PCSI Gender Study Pilot Analyses

Descriptive Statistics

Descriptive stats for each question (continued below)
	vars	n	mean	sd	median	trimmed
gender_cat_cis_1	1	202	40.47	33.74	33.5	35.23
gender_label_cis_1	2	205	42.75	34.02	36	37.04
gender_stereo_cis_1	3	204	57.27	40.19	48	51.8
gender_identify_cis_1	4	204	45.6	37.25	36	38.9
gender_disclose_cis_1	5	205	44.73	37.76	36	37.6
gender_cat_trans_1	6	205	60.4	55.84	36	51.13
gender_label_trans_1	7	205	63.44	55.05	36	53.32
gender_stereo_trans_1	8	204	73.3	53.98	58.5	66.32
gender_identify_tran_1	9	206	87.62	59.17	67	82.17
gender_disclose_tran_1	10	205	101	63.52	94	98.4

	mad	min	max	range	skew	kurtosis	se
gender_cat_cis_1	21.5	0	182	182	1.909	4.484	2.374
gender_label_cis_1	17.79	0	216	216	2.353	6.761	2.376
gender_stereo_cis_1	27.43	0	216	216	1.402	2.088	2.814
gender_identify_cis_1	17.79	0	216	216	2.313	6.111	2.608
gender_disclose_cis_1	17.79	0	216	216	2.238	5.557	2.637
gender_cat_trans_1	26.69	0	216	216	1.376	0.9193	3.9
gender_label_trans_1	19.27	7	216	209	1.482	1.11	3.845
gender_stereo_trans_1	38.55	0	216	216	1.076	0.2836	3.779
gender_identify_tran_1	61.53	6	216	210	0.633	-0.7907	4.122
gender_disclose_tran_1	85.99	0	216	216	0.2356	-1.301	4.436

T-test for differences between cisgender and transgender milestones

Categorization

Categorization (continued below)
Test statistic	df	P value	Alternative hypothesis
-4.296	334.4	0.00002278 * * *	two.sided

mean in group gender_cat_cis_1	mean in group gender_cat_trans_1
40.47	60.11

Labeling

Categorization (continued below)
Test statistic	df	P value	Alternative hypothesis
-4.524	338.5	0.000008402 * * *	two.sided

mean in group gender_label_cis_1	mean in group gender_label_trans_1
42.74	63.27

Stereotyping

Stereotyping (continued below)
Test statistic	df	P value	Alternative hypothesis
-3.413	372.6	0.000714 * * *	two.sided

mean in group gender_stereo_cis_1	mean in group gender_stereo_trans_1
56.96	73.06

Identification

Identification (continued below)
Test statistic	df	P value	Alternative hypothesis
-8.664	341.6	0.0000000000000001842 * * *	two.sided

mean in group gender_identify_cis_1	mean in group gender_identify_tran_1
45.15	87.41

Disclosure

Disclosure (continued below)
Test statistic	df	P value	Alternative hypothesis
-11.02	328	0.000000000000000000000003134 * * *	two.sided

mean in group gender_disclose_cis_1	mean in group gender_disclose_tran_1
44.29	101.2

Exploratory Analyses

Linear Regressions with age difference score (trans age - cis age) predicting secondary DVs

General Autonomy

Estimates for the general autonomy model
term	estimate	std.error	statistic	p.value
(Intercept)	6.0529	0.0889	68.0704	0.0000
gender_comp	-0.0070	0.0018	-3.9586	0.0001

Summary for the general autonomy model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0733	0.0687	0.9922	15.6705	0.0001	1	-281.225	568.45	578.345	194.94	198	200

Estimates for the general autonomy model
term	estimate	std.error	statistic	p.value
(Intercept)	7.2178	0.1114	64.7848	0.0000
gender_comp	-0.0002	0.0014	-0.1543	0.8775
tabs	-0.5439	0.0421	-12.9318	0.0000

Summary for the general autonomy model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.4988	0.4937	0.7316	98.0286	0	2	-219.767	447.534	460.727	105.436	197	200

Medical Autonomy

Estimates for the medical autonomy model
term	estimate	std.error	statistic	p.value
(Intercept)	5.4504	0.101	53.9444	0
gender_comp	-0.0087	0.002	-4.3204	0

Summary for the medical autonomy model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0862	0.0815	1.1274	18.6662	0	1	-306.772	619.545	629.44	251.681	198	200

