Hi Juliet,
Juliet Hannah schrieb:
I should have emphasized, I do not intend to categorize -- mainly
because of all the discussions I have seen on R-help arguing against
this.
Sorry that we all jumped on this ;-)
I just thought it would be problematic to include the variable by
itself. Take other variables, such as a genotype or BMI. If we measure
this variable the next day, it would be the same. However, a hormone's
level would not be the same. I thought this error must be accounted
for somehow.
You are quite correct that fluctuating hormone levels are a problem
(although, strictly speaking, measuring BMI and even genotyping will not
yield exactly the same results the next day, measurement error is always
present). And there may be methods dealing with this, but I don't know
of any.
If you have any idea about the variability of your hormone, you could
always take your data, perturb the hormone levels and run the analysis
again to get a feeling for the stability of your results. This is quite
ad hoc, but if I were the reviewer, a perturbation analysis like this
would greatly reassure me. However, I recently worked with hormones and
had exactly your problem, and we couldn't find any published data on
day-to-day variability, so this was not an option - we finally went
ahead and simply plugged the measurements into R.
Good luck!
Stephan
Thanks again!
Regards,
Juliet
On Sat, Mar 7, 2009 at 1:21 PM, Jonathan Baron <ba...@psych.upenn.edu> wrote:
If you form categories, you add even more error, specifically, the
variation in the distance between each number and the category
boundary.
What's wrong with just including it in the regression?
Yes, the measure X1 will account for less variance than the underlying
variable of real interest (T1, each individual's mean, perhaps), but
X1 could still be useful in two ways. One, it might be a significant
predictor of the dependent variable Y despite the error. Two, it
might increase the sensitivity of the model to other predictors (X2,
X3...) by accounting for what would otherwise be error.
What you cannot conclude in this case (when you measure a predictor
with error) is that the effect of (say) X2 is not accounted for by its
correlation with T1. Some people try to conclude this when X2 remains
a significant predictor of Y when X1 is included in the model. The
trouble is that X1 is an error-prone measure of T1, so the full effect
of T1 is not removed by inclusion of X1.
Jon
On 03/07/09 12:49, Juliet Hannah wrote:
Hi, This is not an R question, but I've seen opinions given on non R
topics, so I wanted
to give it a try. :)
How would one treat a variable that was measured once, but is known to
fluctuate a lot?
For example, I want to include a hormone in my regression as an
explanatory variable. However, this
hormone varies in its levels throughout a day. Nevertheless, its levels differ
substantially between individuals so that there is information there to use.
One simple thing to try would be to form categories, but I assume
there are better ways to handle this. Has anyone worked with such data, or could
anyone suggest some keywords that may be helpful in searching for this
topic. Thanks
for your input.
Regards,
Juliet
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Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page: http://www.sas.upenn.edu/~baron
Editor: Judgment and Decision Making (http://journal.sjdm.org)
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