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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