PCSI Gender Study Pilot Exploratory Analyses
Examining the Scales
Gender Development
Cisgender
##
## Cronbach's alpha for the 'pcsiData[, c("gender_cat_cis_1", "gender_label_cis_1", "gender_stereo_cis_1", ' ' "gender_identify_cis_1", "gender_disclose_cis_1")]' data-set
##
## Items: 5
## Sample units: 223
## alpha: 0.903
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.847 0.937
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = cisdevData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## gender_cat_cis_1 gender_label_cis_1 gender_stereo_cis_1
## 0.127 0.241 0.493
## gender_identify_cis_1 gender_disclose_cis_1
## 0.347 0.416
##
## Loadings:
## Factor1
## gender_cat_cis_1 0.935
## gender_label_cis_1 0.871
## gender_stereo_cis_1 0.712
## gender_identify_cis_1 0.808
## gender_disclose_cis_1 0.764
##
## Factor1
## SS loadings 3.376
## Proportion Var 0.675
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 13.55 on 5 degrees of freedom.
## The p-value is 0.0187Transgender
##
## Cronbach's alpha for the 'pcsiData[, c("gender_cat_trans_1", "gender_label_trans_1", "gender_stereo_trans_1", ' ' "gender_identify_tran_1", "gender_disclose_tran_1")]' data-set
##
## Items: 5
## Sample units: 223
## alpha: 0.891
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.852 0.919
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1##
## Call:
## factanal(x = transdevData, factors = 2, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## gender_cat_trans_1 gender_label_trans_1 gender_stereo_trans_1
## 0.214 0.296 0.214
## gender_identify_tran_1 gender_disclose_tran_1
## 0.005 0.534
##
## Loadings:
## Factor1 Factor2
## gender_cat_trans_1 0.815 0.349
## gender_label_trans_1 0.694 0.471
## gender_stereo_trans_1 0.806 0.369
## gender_identify_tran_1 0.376 0.924
## gender_disclose_tran_1 0.410 0.546
##
## Factor1 Factor2
## SS loadings 2.105 1.632
## Proportion Var 0.421 0.326
## Cumulative Var 0.421 0.747
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 8.73 on 1 degree of freedom.
## The p-value is 0.00312Autonomy
General Autonomy
##
## Cronbach's alpha for the 'pcsiData[, c("autonomy_gen_1", "autonomy_gen_2", "autonomy_gen_3", ' ' "autonomy_gen_4", "autonomy_gen_5", "autonomy_gen_6", "autonomy_gen_8", ' ' "autonomy_gen_9", "autonomy_gen_10")]' data-set
##
## Items: 9
## Sample units: 223
## alpha: 0.942
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.918 0.958
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1##
## Call:
## factanal(x = autongenData, factors = 2, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## autonomy_gen_1 autonomy_gen_2 autonomy_gen_3 autonomy_gen_4 autonomy_gen_5
## 0.191 0.238 0.269 0.306 0.463
## autonomy_gen_6 autonomy_gen_8 autonomy_gen_9 autonomy_gen_10
## 0.253 0.353 0.166 0.244
##
## Loadings:
## Factor1 Factor2
## autonomy_gen_1 0.344 0.831
## autonomy_gen_2 0.696 0.527
## autonomy_gen_3 0.667 0.535
## autonomy_gen_4 0.719 0.421
## autonomy_gen_5 0.319 0.660
## autonomy_gen_6 0.765 0.403
## autonomy_gen_8 0.618 0.515
## autonomy_gen_9 0.863 0.300
## autonomy_gen_10 0.802 0.336
##
## Factor1 Factor2
## SS loadings 4.021 2.496
## Proportion Var 0.447 0.277
## Cumulative Var 0.447 0.724
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 44.24 on 19 degrees of freedom.
## The p-value is 0.000875Medical Autonomy
##
## Cronbach's alpha for the 'pcsiData[, c("autonomy_med_1", "autonomy_med_2", "autonomy_med_3", ' ' "autonomy_med_4", "autonomy_med_5", "autonomy_med_6", "autonomy_med_7", ' ' "autonomy_med_8", "autonomy_med_9r")]' data-set
##
## Items: 9
## Sample units: 223
## alpha: 0.915
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.891 0.932
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1##
## Call:
## factanal(x = autonmedData, factors = 2, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## autonomy_med_1 autonomy_med_2 autonomy_med_3 autonomy_med_4 autonomy_med_5
## 0.299 0.259 0.184 0.124 0.226
## autonomy_med_6 autonomy_med_7 autonomy_med_8 autonomy_med_9r
## 0.247 0.119 0.309 0.955
##
## Loadings:
## Factor1 Factor2
## autonomy_med_1 0.557 0.625
## autonomy_med_2 0.696 0.506
## autonomy_med_3 0.842 0.329
## autonomy_med_4 0.924 0.150
## autonomy_med_5 0.848 0.233
## autonomy_med_6 0.490 0.716
## autonomy_med_7 0.454 0.822
## autonomy_med_8 0.487 0.673
## autonomy_med_9r 0.212
##
## Factor1 Factor2
## SS loadings 3.759 2.518
## Proportion Var 0.418 0.280
## Cumulative Var 0.418 0.697
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 39.22 on 19 degrees of freedom.
