Table 1.
Growth curve models predicting the effect of children’s education on IADL among older adults, China Health and Retirement Longitudinal Study, 2011 to 2018.
| Variable | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Coef. | S.E. | Coef. | S.E. | |
| Child schooling years | -0.066*** | 0.011 | -0.071*** | 0.012 |
| Age (centered on the mean) | 0.199*** | 0.015 | 0.200*** | 0.015 |
| Child schooling*age | -0.004** | 0.001 | -0.004** | 0.001 |
| Education | -0.052*** | 0.012 | -0.053*** | 0.011 |
| Male (ref., Female) | -1.078*** | 0.080 | -0.971*** | 0.077 |
| Urban (ref., Rural) | -0.392*** | 0.107 | -0.305** | 0.103 |
| Married (ref., widowed/separated) | 0.025 | 0.076 | 0.003 | 0.076 |
| Number of living children | 0.076*** | 0.024 | 0.077*** | 0.023 |
| Logged household expenditure per capital | 0.088*** | 0.019 | 0.095*** | 0.019 |
| Random effects-variance component | ||||
| Level 1: within person | 4.553*** | 0.063 | 4.579*** | 0.063 |
| Level 2: in intercept | 4.121*** | 0.129 | 4.565*** | 0.139 |
| Level 2: in linear growth rate | 0.022*** | 0.002 | 0.018*** | 0.003 |
| Constant | 3.011*** | 0.222 | 3.004*** | 0.221 |
| Residual correlation between intercept and linear growth rate | 0.207*** | 0.013 | ||
| AIC | 81910.563 | 81572.874 | ||
| BIC | 82011.263 | 81681.320 | ||
| ICC | 0.470 | 0.470 | ||
| n of persons | 4272 | 4272 | ||
| n of person-year observations | 17,088 | 17,088 | ||
AIC = Akaike information criterion, BIC = Bayesian information criterion, the smaller the better; ref = reference; Coef.=coefficient; S.E.=standard error. p < 0.1 +, p < 0.05 *, p < 0.01 **, p < 0.001 *** (two tailed tests).