Thanks to Stas for giving me the coding examples. Also to Nick for explaining the word for "equidistant in time".
The sample I will be working on has a large sample size. It has over 15,000 cases. Will it take long for gllamm to estimate the growth curve model.
Just to double check:
When I specify student-level weight like gen wt2 = [student level weight], suppose I have three waves (w1weight; w2weight; w3weight), when I list them in a hierarachical order in the data file, the [student level weight] is no more a constant but changes within each respondent, right?
The other thing is that when I specify school-level weight, gen wt3 = [school level weight], I should use the method suggested in Sophia's paper to construct the school-level weight, right?
Thanks,
Alice
> --- On Wed, 6/17/09, Nick Cox <[email protected]>
> wrote:
>
> > From: Nick Cox <[email protected]>
> > Subject: RE: st: growth curve model with weights
> > To: [email protected]
> > Date: Wednesday, June 17, 2009, 1:34 PM
> > "regular" carries the precise sense
> > of equally spaced.
> >
> > It also sometimes carries an informal sense of
> frequent,
> > regardless of
> > spacing.
> >
> > Stas asked: equidistant in time ... is there a proper
> long
> > English word
> > for that?
> >
> > Nick
> > [email protected]
> >
> >
> > Stas Kolenikov
> >
> > Look up chapter 3 of GLLAMM manual. Section 3.2
> describes
> > the general
> > framework for two-level random coefficient model, of
> which
> > the growth
> > model is a special case. You will have two latent
> factors
> > at student
> > level, one will be loading on the vector of 1's (the
> > intercept), and
> > the other one on time (the growth rate; the wave
> number in
> > the simple
> > case if they are equidistant in time... NJC, is there
> a
> > proper long
> > English word for that?).
> >
> > [...]
> >
> > *
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> >
>
>
>
>
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