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Table 1 Details of different prediction models fitted to the master data predicting log weekly wage fixed and random effect parameters (to two significant figures) are reported together with overall residual variance of the model

From: A method for estimating wage, using standardised occupational classifications, for use in medical research in the place of self-reported income

Parameter Model 1: 2-level random intercepts (individuals nested in SOC minor groups) Model 2: 2-level random intercepts and age slopes (individuals nested in SOC minor groups) Model 3: 3-level random intercepts (individuals nested in SOC unit groups nested in SOC minor groups) Model 4: 3-level random intercepts and age slopes (individuals nested in SOC unit groups nested in SOC minor groups)
Fixed effects #     
  Age (increments of one year) 0.0064 0.0052 0.0063 0.005
  Sex (female reference) 0.31 0.33 0.26 0.27
  Intercept 5.00034 5.04 5.1 5.14
Random effects $     
Level - SOC minor     
   Intercept 0.16 0.26 0.13 0.14
  Slope (age)   0.00003   0.0
 Level - SOC unit     
  Intercept    0.05 0.1
  Slope (age)     0.00003
Residual (variance) 0.31 0.31 0.29 0.28
N (for all models) 251,537
  1. Fixed and random effect parameters (to two significant figures) are reported together with overall residual variance of the model.
  2. #Fixed effect parameters are reported on the log wage scale.
  3. $Random effects parameters are reported on the log wage scale and show the standard deviation of the estimated intercepts and slope coefficients at each level.