TABLE 1.

Population values and simulation conditions for the multiple regression model.

Population values for simulation
Parameter	Population value
Means
X1	Fixed to 01
X2	Fixed to 0
Variances
X1	Fixed to 1
X2	Fixed to 1
Y Intercept	1
Y Resid. Var.	0.5
β1	1.0
β2	0.5
Simulation conditions (sample sizes crossed with prior conditions)
Sample sizes	Prior conditions2
n = 25	Informative:
n = 100	(1) β1 ∼ N(0.25, 0.05); β2 ∼ N(0.125, 0.05)
n = 1,000	(2) β1 ∼ N(0.50, 0.05); β2 ∼ N(0.250, 0.05)
	(3) β1 ∼ N(1.00, 0.05); β2 ∼ N(0.500, 0.05)
	(4) β1 ∼ N(2.00, 0.05); β2 ∼ N(1.000, 0.05)
	(5) β1 ∼ N(3.00, 0.05); β2 ∼ N(1.500, 0.05)
	Weakly Informative:
	(6) β1 ∼ N(0.25, 0.1); β2 ∼ N(0.125, 0.1)
	(7) β1 ∼ N(0.50, 0.1); β2 ∼ N(0.250, 0.1)
	(8) β1 ∼ N(1.00, 0.1); β2 ∼ N(0.500, 0.1)
	(9) β1 ∼ N(2.00, 0.1); β2 ∼ N(1.000, 0.1)
	(10) β1 ∼ N(3.00, 0.1); β2 ∼ N(1.500, 0.1)
	Diffuse
	(11) Regression 1 ∼ N(0, 1010); Slope ∼ N(0, 1010)

Y, the continuous outcome in the model. Resid. Var., residual variance. Predictors 1 and 2 (X₁ and X₂) were both continuous predictors. β₁ = Y on X_1. β₂ = Y on X_2.¹The means and variances for the predictors were fixed in the model in order to standardize the predictors. Therefore, estimates are only available for the four remaining parameters. ²The remaining priors in the model were default diffuse prior settings as implemented in Mplus.