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# st: How to assign class membership in latent growth curve (SEM)

 From Mark Ward To statalist@hsphsun2.harvard.edu Subject st: How to assign class membership in latent growth curve (SEM) Date Fri, 12 Apr 2013 21:54:14 +0100

```Dear Statalisters,

I am new to SEM (using 12.1) and relatively new to stata having made
the jump from SPSS some months ago.

I am attempting to develop a latent growth curve model (within the SEM
framework) in order to examine weight(Kg) trajectories for children. I
have 3 time points (birth, 9 months, and 3 years). For now I am using
a sub-set of my data with no covariates. My thinking here is to begin
with as simple a model as possible and gradually build on this.

I have built a model using a sub-set (n=200) of my data:

sem (Intercept@1 -> weightBirth) (Intercept@1 -> weight9Month)
(Intercept@1 -> WEIGHT3Y)(Slope@0 -> weightBirth) (Slope@1 ->
weight9Month) (Slope@3 -> WEIGHT3Y), covstruct(_lexogenous, diagonal)
latent(Intercept Slope ) cov( Intercept*Slope) nocapslatent

The output is
|                 OIM
|      Coef.   Std. Err.      z    P>|z|     [95%
Conf. Interval]
------------------+----------------------------------------------------------------
Measurement       |
weightBirth <-  |
Intercept |          1  (constrained)
_cons |   3.504104   .0968004    36.20   0.000
3.314379    3.693829
----------------+----------------------------------------------------------------
weight9Month <- |
Intercept |          1  (constrained)
Slope |          1  (constrained)
_cons |   9.329375   .3019236    30.90   0.000
8.737616    9.921134
----------------+----------------------------------------------------------------
WEIGHT3Y <-     |
Intercept |          1  (constrained)
Slope |          3  (constrained)
_cons |   28.97687   .6692132    43.30   0.000
27.66524    30.28851
------------------+----------------------------------------------------------------
Variance          |
e.weightBirth |   .3694334   .2133624
.119106    1.145879
e.weight9Month |   1.524365   .4826461
.8195595    2.835289
e.WEIGHT3Y |     3.0223   2.889594
.4639933    19.68628
Intercept |   .0803415   .2002642
.000607    10.63403
Slope |   1.680228    .490534
.9481221     2.97764
------------------+----------------------------------------------------------------
Covariance        |
Intercept       |
Slope |   .5453213   .1805284     3.02   0.003
.1914922    .8991504

A number of articles that have employed a similar approach, albeit
usually with Mplus, suggest there will normally be three classes
(stable, steady increase, and elevated). My question is how do I (1)
to the latent classes?

Any help would be greatly appreciated.

Best,
Mark

--
Mark Ward
PhD candidate
School of Social Work and Social Policy,
Trinity College Dublin
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```