Parameters

Regression Parameters
term coef Wald(0) df p-value
Cluster(1) <- 1 0,0000 10,7665 6 0,096
Cluster(2) <- 1 -0,4068
Cluster(3) <- 1 0,0575
Cluster(4) <- 1 -0,5456
Cluster(5) <- 1 -4,2824
Cluster(6) <- 1 0,0400
Cluster(7) <- 1 0,4644
Cluster(1) <- Agegroup_3r(1) 0,0000 24,7925 12 0,016
Cluster(2) <- Agegroup_3r(1) 0,0000
Cluster(3) <- Agegroup_3r(1) 0,0000
Cluster(4) <- Agegroup_3r(1) 0,0000
Cluster(5) <- Agegroup_3r(1) 0,0000
Cluster(6) <- Agegroup_3r(1) 0,0000
Cluster(7) <- Agegroup_3r(1) 0,0000
Cluster(1) <- Agegroup_3r(2) 0,0000
Cluster(2) <- Agegroup_3r(2) -0,0566
Cluster(3) <- Agegroup_3r(2) 0,1573
Cluster(4) <- Agegroup_3r(2) 0,4371
Cluster(5) <- Agegroup_3r(2) 0,3264
Cluster(6) <- Agegroup_3r(2) 0,3938
Cluster(7) <- Agegroup_3r(2) -0,2372
Cluster(1) <- Agegroup_3r(3) 0,0000
Cluster(2) <- Agegroup_3r(3) -0,1179
Cluster(3) <- Agegroup_3r(3) 0,4587
Cluster(4) <- Agegroup_3r(3) 0,2519
Cluster(5) <- Agegroup_3r(3) 1,3808
Cluster(6) <- Agegroup_3r(3) 0,4792
Cluster(7) <- Agegroup_3r(3) 0,5236
Cluster(1) <- GESLACHT(Man) 0,0000 29,9653 6 4,0e-5
Cluster(2) <- GESLACHT(Man) 0,0000
Cluster(3) <- GESLACHT(Man) 0,0000
Cluster(4) <- GESLACHT(Man) 0,0000
Cluster(5) <- GESLACHT(Man) 0,0000
Cluster(6) <- GESLACHT(Man) 0,0000
Cluster(7) <- GESLACHT(Man) 0,0000
Cluster(1) <- GESLACHT(Woman) 0,0000
Cluster(2) <- GESLACHT(Woman) -0,2743
Cluster(3) <- GESLACHT(Woman) 0,1769
Cluster(4) <- GESLACHT(Woman) -0,5362
Cluster(5) <- GESLACHT(Woman) 0,8538
Cluster(6) <- GESLACHT(Woman) -0,5163
Cluster(7) <- GESLACHT(Woman) -0,5678
Cluster(1) <- Education_level(Low education) 0,0000 147,7902 12 1,6e-25
Cluster(2) <- Education_level(Low education) 0,0000
Cluster(3) <- Education_level(Low education) 0,0000
Cluster(4) <- Education_level(Low education) 0,0000
Cluster(5) <- Education_level(Low education) 0,0000
Cluster(6) <- Education_level(Low education) 0,0000
Cluster(7) <- Education_level(Low education) 0,0000
Cluster(1) <- Education_level(Middle education) 0,0000
Cluster(2) <- Education_level(Middle education) 0,9578
Cluster(3) <- Education_level(Middle education) 0,9599
Cluster(4) <- Education_level(Middle education) 0,0930
Cluster(5) <- Education_level(Middle education) 0,5776
Cluster(6) <- Education_level(Middle education) 0,3633
Cluster(7) <- Education_level(Middle education) -0,4948
Cluster(1) <- Education_level(High education) 0,0000
Cluster(2) <- Education_level(High education) 2,0386
Cluster(3) <- Education_level(High education) 1,7041
Cluster(4) <- Education_level(High education) 1,0101
Cluster(5) <- Education_level(High education) 0,6613
Cluster(6) <- Education_level(High education) 2,0077
Cluster(7) <- Education_level(High education) -0,9243
Cluster(1) <- hh_3r_urbanity(High) 0,0000 19,6521 12 0,074
Cluster(2) <- hh_3r_urbanity(High) 0,0000
Cluster(3) <- hh_3r_urbanity(High) 0,0000
Cluster(4) <- hh_3r_urbanity(High) 0,0000
Cluster(5) <- hh_3r_urbanity(High) 0,0000
Cluster(6) <- hh_3r_urbanity(High) 0,0000
Cluster(7) <- hh_3r_urbanity(High) 0,0000
Cluster(1) <- hh_3r_urbanity(Medium) 0,0000
Cluster(2) <- hh_3r_urbanity(Medium) -0,1418
Cluster(3) <- hh_3r_urbanity(Medium) 0,0802
Cluster(4) <- hh_3r_urbanity(Medium) 0,0367
Cluster(5) <- hh_3r_urbanity(Medium) 0,4744
Cluster(6) <- hh_3r_urbanity(Medium) 0,1002
Cluster(7) <- hh_3r_urbanity(Medium) -1,2850
Cluster(1) <- hh_3r_urbanity(Low) 0,0000
Cluster(2) <- hh_3r_urbanity(Low) -0,4191
Cluster(3) <- hh_3r_urbanity(Low) -0,1412
Cluster(4) <- hh_3r_urbanity(Low) -0,0087
Cluster(5) <- hh_3r_urbanity(Low) -0,4940
Cluster(6) <- hh_3r_urbanity(Low) -0,4417
Cluster(7) <- hh_3r_urbanity(Low) 0,0431
Cluster(1) <- GEZINSCYCLUS(Single) 0,0000 25,4235 18 0,11
