On 5 Mar 2001 16:41:22 -0800, [EMAIL PROTECTED] (Donald Burrill)
wrote:
> On Mon, 5 Mar 2001, Philip Cozzolino wrote in part:
>
> > Yeah, I don't know why I didn't think to compute my eta-squared on the
> > significant trends. As I said, trend analysis is new to me (psych grad
> > student) and I just got startled by the results.
> >
> > The "significant" 4th and 5th order trends only account for 1% of the
> > variance each, so I guess that should tell me something. The linear
> > trend accounts for 44% and the quadratic accounts for 35% more, so 79%
> > of the original 82% omnibus F (this is all practice data).
> >
> > I guess, if I am now interpreting this correctly, the quadratic trend
> > is the best solution.
DB >
> Well, now, THAT depends in part on what the
> spectrum of candidate solutions is, doesn't it? For all that what you
> have is "practice data", I cannot resist asking: Are the linear &
> quadratic components both positive, and is the overall relationship
> monotonically increasing? Then, would the context have an interesting
> interpretation if the relationship were exponential? Does plotting
[ snip, rest ]
"Interesting interpretation" is important. In this example, the
interest (probably) lies mainly with the variance-explained:
in the linear and quadratic.
It's hard for me to be highly interested in an order-5 polynomial,
and sometimes a quadratic seems unnecessarily awkward.
What you want is the convenient, natural explanation.
If "baseline" is far different from what follows, that will induce
a bunch of high order terms if you insist on modeling all the
periods in one repeated measures ANOVA. A sensible
interpretation in that case might be, to describe the "shock effect"
and separately describe what happened later.
Example.
The start of Psychotropic medications has a huge, immediate,
"normalizing" effect on some aspects of sleep of depressed patients
(sleep latency, REM latency, REM time, etc.). Various changes
*after* the initial jolt can be described as no-change; continued
improvement; or return toward the initial baseline.
In real life, linear trends worked fine for describing the on-meds
followup observation nights (with - not accidentally - increasing
intervals between them).
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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