On Mon, 20 Jun 2011 22:45:03 -0500, Jim Clark wrote: > Hi > Thanks for the quotes from Hayes (and for correcting my >use of "observations").
You're welcome. >I do wonder, however, about Hayes use of "error". Is it >really the case that someone with an IQ of 120 deviates >from the norm of 100 because of "error" (which I normally >interpret to mean random influences) rather than the cumulation >of multiple factors that systematically (causally?) contribute >to a higher IQ? I think that Hays is just presenting the traditional line about measurement of psychological attributes which makes an analogy to physical measurement. One classic example of the normal distribution or distribution of errors is Gauss' analysis of astronomical data. The positions of distant heavenly bodies like stars are stable in location but the same astronomer looking at the star across many nights will record locations that differ from night to night. Often the errors are small but sometimes the errors are large. What causes these errors? Some of it may be due to human error, due to atmospheric conditions (e.g., amount of humidity or water vapor in the air, amount of particulate and light population in the sky, etc.). But as long as the conditions are temporary, they can be treated as random effects. So, the position of the star is best modeled by the classical test theory model Y = True + Error What you are suggesting is that the measurement of IQ is better modelled by something like Y = T + SystemaicFactors + Error I'll have more to say about this below. Galton would do similar types of analyses for his "biometric" measurements, the example of one such analysis in provided on the Galton Institute's page which can be accessed here: http://www.galtoninstitute.org.uk/Newsletters/GINL9912/francis_galton.htm Today, I have problems presenting IQ or standardized test results according to the traditional "law of errors/temporary systematic effects" because such variables do not exist in a vacuum. A person's IQ or intelligence score will depend upon our "measurement model" but it also true that over the course one's life systematic factors have operated to either suppress one's intelligence or to facilliate it -- factors such as gender, race, socioeconomic status, one's level of education, the education levels of one's parents, and so on will have had an influence. A better model for IQ or intelligence score for any given person may be: Y = T + GenderEffect + SESEffect + EducSelfEffect + EducPareEffect + ... + Error Some of these issues are addressed in the Handbook of Psychological Assessment which available on books.google.com; here a link to one section on predicting what a person's WAIS-III score would be after one has taken into account demographic and other variables; see: http://tinyurl.com/GooglePsychAssess Quoting from page 123: |In the WAIS-III - WMS-III Technical Manual, it was emphasized that |good practice means that all scores should be evaluated in light of |someone's life history, socioeconomic status, and medical and |psychosocial history. >I wonder if Hayes is using "error" as a substitute for something more >general than just random variation? My reading of Hays is that he is using the traditional descriptions of normal curves (systematic factors would be covered in the sections on ANOVA and regression). YMMV. > When I get a chance I will modify simulation to incorporate correlation >between random influences (Hayes error) to see what impact it has on >the final distribution. -Mike Palij New York University m...@nyu.edu --- You are currently subscribed to tips as: arch...@jab.org. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13090.68da6e6e5325aa33287ff385b70df5d5&n=T&l=tips&o=11091 or send a blank email to leave-11091-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