## The p-value is 0.00413Gender Identity Autonomy
##
## Cronbach's alpha for the 'pcsiData[, c("autonomy_geniden_1", "autonomy_geniden_2", "autonomy_geniden_3", ' ' "autonomy_geniden_4", "autonomy_geniden_5", "autonomy_geniden_6")]' data-set
##
## Items: 6
## Sample units: 223
## alpha: 0.969
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.958 0.977
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = autongenidenData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## autonomy_geniden_1 autonomy_geniden_2 autonomy_geniden_3 autonomy_geniden_4
## 0.232 0.388 0.109 0.061
## autonomy_geniden_5 autonomy_geniden_6
## 0.102 0.064
##
## Loadings:
## Factor1
## autonomy_geniden_1 0.876
## autonomy_geniden_2 0.782
## autonomy_geniden_3 0.944
## autonomy_geniden_4 0.969
## autonomy_geniden_5 0.948
## autonomy_geniden_6 0.967
##
## Factor1
## SS loadings 5.044
## Proportion Var 0.841
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 40.82 on 9 degrees of freedom.
## The p-value is 0.0000054All scales together
## Parallel analysis suggests that the number of factors = 3 and the number of components = 3##
## Call:
## factanal(x = autonData, factors = 3, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## autonomy_gen_1 autonomy_gen_2 autonomy_gen_3 autonomy_gen_4
## 0.412 0.243 0.280 0.314
## autonomy_gen_5 autonomy_gen_6 autonomy_gen_8 autonomy_gen_9
## 0.471 0.249 0.357 0.206
## autonomy_gen_10 autonomy_med_1 autonomy_med_2 autonomy_med_3
## 0.254 0.270 0.251 0.257
## autonomy_med_4 autonomy_med_5 autonomy_med_6 autonomy_med_7
## 0.320 0.373 0.309 0.245
## autonomy_med_8 autonomy_med_9r autonomy_geniden_1 autonomy_geniden_2
## 0.348 0.969 0.225 0.390
## autonomy_geniden_3 autonomy_geniden_4 autonomy_geniden_5 autonomy_geniden_6
## 0.099 0.060 0.104 0.067
##
## Loadings:
## Factor1 Factor2 Factor3
## autonomy_gen_1 0.512 0.425 0.382
## autonomy_gen_2 0.785 0.293 0.236
## autonomy_gen_3 0.707 0.397 0.251
## autonomy_gen_4 0.765 0.190 0.253
## autonomy_gen_5 0.361 0.493 0.393
## autonomy_gen_6 0.800 0.222 0.251
## autonomy_gen_8 0.685 0.307 0.281
## autonomy_gen_9 0.822 0.272 0.210
## autonomy_gen_10 0.798 0.210 0.255
## autonomy_med_1 0.262 0.712 0.394
## autonomy_med_2 0.297 0.762 0.282
## autonomy_med_3 0.425 0.714 0.229
## autonomy_med_4 0.521 0.594 0.236
## autonomy_med_5 0.389 0.649 0.234
## autonomy_med_6 0.223 0.740 0.306
## autonomy_med_7 0.183 0.781 0.333
## autonomy_med_8 0.201 0.709 0.329
## autonomy_med_9r 0.107 0.131
## autonomy_geniden_1 0.261 0.318 0.778
## autonomy_geniden_2 0.286 0.258 0.680
## autonomy_geniden_3 0.218 0.341 0.858
## autonomy_geniden_4 0.277 0.325 0.870
## autonomy_geniden_5 0.321 0.375 0.808
## autonomy_geniden_6 0.290 0.376 0.841
##
## Factor1 Factor2 Factor3
## SS loadings 5.872 5.673 5.382
## Proportion Var 0.245 0.236 0.224
## Cumulative Var 0.245 0.481 0.705
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 618.64 on 207 degrees of freedom.
