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Table 12 Simple and multiple regressions with demographic variables as predictors of AARC losses scores on the AARC-10 SF

From: International relevance of two measures of awareness of age-related change (AARC)

(N = 8639)

Demographic variables as predictors of AARC losses: Simple regressions

Demographic variables as predictors of AARC losses: Multiple regression

 

AARC-10 SF losses

Variables

Coeff.

[95% CI]

p-value

Standardized Coeff.

Coeff.

[95% CI]

p-value

Standardized Coeff.

Age

.11

[.10, .12]

< .0001

.24

.09

[.08, .11]

< .001

.20

Sex

−.80

[−.96, −.64]

< .0001

−.10

−.60

[−.76, −.44]

< .001

−.08

Marital status

−.78

[−.95, −.61]

< .0001

−.10

−.52

[−.68, −.35]

< .001

−.06

Employment

−.96

[−1.1, −.82]

< .0001

−.15

−.11

[−.28, .05]

.18

−.02

University education

−.45

[−.60, −.29]

< .0001

−.06

−.35

[−.50, −.19]

< .001

−.05

Total R2

 

.07

Adjusted R2

 

.07

Model F-test

 

128.74 (5, 8633); p < .001

  1. Note: In the regression models we included only those participants that have no missing data. AARC-10 SF losses = Subscale of the AARC-10 SF assessing AARC losses. 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