Probing for Group Differences in same and other Gender Attitudes
For each analysis, an ANCOVA is conducting covarying age in order to remain consistent with our preregistered analyses. For each new predictor variable (e.g., “parent education level”), two separate ANCOVAs are conducted; first with same gender attitudes as the outcome, then with other gender attitudes as the outcome.
Parent Level Predictors
Parent Education Level
Regression for Same Gender Attitudes
Estimates for the Same Gender model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
3.4497
|
0.4162
|
8.2885
|
0.0000
|
ageAtTest
|
0.1419
|
0.0724
|
1.9599
|
0.0505
|
C_Sex_comb1
|
0.2104
|
0.0808
|
2.6029
|
0.0095
|
P_Edu_comb1
|
0.4375
|
0.6087
|
0.7187
|
0.4726
|
P_Edu_comb2
|
0.3874
|
0.2270
|
1.7069
|
0.0884
|
P_Edu_comb3
|
0.1744
|
0.1177
|
1.4818
|
0.1390
|
P_Edu_comb4
|
0.1194
|
0.0693
|
1.7234
|
0.0854
|
P_Edu_comb5
|
0.0656
|
0.0474
|
1.3824
|
0.1674
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.0199
|
0.1291
|
-0.1541
|
0.8776
|
C_Sex_comb1:P_Edu_comb3
|
-0.0523
|
0.0601
|
-0.8706
|
0.3843
|
C_Sex_comb1:P_Edu_comb4
|
-0.0433
|
0.0289
|
-1.4985
|
0.1346
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Same Gender model
|
2.5 %
|
97.5 %
|
(Intercept)
|
2.6321
|
4.2672
|
ageAtTest
|
-0.0003
|
0.2840
|
C_Sex_comb1
|
0.0516
|
0.3692
|
P_Edu_comb1
|
-0.7583
|
1.6333
|
P_Edu_comb2
|
-0.0584
|
0.8333
|
P_Edu_comb3
|
-0.0568
|
0.4055
|
P_Edu_comb4
|
-0.0167
|
0.2555
|
P_Edu_comb5
|
-0.0276
|
0.1588
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.2735
|
0.2337
|
C_Sex_comb1:P_Edu_comb3
|
-0.1702
|
0.0657
|
C_Sex_comb1:P_Edu_comb4
|
-0.1000
|
0.0135
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
Summary for the Same Gender model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0286
|
0.0108
|
1.2175
|
1.6038
|
0.1019
|
10
|
-892.793
|
1809.59
|
1861.44
|
807.851
|
545
|
556
|
Regression for Other Gender Attitudes
Estimates for the Other Gender model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
4.1688
|
0.5085
|
8.1974
|
0.0000
|
ageAtTest
|
-0.0465
|
0.0885
|
-0.5255
|
0.5995
|
C_Sex_comb1
|
-0.2263
|
0.1001
|
-2.2620
|
0.0241
|
P_Edu_comb1
|
0.4375
|
0.7382
|
0.5927
|
0.5537
|
P_Edu_comb2
|
0.0055
|
0.2763
|
0.0199
|
0.9841
|
P_Edu_comb3
|
-0.0174
|
0.1432
|
-0.1217
|
0.9032
|
P_Edu_comb4
|
-0.0127
|
0.0843
|
-0.1511
|
0.8800
|
P_Edu_comb5
|
-0.0388
|
0.0577
|
-0.6719
|
0.5020
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
0.1455
|
0.1585
|
0.9178
|
0.3591
|
C_Sex_comb1:P_Edu_comb3
|
-0.0215
|
0.0737
|
-0.2914
|
0.7708
|
C_Sex_comb1:P_Edu_comb4
|
-0.0216
|
0.0358
|
-0.6037
|
0.5463
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Other Gender model
|
2.5 %
|
97.5 %
|
(Intercept)
|
3.1697
|
5.1678
|
ageAtTest
|
-0.2204
|
0.1274
|
C_Sex_comb1
|
-0.4229
|
-0.0298
|
P_Edu_comb1
|
-1.0126
|
1.8876
|
P_Edu_comb2
|
-0.5374
|
0.5484
|
P_Edu_comb3
|
-0.2986
|
0.2638
|
P_Edu_comb4
|
-0.1782
|
0.1528
|
P_Edu_comb5
|
-0.1521
|
0.0746
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.1659
|
0.4568
|
C_Sex_comb1:P_Edu_comb3
|
-0.1663
|
0.1233
|
C_Sex_comb1:P_Edu_comb4
|
-0.0918
|
0.0487
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
Summary for the Other Gender model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0355
|
0.0171
|
1.4763
|
1.9265
|
0.0395
|
10
|
-961.993
|
1947.99
|
1999.37
|
1142.1
|
524
|
535
|
Regression for Seating
Estimates for the Seating model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
-0.8503
|
0.5072
|
-1.6765
|
0.0942
|
ageAtTest
|
0.2705
|
0.0887
|
3.0477
|
0.0024
|
C_Sex_comb1
|
0.0636
|
0.1001
|
0.6358
|
0.5252
|
P_Edu_comb1
|
-1.0833
|
0.7354
|
-1.4730
|
0.1413
|
P_Edu_comb2
|
-0.2079
|
0.2765
|
-0.7517
|
0.4526
|
P_Edu_comb3
|
0.0242
|
0.1426
|
0.1699
|
0.8651
|
P_Edu_comb4
|
-0.0183
|
0.0838
|
-0.2178
|
0.8277
|
P_Edu_comb5
|
0.0077
|
0.0575
|
0.1337
|
0.8937
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.0436
|
0.1579
|
-0.2764
|
0.7824
|
C_Sex_comb1:P_Edu_comb3
|
0.0897
|
0.0733
|
1.2235
|
0.2217
|
C_Sex_comb1:P_Edu_comb4
|
0.0342
|
0.0360
|
0.9496
|
0.3428
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Seating model
|
2.5 %
|
97.5 %
|
(Intercept)
|
-1.8467
|
0.1461
|
ageAtTest
|
0.0961
|
0.4448
|
C_Sex_comb1
|
-0.1330
|
0.2603
|
P_Edu_comb1
|
-2.5282
|
0.3615
|
P_Edu_comb2
|
-0.7511
|
0.3354
|
P_Edu_comb3
|
-0.2558
|
0.3043
|
P_Edu_comb4
|
-0.1830
|
0.1465
|
P_Edu_comb5
|
-0.1052
|
0.1206
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.3538
|
0.2665
|
C_Sex_comb1:P_Edu_comb3
|
-0.0543
|
0.2338
|
C_Sex_comb1:P_Edu_comb4
|
-0.0365
|
0.1048
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
Summary for the Seating model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0418
|
0.0232
|
1.4709
|
2.2542
|
0.014
|
10
|
-947.376
|
1918.75
|
1969.98
