Table 2.

Parameter estimates for multilevel model.

Fixed Effects (intercept, slope)	Estimate	SE	t	p
Intercept	2.37	0.45	5.4	< 0.000
Dyadic Coping (${\overline{DC}}_j$)	0.59	0.12	5.0	< 0.000
Dyadic Coping (DCij −${\overline{DC}}_j$)	0.35	0.03	13.5	< 0.000
CONTROL VARIABLES
Gender	−0.04	0.01	−3.0	0.002
Age	−0.01	0.01	−5.6	< 0.000
Education	0.02	0.01	3.8	< 0.000
GDP	0.01	0.01	2.3	0.033
Random Effects ([co-]variances)	Slopes	Intercepts	p
NORTH AMERICA AND WEST EUROPE
Canada	0.54	2.25	0.000
Germany	0.43	2.37	0.000
Great Britain	0.29	2.37	0.000
Italy	0.25	2.54	0.000
Portugal	0.23	2.38	0.000
Spain	0.35	2.48	0.000
Switzerland	0.31	2.37	0.000
United States of America	0.36	2.31	0.000
EAST EUROPE
Bulgaria	0.78	1.76	0.000
Croatia	0.40	2.40	0.000
Greece	0.47	2.21	0.000
Hungary	0.44	2.46	0.000
Poland	0.43	2.38	0.000
Romania	0.56	2.17	0.000
Slovakia	0.55	2.23	0.000
FORMER SOVIET COUNTRIES
Estonia	0.38	2.41	0.000
Kazakhstan	0.25	2.47	0.000
Russia	0.37	2.35	0.000
ASIA
China	0.33	2.46	0.000
Hong Kong	0.56	2.13	0.000
India	0.14	2.42	0.001
Indonesia	0.30	2.50	0.000
Malaysia	0.25	2.49	0.001
Pakistan	0.25	2.45	0.000
South Korea	0.37	2.35	0.000
MIDDLE EAST
Iran	0.22	2.25	0.000
Israel	0.46	2.27	0.000
Saudi Arabia	0.45	1.96	0.000
Turkey	0.36	2.38	0.000
AFRICA
Ghana	0.16	2.55	0.013
Kenya	0.24	2.50	0.000
Nigeria	0.11	2.54	0.000
Uganda	0.26	2.41	0.000
MIDDLE AND SOUTH AMERICA
Brazil	0.27	2.56	0.000
Mexico	0.29	2.51	0.000

N = 7973; All p-values are two-tailed. GDP in $1000; ($\overline{DC}$_j) = nation-level mean of dyadic coping; (DC_ij − $\overline{DC}$_j) = difference between each individual's dyadic coping from their nation-level mean.