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: 0.907
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.855 0.939
## 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 0.0187
Transgender
##
## 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: 0.892
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.854 0.920
## 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
## 0.215 0.296 0.215
## gender_identify_tran_1 gender_disclose_tran_1
## 0.005 0.528
##
## Loadings:
## Factor1 Factor2
## gender_cat_trans_1 0.811 0.356
## gender_label_trans_1 0.689 0.478
## gender_stereo_trans_1 0.803 0.376
## gender_identify_tran_1 0.366 0.928
## gender_disclose_tran_1 0.410 0.552
##
## Factor1 Factor2
## SS loadings 2.080 1.662
## Proportion Var 0.416 0.332
## Cumulative Var 0.416 0.748
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 9.03 on 1 degree of freedom.
## The p-value is 0.00266
Autonomy
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%
## 0.920 0.959
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1
##
## Call:
## factanal(x = autongenData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## autonomy_gen_1 autonomy_gen_2 autonomy_gen_3 autonomy_gen_4 autonomy_gen_5
## 0.470 0.232 0.272 0.303 0.611
## autonomy_gen_6 autonomy_gen_8 autonomy_gen_9 autonomy_gen_10
## 0.255 0.358 0.239 0.282
##
## Loadings:
## Factor1
## autonomy_gen_1 0.728
## autonomy_gen_2 0.876
## autonomy_gen_3 0.853
## autonomy_gen_4 0.835
## autonomy_gen_5 0.624
## autonomy_gen_6 0.863
## autonomy_gen_8 0.801
## autonomy_gen_9 0.872
## autonomy_gen_10 0.848
##
## Factor1
## SS loadings 5.979
## Proportion Var 0.664
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 119.08 on 27 degrees of freedom.
## The p-value is 0.000000000000157
Medical 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")]' data-set
##
## Items: 8
## Sample units: 223
## alpha: 0.944
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.928 0.959
## 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
## 0.298 0.259 0.185 0.121 0.226
## autonomy_med_6 autonomy_med_7 autonomy_med_8
## 0.248 0.124 0.306
##
## Loadings:
## Factor1 Factor2
## autonomy_med_1 0.715 0.437
## autonomy_med_2 0.622 0.595
## autonomy_med_3 0.473 0.769
## autonomy_med_4 0.308 0.886
## autonomy_med_5 0.379 0.794
## autonomy_med_6 0.790 0.358
## autonomy_med_7 0.885 0.304
## autonomy_med_8 0.752 0.359
##
## Factor1 Factor2
## SS loadings 3.334 2.900
## Proportion Var 0.417 0.362
## Cumulative Var 0.417 0.779
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 32 on 13 degrees of freedom.
## The p-value is 0.0024
Gender 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%
## 0.957 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 0.0000054
All scales together
## Parallel analysis suggests that the number of factors = 4 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-42
Prejudice (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%
## 0.964 0.978
## 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 0.00000000000000000147
Interpersonal 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%
## 0.953 0.972
## 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-25
Sex/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%
## 0.943 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 0.0000551
Human 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%
## 0.869 0.947
## 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 0.0546
Anti-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 0.0000000000000000705
Civil 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%
## 0.874 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 0.488
Healthcare
##
## 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%
## 0.933 0.964
## 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 0
Schools & 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%
## 0.895 0.935
## Parallel analysis suggests that the number of factors = 2 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 0.00000000593
Prejudice (TABS) & Legislation Together
All items
##
## 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", "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: 44
## Sample units: 223
## alpha: 0.98
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.976 0.984