|
1118.52
|
517
|
528
|
Regression for Resource Allocation
Estimates for the Resource Allocation model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
1.4910
|
0.3321
|
4.4897
|
0.0000
|
ageAtTest
|
0.1265
|
0.0578
|
2.1889
|
0.0290
|
C_Sex_comb1
|
0.2946
|
0.0642
|
4.5877
|
0.0000
|
P_Edu_comb1
|
-0.2500
|
0.4873
|
-0.5131
|
0.6081
|
P_Edu_comb2
|
-0.1917
|
0.1818
|
-1.0542
|
0.2923
|
P_Edu_comb3
|
-0.1315
|
0.0939
|
-1.4004
|
0.1620
|
P_Edu_comb4
|
-0.0707
|
0.0553
|
-1.2775
|
0.2020
|
P_Edu_comb5
|
-0.0366
|
0.0378
|
-0.9688
|
0.3331
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
0.0325
|
0.1021
|
0.3180
|
0.7506
|
C_Sex_comb1:P_Edu_comb3
|
0.0127
|
0.0475
|
0.2663
|
0.7901
|
C_Sex_comb1:P_Edu_comb4
|
0.0082
|
0.0232
|
0.3549
|
0.7228
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Resource Allocation model
|
2.5 %
|
97.5 %
|
(Intercept)
|
0.8387
|
2.1434
|
ageAtTest
|
0.0130
|
0.2400
|
C_Sex_comb1
|
0.1685
|
0.4207
|
P_Edu_comb1
|
-1.2071
|
0.7071
|
P_Edu_comb2
|
-0.5488
|
0.1655
|
P_Edu_comb3
|
-0.3159
|
0.0529
|
P_Edu_comb4
|
-0.1793
|
0.0380
|
P_Edu_comb5
|
-0.1108
|
0.0376
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.1682
|
0.2331
|
C_Sex_comb1:P_Edu_comb3
|
-0.0807
|
0.1060
|
C_Sex_comb1:P_Edu_comb4
|
-0.0374
|
0.0539
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
Summary for the Resource Allocation model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.1061
|
0.0896
|
0.9745
|
6.4335
|
0
|
10
|
-764.841
|
1553.68
|
1605.47
|
514.725
|
542
|
553
|
Regression for Sticker Choice
Estimates for the Sticker Choice model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
3.9278
|
0.4390
|
8.9480
|
0.0000
|
ageAtTest
|
0.0189
|
0.0763
|
0.2481
|
0.8041
|
C_Sex_comb1
|
0.7915
|
0.0846
|
9.3553
|
0.0000
|
P_Edu_comb1
|
0.0000
|
0.6487
|
0.0000
|
1.0000
|
P_Edu_comb2
|
-0.0871
|
0.2418
|
-0.3604
|
0.7187
|
P_Edu_comb3
|
-0.1091
|
0.1247
|
-0.8752
|
0.3818
|
P_Edu_comb4
|
-0.0264
|
0.0735
|
-0.3595
|
0.7194
|
P_Edu_comb5
|
-0.0324
|
0.0502
|
-0.6464
|
0.5183
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
0.0150
|
0.1355
|
0.1110
|
0.9117
|
C_Sex_comb1:P_Edu_comb3
|
0.0465
|
0.0625
|
0.7450
|
0.4566
|
C_Sex_comb1:P_Edu_comb4
|
0.0007
|
0.0304
|
0.0219
|
0.9825
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Sticker Choice model
|
2.5 %
|
97.5 %
|
(Intercept)
|
3.0656
|
4.7900
|
ageAtTest
|
-0.1309
|
0.1688
|
C_Sex_comb1
|
0.6253
|
0.9577
|
P_Edu_comb1
|
-1.2742
|
1.2742
|
P_Edu_comb2
|
-0.5620
|
0.3877
|
P_Edu_comb3
|
-0.3539
|
0.1358
|
P_Edu_comb4
|
-0.1707
|
0.1179
|
P_Edu_comb5
|
-0.1309
|
0.0661
|
C_Sex_comb1:P_Edu_comb1
|
NA
|
NA
|
C_Sex_comb1:P_Edu_comb2
|
-0.2511
|
0.2812
|
C_Sex_comb1:P_Edu_comb3
|
-0.0762
|
0.1692
|
C_Sex_comb1:P_Edu_comb4
|
-0.0591
|
0.0604
|
C_Sex_comb1:P_Edu_comb5
|
NA
|
NA
|
Summary for the Sticker Choice model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.2856
|
0.2728
|
1.2974
|
22.3465
|
0
|
10
|
-951.652
|
1927.3
|
1979.45
|
940.95
|
559
|
570
|
Parent Income Level
Regression for Same Gender Attitudes
Estimates for the Same Gender model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
3.8294
|
0.3584
|
10.6853
|
0.0000
|
ageAtTest
|
0.1355
|
0.0715
|
1.8941
|
0.0587
|
C_Sex_comb1
|
0.1448
|
0.0641
|
2.2588
|
0.0243
|
P_Income1_comb1
|
-0.0490
|
0.1275
|
-0.3848
|
0.7006
|
P_Income1_comb2
|
0.0643
|
0.0658
|
0.9764
|
0.3293
|
P_Income1_comb3
|
0.0141
|
0.0340
|
0.4144
|
0.6787
|
P_Income1_comb4
|
0.0181
|
0.0224
|
0.8105
|
0.4180
|
C_Sex_comb1:P_Income1_comb1
|
-0.0737
|
0.1275
|
-0.5777
|
0.5637
|
C_Sex_comb1:P_Income1_comb2
|
-0.0261
|
0.0657
|
-0.3976
|
0.6911
|
C_Sex_comb1:P_Income1_comb3
|
0.0027
|
0.0339
|
0.0798
|
0.9364
|
C_Sex_comb1:P_Income1_comb4
|
-0.0013
|
0.0223
|
-0.0582
|
0.9536
|
Confidence intervals for the Same Gender model
|
2.5 %
|
97.5 %
|
(Intercept)
|
3.1254
|
4.5334
|
ageAtTest
|
-0.0050
|
0.2760
|
C_Sex_comb1
|
0.0189
|
0.2707
|
P_Income1_comb1
|
-0.2994
|
0.2014
|
P_Income1_comb2
|
-0.0650
|
0.1936
|
P_Income1_comb3
|
-0.0526
|
0.0808
|
P_Income1_comb4
|
-0.0258
|
0.0621
|
C_Sex_comb1:P_Income1_comb1
|
-0.3242
|
0.1769
|
C_Sex_comb1:P_Income1_comb2
|
-0.1552
|
0.1029
|
C_Sex_comb1:P_Income1_comb3
|
-0.0640
|
0.0694
|
C_Sex_comb1:P_Income1_comb4
|
-0.0451
|
0.0425
|
Summary for the Same Gender model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0242
|
0.0062
|
1.2001
|
1.342
|
0.2046
|
10
|
-876.78
|
1777.56
|
1829.3
|
777.717
|
540
|
551
|
Regression for Other Gender Attitudes
Estimates for the Other Gender model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
4.0984
|
0.4374
|
9.3691
|
0.0000
|
ageAtTest
|
-0.0213
|
0.0872
|
-0.2445
|
0.8070
|
C_Sex_comb1
|
-0.1824
|
0.0781
|
-2.3364
|
0.0198
|
P_Income1_comb1
|
-0.0629
|
0.1534
|
-0.4104
|
0.6817
|
P_Income1_comb2
|
-0.0197
|
0.0811
|
-0.2429
