On Sun, 07 Sep 2008 13:17:10 -0700, Annette Taylor wrote: >(1) I talk with my intro students about the misconception that >mentally ill people generally have a history of violence.
One should probably start by asking what one means by (a) "mentally ill" and (b) "violence" If mentally ill is being used as a synonym for "crazy" then it is a meaningless term. If there is some specific diagnosis, such as one of the DSM, then one should use it. Perhaps when one thinks of mental illness, one imagine a person having a psychotic episodic where one lashes out at random. However, given that many serial killers have antisocial personality disorder and high degrees of psychopathy, one might call them mentally ill but there everyday presentation is usually one of normal functioning and even charming and gregarious. Who is more mentally ill: the person with the psychotic episode or the person with antisocial personality? Consider: was Tony Soprano mentally ill or just "neurotic"? As for violence, it should be remembered that everyone is capable of physically hurting and killing other people and under certain circumstances such as wars or "police actions" violent behavior is not only expected but desired. One part of becoming a member of the armed forces is to learn how to kill others efficiently (a point that Kurt Vonnegut was fond of reminding others of, a point that Stanley Kubrick emphasized in "Full Metal Jacket", a point that Ken Burns makes in his documentary series "The War" when soldiers, once they got over their initial reluctance to kill the "enemy", developed pride in becoming efficient killers). So, what exactly does one mean when one is referring to violence? Socially acceptable forms of violence? Socially prohibited forms of violence -- and if prohibited, prohibited by whom? >And the research evidence seems to support this. But in >thinking about where the misconception comes from, would >it not be correct to say that most people with a history of >violence have had a mental illness? Presumably the armed forces screen out people with mental illness so only "normal" people get taught how be efficient killers. Or are only talking about "amateur" practioners of violence? >In other words, could one be violent or have unmotivated >violence and not be mentally ill? After selecting appropriate definitions for the terms, it probably is likely that one will conclude that not only is it possible but necessary. That's why companies like "Blackwater Worldwide" is used for security by the U.S. in Iraq and other places; see: http://en.wikipedia.org/wiki/Blackwater_USA Last I checked, I didn't hear anyone calling mercenaries mentally ill. They might be crazy though. >A more technical set of questions > >(1) Is it proper to talk about independent and dependent variables >in a correlational study? No. >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? The terms "predictor" and "criterion" are somewhat old-fashioned and imply the use of multiple correlation analysis. It may be useful to make some distinctions: (a) the term "independent variable" has been used in experimental design as referring to a variable selected/manipulated by a researcher. However, people in the social sciences (e.g., economics, sociology) have used the term when they wanted to identify a "causal variable", that is, a variable that causes changes in an outcome measure. So, the relative strength of the dollar might be seen as a causal factor in reducing bond prices or the prices of commodities but it wouldn't be an independent variable in the experimenter's sense. This usage, I believe, has diminished/disappeared because of the next point. (b) In the area of "Structural Equation Modeling" (SEM), researchers often deal with correlational datasets where some variables are identified as "causal" and others are identified as "outcome". Causal variables are called "exogenous" variables and outcome variables are called "endogenous" variables. There are many sources for SEM, one of which is the webiste provided by Ed Rigdon who maintains the SEM-L mailing list; see: http://www2.gsu.edu/~mkteer/sem2.html Although many psychologists have be taught to think about causality only in terms of experimental designs, it is useful to keep in mind that there are many situations where experimentation is not practical but one can make systematic observation that may reveal causal relationships. Astronomy, of course, is based on this. >(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. Things have become much more complicated and today many people in meta-analysis would use "r" instead of "r squared". Rosenthal and DiMatteo (Annual Review of Psychology 2001) make the following statements: |There are two main families of effect sizes, the r family |and the d family. | |The r family of product moment correlations includes Pearson r |when both variables are continuous, phi when both variables |are dichotomous, point biserial r when one variable is continuous |and one is dichotomous, and rho when both variables are in |ranked form, as well as Zr, the Fisher transformation of r. | |This family also includes the various squared indices of r and |related quantities, such as r2, omega squared, epsilon squared, |and eta squared. Squared indices are problematic, however, |because they lose their directionality (although this can be retrieved |through careful analysis of the findings), and the practical magnitude |of these indices is often misinterpreted. In an example regarding the |latter problem, it may be concluded that one percent of the variance |in a dependent variable owing to the independent variable is too |little to matter. However, if the independent variable is a very |inexpensive and safe intervention, and the dependent variable |involves saving lives [as was the case in research on prevention |of heart attacks with low-dose aspirin (Rosenthal & Rosnow 1991)], |the percentage of variance explained may be very small, but its |implications might be quite substantial. >I'd like to hear if this is too crude to be useable. Is there another, >readily calculable effect size? Folks like Rosenthal would say just use "r". >I am very bothered by studies that make a big deal of a significant >correlation of .2 or .3. This is going to sound *SO* wrong but I'll say it anyway: It's not the size of the "r" that's important, it's what you do with it. Rosenthal points out (see aspirin example above) that a small effect can still have significant practical implications but it is more important to realize that the real "significance" of a result is the role it plays in a theoretical explanation. If a theory predicts that there should be a zero correlation between two or more variables, then finding a significant correlation, regardless of its size, is important because it falsifies the theory. If a result allows one to reject a theory, it doesn't matter how large or small the critical finding is. Even small results can be important. -Mike Palij New York University [EMAIL PROTECTED] --- To make changes to your subscription contact: Bill Southerly ([EMAIL PROTECTED])
