PCSI Gender Behavioral Study (Study 3) Analyses
Descriptive Statistics
Full Sample Together
## pcsi3Data[, c("gender_comp", "autonomy_geniden", "legislation", "tabs", "behav_intent", "monetary_behav", "trust_scientists")]
##
## 7 Variables 177 Observations
## --------------------------------------------------------------------------------
## gender_comp
## n missing distinct Info Mean Gmd .05 .10
## 176 1 108 0.994 21.02 31.84 -5.1 -2.4
## .25 .50 .75 .90 .95
## 0.0 8.9 28.8 63.6 88.2
##
## lowest : -64.2 -16.8 -14.4 -11.4 -9.6, highest: 103.0 103.2 114.8 172.8 201.8
## --------------------------------------------------------------------------------
## autonomy_geniden
## n missing distinct Info Mean Gmd .05 .10
## 177 0 18 0.983 5.102 2.016 1.000 2.000
## .25 .50 .75 .90 .95
## 4.000 5.667 6.667 7.000 7.000
##
## lowest : 1.00000 1.33333 1.66667 2.00000 2.66667
## highest: 5.66667 6.00000 6.33333 6.66667 7.00000
##
## 1 (12, 0.068), 1.33333333333333 (2, 0.011), 1.66666666666667 (2, 0.011), 2 (7,
## 0.040), 2.66666666666667 (1, 0.006), 3 (2, 0.011), 3.33333333333333 (6, 0.034),
## 3.66666666666667 (4, 0.023), 4 (11, 0.062), 4.33333333333333 (7, 0.040),
## 4.66666666666667 (13, 0.073), 5 (15, 0.085), 5.33333333333333 (4, 0.023),
## 5.66666666666667 (8, 0.045), 6 (18, 0.102), 6.33333333333333 (13, 0.073),
## 6.66666666666667 (10, 0.056), 7 (42, 0.237)
## --------------------------------------------------------------------------------
## legislation
## n missing distinct Info Mean Gmd .05 .10
## 177 0 34 0.996 4.086 2.116 1.333 1.667
## .25 .50 .75 .90 .95
## 2.833 3.833 5.333 7.000 7.000
##
## lowest : 1.00000 1.33333 1.50000 1.66667 1.83333
## highest: 6.33333 6.50000 6.66667 6.83333 7.00000
## --------------------------------------------------------------------------------
## tabs
## n missing distinct Info Mean Gmd .05 .10
## 177 0 90 1 2.499 1.505 1.000 1.103
## .25 .50 .75 .90 .95
## 1.379 2.103 3.310 4.738 5.366
##
## lowest : 1.00000 1.03448 1.06897 1.10345 1.13793
## highest: 5.65517 5.82759 6.03448 6.10345 6.17241
## --------------------------------------------------------------------------------
## behav_intent
## n missing distinct Info Mean Gmd .05 .10
## 177 0 77 0.995 2.853 1.78 1.000 1.000
## .25 .50 .75 .90 .95
## 1.444 2.500 4.111 4.856 5.611
##
## lowest : 1.00000 1.05556 1.11111 1.16667 1.22222
## highest: 6.05556 6.44444 6.55556 6.66667 7.00000
## --------------------------------------------------------------------------------
## monetary_behav
## n missing distinct Info Mean Gmd
## 177 0 6 0.885 0.9209 1.169
##
## lowest : 0.0 0.5 1.0 1.5 2.0, highest: 0.5 1.0 1.5 2.0 3.0
##
## Value 0.0 0.5 1.0 1.5 2.0 3.0
## Frequency 84 15 20 19 10 29
## Proportion 0.475 0.085 0.113 0.107 0.056 0.164
## --------------------------------------------------------------------------------
## trust_scientists
## n missing distinct Info Mean Gmd
## 177 0 7 0.927 5.333 1.394
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 2 7 7 21 46 65 29
