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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