Table 2.
Stepwise linear regression analyses to predict technology skills.
| M1 | M2 | M3 | ||||
|---|---|---|---|---|---|---|
| Predictors | b (SE) | Value of p | b (SE) | Value of p | b (SE) | Value of p |
| Chronological age | −0.040 (0.009) | 0.000 | −0.039 (0.009) | 0.000 | −0.027 (0.009) | 0.004 |
| Male (ref. female) | 0.507 (0.106) | 0.000 | 0.509 (0.106) | 0.000 | 0.543 (0.102) | 0.000 |
| Education level: high (ref. low) | 0.483 (0.134) | 0.000 | 0.505 (0.135) | 0.000 | 0.464 (0.131) | 0.000 |
| Health status: very good (ref. less good) | 0.823 (0.175) | 0.000 | 0.759 (0.182) | 0.000 | 0.480 (0.189) | 0.011 |
| Health status: good (ref. less good) | 0.566 (0.170) | 0.001 | 0.534 (0.171) | 0.002 | 0.377 (0.170) | 0.028 |
| SA | −0.691 (0.541) | 0.203 | −0.261 (0.532) | 0.624 | ||
| AARC-Gains | 0.079 (0.019) | 0.000 | ||||
| AARC-Losses | −0.076 (0.018) | 0.000 | ||||
| Model fit | F (5,363) = 17.73, p < 0.001 | F (6,362) = 15.07, p < 0.001 | F (8,360) = 15.25, p < 0.001 | |||
| Adjusted R2 | 0.185 | 0.187 | 0.246 | |||
The variable technology skills is calculated as a mean index of different specific technology skills (i.e., skills in using a laptop, smartphone, tablet, and the internet); SA is considered as proportional discrepancy score between felt age and chronological age: subjective age = [felt age − chronological age]/chronological age.