PCSI Gender Intervention Study TESS Analyses
Descriptive Statistics
Full Sample Together
## pcsi2Data[, c("gender_identify_cis", "gender_identify_trans", "gender_comp", "autonomy_geniden", "legislation", "trust_scientists")]
##
## 6 Variables 1563 Observations
## --------------------------------------------------------------------------------
## gender_identify_cis
## n missing distinct Info Mean Gmd .05 .10
## 1480 83 131 0.97 53.96 46.07 6 12
## .25 .50 .75 .90 .95
## 26 36 60 122 180
##
## lowest : 0 1 3 4 6, highest: 220 221 222 225 227
## --------------------------------------------------------------------------------
## gender_identify_trans
## n missing distinct Info Mean Gmd .05 .10
## 1496 67 150 0.984 84.84 69.16 13 24
## .25 .50 .75 .90 .95
## 36 60 130 192 216
##
## lowest : 0 1 3 4 5, highest: 222 223 225 226 227
## --------------------------------------------------------------------------------
## gender_comp
## n missing distinct Info Mean Gmd .05 .10
## 1452 111 180 0.892 30.24 56.72 -12.9 0.0
## .25 .50 .75 .90 .95
## 0.0 0.0 48.0 120.0 164.7
##
## lowest : -211 -203 -191 -168 -156, highest: 209 210 214 215 226
## --------------------------------------------------------------------------------
## autonomy_geniden
## n missing distinct Info Mean Gmd .05 .10
## 1540 23 19 0.994 4.399 1.998 1.000 1.667
## .25 .50 .75 .90 .95
## 3.333 4.333 6.000 7.000 7.000
##
## lowest : 1.00000 1.33333 1.66667 2.00000 2.33333
## highest: 5.66667 6.00000 6.33333 6.66667 7.00000
##
## 1 (112, 0.073), 1.33333333333333 (29, 0.019), 1.66666666666667 (21, 0.014), 2
## (45, 0.029), 2.33333333333333 (38, 0.025), 2.66666666666667 (32, 0.021), 3 (92,
## 0.060), 3.33333333333333 (73, 0.047), 3.66666666666667 (75, 0.049), 4 (172,
## 0.112), 4.33333333333333 (100, 0.065), 4.66666666666667 (97, 0.063), 5 (138,
## 0.090), 5.33333333333333 (70, 0.045), 5.66666666666667 (43, 0.028), 6 (116,
## 0.075), 6.33333333333333 (63, 0.041), 6.66666666666667 (42, 0.027), 7 (182,
## 0.118)
## --------------------------------------------------------------------------------
## legislation
## n missing distinct Info Mean Gmd .05 .10
## 1545 18 37 0.995 4.913 1.84 2.000 2.667
## .25 .50 .75 .90 .95
## 3.833 5.000 6.333 7.000 7.000
##
## lowest : 1.00000 1.16667 1.33333 1.50000 1.66667
## highest: 6.33333 6.50000 6.66667 6.83333 7.00000
## --------------------------------------------------------------------------------
## trust_scientists
## n missing distinct Info Mean Gmd
## 1547 16 7 0.966 4.546 1.919
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 125 97 139 363 280 357 186
## Proportion 0.081 0.063 0.090 0.235 0.181 0.231 0.120
## --------------------------------------------------------------------------------Descriptives by Condition
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range
## gender_identify_cis 1 751 59.66 52.81 45.00 50.01 31.13 0 227 227
## gender_identify_trans 2 757 108.89 63.59 108.00 106.61 71.16 0 227 227
## gender_comp 3 738 48.69 68.26 24.00 41.95 35.58 -191 226 417
## autonomy_geniden 4 779 4.38 1.81 4.33 4.47 1.98 1 7 6
## legislation 5 786 4.90 1.65 5.00 5.02 1.98 1 7 6
## trust_scientists 6 784 4.53 1.73 5.00 4.64 1.48 1 7 6
## skew kurtosis se
## gender_identify_cis 1.62 2.05 1.93
## gender_identify_trans 0.27 -1.09 2.31
## gender_comp 0.58 0.33 2.51
## autonomy_geniden -0.30 -0.83 0.06
## legislation -0.39 -0.75 0.06
## trust_scientists -0.47 -0.61 0.06
## ------------------------------------------------------------
## group: Experiment
## vars n mean sd median trimmed mad min max range
## gender_identify_cis 1 729 48.09 44.55 36.00 38.71 4.45 0 227 227
## gender_identify_trans 2 739 60.21 53.65 36.00 49.41 7.41 0 227 227
## gender_comp 3 714 11.18 40.10 0.00 4.36 0.00 -211 205 416
## autonomy_geniden 4 761 4.42 1.70 4.33 4.49 1.98 1 7 6
## legislation 5 759 4.92 1.59 5.00 5.03 1.73 1 7 6
## trust_scientists 6 763 4.56 1.71 5.00 4.67 1.48 1 7 6
## skew kurtosis se
## gender_identify_cis 2.53 6.37 1.65
## gender_identify_trans 1.82 2.35 1.97
## gender_comp 1.77 9.77 1.50
## autonomy_geniden -0.26 -0.67 0.06
## legislation -0.39 -0.77 0.06
## trust_scientists -0.52 -0.52 0.06Manipulation Check
“As a manipulation check, we will first conduct an independent samples t-test to determine whether perceptions of the age at which transgender youth can identify their gender relative to cisgender youth differs by condition using the age difference score outlined above as the dependent variable.”