Cluster(2) <- GEZINSCYCLUS(Single) 0,0000
Cluster(3) <- GEZINSCYCLUS(Single) 0,0000
Cluster(4) <- GEZINSCYCLUS(Single) 0,0000
Cluster(5) <- GEZINSCYCLUS(Single) 0,0000
Cluster(6) <- GEZINSCYCLUS(Single) 0,0000
Cluster(7) <- GEZINSCYCLUS(Single) 0,0000
Cluster(1) <- GEZINSCYCLUS(Adult household) 0,0000
Cluster(2) <- GEZINSCYCLUS(Adult household) -0,2553
Cluster(3) <- GEZINSCYCLUS(Adult household) -0,3330
Cluster(4) <- GEZINSCYCLUS(Adult household) -0,2793
Cluster(5) <- GEZINSCYCLUS(Adult household) 0,9398
Cluster(6) <- GEZINSCYCLUS(Adult household) -0,8130
Cluster(7) <- GEZINSCYCLUS(Adult household) -0,1257
Cluster(1) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 0,0000
Cluster(2) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) -0,0415
Cluster(3) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 0,1259
Cluster(4) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 0,0705
Cluster(5) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 0,1861
Cluster(6) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) -0,3336
Cluster(7) <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 0,3454
Cluster(1) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) 0,0000
Cluster(2) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) -0,0226
Cluster(3) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) -0,0676
Cluster(4) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) -0,2672
Cluster(5) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) -0,3600
Cluster(6) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) -0,4019
Cluster(7) <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) 0,4909
Cluster(1) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000 19,0396 18 0,39
Cluster(2) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000
Cluster(3) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000
Cluster(4) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000
Cluster(5) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000
Cluster(6) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000
Cluster(7) <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000
Cluster(1) <- hhincome_4r(national benchmark income (€29,500~€43,500)) 0,0000
Cluster(2) <- hhincome_4r(national benchmark income (€29,500~€43,500)) 0,0318
Cluster(3) <- hhincome_4r(national benchmark income (€29,500~€43,500)) -0,4396
Cluster(4) <- hhincome_4r(national benchmark income (€29,500~€43,500)) -0,2514
Cluster(5) <- hhincome_4r(national benchmark income (€29,500~€43,500)) -0,0587
Cluster(6) <- hhincome_4r(national benchmark income (€29,500~€43,500)) -0,0418
Cluster(7) <- hhincome_4r(national benchmark income (€29,500~€43,500)) -0,0609
Cluster(1) <- hhincome_4r(above the national benchmark income (> €43,500)) 0,0000
Cluster(2) <- hhincome_4r(above the national benchmark income (> €43,500)) 0,3422
Cluster(3) <- hhincome_4r(above the national benchmark income (> €43,500)) 0,5312
Cluster(4) <- hhincome_4r(above the national benchmark income (> €43,500)) 0,3012
Cluster(5) <- hhincome_4r(above the national benchmark income (> €43,500)) -0,1582
Cluster(6) <- hhincome_4r(above the national benchmark income (> €43,500)) 0,3357
Cluster(7) <- hhincome_4r(above the national benchmark income (> €43,500)) 0,2340
Cluster(1) <- hhincome_4r(do not know/do not want to say) 0,0000
Cluster(2) <- hhincome_4r(do not know/do not want to say) 0,2005
Cluster(3) <- hhincome_4r(do not know/do not want to say) 0,3361
Cluster(4) <- hhincome_4r(do not know/do not want to say) 0,2353
Cluster(5) <- hhincome_4r(do not know/do not want to say) -0,6538
Cluster(6) <- hhincome_4r(do not know/do not want to say) 0,0570
