Table 1.

Summary of Model Fit Statistics for Alcohol Use Frequency and Quantity

Frequency Models	χ2	df	CFI	RMSEA
Growth Curve Models
Intercept only	2016.0	42	.55	.20
Intercept and Linear	930.0	39	.80	.14
Intercept Linear and Quadratic	319.5	35	.94	.09
Knot Models
Intercept : Intercept	1288.2	39	.72	.17
Intercept : Intercept Linear	1356.1	39	.70	.17
Intercept : Intercept Linear Quadratic	1195.5	35	.74	.17
Intercept Linear : Intercept	704.7	39	.86	.12
Intercept Linear : Intercept Linear	258.0	35	.95	.08
Intercept Linear : Intercept Linear Quadratica	174.0	30	.97	.07
Intercept Linear Quadratic : Intercept	501.7	35	.89	.11
Intercept Linear Quadratic : Intercept Linear	134.9	30	.98	.06
Intercept Linear Quadratic : Intercept Linear Quadratic	71.2	24	.99	.04

Quantity Models	χ2	df	CFI	RMSEA
Growth Curve Models
Intercept only	1379.3	42	.68	.17
Intercept and Linear	299.0	39	.94	.08
Intercept Linear and Quadratic	219.6	35	.96	.07
Knot Models
Intercept : Intercept	1379.3	42	.68	.17
Intercept : Intercept Linear	303.5	39	.94	.08
Intercept : Intercept Linear Quadratic	127.8	35	.98	.05
Intercept Linear : Intercept	1001.6	39	.77	.15
Intercept Linear : Intercept Linear	224.0	35	.96	.07
Intercept Linear : Intercept Linear Quadratica	80.1	30	.99	.04
Intercept Linear Quadratic : Intercept	917.4	35	.79	.15
Intercept Linear Quadratic : Intercept Linear	203.8	30	.96	.07
Intercept Linear Quadratic : Intercept Linear Quadratic	67.2	24	.99	.04

Note. Models are denoted as “effect : effect” where the first effect indicates the trajectory of change prior to graduation, and the second effect indicates the trajectory of change after graduation. p<.001 for all models.

^aDenotes model selected as having the best fit to the data.