Non-weighted
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| 12.82 | 1199 | 2.501e-35 * * * | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 48.69 | 11.18 |
Weighted
test: Two Sample Weighted T-Test (Welch)
coefficients:
t.value df p.value 11.22 1239 0 additional:
Difference Mean.x Mean.y Std. Err 34.55 47.18 12.63 3.078
Serial Mediation Model
“Next, we will test a serial mediation model in which both the perceived gender development age gap and autonomy support mediate the relationship between condition and support for anti-transgender legislation, such that condition will influence the perceived gender development age gap, which will influence autonomy support, and then finally support for anti-trans legislation.”
Non-weighted
## lavaan 0.6.15 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 1422 1563
##
## Model Test User Model:
##
## Test statistic 59.738
## Degrees of freedom 3
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1488.349
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.962
## Tucker-Lewis Index (TLI) 0.923
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12630.926
## Loglikelihood unrestricted model (H1) -12601.057
##
## Akaike (AIC) 25279.851
## Bayesian (BIC) 25327.189
## Sample-size adjusted Bayesian (SABIC) 25298.600
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.115
## 90 Percent confidence interval - lower 0.091
## 90 Percent confidence interval - upper 0.142
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.991
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## gender_comp ~
## conditin (a1) -37.726 2.980 -12.658 0.000 -43.567 -31.884
## autonomy_geniden ~
## gndr_cmp (d21) -0.008 0.001 -10.264 0.000 -0.009 -0.006
## legislation ~
## atnmy_gn (b2) -0.697 0.016 -42.748 0.000 -0.729 -0.665
## Std.lv Std.all
##
## -37.726 -0.318
##
## -0.008 -0.263
##
## -0.697 -0.750
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 86.587 4.692 18.453 0.000 77.390 95.784
## .autonomy_gendn 4.661 0.050 92.722 0.000 4.562 4.759
## .legislation 7.980 0.078 102.793 0.000 7.828 8.132
## Std.lv Std.all
## 86.587 1.461
## 4.661 2.664
## 7.980 4.905
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 3157.015 118.397 26.665 0.000 2924.961 3389.070
## .autonomy_gendn 2.850 0.107 26.665 0.000 2.640 3.059
## .legislation 1.158 0.043 26.665 0.000 1.073 1.243
## Std.lv Std.all
## 3157.015 0.899
## 2.850 0.931
## 1.158 0.438
##
## R-Square:
## Estimate
## gender_comp 0.101
## autonomy_gendn 0.069
## legislation 0.562
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind_eff -0.204 0.026 -7.837 0.000 -0.255 -0.153
## Std.lv Std.all
## -0.204 -0.063## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 14 gender_comp ~ legislation 57.223 10.177 10.177 0.279 0.279
## 17 legislation ~ gender_comp 40.044 0.003 0.003 0.115 0.115
## 15 autonomy_geniden ~ legislation 40.043 1.002 1.002 0.932 0.932
## 13 gender_comp ~ autonomy_geniden 9.712 -8.644 -8.644 -0.255 -0.255
## 16 autonomy_geniden ~ condition 9.712 -0.294 -0.294 -0.084 -0.168Weighted
## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 1422 1563
## Sampling weights variable weights
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 61.407 31.270
## Degrees of freedom 3 3
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.964
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1338.359 572.177
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 2.339
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956 0.950
## Tucker-Lewis Index (TLI) 0.912 0.900
##
## Robust Comparative Fit Index (CFI) 0.958
## Robust Tucker-Lewis Index (TLI) 0.916
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12683.904 -12683.904
## Scaling correction factor 2.214
## for the MLR correction
## Loglikelihood unrestricted model (H1) -12653.200 -12653.200
## Scaling correction factor 2.151
## for the MLR correction
##
## Akaike (AIC) 25385.808 25385.808
## Bayesian (BIC) 25433.146 25433.146
## Sample-size adjusted Bayesian (SABIC) 25404.556 25404.556
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.117 0.081
## 90 Percent confidence interval - lower 0.093 0.064
## 90 Percent confidence interval - upper 0.143 0.100
## P-value H_0: RMSEA <= 0.050 0.000 0.002
## P-value H_0: RMSEA >= 0.080 0.993 0.575
##
## Robust RMSEA 0.114
## 90 Percent confidence interval - lower 0.080
## 90 Percent confidence interval - upper 0.152
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.950
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.039 0.039
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## gender_comp ~
## conditin (a1) -35.336 4.296 -8.226 0.000 -43.755 -26.916
## autonomy_geniden ~
## gndr_cmp (d21) -0.006 0.001 -6.525 0.000 -0.008 -0.004
## legislation ~
## atnmy_gn (b2) -0.668 0.022 -30.383 0.000 -0.711 -0.625
## Std.lv Std.all
##
## -35.336 -0.288
##
## -0.006 -0.225
##
## -0.668 -0.729
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 83.210 7.700 10.807 0.000 68.119 98.302
## .autonomy_gendn 4.526 0.072 62.645 0.000 4.384 4.667
## .legislation 7.893 0.094 84.084 0.000 7.709 8.076
## Std.lv Std.all
## 83.210 1.355
## 4.526 2.634
## 7.893 5.017
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 3458.625 291.014 11.885 0.000 2888.249 4029.002
