. Author manuscript; available in PMC: 2015 Mar 1.

Published in final edited form as: Psychol Aging. 2014 Mar;29(1):57–71. doi: 10.1037/a0035042

Table 2.

Multilevel Longitudinal Models for Hassles and Uplifts Trajectories over Age

	Intensity
	Hassles (n = 1293)		Uplifts (n = 1306)

	B	SE	B	SE
Fixed effects
Constant	1.292^***	.022	1.648^***	.029
Age	−.007^*	.003	.010^**	.004
Age2	.0003^***	.0001	−.0002^†	.0001
Random effects
Slope variance	.009	.002	.011	.003
Intercept variance	.170	.025	.335	.028
Residual variances	.258	.004	.299	.005

LR Test vs. Linear Regression χ² (df)	χ² (3)= 498.32	Prob > χ²=0.000	χ² (3)=1130.02	Prob > χ²=0.000

	Exposure
	Hassles (n = 1313)		Uplifts (n = 1311)

	B	SE	B	SE
Fixed effects
Constant	2.678^***	.065	3.088^***	.044
Age	−.018^**	.007	.018^***	.005
Age2	−.0001	.0002	−.001^***	.0001
Random effects
Slope variance	.074	.003	.047	.002
Intercept variance	1.284	.049	.849	.033
Residual variance	-	-	-	-

LR Test vs. Linear Regression χ² (df)	χ² (3)= 18530.17	Prob > χ²=0.000	χ² (3)=11833.74	Prob > χ²=0.000

	Summary
	Hassles (n = 1313)		Uplifts (n = 1311)

	B	SE	B	SE
Fixed effects
Constant	26.300^***	1.063	46.637^***	1.759
Age	−.830^***	.125	.124	.205
Age2	.019^***	.004	−.019^**	.006
Random effects
Slope variance	.366	.104	.619	.159
Intercept variance	14.037	.858	23.536	1.471
Residual variance	10.175	.155	16.653	.257

LR Test vs. Linear Regression χ² (df)	χ² (3)= 1317.12	Prob > χ² =0.000	χ² (3)=1168.82	Prob > χ²=0.000

Note.

^†

p < .1,

p < .5,

^**

p < .01,

^***

p < .001