Table 1.

Fixed Effects Parameter Estimates For Multilevel Model Predicting Day-To-Day Happiness for Full Sample

Parameter	Estimate	Standard Error	p	95% CI
Fixed effects
Intercept	6.79	.55	<.001	[5.71, 7.87]
Time	0.017	.027	.53	[−.036, .070]
Age	.0083	.0079	.29	[−.0073, .024]
Age2	−.00005	.00039	.89	[−.00082, .00071]
Gender	−.0054	0.28	.98	[−.56, .55]
Years of education	.12	.064	.073	[−.011, .24]
General health *	.63	.21	.004	[.203, 1.047]
Working *	−.61	.30	.043	[−1.20, −.018]
WP SOC score *	.35	.085	<.001	[.18, .52]
Two-way interaction effects
Age X WP SOC score *	0.0077	.0032	.0152	[.0015, .014]
Age2 X WP SOC score *	−.00038	.00017	.028	[−.00072, −.00004]
Covariance parameters
Intercept variance	2.94	.55	<.001
Intercept/slope variance	−.14	.071	.056
Slope variance	.023	.012	.031
Residual	1.96	.11	<.001

Note: WP = within person; SOC = selection, optimization, and compensation strategy usage. Age, Age², years of education and general health centered around grand mean.