Estimates for the medical autonomy model
term	estimate	std.error	statistic	p.value
(Intercept)	6.6247	0.1376	48.1613	0.0000
gender_comp	-0.0018	0.0017	-1.0620	0.2895
tabs	-0.5483	0.0519	-10.5592	0.0000

Summary for the medical autonomy model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.4164	0.4105	0.9032	70.2896	0	2	-261.922	531.844	545.037	160.719	197	200

Gender Identity Autonomy

Estimates for the gender identity autonomy model
term	estimate	std.error	statistic	p.value
(Intercept)	5.6643	0.1372	41.2782	0
gender_comp	-0.0137	0.0027	-4.9942	0

Summary for the gender identity autonomy model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1114	0.1069	1.5379	24.9424	0	1	-370.718	747.436	757.346	470.674	199	201

Estimates for the gender identity autonomy model
term	estimate	std.error	statistic	p.value
(Intercept)	7.6418	0.1575	48.5119	0.0000
gender_comp	-0.0020	0.0020	-1.0040	0.3166
tabs	-0.9244	0.0594	-15.5628	0.0000

Summary for the gender identity autonomy model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.6018	0.5977	1.0329	148.083	0	2	-287.29	582.58	595.754	209.09	196	199

Anti-trans Legislation

Estimates for the legislation model
term	estimate	std.error	statistic	p.value
(Intercept)	2.9459	0.1458	20.2076	0
gender_comp	0.0159	0.0029	5.4989	0

Summary for the legislation model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1325	0.1281	1.6267	30.238	0	1	-380.099	766.198	776.093	523.967	198	200

Estimates for the legislation model
term	estimate	std.error	statistic	p.value
(Intercept)	0.7685	0.1588	4.8402	0.0000
gender_comp	0.0033	0.0020	1.6412	0.1024
tabs	1.0150	0.0599	16.9519	0.0000

Summary for the legislation model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.6482	0.6446	1.0411	180.596	0	2	-288.873	585.745	598.918	212.442	196	199

## 
##  Pearson's product-moment correlation
## 
## data:  pcsiData$gender_comp and pcsiData$tabs
## t = 5.661, df = 198, p-value = 0.0000000523
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.247312 0.486779
## sample estimates:
##      cor 
## 0.373246

## lavaan 0.6.15 ended normally after 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##                                                   Used       Total
##   Number of observations                           199         223
## 
## Model Test User Model:
##                                                       
##   Test statistic                               228.561
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                               443.084
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.484
##   Tucker-Lewis Index (TLI)                      -0.032
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1679.778
##   Loglikelihood unrestricted model (H1)      -1565.497
##                                                       
##   Akaike (AIC)                                3377.555
##   Bayesian (BIC)                              3407.195
##   Sample-size adjusted Bayesian (SABIC)       3378.683
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.615
##   90 Percent confidence interval - lower         0.549
##   90 Percent confidence interval - upper         0.683
##   P-value H_0: RMSEA <= 0.050                    0.000
##   P-value H_0: RMSEA >= 0.080                    1.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.259
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   legislation ~                                                           
##     autonomy_gendn     -0.800    0.053  -15.200    0.000   -0.905   -0.690
##   autonomy_geniden ~                                                      
##     gender_comp        -0.014    0.003   -4.577    0.000   -0.020   -0.008
##   gender_comp ~                                                           
##     tabs               11.176    2.765    4.042    0.000    5.878   16.796
##    Std.lv  Std.all
##                   
##    -0.800   -0.746
##                   
##    -0.014   -0.331
##                   
##    11.176    0.373
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .legislation       7.629    0.280   27.218    0.000    7.028    8.214
##    .autonomy_gendn    5.660    0.129   44.034    0.000    5.412    5.914
##    .gender_comp       2.618    6.412    0.408    0.683  -10.273   15.161
##    Std.lv  Std.all
##     7.629    4.379
##     5.660    3.484
##     2.618    0.066
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .legislation       1.348    0.151    8.929    0.000    1.063    1.667
##    .autonomy_gendn    2.349    0.256    9.194    0.000    1.827    2.842
##    .gender_comp    1361.167  203.492    6.689    0.000  977.005 1753.242
##    Std.lv  Std.all
##     1.348    0.444
##     2.349    0.890
##  1361.167    0.861
## 
## R-Square:
##                    Estimate
##     legislation       0.556
##     autonomy_gendn    0.110
##     gender_comp       0.139

