Hm...I worked the hole day on the model, tested different theoretical assumptions,
but...I don't understand the output with respect to the gini-var and the cross-level-interaction
EJC_gini. Am i right if i say that the overall effect of income inequality to the dependent variable is negative but not signifikant
-->no explanation through gini. And in regard to the c-l-i, the higher the inequality of a country, the more the extrinsic JC influence the
dependent job satisfaction. Is that what the model says?
Mixed-effects ML regression Number of obs = 17737
Group variable: laender Number of groups = 31
Obs per group: min = 299
avg = 572.2
max = 956
Wald chi2(10) = 9436.08
Log likelihood = -23805.018 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
JS | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
intrinsicJC | .369663 .0084099 43.96 0.000 .3531799 .3861462
extrinsicJC | .1572645 .0396179 3.97 0.000 .0796148 .2349141
relatedness | .5013823 .0102363 48.98 0.000 .4813195 .5214451
altersgrup~n | .0652755 .0065998 9.89 0.000 .0523401 .0782109
fullpart | -.054204 .0238984 -2.27 0.023 -.101044 -.007364
SEX | -.0420816 .014417 -2.92 0.004 -.0703384 -.0138249
Bildung | -.0297484 .0063537 -4.68 0.000 -.0422014 -.0172955
ISEI | -.0007213 .0005112 -1.41 0.158 -.0017232 .0002806
gini | -.0050667 .004624 -1.10 0.273 -.0141295 .0039962
EJC_gini | .0031164 .0010966 2.84 0.004 .0009671 .0052657
_cons | .7606644 .171826 4.43 0.000 .4238917 1.097437
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
laender: Identity |
var(_cons) | .0156366 .0044273 .0089771 .0272365
-----------------------------+------------------------------------------------
var(Residual) | .8539798 .0090763 .8363746 .8719556
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 211.44 Prob >= chibar2 = 0.0000
Hope someone can help,
Andy
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