Can I use multilevel even if the LADs and TTWAs are overlapping - e.g.
London is one big TTWA? And I'm not quite sure how the LADs and TTWAs
overlap one another across the rest of UK, I can approximate the
overlapping using the All Field Postcode Directory, but that isn't
entirely accurate IMHO.
On 3/9/06, Clive Nicholas <[email protected]> wrote:
> Ada Ma wrote:
>
> >> > I have a bunch of wage observations and all the observations are
> >> > attached with two geographical identifiers - local authority districts
> >> > (LADs) and travel to work areas (TTWAs). I want to find out how wages
> >> > vary across different areas in UK.
> >> >
> >> > Now I can run wage estimations using either one of the two categorical
> >> > variables as explanatory variable. I would however like to find out
> >> > which categorical variable fits the data better. How do I compare the
> >> > two sets of results given that the explanatory variables are quite
> >> > different?
>
> If you follow Austin Nichols' advice on doing the preliminary analysis,
> and you find differences in wage variances at different spatial levels,
> why not try the multilevel approach via -gllamm- and fit wage models at
> both of your spatial-identifying levels simultaneously? Just a thought.
>
> CLIVE NICHOLAS |t: 0(044)7903 397793
> Politics |e: [email protected]
> Newcastle University |http://www.ncl.ac.uk/geps
>
> Whereever you go and whatever you do, just remember this. No matter how
> many like you, admire you, love you or adore you, the number of people
> turning up to your funeral will be largely determined by local weather
> conditions.
>
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--
Ada Ma
Research Fellow
Health Economics Research Unit
University of Aberdeen, UK.
http://www.abdn.ac.uk/heru/
Tel: +44 (0) 1224 553863
Fax: +44 (0) 1224 550926
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