Re: st: Date: Wed, 22 Sep 2010 08:37:30 +1000

[Steve Samuels <sjsamuels@gmail.com>]

Why don't you resend this with an informative subject, rather than
simply replying to the digest.  You are much more likely to get an
answer.


Steve

On Tue, Sep 21, 2010 at 6:37 PM, John Morton
<john.morton@optusnet.com.au> wrote:
> Hi,
>
> I am seeking advice on analysis of a time series dataset in Stata. The same
> site was visited irregularly 30 times over 3 years (median interval between
> visits 35 days, range 18 to 68 days). At each visit, usually 5 tadpoles (but
> sometimes 6 or 9) were sampled (numbers were limited because this is an
> endangered species). Different tadpoles were sampled at each visit. Each
> tadpole was tested and categorised as test positive or test negative.
> Apparent prevalences were 1.00 at about half of the visits and 0.00 at about
> 25% of visits.
>
> The researcher’s question is whether prevalence varies by month (ie Jan,
> Feb, Mar etc) or by season.
>
> The features of this data that seem important are that the errors would be
> expected to be serially correlation over time, the dependent variable is
> binary, prevalences of 0 and 1 were common, the very small number of
> tadpoles sampled at each visit, and these are not panel data (ie different
> tadpoles were sampled at each visit).
>
> I have done some exploratory modelling treating prevalence as a continuous
> dependent variable (using -regress-) after declaring the data to be
> time-series data (with sequential visit number rather than day number as the
> time variable, using -tsset-). With a null model, tests for serial
> correlation (Durbin-Watson test (-estat dwatson-), Durbin’s alternative (h)
> test (-estat durbinalt-),Breush-Godfrey test ( -estat bgodfrey,lag(6)-),
> Portmaneau (Q) test (-wntestq-) and the autocorrelogram (-ac-)(all from Baum
> 2006) indicate serial correlation. In contrast, after fitting month as a
> fixed effect, these tests do not support rejecting the null hypothesis that
> no serial correlation exists. However treating prevalence (a proportion) as
> a continuous dependent variable (using -regress-) is inappropriate.
>
> Any suggestions on approaches to answer the research question would be much
> appreciated.
>
> Many thanks for any help.
>
> John
>
> ***************************************************************
> Dr John Morton BVSc (Hons) PhD MACVSc (Veterinary Epidemiology)
> Veterinary Epidemiological Consultant
> Jemora Pty Ltd
> PO Box 2277
> Geelong 3220
> Victoria Australia
> Ph:  +61 (0)3 52 982 082
> Mob: 0407 092 558
> Email: john.morton@optusnet.com.au
> ***************************************************************
>
>
>
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