## Parallel analysis suggests that the number of factors = 3 and the number of components = 2
##
## Call:
## factanal(x = legtabsData, factors = 3, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q197_1 Q197_2 Q197_3 Q197_4 Q197_5 Q197_6 Q197_7 Q197_8
## 0.365 0.434 0.183 0.272 0.396 0.337 0.277 0.455
## Q197_10 Q197_11 Q197_12 Q197_13 Q197_14 Q197_15 Q197_16 Q198_1
## 0.160 0.329 0.203 0.444 0.507 0.168 0.448 0.448
## Q198_2r Q198_3r Q198_4 Q198_5 Q198_6 Q198_7 Q198_8r Q198_9r
## 0.422 0.243 0.245 0.516 0.508 0.233 0.347 0.395
## Q198_10r Q198_11 Q198_12 Q198_13 Q198_14r Q198_15 Q198_16r Q198_17r
## 0.183 0.255 0.242 0.220 0.246 0.392 0.561 0.365
## Q198_18r Q198_19r Q198_20r Q198_22 Q198_23r Q198_24r Q198_25 Q198_26r
## 0.181 0.177 0.194 0.213 0.371 0.318 0.200 0.375
## Q198_27r Q198_28 Q198_29 Q198_30
## 0.142 0.307 0.311 0.562
##
## Loadings:
## Factor1 Factor2 Factor3
## Q197_1 0.727 0.149 0.290
## Q197_2 0.725 0.184
## Q197_3 0.867 0.230 0.109
## Q197_4 0.820 0.227
## Q197_5 0.664 0.376 0.148
## Q197_6 0.664 0.444 0.156
## Q197_7 0.831 0.171
## Q197_8 0.650 0.248 0.249
## Q197_10 0.894 0.182
## Q197_11 0.787 0.226
## Q197_12 0.835 0.293 0.117
## Q197_13 0.662 0.264 0.218
## Q197_14 0.393 0.458 0.358
## Q197_15 0.889 0.197
## Q197_16 0.673 0.273 0.158
## Q198_1 0.426 0.497 0.351
## Q198_2r 0.407 0.516 0.382
## Q198_3r 0.248 0.729 0.405
## Q198_4 0.757 0.406 0.130
## Q198_5 0.281 0.537 0.342
## Q198_6 0.611 0.258 0.229
## Q198_7 0.755 0.402 0.186
## Q198_8r 0.391 0.704
## Q198_9r 0.701 0.336
## Q198_10r 0.308 0.807 0.268
## Q198_11 0.467 0.693 0.217
## Q198_12 0.483 0.714 0.127
## Q198_13 0.392 0.783 0.114
## Q198_14r 0.259 0.827
## Q198_15 0.561 0.502 0.201
## Q198_16r 0.167 0.553 0.324
## Q198_17r 0.694 0.340 0.192
## Q198_18r 0.388 0.794 0.193
## Q198_19r 0.277 0.780 0.372
## Q198_20r 0.251 0.731 0.456
## Q198_22 0.772 0.392 0.196
## Q198_23r 0.574 0.463 0.290
## Q198_24r 0.196 0.239 0.766
## Q198_25 0.317 0.772 0.323
## Q198_26r 0.137 0.320 0.710
## Q198_27r 0.138 0.396 0.826
## Q198_28 0.310 0.698 0.331
## Q198_29 0.745 0.326 0.169
## Q198_30 0.314 0.542 0.214
##
## Factor1 Factor2 Factor3
## SS loadings 14.595 10.079 5.175
## Proportion Var 0.332 0.229 0.118
## Cumulative Var 0.332 0.561 0.678
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 1856.04 on 817 degrees of freedom.
## The p-value is 1.38e-82
Just the sex/gender beliefs subscale from TABS with the legislation
items
##
## 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", "Q198_2r", "Q198_4", ' ' "Q198_6", "Q198_7", "Q198_9r", "Q198_15", "Q198_17r", "Q198_22", ' ' "Q198_23r", "Q198_29")]' data-set
##
## Items: 25
## Sample units: 223
## alpha: 0.977
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.973 0.981
## Parallel analysis suggests that the number of factors = 3 and the number of components = 1
##
## Call:
## factanal(x = legtabsData, factors = 3, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## Q197_1 Q197_2 Q197_3 Q197_4 Q197_5 Q197_6 Q197_7 Q197_8
## 0.402 0.412 0.181 0.276 0.112 0.116 0.224 0.462
## Q197_10 Q197_11 Q197_12 Q197_13 Q197_14 Q197_15 Q197_16 Q198_2r
## 0.098 0.305 0.195 0.409 0.573 0.096 0.378 0.520
## Q198_4 Q198_6 Q198_7 Q198_9r Q198_15 Q198_17r Q198_22 Q198_23r
## 0.170 0.496 0.156 0.360 0.443 0.294 0.117 0.383
## Q198_29
## 0.266
##
## Loadings:
## Factor1 Factor2 Factor3
## Q197_1 0.605 0.369 0.311
## Q197_2 0.615 0.418 0.185
## Q197_3 0.709 0.442 0.348
## Q197_4 0.670 0.420 0.315
## Q197_5 0.401 0.223 0.823
## Q197_6 0.338 0.355 0.803
## Q197_7 0.757 0.292 0.343
## Q197_8 0.389 0.470 0.408
## Q197_10 0.813 0.359 0.334
## Q197_11 0.695 0.396 0.233
## Q197_12 0.546 0.573 0.423
## Q197_13 0.378 0.424 0.518
## Q197_14 0.183 0.395 0.487
## Q197_15 0.811 0.315 0.383
## Q197_16 0.396 0.370 0.573
## Q198_2r 0.186 0.454 0.489
## Q198_4 0.411 0.726 0.366
## Q198_6 0.369 0.540 0.276
## Q198_7 0.402 0.727 0.393
## Q198_9r 0.456 0.612 0.241
## Q198_15 0.371 0.571 0.306
## Q198_17r 0.400 0.682 0.285
## Q198_22 0.428 0.762 0.345
## Q198_23r 0.307 0.599 0.405
## Q198_29 0.459 0.653 0.312
##
## Factor1 Factor2 Factor3
## SS loadings 6.622 6.435 4.500
## Proportion Var 0.265 0.257 0.180
## Cumulative Var 0.265 0.522 0.702
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 489.54 on 228 degrees of freedom.
## The p-value is 0.00000000000000000000351
