Table 11.

Simple and multiple regressions with demographic variables as predictors of AARC gains scores on the AARC-10 SF

(N = 8639)	Demographic variables as predictors of AARC gains: Simple regressions				Demographic variables as predictors of AARC gains: Multiple regression
	AARC-10 SF losses
Variables	Coeff.	[95% CI]	p-value	Standardized Coeff.	Coeff.	[95% CI]	p-value	Standardized Coeff.
Age	−.02	[−.04, −.01]	< .0001	−.05	−.03	[−.04, −.01]	< .001	−.05
Sex	1.40	[1.21, 1.60]	< .0001	.15	1.31	[1.11, 1.51]	< .001	.14
Marital status	−.39	[−.59, −.19]	< .0001	−.04	−.27	[−.48, −.06]	.01	−.03
Employment	.04	[−.12, .21]	.60	.01	−.23	[−.43, −.03]	.02	−.03
University education	−.25	[−.44, −.06]	.01	−.03	−.22	[−.41, −.03]	.02	−.02
Total R2					.03
Adjusted R2					.02
Model F-test					44.61 (5, 8633); p < .001

Note: In the regression models we included only those participants that have no missing data. AARC-10 SF gains = Subscale of the AARC-10 SF assessing AARC gains. Marital Status was operationalized as a dichotomous variable capturing whether the participant is married/ civil partnership/ co-habiting or widowed/ separated/ divorced/ single. Employment was operationalized as a dichotomous variable capturing whether the participant is working or not. University education was operationalized as a dichotomous variable. Standardized beta coefficients are calculated by subtracting the mean from the variable and dividing it by its standard deviation