|
0.8082
|
P_Income1_comb3
|
-0.0513
|
0.0415
|
-1.2368
|
0.2167
|
P_Income1_comb4
|
-0.0704
|
0.0275
|
-2.5618
|
0.0107
|
C_Sex_comb1:P_Income1_comb1
|
-0.1797
|
0.1535
|
-1.1709
|
0.2422
|
C_Sex_comb1:P_Income1_comb2
|
-0.0220
|
0.0808
|
-0.2727
|
0.7852
|
C_Sex_comb1:P_Income1_comb3
|
-0.0728
|
0.0415
|
-1.7542
|
0.0800
|
C_Sex_comb1:P_Income1_comb4
|
-0.0094
|
0.0274
|
-0.3414
|
0.7329
|
Confidence intervals for the Other Gender model
|
2.5 %
|
97.5 %
|
(Intercept)
|
3.2390
|
4.9578
|
ageAtTest
|
-0.1926
|
0.1500
|
C_Sex_comb1
|
-0.3358
|
-0.0290
|
P_Income1_comb1
|
-0.3643
|
0.2384
|
P_Income1_comb2
|
-0.1789
|
0.1395
|
P_Income1_comb3
|
-0.1328
|
0.0302
|
P_Income1_comb4
|
-0.1244
|
-0.0164
|
C_Sex_comb1:P_Income1_comb1
|
-0.4812
|
0.1218
|
C_Sex_comb1:P_Income1_comb2
|
-0.1809
|
0.1368
|
C_Sex_comb1:P_Income1_comb3
|
-0.1542
|
0.0087
|
C_Sex_comb1:P_Income1_comb4
|
-0.0632
|
0.0445
|
Summary for the Other Gender model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0468
|
0.0284
|
1.4519
|
2.5516
|
0.0052
|
10
|
-945.901
|
1915.8
|
1967.1
|
1096.19
|
520
|
531
|
Regression for Seating
Estimates for the Seating model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
-0.9726
|
0.4448
|
-2.1865
|
0.0292
|
ageAtTest
|
0.2783
|
0.0885
|
3.1441
|
0.0018
|
C_Sex_comb1
|
0.1623
|
0.0796
|
2.0392
|
0.0419
|
P_Income1_comb1
|
-0.1003
|
0.1572
|
-0.6378
|
0.5239
|
P_Income1_comb2
|
-0.0010
|
0.0822
|
-0.0120
|
0.9904
|
P_Income1_comb3
|
0.0822
|
0.0422
|
1.9471
|
0.0521
|
P_Income1_comb4
|
0.0128
|
0.0280
|
0.4581
|
0.6471
|
C_Sex_comb1:P_Income1_comb1
|
-0.1647
|
0.1574
|
-1.0465
|
0.2958
|
C_Sex_comb1:P_Income1_comb2
|
-0.1068
|
0.0820
|
-1.3021
|
0.1935
|
C_Sex_comb1:P_Income1_comb3
|
-0.0479
|
0.0422
|
-1.1348
|
0.2570
|
C_Sex_comb1:P_Income1_comb4
|
0.0047
|
0.0279
|
0.1695
|
0.8655
|
Confidence intervals for the Seating model
|
2.5 %
|
97.5 %
|
(Intercept)
|
-1.8465
|
-0.0987
|
ageAtTest
|
0.1044
|
0.4523
|
C_Sex_comb1
|
0.0059
|
0.3187
|
P_Income1_comb1
|
-0.4091
|
0.2086
|
P_Income1_comb2
|
-0.1624
|
0.1604
|
P_Income1_comb3
|
-0.0007
|
0.1651
|
P_Income1_comb4
|
-0.0422
|
0.0678
|
C_Sex_comb1:P_Income1_comb1
|
-0.4739
|
0.1445
|
C_Sex_comb1:P_Income1_comb2
|
-0.2679
|
0.0543
|
C_Sex_comb1:P_Income1_comb3
|
-0.1308
|
0.0350
|
C_Sex_comb1:P_Income1_comb4
|
-0.0501
|
0.0596
|
Summary for the Seating model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0418
|
0.0231
|
1.4647
|
2.2388
|
0.0147
|
10
|
-937.963
|
1899.93
|
1951.06
|
1100.62
|
513
|
524
|
Regression for Resource Allocation
Estimates for the Resource Allocation model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
1.2215
|
0.2911
|
4.1959
|
0.0000
|
ageAtTest
|
0.1371
|
0.0580
|
2.3620
|
0.0185
|
C_Sex_comb1
|
0.3595
|
0.0516
|
6.9731
|
0.0000
|
P_Income1_comb1
|
-0.1351
|
0.1013
|
-1.3330
|
0.1831
|
P_Income1_comb2
|
-0.0337
|
0.0533
|
-0.6312
|
0.5282
|
P_Income1_comb3
|
0.0080
|
0.0275
|
0.2921
|
0.7703
|
P_Income1_comb4
|
-0.0120
|
0.0182
|
-0.6590
|
0.5101
|
C_Sex_comb1:P_Income1_comb1
|
-0.1024
|
0.1014
|
-1.0093
|
0.3133
|
C_Sex_comb1:P_Income1_comb2
|
0.0230
|
0.0532
|
0.4317
|
0.6661
|
C_Sex_comb1:P_Income1_comb3
|
-0.0152
|
0.0274
|
-0.5529
|
0.5806
|
C_Sex_comb1:P_Income1_comb4
|
-0.0273
|
0.0182
|
-1.5054
|
0.1328
|
Confidence intervals for the Resource Allocation model
|
2.5 %
|
97.5 %
|
(Intercept)
|
0.6496
|
1.7934
|
ageAtTest
|
0.0231
|
0.2511
|
C_Sex_comb1
|
0.2582
|
0.4607
|
P_Income1_comb1
|
-0.3342
|
0.0640
|
P_Income1_comb2
|
-0.1384
|
0.0711
|
P_Income1_comb3
|
-0.0459
|
0.0620
|
P_Income1_comb4
|
-0.0478
|
0.0238
|
C_Sex_comb1:P_Income1_comb1
|
-0.3017
|
0.0969
|
C_Sex_comb1:P_Income1_comb2
|
-0.0815
|
0.1275
|
C_Sex_comb1:P_Income1_comb3
|
-0.0691
|
0.0387
|
C_Sex_comb1:P_Income1_comb4
|
-0.0630
|
0.0083
|
Summary for the Resource Allocation model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.1152
|
0.0988
|
0.9769
|
7.0072
|
0
|
10
|
-760.622
|
1545.24
|
1596.94
|
513.453
|
538
|
549
|
Regression for Sticker Choice
Estimates for the Sticker Choice model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
3.6340
|
0.3805
|
9.5508
|
0.0000
|
ageAtTest
|
0.0454
|
0.0762
|
0.5959
|
0.5515
|
C_Sex_comb1
|
0.8114
|
0.0672
|
12.0825
|
0.0000
|
P_Income1_comb1
|
0.1473
|
0.1326
|
1.1111
|
0.2670
|
P_Income1_comb2
|
0.0109
|
0.0692
|
0.1575
|
0.8749
|
P_Income1_comb3
|
0.0312
|
0.0358
|
0.8709
|
0.3842
|
P_Income1_comb4
|
-0.0094
|
0.0237
|
-0.3985
|
0.6904
|
C_Sex_comb1:P_Income1_comb1
|
-0.2521
|
0.1327
|
-1.9003
|
0.0579
|
C_Sex_comb1:P_Income1_comb2
|
0.0671
|
0.0690
|
0.9728
|
0.3311
|
C_Sex_comb1:P_Income1_comb3
|
0.0279
|
0.0357
|
0.7811
|
0.4351
|
C_Sex_comb1:P_Income1_comb4
|
-0.0005
|
0.0236
|
-0.0200
|
0.9841
|
Confidence intervals for the Sticker Choice model
|
2.5 %
|
97.5 %
|
(Intercept)
|
2.8866
|
4.3814