## The p-value is 1.31e-42Prejudice (TABS)
Entire Scale
##
## Cronbach's alpha for the 'pcsiData[, c("Q198_1", "Q198_2r", "Q198_3r", "Q198_4", "Q198_5", ' ' "Q198_6", "Q198_7", "Q198_8r", "Q198_9r", "Q198_10r", "Q198_11", ' ' "Q198_12", "Q198_13", "Q198_14r", "Q198_15", "Q198_16r", ' ' "Q198_17r", "Q198_18r", "Q198_19r", "Q198_20r", "Q198_22", ' ' "Q198_23r", "Q198_24r", "Q198_25", "Q198_26r", "Q198_27r", ' ' "Q198_28", "Q198_29", "Q198_30")]' data-set
##
## Items: 29
## Sample units: 223
## alpha: 0.972
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.965 0.977
## Parallel analysis suggests that the number of factors = 3 and the number of components = 2##
## Call:
## factanal(x = tabsData, factors = 3, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q198_1 Q198_2r Q198_3r Q198_4 Q198_5 Q198_6 Q198_7 Q198_8r
## 0.451 0.435 0.239 0.169 0.513 0.495 0.150 0.348
## Q198_9r Q198_10r Q198_11 Q198_12 Q198_13 Q198_14r Q198_15 Q198_16r
## 0.354 0.173 0.253 0.242 0.213 0.239 0.379 0.559
## Q198_17r Q198_18r Q198_19r Q198_20r Q198_22 Q198_23r Q198_24r Q198_25
## 0.300 0.180 0.184 0.191 0.116 0.347 0.323 0.205
## Q198_26r Q198_27r Q198_28 Q198_29 Q198_30
## 0.367 0.148 0.308 0.272 0.565
##
## Loadings:
## Factor1 Factor2 Factor3
## Q198_1 0.454 0.460 0.361
## Q198_2r 0.489 0.412 0.395
## Q198_3r 0.701 0.271 0.443
## Q198_4 0.336 0.839 0.120
## Q198_5 0.505 0.318 0.362
## Q198_6 0.221 0.641 0.211
## Q198_7 0.324 0.844 0.179
## Q198_8r 0.343 0.725
## Q198_9r 0.291 0.748
## Q198_10r 0.791 0.328 0.308
## Q198_11 0.683 0.475 0.235
## Q198_12 0.703 0.490 0.152
## Q198_13 0.777 0.403 0.143
## Q198_14r 0.213 0.842
## Q198_15 0.468 0.600 0.204
## Q198_16r 0.526 0.195 0.356
## Q198_17r 0.269 0.769 0.191
## Q198_18r 0.777 0.402 0.233
## Q198_19r 0.730 0.335 0.413
## Q198_20r 0.694 0.290 0.493
## Q198_22 0.309 0.867 0.189
## Q198_23r 0.406 0.632 0.298
## Q198_24r 0.195 0.220 0.769
## Q198_25 0.727 0.373 0.356
## Q198_26r 0.255 0.195 0.728
## Q198_27r 0.339 0.180 0.840
## Q198_28 0.662 0.346 0.366
## Q198_29 0.272 0.792 0.161
## Q198_30 0.542 0.293 0.235
##
## Factor1 Factor2 Factor3
## SS loadings 7.898 7.285 5.098
## Proportion Var 0.272 0.251 0.176
## Cumulative Var 0.272 0.524 0.699
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 595.46 on 322 degrees of freedom.
## The p-value is 0.00000000000000000147Interpersonal Comfort Subscale
##
## Cronbach's alpha for the 'pcsiData[, c("Q198_1", "Q198_3r", "Q198_5", "Q198_10r", "Q198_11", ' ' "Q198_12", "Q198_13", "Q198_16r", "Q198_18r", "Q198_19r", ' ' "Q198_20r", "Q198_25", "Q198_28", "Q198_30")]' data-set
##
## Items: 14
## Sample units: 223
## alpha: 0.964
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.952 0.973
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = tabsincomData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q198_1 Q198_3r Q198_5 Q198_10r Q198_11 Q198_12 Q198_13 Q198_16r
## 0.502 0.264 0.527 0.178 0.279 0.294 0.255 0.572
## Q198_18r Q198_19r Q198_20r Q198_25 Q198_28 Q198_30
## 0.190 0.191 0.230 0.206 0.306 0.564
##
## Loadings:
## Factor1
## Q198_1 0.706
## Q198_3r 0.858
## Q198_5 0.688
## Q198_10r 0.906
## Q198_11 0.849
## Q198_12 0.840
## Q198_13 0.863
## Q198_16r 0.654
## Q198_18r 0.900
## Q198_19r 0.899
## Q198_20r 0.878
## Q198_25 0.891
## Q198_28 0.833
## Q198_30 0.660
##
## Factor1
## SS loadings 9.442
## Proportion Var 0.674
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 281.6 on 77 degrees of freedom.