## .autonomy_gendn 2.803 0.118 23.690 0.000 2.571 3.035
## .legislation 1.158 0.068 17.070 0.000 1.025 1.291
## Std.lv Std.all
## 3458.625 0.917
## 2.803 0.949
## 1.158 0.468
##
## R-Square:
## Estimate
## gender_comp 0.083
## autonomy_gendn 0.051
## legislation 0.532
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind_eff -0.149 0.031 -4.756 0.000 -0.210 -0.087
## Std.lv Std.all
## -0.149 -0.047## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 14 gender_comp ~ legislation 56.219 10.641 10.641 0.273 0.273
## 15 autonomy_geniden ~ legislation 39.528 1.152 1.152 1.055 1.055
## 17 legislation ~ gender_comp 39.528 0.003 0.003 0.117 0.117
## 21 condition ~ legislation 8.869 0.026 0.026 0.082 0.082
## 16 autonomy_geniden ~ condition 5.196 -0.211 -0.211 -0.061 -0.123Weighted with order as a covariate
## lavaan 0.6.15 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 12
##
## Used Total
## Number of observations 1422 1563
## Sampling weights variable weights
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 68.046 35.447
## Degrees of freedom 3 3
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.920
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1377.657 631.508
## Degrees of freedom 9 9
## P-value 0.000 0.000
## Scaling correction factor 2.182
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.952 0.948
## Tucker-Lewis Index (TLI) 0.857 0.844
##
## Robust Comparative Fit Index (CFI) 0.954
## Robust Tucker-Lewis Index (TLI) 0.862
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -12667.574 -12667.574
## Scaling correction factor 2.138
## for the MLR correction
## Loglikelihood unrestricted model (H1) -12633.551 -12633.551
## Scaling correction factor 2.094
## for the MLR correction
##
## Akaike (AIC) 25359.147 25359.147
## Bayesian (BIC) 25422.265 25422.265
## Sample-size adjusted Bayesian (SABIC) 25384.145 25384.145
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.123 0.087
## 90 Percent confidence interval - lower 0.099 0.069
## 90 Percent confidence interval - upper 0.150 0.106
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.998 0.758
##
## Robust RMSEA 0.121
## 90 Percent confidence interval - lower 0.087
## 90 Percent confidence interval - upper 0.158
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 0.976
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.034 0.034
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## gender_comp ~
## conditin (a1) -35.586 4.252 -8.368 0.000 -43.921 -27.252
## gndr_dn_ -15.875 4.250 -3.735 0.000 -24.205 -7.546
## autonomy_geniden ~
## gndr_cmp (d21) -0.006 0.001 -6.349 0.000 -0.008 -0.004
## gndr_dn_ 0.015 0.123 0.118 0.906 -0.226 0.255
## legislation ~
## atnmy_gn (b2) -0.669 0.022 -30.646 0.000 -0.712 -0.626
## gndr_dn_ 0.146 0.081 1.798 0.072 -0.013 0.304
## Std.lv Std.all
##
## -35.586 -0.290
## -15.875 -0.129
##
## -0.006 -0.225
## 0.015 0.004
##
## -0.669 -0.731
## 0.146 0.046
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 107.189 9.866 10.864 0.000 87.851 126.526
## .autonomy_gendn 4.504 0.210 21.447 0.000 4.092 4.915
## .legislation 7.682 0.150 51.049 0.000 7.387 7.977
## Std.lv Std.all
## 107.189 1.746
## 4.504 2.621
## 7.682 4.883
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 3395.679 286.099 11.869 0.000 2834.936 3956.423
## .autonomy_gendn 2.803 0.118 23.690 0.000 2.571 3.035
## .legislation 1.153 0.067 17.109 0.000 1.021 1.285
## Std.lv Std.all
## 3395.679 0.901
## 2.803 0.949
## 1.153 0.466
##
## R-Square:
## Estimate
## gender_comp 0.099
## autonomy_gendn 0.051
## legislation 0.534
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind_eff -0.150 0.032 -4.694 0.000 -0.212 -0.087
## Std.lv Std.all
## -0.150 -0.048## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 20 gender_comp ~ legislation 62.364 11.107 11.107 0.285 0.285
## 21 autonomy_geniden ~ legislation 44.331 1.235 1.235 1.131 1.131
## 23 legislation ~ gender_comp 44.331 0.003 0.003 0.125 0.125
## 31 gender_identify_order ~ legislation 11.780 2.167 2.167 6.820 6.820
## 27 condition ~ legislation 9.024 0.026 0.026 0.083 0.083Secondary Analyses
Additional Moderation Models
“We will measure a secondary dependent variable which will be used for additional analyses (see below). We will measure participants’ trust in scientists on a single-item scale in which participants will be asked to indicate on a scale from 1 (strongly distrust) to 7 (strongly trust) how much they trust or distrust scientists as a source of information about gender development (adapted from Hmielowski et al., 2014).”
“In addition to our planned serial mediation model, we will also test two moderation models. As described above, we expect participants who receive the intervention (relative to those who do not) will show a reduced age gap in their perception of gender identity development. We further predict that this effect will be greater for participants who express more trust in scientists, as has been the case in previous work examining motivated reasoning about scientific findings (Drummond & Fischhoff, 2017).”.
“Additionally, because transgender rights are a highly politicized topic (Hatch et al., 2022), we expect the predicted effect will be greater among more highly educated liberal participants, in line with research from other similarly politicized domains (e.g., global warming; Drummond & Fischhoff, 2017). Thus, we will test moderation by these variables through two linear regression models (one model with condition and trust in scientists and another with condition, political ideology, and education).”
