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st: Logistic growth curve question

 From Dave Garson <[email protected]> To [email protected] Subject st: Logistic growth curve question Date Sun, 13 Apr 2014 17:07:10 -0400

For instructional purposes, I am trying to replicate the classic Pothoff & Roy linear growth model in SPSS, SAS, and Stata. While there are variations on this model, the version I am replicating has the following attributes and constraints:
```1. y1 through y4 are distance measurements at ages 8, 10, 12, and 14
```
2. ICEPT is the intercept latent variable, whose slopes are constrained to 1.0. It is modeled as a cause of y1 through y4. 3. SLOPE is the slope latent variable, whose paths are constrained to 8, 10, 12, and 14. It is also modeled as a cause of y1 through y4.
```4. Error variances are constrained equal.
5. Dependent means are constrained to 0.

```
I get the same results in SAS and SPSS but very different coefficients in Stata, with which I am less familiar. The estat framework postestimation command in Stata shows the five model aspects above are met, but coefficients differ a great deal. Below is my SAS code and my Stata code. My question is, what needs to be changed in the Stata code to give results consistent with SPSS, SAS, and the model described above?
```SAS SYNTAX:
PROC CALIS  DATA=in.royfm MODIFICATION EFFPART PLATCOV;
```
/* Above, MODIFICATION requests Lagrange multiplier modification indices */
```      /* Above, EFFPART requests a partition of total effects */
```
/* Above, PLATCOV requests latent variable covariances and score coefficients */
```PATH
/* Below, there are four time periods, y1 - y4 */
y1 y2 y3 y4 <--- ICEPT = 1.0 1.0 1.0 1.0,
y1 y2 y3 y4 <--- SLOPE = 8.0 10.0 12.0 14.0;
```
/*Above, intercept paths constrained to 1, slopes are costrained to linear growth */
```PVAR
y1       = variance1,
y2       = variance1,
y3       = variance1,
y4       = variance1,
ICEPT    = ivariance,
SLOPE    = svariance;
```
/* Above, time variables are constrained to have equal error variance */ /* Above, variances of ICEPT and SLOPE are freely estimated as ivariance and svariance */
```PCOV
ICEPT SLOPE   = cov1;
/* Above, ICEPT and SLOPE covariance is freely estimated as cov1 */
MEAN
y1       = 0,
y2       = 0,
y3       = 0,
y4       = 0,
ICEPT    = imean,
SLOPE    = smean;
/* Above, time variables constrained to a mean of 0 */
```
/* Above, means of ICEPT and SLOPE are freely estimated as imean and smean */
```RUN;

STATA SYNTAX
sem
(ICEPT -> y1@1 y2@1 y3@1 y4@1)
(SLOPE -> y1@8  y2@10  y3@12  y4@14),
latent(ICEPT SLOPE)
```
cov( e.y1@variance1 e.y2@variance1 e.y3@variance1 e.y4@variance1 SLOPE*ICEPT)
```means(ICEPT SLOPE)
noconstant

Advice appreciated on list or to [email protected]
Best to all,
Dave

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```