|
ageAtTest
|
-0.1042
|
0.1950
|
C_Sex_comb1
|
0.6795
|
0.9433
|
P_Income1_comb1
|
-0.1131
|
0.4077
|
P_Income1_comb2
|
-0.1250
|
0.1468
|
P_Income1_comb3
|
-0.0391
|
0.1014
|
P_Income1_comb4
|
-0.0560
|
0.0371
|
C_Sex_comb1:P_Income1_comb1
|
-0.5127
|
0.0085
|
C_Sex_comb1:P_Income1_comb2
|
-0.0684
|
0.2027
|
C_Sex_comb1:P_Income1_comb3
|
-0.0423
|
0.0981
|
C_Sex_comb1:P_Income1_comb4
|
-0.0468
|
0.0459
|
Summary for the Sticker Choice model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.2991
|
0.2864
|
1.2905
|
23.721
|
0
|
10
|
-943.603
|
1911.21
|
1963.29
|
926.013
|
556
|
567
|
Parent Political Orientation
Regression for Same Gender Attitudes
Estimates for the Same Gender model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
3.5257
|
0.3787
|
9.3112
|
0.0000
|
ageAtTest
|
0.1782
|
0.0728
|
2.4468
|
0.0147
|
C_Sex_comb1
|
0.0646
|
0.1178
|
0.5482
|
0.5838
|
P_Politcal_comb
|
0.0412
|
0.0360
|
1.1432
|
0.2535
|
C_Sex_comb1:P_Politcal_comb
|
0.0257
|
0.0361
|
0.7138
|
0.4757
|
Confidence intervals for the Same Gender model
|
2.5 %
|
97.5 %
|
(Intercept)
|
2.7818
|
4.2696
|
ageAtTest
|
0.0351
|
0.3214
|
C_Sex_comb1
|
-0.1668
|
0.2959
|
P_Politcal_comb
|
-0.0296
|
0.1120
|
C_Sex_comb1:P_Politcal_comb
|
-0.0451
|
0.0966
|
Summary for the Same Gender model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0278
|
0.0203
|
1.213
|
3.6879
|
0.0057
|
4
|
-837.341
|
1686.68
|
1712.22
|
759.176
|
516
|
521
|
Regression for Other Gender Attitudes
Estimates for the Other Gender model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
4.0703
|
0.4621
|
8.8091
|
0.0000
|
ageAtTest
|
-0.0298
|
0.0889
|
-0.3350
|
0.7377
|
C_Sex_comb1
|
-0.2048
|
0.1445
|
-1.4173
|
0.1570
|
P_Politcal_comb
|
-0.0135
|
0.0443
|
-0.3047
|
0.7607
|
C_Sex_comb1:P_Politcal_comb
|
-0.0038
|
0.0443
|
-0.0853
|
0.9321
|
Confidence intervals for the Other Gender model
|
2.5 %
|
97.5 %
|
(Intercept)
|
3.1625
|
4.9782
|
ageAtTest
|
-0.2044
|
0.1448
|
C_Sex_comb1
|
-0.4887
|
0.0791
|
P_Politcal_comb
|
-0.1005
|
0.0735
|
C_Sex_comb1:P_Politcal_comb
|
-0.0908
|
0.0832
|
Summary for the Other Gender model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0216
|
0.0138
|
1.4679
|
2.7491
|
0.0277
|
4
|
-902.481
|
1816.96
|
1842.27
|
1070.91
|
497
|
502
|
Regression for Seating
Estimates for the Seating model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
-0.7540
|
0.4576
|
-1.6476
|
0.1001
|
ageAtTest
|
0.2916
|
0.0880
|
3.3147
|
0.0010
|
C_Sex_comb1
|
0.2558
|
0.1427
|
1.7922
|
0.0737
|
P_Politcal_comb
|
-0.0701
|
0.0436
|
-1.6055
|
0.1090
|
C_Sex_comb1:P_Politcal_comb
|
-0.0382
|
0.0437
|
-0.8759
|
0.3815
|
Confidence intervals for the Seating model
|
2.5 %
|
97.5 %
|
(Intercept)
|
-1.6531
|
0.1452
|
ageAtTest
|
0.1188
|
0.4645
|
C_Sex_comb1
|
-0.0246
|
0.5363
|
P_Politcal_comb
|
-0.1558
|
0.0157
|
C_Sex_comb1:P_Politcal_comb
|
-0.1240
|
0.0475
|
Summary for the Seating model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.036
|
0.0282
|
1.4454
|
4.5923
|
0.0012
|
4
|
-885.793
|
1783.59
|
1808.84
|
1027.9
|
492
|
497
|
Regression for Resource Allocation
Estimates for the Resource Allocation model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
1.2046
|
0.3036
|
3.9672
|
0.0001
|
ageAtTest
|
0.1407
|
0.0584
|
2.4101
|
0.0163
|
C_Sex_comb1
|
0.3078
|
0.0941
|
3.2728
|
0.0011
|
P_Politcal_comb
|
-0.0020
|
0.0287
|
-0.0693
|
0.9448
|
C_Sex_comb1:P_Politcal_comb
|
-0.0020
|
0.0287
|
-0.0713
|
0.9432
|
Confidence intervals for the Resource Allocation model
|
2.5 %
|
97.5 %
|
(Intercept)
|
0.6081
|
1.8012
|
ageAtTest
|
0.0260
|
0.2554
|
C_Sex_comb1
|
0.1230
|
0.4926
|
P_Politcal_comb
|
-0.0584
|
0.0544
|
C_Sex_comb1:P_Politcal_comb
|
-0.0584
|
0.0543
|
Summary for the Resource Allocation model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.0955
|
0.0885
|
0.9762
|
13.5733
|
0
|
4
|
-721.436
|
1454.87
|
1480.38
|
489.863
|
514
|
519
|
Regression for Sticker Choice
Estimates for the Sticker Choice model
term
|
estimate
|
std.error
|
statistic
|
p.value
|
(Intercept)
|
3.7242
|
0.3975
|
9.3683
|
0.0000
|
ageAtTest
|
0.0111
|
0.0768
|
0.1452
|
0.8846
|
C_Sex_comb1
|
0.9129
|
0.1228
|
7.4367
|
0.0000
|
P_Politcal_comb
|
0.0413
|
0.0376
|
1.0981
|
0.2727
|
C_Sex_comb1:P_Politcal_comb
|
-0.0319
|
0.0377
|
-0.8475
|
0.3971
|
Confidence intervals for the Sticker Choice model
|
2.5 %
|
97.5 %
|
(Intercept)
|
2.9432
|
4.5051
|
ageAtTest
|
-0.1397
|
0.1620
|
C_Sex_comb1
|
0.6718
|
1.1541
|
P_Politcal_comb
|
-0.0326
|
0.1153
|
C_Sex_comb1:P_Politcal_comb
|
-0.1059
|
0.0421
|
Summary for the Sticker Choice model
r.squared
|
adj.r.squared
|
sigma
|
statistic
|
p.value
|
df
|
logLik
|
AIC
|
BIC
|
deviance
|
df.residual
|
nobs
|
0.2891
|
0.2837
|
1.2939
|
53.8817
|
0
|
4
|
-894.48
|
1800.96
|
1826.65
|
887.35
|
530
|
535
|
Zipcode Level Predictors