## The p-value is 4.26e-25Sex/Gender Beliefs Subscale
##
## Cronbach's alpha for the 'pcsiData[, c("Q198_2r", "Q198_4", "Q198_6", "Q198_7", "Q198_9r", ' ' "Q198_15", "Q198_17r", "Q198_22", "Q198_23r", "Q198_29")]' data-set
##
## Items: 10
## Sample units: 223
## alpha: 0.952
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.942 0.960
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = tabssgbelData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q198_2r Q198_4 Q198_6 Q198_7 Q198_9r Q198_15 Q198_17r Q198_22
## 0.598 0.178 0.499 0.150 0.368 0.430 0.299 0.119
## Q198_23r Q198_29
## 0.385 0.274
##
## Loadings:
## Factor1
## Q198_2r 0.634
## Q198_4 0.907
## Q198_6 0.708
## Q198_7 0.922
## Q198_9r 0.795
## Q198_15 0.755
## Q198_17r 0.837
## Q198_22 0.939
## Q198_23r 0.784
## Q198_29 0.852
##
## Factor1
## SS loadings 6.699
## Proportion Var 0.670
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 76.97 on 35 degrees of freedom.
## The p-value is 0.0000551Human Value Subscale
##
## Cronbach's alpha for the 'pcsiData[, c("Q198_8r", "Q198_14r", "Q198_24r", "Q198_26r", "Q198_27r")]' data-set
##
## Items: 5
## Sample units: 223
## alpha: 0.918
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.875 0.946
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = tabshvData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q198_8r Q198_14r Q198_24r Q198_26r Q198_27r
## 0.364 0.249 0.340 0.358 0.142
##
## Loadings:
## Factor1
## Q198_8r 0.797
## Q198_14r 0.866
## Q198_24r 0.812
## Q198_26r 0.801
## Q198_27r 0.926
##
## Factor1
## SS loadings 3.547
## Proportion Var 0.709
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 10.84 on 5 degrees of freedom.
## The p-value is 0.0546Anti-trans Legislation
Entire Scale
##
## Cronbach's alpha for the 'pcsiData[, c("Q197_1", "Q197_2", "Q197_3", "Q197_4", "Q197_5", ' ' "Q197_6", "Q197_7", "Q197_8", "Q197_10", "Q197_11", "Q197_12", ' ' "Q197_13", "Q197_14", "Q197_15", "Q197_16")]' data-set
##
## Items: 15
## Sample units: 223
## alpha: 0.965
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.957 0.971
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1##
## Call:
## factanal(x = legislationData, factors = 2, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q197_1 Q197_2 Q197_3 Q197_4 Q197_5 Q197_6 Q197_7 Q197_8 Q197_10 Q197_11
## 0.402 0.418 0.180 0.270 0.153 0.107 0.238 0.488 0.105 0.307
## Q197_12 Q197_13 Q197_14 Q197_15 Q197_16
## 0.243 0.409 0.614 0.117 0.380
##
## Loadings:
## Factor1 Factor2
## Q197_1 0.664 0.395
## Q197_2 0.706 0.289
## Q197_3 0.780 0.460
## Q197_4 0.745 0.418
## Q197_5 0.371 0.842
## Q197_6 0.347 0.879
## Q197_7 0.767 0.417
## Q197_8 0.488 0.524
## Q197_10 0.841 0.433
## Q197_11 0.759 0.342
## Q197_12 0.659 0.569
## Q197_13 0.449 0.624
## Q197_14 0.268 0.561
## Q197_15 0.818 0.462
## Q197_16 0.438 0.654
##
## Factor1 Factor2
## SS loadings 6.038 4.530
## Proportion Var 0.403 0.302
## Cumulative Var 0.403 0.705
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 226.48 on 76 degrees of freedom.
## The p-value is 0.0000000000000000705Civil Rights
##
## Cronbach's alpha for the 'pcsiData[, c("Q197_1", "Q197_2", "Q197_3", "Q197_5", "Q197_6", ' ' "Q197_14")]' data-set
##
## Items: 6
## Sample units: 223
## alpha: 0.897
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.873 0.916
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1##
## Call:
## factanal(x = legcivData, factors = 2, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q197_1 Q197_2 Q197_3 Q197_5 Q197_6 Q197_14
## 0.336 0.406 0.207 0.183 0.035 0.633
##
## Loadings:
## Factor1 Factor2
## Q197_1 0.353 0.734
## Q197_2 0.260 0.725
## Q197_3 0.449 0.769
## Q197_5 0.810 0.401
## Q197_6 0.923 0.336
## Q197_14 0.488 0.358
##
## Factor1 Factor2
## SS loadings 2.140 2.058
## Proportion Var 0.357 0.343
## Cumulative Var 0.357 0.700
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 3.43 on 4 degrees of freedom.
