Date: Wed, 19 Oct 2005 11:29:03 -0700
Reply-To: Dale Glaser <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Dale Glaser <firstname.lastname@example.org>
Subject: Time Series inquiry.......
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Hi all.....I will be working on a project whereby there are 99 matches (i.e., 198 participants in the study) with approximately one dozen variables measured on a varying basis over a varying number of days. For example from one matched pair one individual from the experimental group is measured on one variable (e.g., respiratory rate) 8 times on day 1, then 9 times on day 2, and then 4 times on day 6; their matched cohort (control group) may be in the study for five days and have a very different number of measures per day. Also, the time by which they were measured was not standardized (i.e., not all were measured at 8 am, 9 am, etc.). There can be from 10 to 80 measures per person on a given variable given the length of their participation. I was envisioning a multi-sample latent growth curve approach until I found out the # of measures (note: the client would indeed like to model ALL the measures as opposed to collapsing across time/days). Subsequently, a time series approach
was proposed (e.g., ARIMA) though I am having difficulty how to best pool the 198 particpants given the varying rates of measures across a varying rate of days.
If anyone has had experience with such a design, I would be most appreciative if you would be wiling to shed insight about the optimal way to handle such a time-unstructured design. I have pondered a mixed-model approach or pooled time series, but the number of measures across time see to prohibit a mixed-model approach (I do have HLM6.0 and Mplus) and I"m not sure if pooled time series is appropriate (i.e, does each column represent an individual's 10- 80 or so measures, and are they collapsed?).
Again, thank you for your time...Dale Glaser
Dale Glaser, Ph.D.
4003 Goldfinch St, Suite G
San Diego, CA 92103