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Table 11 Simple and multiple regressions with demographic variables as predictors of AARC gains 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 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
  1. 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