##                 lhs op              rhs      mi    epc sepc.lv sepc.all
## 18             tabs  ~      legislation 116.187  0.627   0.627    0.822
## 15 autonomy_geniden  ~             tabs 109.990 -0.924  -0.924   -0.756
## 17      gender_comp  ~ autonomy_geniden 109.990 47.930  47.930    1.958
## 19             tabs  ~ autonomy_geniden 109.990 -0.695  -0.695   -0.849
## 13      legislation  ~             tabs  22.982  0.299   0.299    0.228
##    sepc.nox
## 18    0.822
## 15   -0.569
## 17    1.958
## 19   -0.849
## 13    0.172

## lavaan 0.6.15 ended normally after 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           200         223
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                               192.198
##   Degrees of freedom                                 3
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -678.807
##   Loglikelihood unrestricted model (H1)       -678.807
##                                                       
##   Akaike (AIC)                                1371.613
##   Bayesian (BIC)                              1394.701
##   Sample-size adjusted Bayesian (SABIC)       1372.525
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   legislation ~                                                           
##     gender_cmp (c)      0.006    0.002    3.168    0.002    0.002    0.009
##   autonomy_geniden ~                                                      
##     gender_cmp (a)     -0.014    0.003   -4.551    0.000   -0.020   -0.008
##   legislation ~                                                           
##     atnmy_gndn (b)     -0.751    0.055  -13.589    0.000   -0.858   -0.643
##    Std.lv  Std.all
##                   
##     0.006    0.132
##                   
##    -0.014   -0.331
##                   
##    -0.751   -0.701
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .legislation       7.192    0.324   22.226    0.000    6.588    7.837
##    .autonomy_gendn    5.654    0.133   42.497    0.000    5.403    5.918
##    Std.lv  Std.all
##     7.192    4.139
##     5.654    3.484
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .legislation       1.297    0.155    8.348    0.000    0.991    1.577
##    .autonomy_gendn    2.345    0.248    9.465    0.000    1.849    2.832
##    Std.lv  Std.all
##     1.297    0.429
##     2.345    0.891
## 
## R-Square:
##                    Estimate
##     legislation       0.571
##     autonomy_gendn    0.109
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab                0.010    0.002    4.335    0.000    0.006    0.016
##     total             0.016    0.003    5.506    0.000    0.011    0.022
##    Std.lv  Std.all
##     0.010    0.232
##     0.016    0.364

## [1] lhs      op       rhs      mi       epc      sepc.lv  sepc.all sepc.nox
## <0 rows> (or 0-length row.names)

## lavaan 0.6.15 ended normally after 26 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           199         223
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                               213.097
##   Degrees of freedom                                 3
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -611.278
##   Loglikelihood unrestricted model (H1)       -611.278
##                                                       
##   Akaike (AIC)                                1236.556
##   Bayesian (BIC)                              1259.609
##   Sample-size adjusted Bayesian (SABIC)       1237.433
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   tabs ~                                                                  
##     gender_cmp (c)      0.004    0.002    2.055    0.040    0.000    0.008
##   autonomy_geniden ~                                                      
##     gender_cmp (a)     -0.014    0.003   -4.440    0.000   -0.020   -0.008
##   tabs ~                                                                  
##     atnmy_gndn (b)     -0.598    0.049  -12.152    0.000   -0.699   -0.497
##    Std.lv  Std.all
##                   
##     0.004    0.131
##                   
##    -0.014   -0.331
##                   
##    -0.598   -0.731
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .tabs              5.528    0.313   17.638    0.000    4.899    6.165
##    .autonomy_gendn    5.660    0.133   42.683    0.000    5.394    5.908
##    Std.lv  Std.all
##     5.528    4.160
##     5.660    3.484
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .tabs              0.680    0.079    8.562    0.000    0.523    0.840
##    .autonomy_gendn    2.349    0.261    9.014    0.000    1.849    2.869
##    Std.lv  Std.all
##     0.680    0.385
##     2.349    0.890
## 
## R-Square:
##                    Estimate
##     tabs              0.615
##     autonomy_gendn    0.110
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab                0.008    0.002    4.386    0.000    0.005    0.012
##     total             0.012    0.003    4.976    0.000    0.007    0.017
##    Std.lv  Std.all
##     0.008    0.242
##     0.012    0.373

## [1] lhs      op       rhs      mi       epc      sepc.lv  sepc.all sepc.nox
## <0 rows> (or 0-length row.names)