one can make causal > inferences with confidence from the results of two-group t tests and > ANOVA but not with the results of correlation/regression techniques. I > believe that this delusion stems from the fact that experimental research > typically involves a small number of experimental treatments that data > from such research are conveniently evaluated with two-group t tests and > ANOVA. Accordingly, t tests and ANOVA are covered when students are > learning about experimental research. Students then confuse the > statistical technique with the experimental method. I also feel that the > use of the term "correlational design" contributes to the problem. When > students are taught to use the term "correlational design" to describe > nonexperimental methods of collecting data, and cautioned regarding the > problems associated with inferring causality from such data, the students > mistake correlational statistical techniques with "correlational" data > collection methods. I refuse to use the word "correlational" when > describing a design. I much prefer "nonexperimental" or "observational." > > > > In closing, let me be a bit picky about the meaning of the word > "imply." Today this word is used most often to mean "to hint" or "to > suggest" rather than "to have as a necessary part." Accordingly, I argue > that correlation does imply (hint at) causation, even when the > correlation is observed in data not collected by experimental means. Of > course, with nonexperimental models, the potential causal explanations of > the observed correlation between X and Y must include models that involve > additional variables and which differ with respect to which events are > causes and which effects. > >---------- >Karl L. Wuensch, Department of Psychology, >East Carolina University, Greenville NC 27858-4353 >Voice: 252-328-4102 Fax: 252-328-6283 ><mailto:[EMAIL PROTECTED]>[EMAIL PROTECTED] >http://core.ecu.edu/psyc/wuenschk/klw.htm _________________________________________________________ dennis roberts, educational psychology, penn state university 208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED] http://roberts.ed.psu.edu/users/droberts/drober~1.htm ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
