Table 4 -.

Latent Growth Model Fit in Males

Model	# Cl	# Params	LogLik	BIC	Entropy	Class     Percentages
Mother Reports (Age 7-12)
1. No Random Effects	1	4	-24172	48379
	2	7	-22520	45103	.66	60, 40
	3	10	-22169	44427	.61	46, 36, 18
	4	13	-22121	44359	.63	45, 29, 22, 04
	5	Failed to replicate likelihood
2. Random Intercept	1	5	-22189	44423
	2	Failed to converge
3. Random Intercept and Slope	1	6	-22189	44431
	2	Failed to replicate likelihood
Self-Reports (Age 14-18)
4. No Random Effects	1	4	-4502	9033
	2	7	-4317	8687	.60	67, 33
	3	10	-4287	8650	.50	43, 42, 15,
	4	13	-4283	8663	.42	35, 28, 21, 16
5. Random Intercept	1	5	-4291	8619
	2	Failed to replicate likelihood
6. Random Intercept and Slope	1	6	-4288	8622
	2	Failed to replicate likelihood
Piecewise Models (Age 7-18)
7. Random intercept	1	9	-29596	59273
	2	15	-29545	59224	.4	76, 24
8. Rater Mean Difference = 0*	1	8	-29612	59297

* Δχ2=25.4, df=1, p = 4.5E-8

Latent class growth models for maternal and self-ratings separately, and jointly using a piecewise model. Models were estimated with an increasing number of classes (Cl). Shown are the number of estimated parameters in the model, the final log likelihood value, the Bayesian information criterion (BIC), Entropy, and class proportions. Models with multiple classes did not have significantly lower BIC than models with a single class (bold). For the piecewise models, we selected a single class model because entropy was low indicating poor class assignment. Secondly, the BIC of the two class model fell within 1 SD of bootstrapped BIC for the single class model (BIC SD = 1142). Model 8 tested whether the second intercept was necessary to model the discontinuity between raters at ages 12 and 14. This was done by testing whether the mean of the second intercept could be set equal to 0. This model fit significantly worse than the unconstrained model, indicating a mean difference between raters.