## The p-value is 0.488Healthcare
##
## Cronbach's alpha for the 'pcsiData[, c("Q197_7", "Q197_10", "Q197_15")]' data-set
##
## Items: 3
## Sample units: 223
## alpha: 0.95
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.932 0.963
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = leghealthData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q197_7 Q197_10 Q197_15
## 0.230 0.111 0.066
##
## Loadings:
## Factor1
## Q197_7 0.877
## Q197_10 0.943
## Q197_15 0.967
##
## Factor1
## SS loadings 2.594
## Proportion Var 0.865
##
## The degrees of freedom for the model is 0 and the fit was 0Schools & Educations
##
## Cronbach's alpha for the 'pcsiData[, c("Q197_4", "Q197_8", "Q197_11", "Q197_12", "Q197_13", ' ' "Q197_16")]' data-set
##
## Items: 6
## Sample units: 223
## alpha: 0.917
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.896 0.934
## Parallel analysis suggests that the number of factors = 1 and the number of components = 1##
## Call:
## factanal(x = legeduData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q197_4 Q197_8 Q197_11 Q197_12 Q197_13 Q197_16
## 0.314 0.424 0.358 0.179 0.407 0.413
##
## Loadings:
## Factor1
## Q197_4 0.828
## Q197_8 0.759
## Q197_11 0.801
## Q197_12 0.906
## Q197_13 0.770
## Q197_16 0.766
##
## Factor1
## SS loadings 3.905
## Proportion Var 0.651
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 56.64 on 9 degrees of freedom.
## The p-value is 0.00000000593pcsi_clean <- var.count.mean(pcsi_clean, “tabs_count2”, “tabs”, pcsi_clean[c(“Q198_1”, “Q198_2r”, “Q198_3r”, “Q198_4”, “Q198_5”, “Q198_6”, “Q198_7”, “Q198_8r”, “Q198_9r”, “Q198_10r”, “Q198_11”, “Q198_12”, “Q198_13”, “Q198_14r”, “Q198_15”, “Q198_16r”, “Q198_17r”, “Q198_18r”, “Q198_19r”, “Q198_20r”, “Q198_22”, “Q198_23r”, “Q198_24r”, “Q198_25”, “Q198_26r”, “Q198_27r”, “Q198_28”, “Q198_29”, “Q198_30”)])
pcsi_clean <- var.count.mean(pcsi_clean, “legislation_count2”, “legislation”, pcsi_clean[c(“Q197_1”, “Q197_2”, “Q197_3”, “Q197_4”, “Q197_5”, “Q197_6”, “Q197_7”, “Q197_8”, “Q197_10”, “Q197_11”, “Q197_12”, “Q197_13”, “Q197_14”, “Q197_15”, “Q197_16”)])
Examining Distributions
Gender Development
Composites
Categorization
Labelling
Stereotyping
Identification
Disclosure
Autonomy
General Autonomy
Medical Autonomy
Gender Identity Autonomy
Prejudice (TABS)
Entire Scale
Interpersonal Comfort Subscale
Sex/Gender Beliefs Subscale
Human Value Subscale
Anti-trans Legislation
Entire Scale
Civil Rights
Healthcare
Schools & Educations
Exploratory Analyses Probing Findings
Does the gender composite predict different types of legislation differently?
