Level-1 Model CPij = β0j + β1j*(CQij) + rij
Level-2 Model β0j = γ00 + u0j
β1j = γ10 + γ11*(INDj) + u1j
CQ has been centered around the group mean.
Mixed Model CPij = γ00
+ γ10*CQij + γ11*INDj*CQij + u0j + u1j*CQij + rij
Final Results - Iteration 51Iterations stopped due to small change in likelihood function
σ2
= 0.37558
τ| INTRCPT1,β0 | 0.14415 | -0.10747 |
| CQ,β1 | -0.10747 | 0.17848 |
τ (as correlations)| INTRCPT1,β0 | 1.000 | -0.670 |
| CQ,β1 | -0.670 | 1.000 |
Random level-1 coefficient | Reliability estimate |
| INTRCPT1,β0 | 0.629 |
CQ,β1 | 0.352 |
The value of the log-likelihood function at iteration 51 = -2.467813E+002
Final estimation of fixed effects:
Fixed Effect | Coefficient | Standard
error | t-ratio | Approx.
d.f. | p-value |
For INTRCPT1, β0 |
| INTRCPT2, γ00 | 3.745728 | 0.067681 | 55.344 | 49 | <0.001 |
For CQ slope, β1 |
| INTRCPT2, γ10 | -0.514342 | 0.562072 | -0.915 | 48 | 0.365 |
IND, γ11 | 0.308760 | 0.154929 | 1.993 | 48 | 0.052 |
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 | 3.745728 | 0.067126 | 55.801 | 49 | <0.001 |
For CQ slope, β1 |
| INTRCPT2, γ10 | -0.514342 | 0.524630 | -0.980 | 48 | 0.332 |
IND, γ11 | 0.308760 | 0.141440 | 2.183 | 48 | 0.034 |
Final estimation of variance componentsRandom Effect | Standard
Deviation | Variance
Component | d.f. | χ2 | p-value |
INTRCPT1, u0 | 0.37967 | 0.14415 | 49 | 135.20402 | <0.001 |
CQ slope, u1 | 0.42246 | 0.17848 | 48 | 82.25021 | 0.002 |
level-1, r | 0.61284 | 0.37558 | | | |
Statistics for current covariance components model
这个结果请问证明调节作用有效吗？怎么根据结果画交互作用图？根据这个做法：http://www.jeremydawson.co.uk/slopes.htm
应该用哪个模型呢？用标准化变量还是非标准化变量呢？怎非标的话应该怎么看变量的系数
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