Moderation by trust in scientists
Non-weighted
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 50.2563 | 4.2177 | 11.9156 | 0.0000 |
| condition1 | -23.4851 | 4.2177 | -5.5683 | 0.0000 |
| trust_scientists | -4.4168 | 0.8597 | -5.1375 | 0.0000 |
| condition1:trust_scientists | 0.9885 | 0.8597 | 1.1498 | 0.2504 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1201 | 0.1182 | 55.5897 | 65.3108 | 0 | 3 | -7827.19 | 15664.4 | 15690.7 | 4437551 | 1436 | 1440 |
Weighted
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 46.7758 | 4.4034 | 10.6226 | 0.0000 |
| condition1 | -14.9555 | 4.4034 | -3.3963 | 0.0007 |
| trust_scientists | -3.7260 | 0.9098 | -4.0954 | 0.0000 |
| condition1:trust_scientists | -0.5108 | 0.9098 | -0.5615 | 0.5746 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0912 | 0.0893 | 58.2123 | 48.0086 | 0 | 3 | -8151.79 | 16313.6 | 16339.9 | 4866137 | 1436 | 1440 |
Weighted with order as covariate
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 46.9074 | 4.3699 | 10.7342 | 0.0000 |
| condition1 | -14.7021 | 4.3701 | -3.3642 | 0.0008 |
| trust_scientists | -3.8162 | 0.9031 | -4.2258 | 0.0000 |
| gender_identify_order1 | -7.3626 | 1.5299 | -4.8124 | 0.0000 |
| condition1:trust_scientists | -0.5944 | 0.9030 | -0.6583 | 0.5105 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1056 | 0.1031 | 57.7683 | 42.3517 | 0 | 4 | -8140.26 | 16292.5 | 16324.2 | 4788852 | 1435 | 1440 |
Moderation by political ideology and education
A higher score in political ideology is more conservative
1 Very liberal 2 Somewhat liberal 3 Moderate 4 Somewhat conservative 5 Very conservative
Less than high school is the reference level for education
Non-weighted
##
## Less than HS HS graduate or equivalent
## 60 278
## Some college/ associates degree Bachelor's degree
## 618 338
## Post grad study/professional degree
## 269| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 14.5013 | 6.4332 | 2.2541 | 0.0243 |
| condition1 | -16.7280 | 6.4332 | -2.6003 | 0.0094 |
| pol_ideology_missing | 4.8176 | 2.0547 | 2.3447 | 0.0192 |
| education_factor1 | -11.7953 | 14.5171 | -0.8125 | 0.4166 |
| education_factor2 | -8.0771 | 5.3722 | -1.5035 | 0.1329 |
| education_factor3 | -6.1683 | 3.2342 | -1.9072 | 0.0567 |
| education_factor4 | -4.7703 | 2.3189 | -2.0571 | 0.0399 |
| condition1:pol_ideology_missing | -0.2635 | 2.0547 | -0.1282 | 0.8980 |
| condition1:education_factor1 | 26.7706 | 14.5171 | 1.8441 | 0.0654 |
| condition1:education_factor2 | 10.6964 | 5.3722 | 1.9911 | 0.0467 |
| condition1:education_factor3 | 6.4658 | 3.2342 | 1.9992 | 0.0458 |
| condition1:education_factor4 | 2.4490 | 2.3189 | 1.0561 | 0.2911 |
| pol_ideology_missing:education_factor1 | 4.3549 | 4.5971 | 0.9473 | 0.3436 |
| pol_ideology_missing:education_factor2 | 3.1209 | 1.6934 | 1.8429 | 0.0655 |
| pol_ideology_missing:education_factor3 | 2.0300 | 1.0359 | 1.9596 | 0.0502 |
| pol_ideology_missing:education_factor4 | 1.1234 | 0.7772 | 1.4454 | 0.1486 |
| condition1:pol_ideology_missing:education_factor1 | -8.0585 | 4.5971 | -1.7529 | 0.0798 |
| condition1:pol_ideology_missing:education_factor2 | -4.3794 | 1.6934 | -2.5861 | 0.0098 |
| condition1:pol_ideology_missing:education_factor3 | -2.6485 | 1.0359 | -2.5566 | 0.0107 |
| condition1:pol_ideology_missing:education_factor4 | -0.5600 | 0.7772 | -0.7206 | 0.4713 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1508 | 0.1395 | 54.7193 | 13.2752 | 0 | 19 | -7796.39 | 15634.8 | 15745.5 | 4251765 | 1420 | 1440 |
Weighted
##
## Less than HS HS graduate or equivalent
## 60 278
## Some college/ associates degree Bachelor's degree
## 618 338
## Post grad study/professional degree
## 269| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 13.7927 | 5.0004 | 2.7583 | 0.0059 |
| condition1 | -17.7661 | 5.0004 | -3.5529 | 0.0004 |
| pol_ideology_missing | 5.0144 | 1.5962 | 3.1414 | 0.0017 |
| education_factor1 | -15.5407 | 9.3323 | -1.6652 | 0.0961 |
| education_factor2 | -7.3790 | 4.0292 | -1.8314 | 0.0673 |
| education_factor3 | -7.1820 | 3.0418 | -2.3611 | 0.0184 |
| education_factor4 | -8.5175 | 2.4234 | -3.5147 | 0.0005 |
| condition1:pol_ideology_missing | 0.4934 | 1.5962 | 0.3091 | 0.7573 |
| condition1:education_factor1 | 33.2714 | 9.3323 | 3.5652 | 0.0004 |
| condition1:education_factor2 | 9.2536 | 4.0292 | 2.2966 | 0.0218 |