Cluster(7) <- hhincome_4r(do not know/do not want to say) 0,2654
Cluster(1) <- Occupation_status(self-employed) 0,0000 172,9996 12 1,2e-30
Cluster(2) <- Occupation_status(self-employed) 0,0000
Cluster(3) <- Occupation_status(self-employed) 0,0000
Cluster(4) <- Occupation_status(self-employed) 0,0000
Cluster(5) <- Occupation_status(self-employed) 0,0000
Cluster(6) <- Occupation_status(self-employed) 0,0000
Cluster(7) <- Occupation_status(self-employed) 0,0000
Cluster(1) <- Occupation_status(paid job (govenment or non)) 0,0000
Cluster(2) <- Occupation_status(paid job (govenment or non)) -0,5365
Cluster(3) <- Occupation_status(paid job (govenment or non)) -1,9516
Cluster(4) <- Occupation_status(paid job (govenment or non)) -1,5521
Cluster(5) <- Occupation_status(paid job (govenment or non)) -0,9894
Cluster(6) <- Occupation_status(paid job (govenment or non)) -1,7074
Cluster(7) <- Occupation_status(paid job (govenment or non)) -2,2664
Cluster(1) <- Occupation_status(others) 0,0000
Cluster(2) <- Occupation_status(others) -2,2288
Cluster(3) <- Occupation_status(others) 0,3694
Cluster(4) <- Occupation_status(others) -0,5420
Cluster(5) <- Occupation_status(others) 4,8356
Cluster(6) <- Occupation_status(others) 0,2197
Cluster(7) <- Occupation_status(others) -0,2160
Cluster(1) <- Sector(Government) 0,0000 192,2973 30 1,4e-25
Cluster(2) <- Sector(Government) 0,0000
Cluster(3) <- Sector(Government) 0,0000
Cluster(4) <- Sector(Government) 0,0000
Cluster(5) <- Sector(Government) 0,0000
Cluster(6) <- Sector(Government) 0,0000
Cluster(7) <- Sector(Government) 0,0000
Cluster(1) <- Sector(Education) 0,0000
Cluster(2) <- Sector(Education) -0,8185
Cluster(3) <- Sector(Education) -2,2507
Cluster(4) <- Sector(Education) -0,1313
Cluster(5) <- Sector(Education) -0,5723
Cluster(6) <- Sector(Education) -2,4308
Cluster(7) <- Sector(Education) -0,8992
Cluster(1) <- Sector(Professional and Business Services) 0,0000
Cluster(2) <- Sector(Professional and Business Services) -0,5630
Cluster(3) <- Sector(Professional and Business Services) -0,5369
Cluster(4) <- Sector(Professional and Business Services) -0,7360
Cluster(5) <- Sector(Professional and Business Services) 0,4432
Cluster(6) <- Sector(Professional and Business Services) -1,5818
Cluster(7) <- Sector(Professional and Business Services) -0,1853
Cluster(1) <- Sector(Vital Sector) 0,0000
Cluster(2) <- Sector(Vital Sector) -1,4227
Cluster(3) <- Sector(Vital Sector) -2,2207
Cluster(4) <- Sector(Vital Sector) -0,3568
Cluster(5) <- Sector(Vital Sector) -0,1615
Cluster(6) <- Sector(Vital Sector) -1,8419
Cluster(7) <- Sector(Vital Sector) -0,1525
Cluster(1) <- Sector(Service Sector) 0,0000
Cluster(2) <- Sector(Service Sector) -2,1278
Cluster(3) <- Sector(Service Sector) -1,8152
Cluster(4) <- Sector(Service Sector) -1,2568
Cluster(5) <- Sector(Service Sector) 0,6593
Cluster(6) <- Sector(Service Sector) -3,3170
Cluster(7) <- Sector(Service Sector) 0,1559
Cluster(1) <- Sector(Others) 0,0000
Cluster(2) <- Sector(Others) -0,4707
Cluster(3) <- Sector(Others) -1,2222
Cluster(4) <- Sector(Others) -0,0132
Cluster(5) <- Sector(Others) 0,2508
Cluster(6) <- Sector(Others) -0,7058
Cluster(7) <- Sector(Others) 0,3951
SUM_SWLS <- 1 20,6342 537,3669 1 7,1e-119
SUM_SWLS <- Cluster(1) 0,0000 8,2718 6 0,22
SUM_SWLS <- Cluster(2) 0,0271
SUM_SWLS <- Cluster(3) -0,7900
SUM_SWLS <- Cluster(4) 0,4068
SUM_SWLS <- Cluster(5) -0,7738
SUM_SWLS <- Cluster(6) 0,8349
SUM_SWLS <- Cluster(7) -0,2152
SUM_SWLS <- Agegroup_3r(1) 0,0000 8,1734 2 0,017
SUM_SWLS <- Agegroup_3r(2) -0,0789
SUM_SWLS <- Agegroup_3r(3) 0,9356
SUM_SWLS <- GESLACHT(Man) 0,0000 0,5123 1 0,47
