At 06:42 PM 2/3/2003, Simon, Steve, PhD wrote:
There's a trade-off here. By removing the middle third, you increase the separation of the two groups, which is good
why is this good?
why?while at the same time reducing the sample size, which is bad.
if you are interested in generalization to a larger population ... is it a population of top and bottoms or ... the full range? if it is the top and bottom OF a population ... then, you are using exactly what you want
you can always take a larger n group ... and split at top and bottom and not have that problem but, then ... the issue is of ... your estimate of error is estimating sampling fluctuation of WHAT population?
i think that other than maybe making a hand calculation simpler ... but, who analyzes data that way anymore? ... the trade off in loss of information is always bad ..Usually the trade-off is good.
i would be interested in how this visualization would look ... in terms of fat consumption ... we can't really visualize can we, volume of fat very well ...I would not be as critical as some of the others on the list. Sometimes a categorical variable is easier to interpret. A lot of dietary research, for example, looks at the highest quintile of fat consumption and compares it to the lowest quintile. I can visualize those two groups pretty well.
just to use an example (and, this might be off the wall) ... if the 75th PR in fat consumption is 6 parts per million ... and the 25th PR is 3 parts per million ... you still have to know what a "part per million" means or ... 6 doesn't have any more meaning that ...
saying on a more continuous basis ... the 54th PR is 5 and the 35th PR is 3.9 ...
how can this be? what if you want to estimate the true score for someone in the middle? you have eliminated all the middle data ... in a sense, the standard error of measurement has no meaning in this midrange anymore since, you estimate of it based on the top and bottom only groups ignores the middleFurthermore, categorization mitigates some of the problems caused by measurement error.
_________________________________________________________If I were doing it myself, I would almost never dichotomize. But I wouldn't be too upset if someone else did it, especially if the data set were already quite large. Steve Simon, [EMAIL PROTECTED], Standard Disclaimer. The STATS web page has moved to http://www.childrens-mercy.org/stats. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
dennis roberts, educational psychology, penn state university
208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED]
http://roberts.ed.psu.edu/users/droberts/drober~1.htm
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