GINI Index (representing wealth inequality by zipcode)
Regression for Same Gender Attitudes
Estimates for the Same Gender model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
3.5556
|
0.6289
|
5.6536
|
138.0264
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
0.1533
|
0.0719
|
2.1329
|
551.4398
|
0.0334
|
fixed
|
NA
|
C_Sex_comb1
|
0.4385
|
0.4707
|
0.9317
|
554.4957
|
0.3519
|
fixed
|
NA
|
gini_index
|
0.4377
|
1.0855
|
0.4033
|
50.4273
|
0.6885
|
fixed
|
NA
|
C_Sex_comb1:gini_index
|
-0.7127
|
1.0572
|
-0.6741
|
554.6702
|
0.5005
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).gini_index
|
NaN
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__gini_index
|
0.2131
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.2102
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Same Gender model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
2.0943
|
.sig02
|
-1.0000
|
0.3705
|
.sig03
|
0.0000
|
4.3208
|
.sigma
|
1.1183
|
1.2822
|
(Intercept)
|
2.2293
|
4.8001
|
ageAtTest
|
0.0170
|
0.2835
|
C_Sex_comb1
|
-0.4393
|
1.3063
|
gini_index
|
-1.7248
|
2.6974
|
C_Sex_comb1:gini_index
|
-2.6153
|
1.2983
|
Summary for the Same Gender model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.2102
|
-904.48
|
1826.96
|
1865.91
|
1808.96
|
551
|
Regression for Other Gender Attitudes
Estimates for the Other Gender model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
3.9331
|
0.7724
|
5.0919
|
182.368
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
-0.0568
|
0.0870
|
-0.6520
|
530.595
|
0.5147
|
fixed
|
NA
|
C_Sex_comb1
|
1.0291
|
0.5754
|
1.7885
|
478.515
|
0.0743
|
fixed
|
NA
|
gini_index
|
0.5264
|
1.3423
|
0.3922
|
83.240
|
0.6959
|
fixed
|
NA
|
C_Sex_comb1:gini_index
|
-2.8689
|
1.2912
|
-2.2219
|
480.170
|
0.0268
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
1.3700
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).gini_index
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__gini_index
|
2.6868
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.4405
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Other Gender model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
3.3603
|
.sig02
|
-1.0000
|
0.0000
|
.sig03
|
0.0000
|
7.1407
|
.sigma
|
1.3270
|
1.5275
|
(Intercept)
|
2.2738
|
5.4109
|
ageAtTest
|
-0.2138
|
0.1173
|
C_Sex_comb1
|
-0.1590
|
2.1534
|
gini_index
|
-2.0414
|
3.0193
|
C_Sex_comb1:gini_index
|
-5.3492
|
-0.3321
|
Summary for the Other Gender model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.4405
|
-967.949
|
1953.9
|
1992.51
|
1935.9
|
530
|
## SIMPLE SLOPES ANALYSIS
##
## Slope of gini_index when C_Sex_comb = female:
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -2.20 1.93 -1.14 0.26
##
## Slope of gini_index when C_Sex_comb = male:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 3.60 1.83 1.97 0.05
Regression for Seating
Estimates for the Seating model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
-0.7375
|
0.7627
|
-0.9669
|
528
|
0.3340
|
fixed
|
NA
|
ageAtTest
|
0.2833
|
0.0881
|
3.2160
|
528
|
0.0014
|
fixed
|
NA
|
C_Sex_comb1
|
-0.0053
|
0.5804
|
-0.0092
|
528
|
0.9927
|
fixed
|
NA
|
gini_index
|
-0.4769
|
1.3099
|
-0.3641
|
528
|
0.7159
|
fixed
|
NA
|
C_Sex_comb1:gini_index
|
0.2944
|
1.3044
|
0.2257
|
528
|
0.8215
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).gini_index
|
NaN
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__gini_index
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.4680
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Seating model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
0.3980
|
.sig02
|
0.0000
|
1.0000
|
.sig03
|
0.0000
|
0.7209
|
.sigma
|
1.3701
|
1.5460
|
(Intercept)
|
-2.2316
|
0.7087
|
ageAtTest
|
0.1076
|
0.4618
|
C_Sex_comb1
|
-1.1372
|
1.0213
|
gini_index
|
-3.1358
|
2.2363
|
C_Sex_comb1:gini_index
|
-2.1238
|
2.8085
|
Summary for the Seating model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.468
|
-961.24
|
1940.48
|
1978.99
|
1922.48
|
524
|
Regression for Resource Allocation
Estimates for the Resource Allocation model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
1.3457
|
0.5015
|
2.6835
|
135.8339
|
0.0082
|
fixed
|
NA
|
ageAtTest
|
0.1273
|
0.0572
|
2.2248
|
548.7618
|
0.0265
|
fixed
|
NA
|
C_Sex_comb1
|
-0.2435
|
0.3754
|
-0.6487
|
549.7114
|
0.5168
|
fixed
|
NA
|
gini_index
|
-0.1835
|
0.8748
|
-0.2097
|
49.5118
|
0.8347
|
fixed
|
NA
|
C_Sex_comb1:gini_index
|
1.2753
|
0.8433
|
1.5122
|
549.9250
|
0.1310
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).gini_index
|
NaN
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__gini_index
|
0.2052
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
0.9661
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Resource Allocation model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
