Table 5.

Key model parameters for latent growth models of child anxiety symptoms (parameters excluded are the variances for the intercept, slope, and observed child anxiety symptoms at 18, 27, and 54 months

Effect	b	95% HPD	Posterior SD	Prior distribution
Maternal model (n = 500)
αintercept	1.503	[1.262, 1.751]	0.125	norm(1.6, 0.277)
ψintercept	1.599	[1.279, 1.931]	0.167	gamma(1, 0.5)
αslope	0.027	[0.011, 0.043]	0.008	norm(1.6, 0.277)
ψslope	0.006	[0.005, 0.007]	0.000	gamma(1, 0.5)
Cov(i, s)	−0.038	[ − 0.053, −0.022]	0.008	beta(1, 1)
Inheritedinfluencei	0.003	[ − 0.012, 0.018]	0.008	norm(0.1, 200)
Inheritedinfluences	0.000	[ − 0.001, 0.001]	0.001	norm(0.1, 200)
RPAi	0.049	[0.011, 0.087]	0.019	norm(0.1, 200)
RPAS	0.001	[ − 0.001, 0.004]	0.001	norm(0.1, 200)
Paternal model (n = 489)
αintercept	1.482	[1.254, 1.727]	0.120	norm(1.6, 0.277)
ψintercept	1.614	[1.303, 1.952]	0.167	gamma(1, 0.5)
αslope	0.030	[0.015, 0.045]	0.008	norm(1.6, 0.277)
ψslope	0.006	[0.005, 0.007]	0.000	gamma(1, 0.5)
Cov(i, s)	−0.038	[ − 0.053, −0.023]	0.008	beta(1, 1)
Inheritedinfluencei	0.002	[ − 0.013, 0.018]	0.008	norm(0.1, 200)
Inheritedinfluenees	0.000	[ − 0.001, 0.001]	0.001	norm(0.1, 200)
RPAi	0.076	[0.030, 0.117]	0.022	norm(0.1, 200)
RPAs	0.000	[ − 0.002, 0.003]	0.001	norm(0.1, 200)
Maternal-paternal difference
RPAi difference	−0.027	[ − 0.084, 0.031]	0.029
RPAs difference	0.001	[ − 0.003, 0.005]	0.002

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. Baseline (intercept) child anxiety symptoms are predicted from inherited influence (Inherited influence_i) and rearing parent anxiety (RPA_i). The rate of change of child anxiety symptoms is predicted from inherited influence (Inherited influence_s) and rearing parent anxiety (RPA_s). 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 $\sqrt{(1}/\text{value})$.