Table 4.

Key model parameters for the unconditional latent growth model of child anxiety symptoms (parameters excluded are the variances for the observed child anxiety symptoms at 18, 27, and 54 months).

Effect	b	95% HPD	Posterior SD	Prior distribution
αintercept	1.707	[1.587, 1.831]	0.063	norm(1.6, 0.277)
ψintercept	1.434	[1.149, 1.743]	0.152	wish(iden, 3)
αslope	0.029	[0.021, 0.037]	0.004	norm(1.6, 0.277)
ψslope	0.006	[0.005, 0.007]	0.000	wish(iden, 3)
Cov(i, s)	−0.019	[ − 0.030, −0.008]	0.006	wish(iden, 3)

Note. α_intercept and ψ_intercept are the mean and variance of the intercept respectively. The corresponding terms for the slope are α_slope and ψ_slope. Cov(i, s) is the covariance between the intercept and slope. HPD = Highest Posterior Density interval. Prior distributions are represented by the functions used to create them. For example norm(x, y) represents a normal distribution with M = x, and a precision of y. The precision is used rather than the standard deviation to be consistent with the convention in JAGS (the engine behind the Bayesian estimation process). To convert the value to the standard deviation use $\sqrt{(1}/\text{value})$.