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Statistics for the current model
Deviance = 47049.462733
Number of estimated parameters = 6
This is followed by the results for the heterogeneous model.
Var(R) = σ2 and log(σ2) = α0 + α1(FEMALE)
![HLM中文教程](//l.b2b168.com/2019/07/18/10/201907181030459619184.jpg)
To fit models in HLM, 8 statistical applications are used: HLM2, which fits 2-level linear and nonlinear (HGLM) models; HLM3, which fits 3-level linear/nonlinear models; HLM4,
![HLM中文教程](//l.b2b168.com/2020/06/18/17/202006181725027765464.jpg)
allows the fitting of hierarchical models with dependent random effects (spatial design models).
has the ability to estimate an HLM from incomplete data in the form of a completely automated approach that generates and analyses multiply imputed data sets from incomplete data.
![HLM中文教程](//l.b2b168.com/2019/07/29/10/20190729103943513724.jpg)
Final estimation of fixed effects:
Fixed Effect Coefficient Standard
error
t-ratio Approx.
d.f. p-value
For INTRCPT1, β0
INTRCPT2, γ00 13.347288 0.261178 51.104 159 <0.001
For FEMALE slope, β1
INTRCPT2, γ10 -1.384269 0.186401 -7.426 159 <0.001
Final estimation of fixed effects
(with robust standard errors)
Fixed Effect Coefficient Standard
error
t-ratio Approx.
d.f. p-value
For INTRCPT1, β0
INTRCPT2, γ00 13.347288 0.261028 51.134 159 <0.001
For FEMALE slope, β1
INTRCPT2, γ10 -1.384269 0.185836 -7.449 159 <0.001
Final estimation of variance components
Random Effect Standard
Deviation
Variance
Component d.f. χ2 p-value
INTRCPT1, u0 2.91733 8.51079 122 561.99707 <0.001
FEMALE slope, u1 0.80941 0.65515 122 139.31550 0.135
Note: The chi-square statistics reported above are based on only 123 of 160
units that had sufficient data for computation. Fixed effects and variance
components are based on all the data.
Statistics for the current model
Deviance = 47042.277245
Number of estimated parameters = 7
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