Predicting support for civil rights legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.7104 | 0.1389 | 19.5163 | 0 |
| gender_comp | 0.0156 | 0.0028 | 5.6311 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.138 | 0.1337 | 1.5497 | 31.709 | 0 | 1 | -370.4 | 746.8 | 756.694 | 475.534 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.5669 | 0.1434 | 3.9528 | 0.0001 |
| gender_comp | 0.0031 | 0.0018 | 1.7062 | 0.0895 |
| tabs | 0.9998 | 0.0541 | 18.4885 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6859 | 0.6827 | 0.9403 | 213.977 | 0 | 2 | -268.61 | 545.219 | 558.392 | 173.3 | 196 | 199 |
Predicting support for healthcare legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.2879 | 0.1802 | 18.2437 | 0 |
| gender_comp | 0.0177 | 0.0036 | 4.9414 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1098 | 0.1053 | 2.0111 | 24.4173 | 0 | 1 | -422.517 | 851.034 | 860.929 | 800.8 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.0865 | 0.2392 | 4.5415 | 0.0000 |
| gender_comp | 0.0049 | 0.0030 | 1.6289 | 0.1049 |
| tabs | 1.0264 | 0.0902 | 11.3774 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4639 | 0.4584 | 1.5686 | 84.7898 | 0 | 2 | -370.446 | 748.891 | 762.065 | 482.268 | 196 | 199 |
Predicting support for schools & education legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.0103 | 0.1526 | 19.7287 | 0 |
| gender_comp | 0.0154 | 0.0030 | 5.0907 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1157 | 0.1113 | 1.7027 | 25.9151 | 0 | 1 | -389.224 | 784.449 | 794.344 | 574.031 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.8112 | 0.1745 | 4.6502 | 0.0000 |
| gender_comp | 0.0027 | 0.0022 | 1.2149 | 0.2258 |
| tabs | 1.0244 | 0.0658 | 15.5728 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6048 | 0.6008 | 1.1439 | 149.966 | 0 | 2 | -307.607 | 623.213 | 636.387 | 256.455 | 196 | 199 |
Do different gender development questions predict support for legislation differently?
Categorization composite predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.2757 | 0.1295 | 25.2974 | 0.0000 |
| gender_cat_comp | -0.0085 | 0.0025 | -3.3637 | 0.0009 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0541 | 0.0493 | 1.6987 | 11.3143 | 0.0009 | 1 | -388.754 | 783.508 | 793.403 | 571.338 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7734 | 0.1603 | 4.8232 | 0.0000 |
| gender_cat_comp | 0.0004 | 0.0016 | 0.2701 | 0.7874 |
| tabs | 1.0565 | 0.0587 | 18.0044 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6435 | 0.6399 | 1.048 | 176.922 | 0 | 2 | -290.194 | 588.387 | 601.56 | 215.281 | 196 | 199 |
Labelling composite predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.2758 | 0.1290 | 25.3976 | 0.0000 |
| gender_label_comp | -0.0083 | 0.0024 | -3.4325 | 0.0007 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0562 | 0.0514 | 1.6968 | 11.7819 | 0.0007 | 1 | -388.531 | 783.062 | 792.957 | 570.065 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7793 | 0.1588 | 4.9062 | 0.0000 |
| gender_label_comp | -0.0023 | 0.0015 | -1.5348 | 0.1264 |
| tabs | 1.0328 | 0.0569 | 18.1389 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6476 | 0.644 | 1.042 | 180.122 | 0 | 2 | -289.042 | 586.084 | 599.257 | 212.804 | 196 | 199 |
Stereotyping composite predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.2280 | 0.1249 | 25.8350 | 0 |
| gender_stereo_comp | -0.0126 | 0.0026 | -4.7798 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1034 | 0.0989 | 1.6537 | 22.8464 | 0 | 1 | -383.391 | 772.782 | 782.677 | 541.504 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7552 | 0.1637 | 4.6118 | 0.0000 |
| gender_stereo_comp | 0.0011 | 0.0018 | 0.6024 | 0.5476 |
| tabs | 1.0677 | 0.0619 | 17.2546 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6441 | 0.6404 | 1.0473 | 177.328 | 0 | 2 | -290.047 | 588.093 | 601.266 | 214.964 | 196 | 199 |
Identification composite predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.0360 | 0.1463 | 20.7532 | 0 |
| gender_identify_comp | -0.0096 | 0.0021 | -4.6078 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0968 | 0.0923 | 1.6598 | 21.2314 | 0 | 1 | -384.125 | 774.25 | 784.145 | 545.493 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7511 | 0.1590 | 4.7231 | 0.0000 |
| gender_identify_comp | -0.0025 | 0.0014 | -1.8546 | 0.0652 |
| tabs | 1.0199 | 0.0580 | 17.5794 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6496 | 0.646 | 1.0392 | 181.642 | 0 | 2 | -288.5 | 585 | 598.173 | 211.648 | 196 | 199 |
Disclosure composite predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9594 | 0.1627 | 18.1859 | 0 |
| gender_disclose_comp | -0.0084 | 0.0020 | -4.2806 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0847 | 0.0801 | 1.6709 | 18.3238 | 0 | 1 | -385.46 | 776.92 | 786.815 | 552.825 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.6652 | 0.1615 | 4.1186 | 0.0001 |
| gender_disclose_comp | -0.0035 | 0.0012 | -2.8344 | 0.0051 |
| tabs | 1.0168 | 0.0562 | 18.1007 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6574 | 0.6539 | 1.0274 | 188.084 | 0 | 2 | -286.233 | 580.467 | 593.64 | 206.881 | 196 | 199 |
Do the transgender development questions predict support for legislation on their own?