1.7215
|
.sig02
|
-1.0000
|
0.3479
|
.sig03
|
0.0000
|
3.6675
|
.sigma
|
0.8986
|
1.0194
|
(Intercept)
|
0.2370
|
2.3360
|
ageAtTest
|
0.0020
|
0.2449
|
C_Sex_comb1
|
-1.0072
|
0.4480
|
gini_index
|
-1.9395
|
1.8102
|
C_Sex_comb1:gini_index
|
-0.3055
|
2.9755
|
Summary for the Resource Allocation model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
0.9661
|
-775.989
|
1569.98
|
1608.88
|
1551.98
|
548
|
Regression for Sticker Choice
Estimates for the Sticker Choice model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
4.5900
|
0.6667
|
6.8846
|
166.1850
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
0.0065
|
0.0757
|
0.0859
|
568.5361
|
0.9315
|
fixed
|
NA
|
C_Sex_comb1
|
0.7687
|
0.4972
|
1.5461
|
569.1409
|
0.1226
|
fixed
|
NA
|
gini_index
|
-1.6696
|
1.1678
|
-1.4297
|
63.2188
|
0.1577
|
fixed
|
NA
|
C_Sex_comb1:gini_index
|
0.0737
|
1.1171
|
0.0660
|
569.2406
|
0.9474
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).gini_index
|
NaN
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__gini_index
|
0.3137
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.2870
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Sticker Choice model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
2.4885
|
.sig02
|
-1.0000
|
0.9092
|
.sig03
|
0.0000
|
5.3661
|
.sigma
|
1.1958
|
1.3630
|
(Intercept)
|
3.3581
|
5.9034
|
ageAtTest
|
-0.1504
|
0.1356
|
C_Sex_comb1
|
-0.2212
|
1.7654
|
gini_index
|
-4.0743
|
0.6035
|
C_Sex_comb1:gini_index
|
-2.2270
|
2.2360
|
Summary for the Sticker Choice model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.287
|
-966.94
|
1951.88
|
1991.08
|
1933.88
|
567
|
Females unemployed by zipcode
Regression for Same Gender Attitudes
Estimates for the Same Gender model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
3.8412
|
0.3981
|
9.6483
|
365.6376
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
0.1614
|
0.0731
|
2.2079
|
534.8681
|
0.0277
|
fixed
|
NA
|
C_Sex_comb1
|
0.1091
|
0.0981
|
1.1117
|
488.0882
|
0.2668
|
fixed
|
NA
|
unemp_all_f
|
-0.0004
|
0.0003
|
-1.2352
|
29.1496
|
0.2266
|
fixed
|
NA
|
C_Sex_comb1:unemp_all_f
|
0.0001
|
0.0002
|
0.3439
|
510.3028
|
0.7311
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
1.1437
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).unemp_all_f
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__unemp_all_f
|
0.0021
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.1524
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Same Gender model
|
2.5 %
|
97.5 %
|
.sig01
|
0.8283
|
1.4133
|
.sig02
|
-1.0000
|
-0.9580
|
.sig03
|
0.0014
|
0.0027
|
.sigma
|
1.0697
|
1.2280
|
(Intercept)
|
3.0233
|
4.6233
|
ageAtTest
|
0.0221
|
0.3109
|
C_Sex_comb1
|
-0.1018
|
0.3031
|
unemp_all_f
|
-0.0009
|
0.0002
|
C_Sex_comb1:unemp_all_f
|
-0.0003
|
0.0004
|
Summary for the Same Gender model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.1524
|
-937.062
|
1892.12
|
1931.08
|
1874.12
|
551
|
Regression for Other Gender Attitudes
Estimates for the Other Gender model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
4.0326
|
0.4757
|
8.4766
|
370.5799
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
-0.0364
|
0.0876
|
-0.4155
|
531.5034
|
0.6780
|
fixed
|
NA
|
C_Sex_comb1
|
-0.1830
|
0.1188
|
-1.5400
|
486.0075
|
0.1242
|
fixed
|
NA
|
unemp_all_f
|
0.0001
|
0.0003
|
0.2246
|
18.7012
|
0.8247
|
fixed
|
NA
|
C_Sex_comb1:unemp_all_f
|
-0.0001
|
0.0002
|
-0.3857
|
411.6042
|
0.6999
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
1.3583
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).unemp_all_f
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__unemp_all_f
|
0.0022
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.3632
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Other Gender model
|
2.5 %
|
97.5 %
|
.sig01
|
0.9297
|
1.6889
|
.sig02
|
-1.0000
|
-0.9477
|
.sig03
|
0.0013
|
0.0029
|
.sigma
|
1.2556
|
1.4474
|
(Intercept)
|
3.0354
|
5.0229
|
ageAtTest
|
-0.2322
|
0.1432
|
C_Sex_comb1
|
-0.4427
|
0.0509
|
unemp_all_f
|
-0.0007
|
0.0007
|
C_Sex_comb1:unemp_all_f
|
-0.0005
|
0.0004
|
Summary for the Other Gender model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.3632
|
-995.16
|
2008.32
|
2046.93
|
1990.32
|
530
|
Regression for Seating
Estimates for the Seating model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
-1.0368
|
0.4870
|
-2.1289
|
29.3935
|
0.0418
|
fixed
|
NA
|
ageAtTest
|
0.2656
|
0.0894
|
2.9693
|
311.2139
|
0.0032
|
fixed
|
NA
|
C_Sex_comb1
|
0.1060
|
0.1209
|
0.8763
|
144.7712
|
0.3823
|
fixed
|
NA
|
unemp_all_f
|
0.0003
|
0.0004
|
0.9602
|
0.5979
|
0.5848
|
fixed
|
NA
|
C_Sex_comb1:unemp_all_f
|
0.0000
|
0.0002
|
0.1565
|
54.0746
|
0.8762
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
1.3837
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).unemp_all_f