## Proportion 0.011 0.040 0.040 0.119 0.260 0.367 0.164
## --------------------------------------------------------------------------------Descriptives by Condition
##
## Descriptive statistics by group
## group: Control
## vars n mean sd median trimmed mad min max range
## gender_comp 1 86 29.54 35.40 22.30 25.79 29.50 -64.2 172.80 237.00
## autonomy_geniden 2 87 5.07 1.81 5.67 5.30 1.48 1.0 7.00 6.00
## legislation 3 87 4.05 1.65 3.83 4.00 1.48 1.0 7.00 6.00
## tabs 4 87 2.49 1.37 2.07 2.30 1.02 1.0 6.17 5.17
## behav_intent 5 87 2.78 1.54 2.44 2.69 2.14 1.0 6.44 5.44
## monetary_behav 6 87 0.92 1.09 0.50 0.79 0.74 0.0 3.00 3.00
## trust_scientists 7 87 5.51 1.15 6.00 5.62 1.48 1.0 7.00 6.00
## skew kurtosis se
## gender_comp 1.11 2.33 3.82
## autonomy_geniden -0.94 -0.08 0.19
## legislation 0.35 -0.80 0.18
## tabs 1.08 0.19 0.15
## behav_intent 0.35 -1.21 0.16
## monetary_behav 0.84 -0.68 0.12
## trust_scientists -1.19 2.20 0.12
## ------------------------------------------------------------
## group: Experiment
## vars n mean sd median trimmed mad min max range
## gender_comp 1 90 12.89 29.06 2.40 6.45 7.12 -11.4 201.80 213.20
## autonomy_geniden 2 90 5.14 1.88 5.67 5.38 1.98 1.0 7.00 6.00
## legislation 3 90 4.12 2.03 4.00 4.14 2.72 1.0 7.00 6.00
## tabs 4 90 2.51 1.39 2.16 2.34 1.46 1.0 6.03 5.03
## behav_intent 5 90 2.92 1.62 2.61 2.76 1.81 1.0 7.00 6.00
## monetary_behav 6 90 0.92 1.14 0.50 0.78 0.74 0.0 3.00 3.00
## trust_scientists 7 90 5.17 1.45 5.00 5.32 1.48 1.0 7.00 6.00
## skew kurtosis se
## gender_comp 3.82 18.88 3.06
## autonomy_geniden -0.80 -0.49 0.20
## legislation 0.11 -1.30 0.21
## tabs 0.77 -0.44 0.15
## behav_intent 0.66 -0.43 0.17
## monetary_behav 0.87 -0.79 0.12
## trust_scientists -0.79 0.04 0.15Correlations
| row | column | n | cor | p |
|---|---|---|---|---|
| gender_comp | autonomy_geniden | 176 | -0.2377847 | 0.001483985644449070662 |
| gender_comp | legislation | 176 | 0.2042872 | 0.006535967053502567126 |
| autonomy_geniden | legislation | 177 | -0.7607408 | 0.000000000000000000000 |
| gender_comp | tabs | 176 | 0.3617518 | 0.000000809398881251866 |
| autonomy_geniden | tabs | 177 | -0.8100476 | 0.000000000000000000000 |
| legislation | tabs | 177 | 0.7799376 | 0.000000000000000000000 |
| gender_comp | behav_intent | 176 | -0.1814689 | 0.015937208153809834243 |
| autonomy_geniden | behav_intent | 177 | 0.5674803 | 0.000000000000000222045 |
| legislation | behav_intent | 177 | -0.7201486 | 0.000000000000000000000 |
| tabs | behav_intent | 177 | -0.6329067 | 0.000000000000000000000 |
| gender_comp | monetary_behav | 176 | -0.1401948 | 0.063479498807119671966 |
| autonomy_geniden | monetary_behav | 177 | 0.4267148 | 0.000000003171027440629 |
| legislation | monetary_behav | 177 | -0.4689116 | 0.000000000046196824144 |
| tabs | monetary_behav | 177 | -0.5131374 | 0.000000000000281996648 |
| behav_intent | monetary_behav | 177 | 0.4537386 | 0.000000000225991669822 |
| gender_comp | trust_scientists | 176 | -0.0836369 | 0.269767836021125084756 |
| autonomy_geniden | trust_scientists | 177 | 0.4860468 | 0.000000000006985079182 |
| legislation | trust_scientists | 177 | -0.4778218 | 0.000000000017525536578 |
| tabs | trust_scientists | 177 | -0.5344717 | 0.000000000000018207658 |
| behav_intent | trust_scientists | 177 | 0.3089385 | 0.000028661721551248576 |
| monetary_behav | trust_scientists | 177 | 0.2704055 | 0.000272562562991351953 |
| gender_comp | pol_or | 175 | 0.1854310 | 0.014018558972733963230 |
| autonomy_geniden | pol_or | 176 | -0.6100456 | 0.000000000000000000000 |
| legislation | pol_or | 176 | 0.6634318 | 0.000000000000000000000 |
| tabs | pol_or | 176 | 0.6514551 | 0.000000000000000000000 |
| behav_intent | pol_or | 176 | -0.4244636 | 0.000000004331258685042 |
| monetary_behav | pol_or | 176 | -0.2851590 | 0.000125023334211604720 |
| trust_scientists | pol_or | 176 | -0.5165717 | 0.000000000000215383267 |
Manipulation 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.”