| condition1:education_factor3 | 8.0456 | 3.0418 | 2.6450 | 0.0083 |
| condition1:education_factor4 | 4.7931 | 2.4234 | 1.9779 | 0.0481 |
| pol_ideology_missing:education_factor1 | 5.5357 | 2.9484 | 1.8776 | 0.0606 |
| pol_ideology_missing:education_factor2 | 2.6329 | 1.2557 | 2.0968 | 0.0362 |
| pol_ideology_missing:education_factor3 | 2.5997 | 0.9664 | 2.6901 | 0.0072 |
| pol_ideology_missing:education_factor4 | 1.9927 | 0.8032 | 2.4809 | 0.0132 |
| condition1:pol_ideology_missing:education_factor1 | -10.3234 | 2.9484 | -3.5014 | 0.0005 |
| condition1:pol_ideology_missing:education_factor2 | -3.8792 | 1.2557 | -3.0893 | 0.0020 |
| condition1:pol_ideology_missing:education_factor3 | -2.8665 | 0.9664 | -2.9662 | 0.0031 |
| condition1:pol_ideology_missing:education_factor4 | -0.7636 | 0.8032 | -0.9507 | 0.3419 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1341 | 0.1225 | 56.6333 | 11.572 | 0 | 19 | -8102.94 | 16247.9 | 16358.6 | 4554406 | 1420 | 1440 |
Exploring the three-way interactions
intercept for condition=control & education_factor=“Less than HS”
## (Intercept)
## 13.7927intercept for condition=experiment & education_factor=“Less than HS”
## (Intercept)
## -3.9734intercept for condition=control & education_factor=“HS graduate or equivalent”
## (Intercept)
## -1.74803intercept for condition=experiment & education_factor=“HS graduate or equivalent”
## (Intercept)
## 13.7574intercept for condition=control & education_factor=“Some college/ associates degree”
## (Intercept)
## 6.41365intercept for condition=experiment & education_factor=“Some college/ associates degree”
## (Intercept)
## -2.09882intercept for condition=control & education_factor=“Bachelor’s degree”
## (Intercept)
## 6.61063intercept for condition=experiment & education_factor=“Bachelor’s degree”
## (Intercept)
## -3.10987intercept for condition=control & education_factor=“Post grad study/professional degree”
## (Intercept)
## 5.27513intercept for condition=experiment & education_factor=“Post grad study/professional degree”
## (Intercept)
## -7.69781
Weighted with order as a covariate
##
## Less than HS HS graduate or equivalent
## 60 278
## Some college/ associates degree Bachelor's degree
## 618 338
## Post grad study/professional degree
## 269| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 13.6297 | 4.9619 | 2.7469 | 0.0061 |
| condition1 | -19.7912 | 4.9795 | -3.9745 | 0.0001 |
| pol_ideology_missing | 5.0468 | 1.5839 | 3.1864 | 0.0015 |
| education_factor1 | -12.9906 | 9.2754 | -1.4006 | 0.1616 |
| education_factor2 | -7.8791 | 3.9994 | -1.9701 | 0.0490 |
| education_factor3 | -7.5291 | 3.0192 | -2.4938 | 0.0128 |
| education_factor4 | -7.9837 | 2.4072 | -3.3166 | 0.0009 |
| gender_identify_order1 | -7.2437 | 1.5039 | -4.8167 | 0.0000 |
| condition1:pol_ideology_missing | 1.1036 | 1.5889 | 0.6946 | 0.4874 |
| condition1:education_factor1 | 37.0405 | 9.2932 | 3.9857 | 0.0001 |
| condition1:education_factor2 | 10.4519 | 4.0058 | 2.6092 | 0.0092 |
| condition1:education_factor3 | 8.0501 | 3.0183 | 2.6671 | 0.0077 |
| condition1:education_factor4 | 5.0121 | 2.4051 | 2.0840 | 0.0373 |
| pol_ideology_missing:education_factor1 | 4.6189 | 2.9318 | 1.5755 | 0.1154 |
| pol_ideology_missing:education_factor2 | 2.6422 | 1.2460 | 2.1206 | 0.0341 |
| pol_ideology_missing:education_factor3 | 2.6569 | 0.9590 | 2.7705 | 0.0057 |
| pol_ideology_missing:education_factor4 | 1.7787 | 0.7983 | 2.2282 | 0.0260 |
| condition1:pol_ideology_missing:education_factor1 | -11.3912 | 2.9340 | -3.8825 | 0.0001 |
| condition1:pol_ideology_missing:education_factor2 | -4.2704 | 1.2486 | -3.4201 | 0.0006 |
| condition1:pol_ideology_missing:education_factor3 | -2.8590 | 0.9589 | -2.9815 | 0.0029 |
| condition1:pol_ideology_missing:education_factor4 | -0.8606 | 0.7973 | -1.0794 | 0.2806 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.148 | 0.136 | 56.1957 | 12.3253 | 0 | 20 | -8091.26 | 16226.5 | 16342.5 | 4481140 | 1419 | 1440 |
Moderated mediation model
“Secondary analyses will also include a moderated mediation model to determine if this model is a better fit than our planned serial mediation model. Specifically, we will also test a moderated mediation model where the relation between the perceived gender development age gap and autonomy support varies by condition.”