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__unemp_all_f
|
0.0023
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.3877
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Seating model
|
2.5 %
|
97.5 %
|
.sig01
|
0.9864
|
1.7212
|
.sig02
|
-1.0000
|
-0.9432
|
.sig03
|
0.0015
|
0.0030
|
.sigma
|
1.2705
|
1.4715
|
(Intercept)
|
-1.9067
|
0.0456
|
ageAtTest
|
0.0880
|
0.4322
|
C_Sex_comb1
|
-0.1483
|
0.3380
|
unemp_all_f
|
-0.0003
|
0.0010
|
C_Sex_comb1:unemp_all_f
|
-0.0004
|
0.0005
|
Summary for the Seating model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.3877
|
-993.007
|
2004.01
|
2042.52
|
1986.01
|
524
|
Regression for Resource Allocation
Estimates for the Resource Allocation model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
1.1893
|
0.3144
|
3.7828
|
362.703
|
0.0002
|
fixed
|
NA
|
ageAtTest
|
0.1333
|
0.0577
|
2.3111
|
544.948
|
0.0212
|
fixed
|
NA
|
C_Sex_comb1
|
0.2286
|
0.0781
|
2.9273
|
485.555
|
0.0036
|
fixed
|
NA
|
unemp_all_f
|
0.0001
|
0.0002
|
0.3526
|
19.189
|
0.7282
|
fixed
|
NA
|
C_Sex_comb1:unemp_all_f
|
0.0002
|
0.0001
|
1.3964
|
427.704
|
0.1633
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.9148
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).unemp_all_f
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__unemp_all_f
|
0.0014
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
0.9074
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Resource Allocation model
|
2.5 %
|
97.5 %
|
.sig01
|
0.6593
|
1.1356
|
.sig02
|
-1.0000
|
-0.9466
|
.sig03
|
0.0008
|
0.0019
|
.sigma
|
0.8367
|
0.9635
|
(Intercept)
|
0.5487
|
1.7394
|
ageAtTest
|
0.0249
|
0.2491
|
C_Sex_comb1
|
0.0622
|
0.3928
|
unemp_all_f
|
-0.0004
|
0.0005
|
C_Sex_comb1:unemp_all_f
|
-0.0001
|
0.0004
|
Summary for the Resource Allocation model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
0.9074
|
-803.889
|
1625.78
|
1664.68
|
1607.78
|
548
|
Regression for Sticker Choice
Estimates for the Sticker Choice model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
3.8926
|
0.4184
|
9.3027
|
330.830
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
0.0069
|
0.0763
|
0.0903
|
556.452
|
0.9281
|
fixed
|
NA
|
C_Sex_comb1
|
0.9240
|
0.1022
|
9.0434
|
500.518
|
0.0000
|
fixed
|
NA
|
unemp_all_f
|
-0.0001
|
0.0004
|
-0.2884
|
12.712
|
0.7777
|
fixed
|
NA
|
C_Sex_comb1:unemp_all_f
|
-0.0003
|
0.0002
|
-1.4145
|
564.249
|
0.1578
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
1.2121
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).unemp_all_f
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__unemp_all_f
|
0.0026
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.2225
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Sticker Choice model
|
2.5 %
|
97.5 %
|
.sig01
|
0.8608
|
1.5198
|
.sig02
|
-1.0000
|
-0.9630
|
.sig03
|
0.0018
|
0.0032
|
.sigma
|
1.1310
|
1.2944
|
(Intercept)
|
3.1057
|
4.7604
|
ageAtTest
|
-0.1491
|
0.1506
|
C_Sex_comb1
|
0.7104
|
1.1212
|
unemp_all_f
|
-0.0009
|
0.0006
|
C_Sex_comb1:unemp_all_f
|
-0.0006
|
0.0001
|
Summary for the Sticker Choice model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.2225
|
-996.639
|
2011.28
|
2050.48
|
1993.28
|
567
|
Percent college graduates by zipcode
Regression for Same Gender Attitudes
Estimates for the Same Gender model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
3.4516
|
0.4766
|
7.2425
|
555
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
0.1615
|
0.0714
|
2.2618
|
555
|
0.0241
|
fixed
|
NA
|
C_Sex_comb1
|
0.6722
|
0.3144
|
2.1378
|
555
|
0.0330
|
fixed
|
NA
|
all_col_grad
|
0.5692
|
0.7070
|
0.8052
|
555
|
0.4211
|
fixed
|
NA
|
C_Sex_comb1:all_col_grad
|
-1.2482
|
0.7087
|
-1.7611
|
555
|
0.0788
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).all_col_grad
|
NaN
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__all_col_grad
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.2102
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Same Gender model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
0.2898
|
.sig02
|
-1.0000
|
0.0000
|
.sig03
|
0.0000
|
0.6186
|
.sigma
|
1.1153
|
1.2882
|
(Intercept)
|
2.4227
|
4.3694
|
ageAtTest
|
0.0201
|
0.2967
|
C_Sex_comb1
|
0.0728
|
1.2744
|
all_col_grad
|
-0.8980
|
2.0921
|
C_Sex_comb1:all_col_grad
|
-2.6568
|
0.1127
|
Summary for the Same Gender model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.2102
|
-903.624
|
1825.25
|
1864.2
|
1807.25
|
551
|
Regression for Other Gender Attitudes
Estimates for the Other Gender model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
3.6599
|
0.5884
|
6.2203
|
321.828
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
-0.0478
|
0.0871
|
-0.5491
|
528.740
|
0.5831
|
fixed
|
NA
|
C_Sex_comb1
|
0.0260
|
0.3880
|
0.0671
|
442.000
|
0.9465
|
fixed
|
NA
|
all_col_grad
|
1.0501
|
0.8870
|
