Date: Tue, 21 Jun 2011 16:51:34 -0500
Reply-To: "Steve Simon, P.Mean Consulting" <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Steve Simon, P.Mean Consulting" <firstname.lastname@example.org>
Subject: Re: mixed models - time as DV
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On 6/21/2011 4:40 PM, Parise, Carol A. wrote:
> This one of probably many questions i will likely be posting on using
> linear mixed models over the next few months. It's my first crack at
> using this and i'm slowly working through the lingo by reading as
> much as I can. I read the SPSS technical report on this and i found
> an example from nice little article that i am using to mess with my
> data. http://www.indiana.edu/~statmath/stat/all/hlm/hlm.pdf
> The article explains the interpretation of the intercept term in the
> empty model is equivalent to the overall math achievement score.
> My data have time in hh:mm:ss as the DV.
> In my sample analysis, the scale of the intercept is not in hh:mm:ss
> and i can't seem to adjust to to be in this format with the "cell
> properties" when i click on the output.
> It would be really helpful to have the correct scale versus just the
> p-value for interpretation purposes. Anyone have thoughts on how I
> can do this?
Using time as a dependent variable is rather unusual. Are you sure this
makes sense from a scientific perspective. Do you expect the time to be
a random variable? Do you expect a normal distribution of time values
around some value?
Frequently, when time to an event is the variable of interest, you are
often going to want a survival analysis. Without knowing more about the
purpose of your research, of course, I can only speculate.
If it does make sense to use time and a dependent variable, then the
logical thing to do is to convert from a time value (which represents
the number of seconds since October 14, 1582) to a different value, such
as the number of seconds since the start of your study.
Steve Simon, email@example.com, Standard Disclaimer.
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