Non-weighted
## lavaan 0.6.15 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 1422 1563
##
## Model Test User Model:
##
## Test statistic 12.057
## Degrees of freedom 2
## P-value (Chi-square) 0.002
##
## Model Test Baseline Model:
##
## Test statistic 1342.157
## Degrees of freedom 7
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.992
## Tucker-Lewis Index (TLI) 0.974
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4857.736
## Loglikelihood unrestricted model (H1) -4851.708
##
## Akaike (AIC) 9733.472
## Bayesian (BIC) 9780.811
## Sample-size adjusted Bayesian (SABIC) 9752.221
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent confidence interval - lower 0.030
## 90 Percent confidence interval - upper 0.094
## P-value H_0: RMSEA <= 0.050 0.261
## P-value H_0: RMSEA >= 0.080 0.177
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.011
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## autonomy_geniden ~
## gndr_cm (a11) -0.012 0.002 -5.157 0.000 -0.017 -0.008
## cndtn_n (a1m1) -0.359 0.101 -3.546 0.000 -0.558 -0.161
## gndr_:_ (a111) 0.003 0.002 1.720 0.085 -0.000 0.007
## legislation ~
## atnmy_g (b11) -0.669 0.017 -40.155 0.000 -0.702 -0.636
## gndr_cm (c11) 0.003 0.000 6.419 0.000 0.002 0.004
## Std.lv Std.all
##
## -0.012 -0.423
## -0.359 -0.103
## 0.003 0.136
##
## -0.669 -0.720
## 0.003 0.115
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .autonomy_gendn 5.228 0.168 31.210 0.000 4.900 5.556
## .legislation 7.760 0.084 92.547 0.000 7.595 7.924
## Std.lv Std.all
## 5.228 2.988
## 7.760 4.770
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .autonomy_gendn 2.825 0.106 26.665 0.000 2.617 3.032
## .legislation 1.125 0.042 26.665 0.000 1.043 1.208
## Std.lv Std.all
## 2.825 0.923
## 1.125 0.425
##
## R-Square:
## Estimate
## autonomy_gendn 0.077
## legislation 0.575
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## a11_cond1 -0.012 0.002 -5.157 0.000 -0.017 -0.008
## a11_cond2 -0.009 0.001 -10.170 0.000 -0.011 -0.008
## i_y1m1x1mod1 -0.002 0.001 -1.718 0.086 -0.004 0.000
## ind_b11_11_cn1 0.008 0.002 5.115 0.000 0.005 0.012
## ind_b11_11_cn2 0.006 0.001 9.858 0.000 0.005 0.007
## Std.lv Std.all
## -0.012 -0.423
## -0.009 -0.287
## -0.002 -0.098
## 0.008 0.304
## 0.006 0.206## lhs op rhs mi epc sepc.lv
## 24 autonomy_geniden ~ legislation 12.004 1.544 1.544
## 32 condition_numeric ~ legislation 10.119 0.032 0.032
## 25 legislation ~ condition_numeric 9.342 0.182 0.182
## 28 gender_comp ~ legislation 3.208 0.800 0.800
## 36 gender_comp:condition_numeric ~ legislation 2.241 -0.846 -0.846
## sepc.all sepc.nox
## 24 1.435 1.435
## 32 0.106 0.106
## 25 0.056 0.112
## 28 0.022 0.022
## 36 -0.018 -0.018Weighted
## lavaan 0.6.15 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 1422 1563
## Sampling weights variable weights
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 20.114 10.240
## Degrees of freedom 2 2
## P-value (Chi-square) 0.000 0.006
## Scaling correction factor 1.964
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1225.395 572.215
## Degrees of freedom 7 7
## P-value 0.000 0.000
## Scaling correction factor 2.141
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.985 0.985
## Tucker-Lewis Index (TLI) 0.948 0.949
##
## Robust Comparative Fit Index (CFI) 0.987
## Robust Tucker-Lewis Index (TLI) 0.953
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4846.900 -4846.900
## Scaling correction factor 1.893
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4836.843 -4836.843
## Scaling correction factor 1.906
## for the MLR correction
##
## Akaike (AIC) 9711.800 9711.800
## Bayesian (BIC) 9759.138 9759.138
## Sample-size adjusted Bayesian (SABIC) 9730.549 9730.549
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.080 0.054
## 90 Percent confidence interval - lower 0.051 0.032
## 90 Percent confidence interval - upper 0.113 0.078
## P-value H_0: RMSEA <= 0.050 0.047 0.349
## P-value H_0: RMSEA >= 0.080 0.541 0.038
##
## Robust RMSEA 0.075
## 90 Percent confidence interval - lower 0.034
## 90 Percent confidence interval - upper 0.124
## P-value H_0: Robust RMSEA <= 0.050 0.138
## P-value H_0: Robust RMSEA >= 0.080 0.497
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.015 0.015
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## autonomy_geniden ~
## gndr_cm (a11) -0.012 0.003 -3.837 0.000 -0.018 -0.006
## cndtn_n (a1m1) -0.303 0.146 -2.080 0.038 -0.589 -0.017
## gndr_:_ (a111) 0.004 0.002 1.730 0.084 -0.001 0.009
## legislation ~
## atnmy_g (b11) -0.644 0.023 -28.291 0.000 -0.688 -0.599
## gndr_cm (c11) 0.003 0.001 4.812 0.000 0.002 0.004
## Std.lv Std.all
##
## -0.012 -0.429
## -0.303 -0.088
## 0.004 0.191
##
## -0.644 -0.703
## 0.003 0.117
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .autonomy_gendn 5.005 0.238 21.028 0.000 4.539 5.472
## .legislation 7.698 0.104 74.043 0.000 7.494 7.901
## Std.lv Std.all
## 5.005 2.913
## 7.698 4.893
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .autonomy_gendn 2.781 0.120 23.270 0.000 2.547 3.015