1.1839
|
139.509
|
0.2385
|
fixed
|
NA
|
C_Sex_comb1:all_col_grad
|
-0.5934
|
0.8742
|
-0.6787
|
488.945
|
0.4976
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.2765
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).all_col_grad
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__all_col_grad
|
0.2681
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.4542
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Other Gender model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
1.8756
|
.sig02
|
-1.0000
|
1.0000
|
.sig03
|
0.0000
|
4.0257
|
.sigma
|
1.3401
|
1.5319
|
(Intercept)
|
2.5235
|
4.9136
|
ageAtTest
|
-0.2207
|
0.1172
|
C_Sex_comb1
|
-0.8351
|
0.8170
|
all_col_grad
|
-0.5688
|
2.7096
|
C_Sex_comb1:all_col_grad
|
-2.4005
|
1.3438
|
Summary for the Other Gender model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.4542
|
-970.925
|
1959.85
|
1998.46
|
1941.85
|
530
|
Regression for Seating
Estimates for the Seating model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
-0.6736
|
0.5926
|
-1.1367
|
250.2755
|
0.2567
|
fixed
|
NA
|
ageAtTest
|
0.2931
|
0.0877
|
3.3407
|
526.4748
|
0.0009
|
fixed
|
NA
|
C_Sex_comb1
|
0.6124
|
0.3922
|
1.5613
|
303.3730
|
0.1195
|
fixed
|
NA
|
all_col_grad
|
-0.7561
|
0.8913
|
-0.8483
|
80.0781
|
0.3988
|
fixed
|
NA
|
C_Sex_comb1:all_col_grad
|
-1.1204
|
0.8829
|
-1.2690
|
343.4721
|
0.2053
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.7220
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).all_col_grad
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__all_col_grad
|
1.5488
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.4602
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Seating model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
2.1742
|
.sig02
|
-1.0000
|
0.0000
|
.sig03
|
0.0000
|
4.8050
|
.sigma
|
1.3477
|
1.5346
|
(Intercept)
|
-1.9344
|
0.4848
|
ageAtTest
|
0.1062
|
0.4669
|
C_Sex_comb1
|
-0.1689
|
1.3148
|
all_col_grad
|
-2.5407
|
1.0689
|
C_Sex_comb1:all_col_grad
|
-2.7287
|
0.5868
|
Summary for the Seating model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.4602
|
-960.952
|
1939.9
|
1978.41
|
1921.9
|
524
|
Regression for Resource Allocation
Estimates for the Resource Allocation model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
1.5558
|
0.3898
|
3.9915
|
268.466
|
0.0001
|
fixed
|
NA
|
ageAtTest
|
0.1208
|
0.0570
|
2.1196
|
549.975
|
0.0345
|
fixed
|
NA
|
C_Sex_comb1
|
0.0150
|
0.2548
|
0.0589
|
382.403
|
0.9530
|
fixed
|
NA
|
all_col_grad
|
-0.5841
|
0.5939
|
-0.9835
|
86.923
|
0.3281
|
fixed
|
NA
|
C_Sex_comb1:all_col_grad
|
0.6946
|
0.5736
|
1.2108
|
422.517
|
0.2266
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.7369
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).all_col_grad
|
-1.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__all_col_grad
|
1.6665
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
0.9628
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Resource Allocation model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
1.5757
|
.sig02
|
-1.0000
|
0.9998
|
.sig03
|
0.0000
|
3.6090
|
.sigma
|
0.8933
|
1.0164
|
(Intercept)
|
0.8533
|
2.3909
|
ageAtTest
|
0.0096
|
0.2243
|
C_Sex_comb1
|
-0.4848
|
0.5061
|
all_col_grad
|
-1.8765
|
0.5445
|
C_Sex_comb1:all_col_grad
|
-0.4223
|
1.8070
|
Summary for the Resource Allocation model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
0.9628
|
-776.667
|
1571.33
|
1610.24
|
1553.33
|
548
|
Regression for Sticker Choice
Estimates for the Sticker Choice model
effect
|
group
|
term
|
estimate
|
std.error
|
statistic
|
df
|
p.value
|
fixed
|
NA
|
(Intercept)
|
4.6014
|
0.4977
|
9.2462
|
471.999
|
0.0000
|
fixed
|
NA
|
ageAtTest
|
0.0036
|
0.0750
|
0.0483
|
565.946
|
0.9615
|
fixed
|
NA
|
C_Sex_comb1
|
0.0968
|
0.3258
|
0.2971
|
568.869
|
0.7665
|
fixed
|
NA
|
all_col_grad
|
-1.6625
|
0.7453
|
-2.2306
|
178.251
|
0.0270
|
fixed
|
NA
|
C_Sex_comb1:all_col_grad
|
1.5959
|
0.7363
|
2.1677
|
570.532
|
0.0306
|
ran_pars
|
Zipcode_comb
|
sd__(Intercept)
|
0.0000
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
cor__(Intercept).all_col_grad
|
NaN
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Zipcode_comb
|
sd__all_col_grad
|
0.2653
|
NA
|
NA
|
NA
|
NA
|
ran_pars
|
Residual
|
sd__Observation
|
1.2797
|
NA
|
NA
|
NA
|
NA
|
Confidence intervals for the Sticker Choice model
|
2.5 %
|
97.5 %
|
.sig01
|
0.0000
|
1.7110
|
.sig02
|
-1.0000
|
0.7567
|
.sig03
|
0.0000
|
3.7455
|
.sigma
|
1.1894
|
1.3450
|
(Intercept)
|
3.6869
|
5.6281
|
ageAtTest
|
-0.1478
|
0.1473
|
C_Sex_comb1
|
-0.4702
|
0.6982
|
all_col_grad
|
-3.1397
|
-0.2026
|
C_Sex_comb1:all_col_grad
|
0.1854
|
2.8319
|
Summary for the Sticker Choice model
sigma
|
logLik
|
AIC
|
BIC
|
REMLcrit
|
df.residual
|
1.2797
|
-963.677
|
1945.35
|
1984.56
|
1927.35
|
567
|