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| 3.401 | 164.6 | 0.0008419 * * * | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 29.54 | 12.89 |
0.5152
Serial Mediation Models
Next, we will test three separate serial mediation models. The first two will assess whether both the perceived gender development age gap and autonomy support mediate the relationship between condition and behavioral support for transgender youth, such that condition will influence the perceived gender development age gap, which will influence autonomy support, and then finally behavioral support for transgender youth. In the first model, behavioral support for transgender youth will be operationalized through the scale assessing behavioral intentions to support the rights of transgender youth, while in the second model, support for transgender youth will be operationalized through participants’ monetary behavioral support for transgender youth. The third and final serial mediation model will test whether 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.
Behavioral Intentions
## lavaan 0.6.15 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 176 177
##
## Model Test User Model:
##
## Test statistic 1.031
## Degrees of freedom 3
## P-value (Chi-square) 0.794
##
## Model Test Baseline Model:
##
## Test statistic 91.050
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.046
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1507.792
## Loglikelihood unrestricted model (H1) -1507.277
##
## Akaike (AIC) 3033.584
## Bayesian (BIC) 3062.119
## Sample-size adjusted Bayesian (SABIC) 3033.618
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.081
## P-value H_0: RMSEA <= 0.050 0.879
## P-value H_0: RMSEA >= 0.080 0.052
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.017
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 10000
## Number of successful bootstrap draws 10000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## gender_comp ~
## conditin (a1) -16.648 4.953 -3.361 0.001 -26.256 -6.787
## autonomy_geniden ~
## gndr_cmp (d21) -0.013 0.004 -3.232 0.001 -0.022 -0.005
## behav_intent ~
## atnmy_gn (b2) 0.485 0.040 12.098 0.000 0.408 0.565
## Std.lv Std.all
##
## -16.648 -0.251
##
## -0.013 -0.238
##
## 0.485 0.567
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 46.186 8.256 5.594 0.000 30.327 62.730
## .autonomy_gendn 5.381 0.164 32.869 0.000 5.054 5.698
## .behav_intent 0.379 0.181 2.092 0.036 0.021 0.733
## Std.lv Std.all
## 46.186 1.391
## 5.381 2.925
## 0.379 0.241
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 1032.408 245.239 4.210 0.000 609.081 1552.243
## .autonomy_gendn 3.194 0.340 9.384 0.000 2.492 3.823
## .behav_intent 1.681 0.168 9.994 0.000 1.353 2.017
## Std.lv Std.all
## 1032.408 0.937
## 3.194 0.943
## 1.681 0.678
##
## R-Square:
## Estimate
## gender_comp 0.063
## autonomy_gendn 0.057
## behav_intent 0.322
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind_eff 0.107 0.055 1.944 0.052 0.023 0.235
## Std.lv Std.all
## 0.107 0.034## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 15 autonomy_geniden ~ behav_intent 0.597 -0.337 -0.337 -0.289 -0.289
## 17 behav_intent ~ gender_comp 0.597 -0.002 -0.002 -0.049 -0.049
## 14 gender_comp ~ behav_intent 0.571 -1.429 -1.429 -0.068 -0.068
## 13 gender_comp ~ autonomy_geniden 0.340 -3.153 -3.153 -0.175 -0.175
## 16 autonomy_geniden ~ condition 0.340 -0.162 -0.162 -0.044 -0.088Monetary Behavioral Support
## lavaan 0.6.15 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 176 177
##
## Model Test User Model:
##
## Test statistic 0.701
## Degrees of freedom 3
## P-value (Chi-square) 0.873
##
## Model Test Baseline Model:
##
## Test statistic 58.215
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.088
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1462.260
## Loglikelihood unrestricted model (H1) -1461.909
##
## Akaike (AIC) 2942.520
## Bayesian (BIC) 2971.054
## Sample-size adjusted Bayesian (SABIC) 2942.553
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.064