## .legislation 1.126 0.068 16.447 0.000 0.992 1.260
## Std.lv Std.all
## 2.781 0.942
## 1.126 0.455
##
## R-Square:
## Estimate
## autonomy_gendn 0.058
## legislation 0.545
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## a11_cond1 -0.012 0.003 -3.837 0.000 -0.018 -0.006
## a11_cond2 -0.008 0.001 -6.640 0.000 -0.010 -0.006
## i_y1m1x1mod1 -0.003 0.002 -1.722 0.085 -0.006 0.000
## ind_b11_11_cn1 0.008 0.002 3.775 0.000 0.004 0.012
## ind_b11_11_cn2 0.005 0.001 6.384 0.000 0.004 0.007
## Std.lv Std.all
## -0.012 -0.429
## -0.008 -0.238
## -0.003 -0.134
## 0.008 0.302
## 0.005 0.167## lhs op rhs mi epc sepc.lv
## 24 autonomy_geniden ~ legislation 17.532 1.978 1.978
## 32 condition_numeric ~ legislation 17.309 0.043 0.043
## 25 legislation ~ condition_numeric 16.023 0.236 0.236
## 28 gender_comp ~ legislation 4.831 1.052 1.052
## 36 gender_comp:condition_numeric ~ legislation 3.388 -1.143 -1.143
## sepc.all sepc.nox
## 24 1.811 1.811
## 32 0.136 0.136
## 25 0.075 0.150
## 28 0.027 0.027
## 36 -0.022 -0.022Weightedv with order as a covariate
## lavaan 0.6.15 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 11
##
## Used Total
## Number of observations 1422 1563
## Sampling weights variable weights
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 23.281 12.669
## Degrees of freedom 2 2
## P-value (Chi-square) 0.000 0.002
## Scaling correction factor 1.838
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 1240.056 603.256
## Degrees of freedom 9 9
## P-value 0.000 0.000
## Scaling correction factor 2.056
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.983 0.982
## Tucker-Lewis Index (TLI) 0.922 0.919
##
## Robust Comparative Fit Index (CFI) 0.984
## Robust Tucker-Lewis Index (TLI) 0.928
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4841.153 -4841.153
## Scaling correction factor 1.891
## for the MLR correction
## Loglikelihood unrestricted model (H1) -4829.512 -4829.512
## Scaling correction factor 1.883
## for the MLR correction
##
## Akaike (AIC) 9704.306 9704.306
## Bayesian (BIC) 9762.164 9762.164
## Sample-size adjusted Bayesian (SABIC) 9727.220 9727.220
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.087 0.061
## 90 Percent confidence interval - lower 0.057 0.039
## 90 Percent confidence interval - upper 0.120 0.086
## P-value H_0: RMSEA <= 0.050 0.022 0.187
## P-value H_0: RMSEA >= 0.080 0.673 0.113
##
## Robust RMSEA 0.083
## 90 Percent confidence interval - lower 0.043
## 90 Percent confidence interval - upper 0.129
## P-value H_0: Robust RMSEA <= 0.050 0.081
## P-value H_0: Robust RMSEA >= 0.080 0.603
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.014 0.014
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## autonomy_geniden ~
## gndr_cm (a11) -0.012 0.003 -3.823 0.000 -0.018 -0.006
## cndtn_n (a1m1) -0.304 0.145 -2.095 0.036 -0.589 -0.020
## gndr_:_ (a111) 0.004 0.002 1.739 0.082 -0.001 0.009
## gndr_d_ -0.016 0.122 -0.127 0.899 -0.254 0.223
## legislation ~
## atnmy_g (b11) -0.644 0.023 -28.620 0.000 -0.688 -0.600
## gndr_cm (c11) 0.003 0.001 5.179 0.000 0.002 0.004
## gndr_d_ 0.192 0.080 2.401 0.016 0.035 0.348
## Std.lv Std.all
##
## -0.012 -0.431
## -0.304 -0.089
## 0.004 0.192
## -0.016 -0.005
##
## -0.644 -0.703
## 0.003 0.125
## 0.192 0.061
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .autonomy_gendn 5.031 0.309 16.282 0.000 4.425 5.637
## .legislation 7.408 0.158 46.802 0.000 7.098 7.718
## Std.lv Std.all
## 5.031 2.928
## 7.408 4.709
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .autonomy_gendn 2.781 0.120 23.272 0.000 2.547 3.015
## .legislation 1.117 0.068 16.484 0.000 0.984 1.249
## Std.lv Std.all
## 2.781 0.942
## 1.117 0.451
##
## R-Square:
## Estimate
## autonomy_gendn 0.058
## legislation 0.549
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## a11_cond1 -0.012 0.003 -3.823 0.000 -0.018 -0.006
## a11_cond2 -0.008 0.001 -6.511 0.000 -0.010 -0.006
## i_y1m1x1mod1 -0.003 0.002 -1.729 0.084 -0.006 0.000
## ind_b11_11_cn1 0.008 0.002 3.751 0.000 0.004 0.012
## ind_b11_11_cn2 0.005 0.001 6.250 0.000 0.004 0.007
## Std.lv Std.all
## -0.012 -0.431
## -0.008 -0.239
## -0.003 -0.135
## 0.008 0.303
## 0.005 0.168## lhs op rhs mi epc sepc.lv sepc.all
## 31 autonomy_geniden ~ legislation 20.849 2.166 2.166 1.983
## 40 condition_numeric ~ legislation 19.570 0.046 0.046 0.143
## 32 legislation ~ condition_numeric 17.717 0.247 0.247 0.079
## 50 gender_identify_order ~ legislation 12.940 0.375 0.375 1.181
## 35 gender_comp ~ legislation 6.292 1.186 1.186 0.030
## sepc.nox
## 31 1.983
## 40 0.143
## 32 0.157
## 50 1.181
## 35 0.030Testing for direct effects of condition
“We will also conduct two separate independent samples t-test to determine whether there is a direct effect of condition on support for youth’s gender identity autonomy and support for anti-trans legislation, respectively.”
