--- Kristian Karlson <[email protected]> wrote:
> The main point of the study is to delve into what the unobserved
> components in educational decisions consist of, i.e. describing the
> latent classes that account for unobserved heterogeneity with
> variables that indicate different forms of motivation.
Just for clarification: You have observed variables that are indicators
of a latent variable, and you are interested what part of the
unobserved heterogeneity can be captured by this latent variable?
Have you already found www.gllamm.org, which has the manual and lots of
example code?
> The main point is that the more complex a model (i.e. the more
> realistic model of the educational system), the better the
> identification of the unobserved heterogeneity influencing
> educational decisions net of respondent background characteristics
> (family background, gender, ability, etc.).
I am not sure I buy this point.
First, I always think of identification in terms of where the
information you are using in your estimation comes from: is the data,
the design, or your assumptions. Better identification for me means
that you are making better use of the observed data or the study
design, rather than assumptions. More complex models typically mean
that you are making more use of assumptions.
Second, controlling for variables is not an aim in it's own right, but
a means of controlling confounding variables. So, a clear (theoretical)
idea about which variables are confounders and which variables are
intervening variables is crucial. So controlling for background
characteristic does _not_ necessarily lead to better estimates, it
depends on the place of these variables in the causal chain.
-- Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room N515
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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