Table 3. Multi-level Modeling Analyses Predicting Daily Emotion Regulation Flexibility.

		Model 1				Model 2
		Age		Age2		Age			Age2		Cognitive functioning
Parameter	Intercept	γ(SE)	R2	γ(SE)	R2	γ(SE)	R2	BF	γ(SE)	R2	BF	γ(SE)	R2	BF
Categorical variability	4.14(.13)	-.007(.004)	.008	<-.001(<.001)	.003	-.009(.005)	.016	.47	<-.001(<.001)	.022	.73	-.028(.06)	<.001	.57
Temporal variability
Situation selection		-.008(.001)*	.109	<.001(<.001)	<.001	-.003(.002)	.009	.68	<.001(<.001)	.006	1.10	.024(.02)	.004	1.30
Situation modification	.911(.05)	-.006(.001)*	.062	<-.001(<.001)	<.001	-.002(.002)	.013	.80	<.001(<.001)	.006	1.40	-.013(.02)	.001	.98
Distraction	1.04(.05)	-.006(.001)*	.056	<.001(<.001)	<.001	-.002(<.001)	.010	.73	<.001(<.001)	.015	.86	.013(.02)	.001	.89
Detached reappraisal	1.13(.05)	-.007(.001)*	.068	<-.001(<.001)	<.001	-.004(.002)	.014	.66	<.001(<.001)	<.001	1.40	.034(.02)	.008	.94
Positive reappraisal	1.25(.05)	-.006(.001)*	.055	-.001(<.001)	.004	-.003(.002)	.007	.92	<-.001(<.001)	<.001	.77	.020(.02)	.002	1.10
Suppression	1.26(.05)	-.009(.001)*	.101	<-.001(<.001)	.003	-.003(.002)	.014	.89	<.001(<.001)	.006	.61	.023(.02)	.004	1.50
Repertoire	1.17(.05)	<-.001(.002)	<.001	<.001(<.001)*	.024	-.001(.002)	.003	.63	<-.001(<.001)	<.001	.96	-.011(.01)	.001	1.10

Note. γ(SE) = unstandardized fixed effect estimates with standard errors in parentheses. R² = semi-partial R² effect size. BF = Bayes factors. Cognitive functioning = fluid cognitive functioning latent score. We also controlled for the mean-level of outcome (i.e., strategy use or repertoire) when predicting temporal variability.

^*p < .05.