Thank you for your responses. 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.
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. 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 >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > -- > 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) > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
