Table 2.

Mixed effects regression (Poisson) predicting number of services received and needed

	Services Received: Final Model				Services Needed: Final Model
	b	S.E.	p	eb	b	S.E.	p	eb
Intercept	0.58	0.12	< .001	1.78	−0.09	0.17	. 581	0.91
Time	−0.07	0.02	< .001	0.93	0.02	0.02	.272	1.02
Exited High School (HS)	0.07	0.15	0.649	1.07	0.31	0.12	.009	1.37
Has ID	0.56	0.11	< .001	1.75	0.10	0.11	.348	1.11
Mass.(vs. Wisc.)	0.27	0.07	< .001	1.31	−0.01	0.11	.890	0.99
Lives with Family	−0.11	0.07	.120	0.90	0.24	0.10	.022	1.27
Sex: Female	0.09	0.08	.231	1.09	−0.26	0.12	.037	0.77
Income: <$70,000	0.02	0.06	.771	1.02	0.21	0.09	.016	1.24
Time X Exited HS	0.03	0.02	.046	1.03	−0.05	0.02	.020	0.95
Exited HS X ID	−0.37	0.19	.047	0.69	---	---	---	---
Time X ID	0.04	0.02	.012	1.04	---	---	---	---
AIC	2763.12				2280.17
BIC	2818.15				2326.03
Log Likelihood	−1369.56				−1130.08
Number of Observations	725				725
Number of Groups (Participants)	204				204
Variance: Intercept	0.12 (0.34)				0.33 (0.57)

e^b The exponentiated regression coefficients are rate ratios, which are a type of effect size measure for Poisson regression. For example, prior to high school exit, the rate of service use by individuals without ID was e^(−0.07) = 0.93 times less each year; that is, there was a 1.00 – 0.93 = 7% decrease each year. Similarly, the rate of unmet service need was e^(0.31) = 1.37 times greater immediately after high school exit than just prior.