Table 3.
Parameter estimates and robust standard errors for multilevel regression models predicting self-esteem from 1989–2002.
| Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|
| Independent Variables | b (SE) | b (SE) | b (SE) | b (SE) |
| Intercept | 0.122*** (0.017) | 0.110*** (0.017) | 0.110*** (0.017) | −0.111*** (0.020) |
| Time-constant predictors | ||||
| Baseline Age | 0.002 (0.018) | −0.032* (0.016) | −0.057*** (0.016) | −0.002 (0.018) |
| Race | −0.015 (0.016) | −0.024 (0.015) | −0.014 (0.015) | −0.015 (0.014) |
| Time-varying predictors | ||||
| Time since baseline | 0.048** (0.017) | 0.048** (0.017) | 0.044** (0.017) | 0.057*** (0.017) |
| Functional status (lagged) | 0.028* (0.013) | |||
| Δ Functional status | 0.021* (0.010) | |||
| Financial strain (lagged) | −0.058*** (0.013) | |||
| Work status (lagged) | 0.133*** (0.033) | |||
| Δ Work status | 0.091** (0.030) | |||
| Interactions | ||||
| Age × Race | 0.005 (0.016) | −0.008 (0.015) | −0.005 (0.015) | 0.010 (0.019) |
| Age × Time | −0.059*** (0.011) | −0.053*** (0.012) | −0.051*** (0.011) | −0.045*** (0.012) |
| Race × Time | 0.004 (0.011) | 0.002 (0.011) | 0.006 (0.011) | 0.011 (0.011) |
| Age × Race × Time | −0.028* (0.012) | −0.025* (0.012) | −0.026* (0.012) | −0.022 (0.012) |
| Age × Functional status (lag) | −0.002 (0.013) | |||
| Race × Functional status (lag) | −0.001 (0.013) | |||
| Age × Δ Functional status | 0.006 (0.011) | |||
| Race × Δ Functional status | 0.007 (0.011) | |||
| Age × Financial strain (lag) | 0.016 (0.013) | |||
| Race × Financial strain (lag) | 0.015 (0.012) | |||
| Age × Work status (lag) | −0.043 (0.034) | |||
| Race × Work status (lag) | 0.068 * (0.034) | |||
| Age × Δ Work status | −0.027 (0.035) | |||
| Race × Δ Work status | 0.036 (0.029) | |||
| Random effects | ||||
| Variances (% explained) | ||||
| Intercept | 0.295*** (13.24) | 0.202*** (40.59) | 0.197*** (42.06) | 0.199*** (41.47) |
| Slope | 0.013** (0.00) | 0.010* (23.08) | 0.011* (15.38) | 0.010* (23.08) |
Notes: Models control for attrition and mortality status, self-esteem at baseline, gender, and education. Also, the inferences drawn from these models utilize the robust standard errors produced by hierarchical linear modeling because they are somewhat tolerant of violations to the assumption of normally distributed response variables (Hox, 2002). To compute the percentage of explained variance, we used as a benchmark variances from a model including only the intercept and time.
*
p ≤ .05
**
p ≤ .01
***
p ≤ .001