Trasngender age composite predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.2998 | 0.2124 | 10.8254 | 0 |
| gender_trans_mean | 0.0148 | 0.0023 | 6.3207 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1679 | 0.1637 | 1.5932 | 39.9515 | 0 | 1 | -375.931 | 757.862 | 767.757 | 502.578 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.6011 | 0.1713 | 3.5086 | 0.0006 |
| gender_trans_mean | 0.0042 | 0.0016 | 2.5807 | 0.0106 |
| tabs | 0.9921 | 0.0596 | 16.6338 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6551 | 0.6516 | 1.0309 | 186.157 | 0 | 2 | -286.906 | 581.812 | 594.985 | 208.284 | 196 | 199 |
Trasngender categorization age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9090 | 0.1748 | 16.6435 | 0.0000 |
| gender_cat_trans_1 | 0.0089 | 0.0022 | 4.1211 | 0.0001 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.079 | 0.0743 | 1.6761 | 16.9832 | 0.0001 | 1 | -386.082 | 778.164 | 788.059 | 556.273 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7453 | 0.1641 | 4.5420 | 0.000 |
| gender_cat_trans_1 | 0.0012 | 0.0014 | 0.8274 | 0.409 |
| tabs | 1.0367 | 0.0587 | 17.6627 | 0.000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6446 | 0.641 | 1.0464 | 177.779 | 0 | 2 | -289.884 | 587.767 | 600.94 | 214.612 | 196 | 199 |
Transgender labelling age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.8325 | 0.1798 | 15.7572 | 0 |
| gender_label_trans_1 | 0.0097 | 0.0022 | 4.4602 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0913 | 0.0867 | 1.6649 | 19.893 | 0 | 1 | -384.737 | 775.475 | 785.37 | 548.844 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.6974 | 0.1652 | 4.2222 | 0.0000 |
| gender_label_trans_1 | 0.0024 | 0.0014 | 1.7242 | 0.0862 |
| tabs | 1.0227 | 0.0580 | 17.6356 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6487 | 0.6451 | 1.0404 | 180.988 | 0 | 2 | -288.733 | 585.465 | 598.639 | 212.144 | 196 | 199 |
Transgender stereotyping age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.5229 | 0.1927 | 13.0890 | 0 |
| gender_stereo_trans_1 | 0.0124 | 0.0021 | 5.8882 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.149 | 0.1447 | 1.6112 | 34.6706 | 0 | 1 | -378.175 | 762.351 | 772.246 | 513.985 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.6942 | 0.1659 | 4.1852 | 0.0000 |
| gender_stereo_trans_1 | 0.0025 | 0.0015 | 1.7066 | 0.0895 |
| tabs | 1.0104 | 0.0605 | 16.6886 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6486 | 0.645 | 1.0405 | 180.903 | 0 | 2 | -288.763 | 585.526 | 598.699 | 212.208 | 196 | 199 |
Transgender identification age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.3564 | 0.2012 | 11.7112 | 0 |
| gender_identify_tran_1 | 0.0124 | 0.0019 | 6.4778 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1749 | 0.1707 | 1.5865 | 41.9623 | 0 | 1 | -375.09 | 756.179 | 766.074 | 498.367 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.6163 | 0.1676 | 3.6777 | 0.0003 |
| gender_identify_tran_1 | 0.0037 | 0.0013 | 2.7235 | 0.0070 |
| tabs | 0.9880 | 0.0597 | 16.5591 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6564 | 0.6529 | 1.0289 | 187.219 | 0 | 2 | -286.535 | 581.07 | 594.243 | 207.509 | 196 | 199 |
Transgender disclosure age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.4961 | 0.2198 | 11.3557 | 0 |
| gender_disclose_tran_1 | 0.0093 | 0.0018 | 5.0503 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1141 | 0.1096 | 1.6439 | 25.5054 | 0 | 1 | -382.194 | 770.388 | 780.283 | 535.062 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.4904 | 0.1765 | 2.7784 | 0.0060 |
| gender_disclose_tran_1 | 0.0040 | 0.0012 | 3.4138 | 0.0008 |
| tabs | 1.0036 | 0.0561 | 17.8793 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6634 | 0.66 | 1.0184 | 193.16 | 0 | 2 | -284.484 | 576.967 | 590.141 | 203.275 | 196 | 199 |
Do the transgender development ACCURACY questions predict support for legislation on their own?