SUM_SWLS <- GESLACHT(Woman) 0,1677
SUM_SWLS <- Education_level(Low education) 0,0000 14,9674 2 0,00056
SUM_SWLS <- Education_level(Middle education) 0,9021
SUM_SWLS <- Education_level(High education) 1,5855
SUM_SWLS <- hh_3r_urbanity(High) 0,0000 1,6789 2 0,43
SUM_SWLS <- hh_3r_urbanity(Medium) -0,4021
SUM_SWLS <- hh_3r_urbanity(Low) 0,0116
SUM_SWLS <- GEZINSCYCLUS(Single) 0,0000 31,8491 3 5,6e-7
SUM_SWLS <- GEZINSCYCLUS(Adult household) 1,5752
SUM_SWLS <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 1,8424
SUM_SWLS <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) 1,8615
SUM_SWLS <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000 35,0190 3 1,2e-7
SUM_SWLS <- hhincome_4r(national benchmark income (€29,500~€43,500)) 1,7833
SUM_SWLS <- hhincome_4r(above the national benchmark income (> €43,500)) 2,7320
SUM_SWLS <- hhincome_4r(do not know/do not want to say) 2,4362
SUM_SWLS <- Occupation_status(self-employed) 0,0000 3,9620 2 0,14
SUM_SWLS <- Occupation_status(paid job (govenment or non)) -0,4884
SUM_SWLS <- Occupation_status(others) -1,8649
SUM_SWLS <- Sector(Government) 0,0000 5,4920 5 0,36
SUM_SWLS <- Sector(Education) 0,0498
SUM_SWLS <- Sector(Professional and Business Services) -0,0587
SUM_SWLS <- Sector(Vital Sector) 0,2568
SUM_SWLS <- Sector(Service Sector) -0,8554
SUM_SWLS <- Sector(Others) -0,2971
WFH_experience(Strongly disagree) <- 1 0,0000 474,8078 4 1,9e-101
WFH_experience(Disagree) <- 1 1,4586
WFH_experience(Neither disagree or agree) <- 1 4,1124
WFH_experience(Agree) <- 1 1,2067
WFH_experience(Strongly agree) <- 1 -2,5990
WFH_experience <- Cluster(1) 0,0000 190,6223 6 1,9e-38
WFH_experience <- Cluster(2) 3,4194
WFH_experience <- Cluster(3) 4,0395
WFH_experience <- Cluster(4) 1,4837
WFH_experience <- Cluster(5) 1,3143
WFH_experience <- Cluster(6) 3,8897
WFH_experience <- Cluster(7) 0,7920
WFH_experience <- Agegroup_3r(1) 0,0000 0,0246 2 0,99
WFH_experience <- Agegroup_3r(2) 0,0080
WFH_experience <- Agegroup_3r(3) 0,0223
WFH_experience <- GESLACHT(Man) 0,0000 0,2886 1 0,59
WFH_experience <- GESLACHT(Woman) 0,0457
WFH_experience <- Education_level(Low education) 0,0000 1,0819 2 0,58
WFH_experience <- Education_level(Middle education) 0,0585
WFH_experience <- Education_level(High education) 0,1358
WFH_experience <- hh_3r_urbanity(High) 0,0000 2,4086 2 0,30
WFH_experience <- hh_3r_urbanity(Medium) -0,1931
WFH_experience <- hh_3r_urbanity(Low) -0,0381
WFH_experience <- GEZINSCYCLUS(Single) 0,0000 8,5074 3 0,037
WFH_experience <- GEZINSCYCLUS(Adult household) -0,0729
WFH_experience <- GEZINSCYCLUS(Household with a youngest child with the age <= 12) 0,2422
WFH_experience <- GEZINSCYCLUS(Household with a youngest child with the age of 13 up to 17) 0,0624
WFH_experience <- hhincome_4r(below the national benchmark income(<€29,500)) 0,0000 8,0734 3 0,045
WFH_experience <- hhincome_4r(national benchmark income (€29,500~€43,500)) 0,4079
WFH_experience <- hhincome_4r(above the national benchmark income (> €43,500)) 0,4153
WFH_experience <- hhincome_4r(do not know/do not want to say) 0,2931
WFH_experience <- Occupation_status(self-employed) 0,0000 1,3825 2 0,50
WFH_experience <- Occupation_status(paid job (govenment or non)) -0,1873
WFH_experience <- Occupation_status(others) -0,1214
WFH_experience <- Sector(Government) 0,0000 5,3156 5 0,38
WFH_experience <- Sector(Education) -0,2543
WFH_experience <- Sector(Professional and Business Services) 0,0721
WFH_experience <- Sector(Vital Sector) -0,0227
WFH_experience <- Sector(Service Sector) -0,0409
WFH_experience <- Sector(Others) 0,0628
Variances
term coef Wald(0) df p-value
SUM_SWLS 24,9556 828,6861 1 3,1e-182