Gender identity autonomy
Non-weighted
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.4239 | 1536 | 0.6717 | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 4.38 | 4.418 |
Weighted
test: Two Sample Weighted T-Test (Welch)
coefficients:
t.value df p.value -0.2752 1535 0.7832 additional:
Difference Mean.x Mean.y Std. Err -0.02408 4.3 4.324 0.08753
Weighted with order as a covariate
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.3133 | 0.0437 | 98.6009 | 0.0000 |
| condition1 | 0.0125 | 0.0437 | 0.2855 | 0.7753 |
| gender_identify_order1 | 0.0328 | 0.0437 | 0.7505 | 0.4531 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0004 | -0.0009 | 1.7144 | 0.3195 | 0.7265 | 2 | -3289.48 | 6586.96 | 6608.32 | 4517.37 | 1537 | 1540 |
Support for anti-trans legislation
Non-weighted
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.2672 | 1543 | 0.7893 | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 4.902 | 4.924 |
Weighted
test: Two Sample Weighted T-Test (Welch)
coefficients:
t.value df p.value -1.039 1546 0.2988 additional:
Difference Mean.x Mean.y Std. Err -0.08223 4.975 5.057 0.07912
Weighted with order as a covariate
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 5.0169 | 0.0396 | 126.6349 | 0.0000 |
| condition1 | 0.0413 | 0.0396 | 1.0435 | 0.2969 |
| gender_identify_order1 | 0.0219 | 0.0396 | 0.5531 | 0.5803 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0009 | -0.0004 | 1.558 | 0.6918 | 0.5008 | 2 | -3151.78 | 6311.57 | 6332.94 | 3742.95 | 1542 | 1545 |
Testing for order effects
“Last, we will conduct an independent samples t-test to test for order effects on participants’ age gap in their perceptions of gender identity development for participants who are first asked about gender development of cisgender people compared to gender development of transgender people.”
Planned analyses
Non-weighted
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| 5.599 | 1436 | 0.00000002578 * * * | two.sided |
| mean in group Cis first | mean in group Trans first |
|---|---|
| 38.45 | 21.33 |
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -3.436 | 1357 | 0.0006085 * * * | two.sided |
| mean in group Cis first | mean in group Trans first |
|---|---|
| 49.76 | 58.63 |
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| 2.085 | 1493 | 0.03723 * | two.sided |
| mean in group Cis first | mean in group Trans first |
|---|---|
| 88.21 | 81.36 |
Weighted
test: Two Sample Weighted T-Test (Welch)
coefficients:
t.value df p.value 4.472 1438 0.000008375 additional:
Difference Mean.x Mean.y Std. Err 14.24 36.73 22.49 3.184
test: Two Sample Weighted T-Test (Welch)
coefficients:
t.value df p.value -3.803 1342 0.0001492 additional:
Difference Mean.x Mean.y Std. Err -9.999 49.64 59.64 2.629
test: Two Sample Weighted T-Test (Welch)
coefficients:
t.value df p.value 0.7247 1487 0.4688 additional:
Difference Mean.x Mean.y Std. Err 2.398 86.64 84.24 3.309
2x2 model with the perceptions of gender identity regression on order and condition
## Anova Table (Type III tests)
##
## Response: pcsi2Data$gender_comp
## Sum Sq Df F value
## (Intercept) 1261435 1 411.97
## pcsi2Data$condition 489843 1 159.98
## pcsi2Data$gender_identify_order 96828 1 31.62
## pcsi2Data$condition:pcsi2Data$gender_identify_order 47917 1 15.65
## Residuals 4433679 1448
## Pr(>F)
## (Intercept) < 0.0000000000000002 ***
## pcsi2Data$condition < 0.0000000000000002 ***
## pcsi2Data$gender_identify_order 0.0000000224 ***
## pcsi2Data$condition:pcsi2Data$gender_identify_order 0.0000799239 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## eta.sq eta.sq.part
## pcsi2Data$condition 0.09621689 0.0994905
## pcsi2Data$gender_identify_order 0.01901935 0.0213725
## pcsi2Data$condition:pcsi2Data$gender_identify_order 0.00941213 0.0106921## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = pcsi2Data$gender_comp ~ pcsi2Data$condition * pcsi2Data$gender_identify_order)
##
## $`pcsi2Data$condition`
## diff lwr upr p adj
## Experiment-Control -37.5063 -43.2042 -31.8084 0
##
## $`pcsi2Data$gender_identify_order`
## diff lwr upr p adj
## Trans first-Cis first -16.5212 -22.2232 -10.8192 0
##
## $`pcsi2Data$condition:pcsi2Data$gender_identify_order`
## diff lwr upr
## Experiment:Cis first-Control:Cis first -48.27680 -58.63441 -37.9192
## Control:Trans first-Control:Cis first -27.85212 -38.34701 -17.3572
## Experiment:Trans first-Control:Cis first -53.12511 -63.62000 -42.6302
## Control:Trans first-Experiment:Cis first 20.42467 9.76876 31.0806
## Experiment:Trans first-Experiment:Cis first -4.84831 -15.50423 5.8076
## Experiment:Trans first-Control:Trans first -25.27299 -36.06238 -14.4836
## p adj
## Experiment:Cis first-Control:Cis first 0.000000
## Control:Trans first-Control:Cis first 0.000000
## Experiment:Trans first-Control:Cis first 0.000000
## Control:Trans first-Experiment:Cis first 0.000005
## Experiment:Trans first-Experiment:Cis first 0.645780
## Experiment:Trans first-Control:Trans first 0.000000| condition | gender_identify_order | mean | sd | n | sem |
|---|---|---|---|---|---|
| Control | Cis first | 61.82 | 71.05 | 414 | 3.492 |
| Control | Trans first | 33.97 | 61.85 | 378 | 3.181 |
| Experiment | Cis first | 13.54 | 41.89 | 392 | 2.116 |
| Experiment | Trans first | 8.695 | 38.03 | 379 | 1.954 |