Trasngender categorization age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9356 | 0.1701 | 17.2596 | 0.0000 |
| gender_cat_trans_acc | 0.0089 | 0.0022 | 4.1211 | 0.0001 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.079 | 0.0743 | 1.6761 | 16.9832 | 0.0001 | 1 | -386.082 | 778.164 | 788.059 | 556.273 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7488 | 0.1631 | 4.5898 | 0.000 |
| gender_cat_trans_acc | 0.0012 | 0.0014 | 0.8274 | 0.409 |
| tabs | 1.0367 | 0.0587 | 17.6627 | 0.000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6446 | 0.641 | 1.0464 | 177.779 | 0 | 2 | -289.884 | 587.767 | 600.94 | 214.612 | 196 | 199 |
Transgender labelling age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.0642 | 0.1446 | 21.1977 | 0 |
| gender_label_trans_acc | 0.0097 | 0.0022 | 4.4602 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0913 | 0.0867 | 1.6649 | 19.893 | 0 | 1 | -384.737 | 775.475 | 785.37 | 548.844 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7559 | 0.1591 | 4.7524 | 0.0000 |
| gender_label_trans_acc | 0.0024 | 0.0014 | 1.7242 | 0.0862 |
| tabs | 1.0227 | 0.0580 | 17.6356 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6487 | 0.6451 | 1.0404 | 180.988 | 0 | 2 | -288.733 | 585.465 | 598.639 | 212.144 | 196 | 199 |
Transgender stereotyping age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9704 | 0.1389 | 21.3837 | 0 |
| gender_stereo_trans_acc | 0.0124 | 0.0021 | 5.8882 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.149 | 0.1447 | 1.6112 | 34.6706 | 0 | 1 | -378.175 | 762.351 | 772.246 | 513.985 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7856 | 0.1587 | 4.9508 | 0.0000 |
| gender_stereo_trans_acc | 0.0025 | 0.0015 | 1.7066 | 0.0895 |
| tabs | 1.0104 | 0.0605 | 16.6886 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6486 | 0.645 | 1.0405 | 180.903 | 0 | 2 | -288.763 | 585.526 | 598.699 | 212.208 | 196 | 199 |
Transgender identification age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.6529 | 0.1652 | 16.0607 | 0 |
| gender_identify_trans_acc | 0.0124 | 0.0019 | 6.4778 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1749 | 0.1707 | 1.5865 | 41.9623 | 0 | 1 | -375.09 | 756.179 | 766.074 | 498.367 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.7043 | 0.1591 | 4.4270 | 0.000 |
| gender_identify_trans_acc | 0.0037 | 0.0013 | 2.7235 | 0.007 |
| tabs | 0.9880 | 0.0597 | 16.5591 | 0.000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6564 | 0.6529 | 1.0289 | 187.219 | 0 | 2 | -286.535 | 581.07 | 594.243 | 207.509 | 196 | 199 |
Transgender disclosure age predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.8308 | 0.1673 | 16.9225 | 0 |
| gender_disclose_trans_acc | 0.0093 | 0.0018 | 5.0503 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1141 | 0.1096 | 1.6439 | 25.5054 | 0 | 1 | -382.194 | 770.388 | 780.283 | 535.062 | 198 | 200 |
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.6353 | 0.1607 | 3.9530 | 0.0001 |
| gender_disclose_trans_acc | 0.0040 | 0.0012 | 3.4138 | 0.0008 |
| tabs | 1.0036 | 0.0561 | 17.8793 | 0.0000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6634 | 0.66 | 1.0184 | 193.16 | 0 | 2 | -284.484 | 576.967 | 590.141 | 203.275 | 196 | 199 |
Do the different prejudice subscales predict support for anti-trans legislation differently?
Interpersonal Comfort Subscale predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.4653 | 0.1729 | 8.4759 | 0 |
| tabs.incom | 0.8253 | 0.0618 | 13.3450 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4735 | 0.4709 | 1.2673 | 178.09 | 0 | 1 | -330.155 | 666.31 | 676.205 | 317.98 | 198 | 200 |
Sex/Gender Beliefs Subscale predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.5586 | 0.1225 | 4.5605 | 0 |
| tabs.sgbel | 0.9209 | 0.0346 | 26.6493 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.782 | 0.7809 | 0.8155 | 710.187 | 0 | 1 | -241.993 | 489.986 | 499.88 | 131.679 | 198 | 200 |
Human Value Subscale predicting support for anti-trans legislation
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.3770 | 0.2141 | 11.1031 | 0 |
| tabs.hv | 0.6045 | 0.1031 | 5.8614 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1485 | 0.1442 | 1.6157 | 34.3556 | 0 | 1 | -376.835 | 759.67 | 769.55 | 514.251 | 197 | 199 |