## P-value H_0: RMSEA <= 0.050 0.928
## P-value H_0: RMSEA >= 0.080 0.029
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.016
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 10000
## Number of successful bootstrap draws 10000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## gender_comp ~
## conditin (a1) -16.648 4.882 -3.410 0.001 -26.198 -6.976
## autonomy_geniden ~
## gndr_cmp (d21) -0.013 0.004 -3.214 0.001 -0.022 -0.005
## monetary_behav ~
## atnmy_gn (b2) 0.259 0.033 7.723 0.000 0.195 0.326
## Std.lv Std.all
##
## -16.648 -0.251
##
## -0.013 -0.238
##
## 0.259 0.429
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 46.186 8.168 5.654 0.000 30.458 62.224
## .autonomy_gendn 5.381 0.162 33.202 0.000 5.067 5.694
## .monetary_behav -0.405 0.148 -2.740 0.006 -0.703 -0.118
## Std.lv Std.all
## 46.186 1.391
## 5.381 2.925
## -0.405 -0.366
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 1032.408 245.119 4.212 0.000 611.273 1556.075
## .autonomy_gendn 3.194 0.338 9.437 0.000 2.501 3.825
## .monetary_behav 1.002 0.095 10.525 0.000 0.810 1.186
## Std.lv Std.all
## 1032.408 0.937
## 3.194 0.943
## 1.002 0.816
##
## R-Square:
## Estimate
## gender_comp 0.063
## autonomy_gendn 0.057
## monetary_behav 0.184
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind_eff 0.057 0.029 1.926 0.054 0.013 0.127
## Std.lv Std.all
## 0.057 0.026## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 14 gender_comp ~ monetary_behav 0.439 -1.637 -1.637 -0.055 -0.055
## 20 condition ~ autonomy_geniden 0.340 -0.013 -0.013 -0.047 -0.047
## 13 gender_comp ~ autonomy_geniden 0.340 -3.153 -3.153 -0.175 -0.175
## 16 autonomy_geniden ~ condition 0.340 -0.162 -0.162 -0.044 -0.088
## 17 monetary_behav ~ gender_comp 0.332 -0.001 -0.001 -0.040 -0.040Anti-transgender Legislation
## lavaan 0.6.15 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 176 177
##
## Model Test User Model:
##
## Test statistic 1.168
## Degrees of freedom 3
## P-value (Chi-square) 0.761
##
## Model Test Baseline Model:
##
## Test statistic 176.290
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.022
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1493.073
## Loglikelihood unrestricted model (H1) -1492.489
##
## Akaike (AIC) 3004.146
## Bayesian (BIC) 3032.681
## Sample-size adjusted Bayesian (SABIC) 3004.180
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.086
## P-value H_0: RMSEA <= 0.050 0.858
## P-value H_0: RMSEA >= 0.080 0.063
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.021
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 10000
## Number of successful bootstrap draws 10000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## gender_comp ~
## conditin (a1) -16.648 4.885 -3.408 0.001 -25.920 -6.915
## autonomy_geniden ~
## gndr_cmp (d21) -0.013 0.004 -3.239 0.001 -0.022 -0.005
## legislation ~
## atnmy_gn (b2) -0.765 0.035 -21.549 0.000 -0.835 -0.694
## Std.lv Std.all
##
## -16.648 -0.251
##
## -0.013 -0.238
##
## -0.765 -0.763
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 46.186 8.186 5.642 0.000 30.029 62.108
## .autonomy_gendn 5.381 0.162 33.119 0.000 5.060 5.696
## .legislation 7.996 0.175 45.730 0.000 7.654 8.349
## Std.lv Std.all
## 46.186 1.391
## 5.381 2.925
## 7.996 4.335
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .gender_comp 1032.408 248.359 4.157 0.000 609.303 1565.739
## .autonomy_gendn 3.194 0.338 9.444 0.000 2.490 3.820
## .legislation 1.422 0.172 8.273 0.000 1.090 1.759
## Std.lv Std.all
## 1032.408 0.937
## 3.194 0.943
## 1.422 0.418
##
## R-Square:
## Estimate
## gender_comp 0.063
## autonomy_gendn 0.057
## legislation 0.582
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind_eff -0.168 0.084 -1.993 0.046 -0.361 -0.040
## Std.lv Std.all
## -0.168 -0.045## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 21 condition ~ legislation 0.722 0.018 0.018 0.066 0.066
## 14 gender_comp ~ legislation 0.643 1.605 1.605 0.089 0.089
## 18 legislation ~ condition 0.387 0.112 0.112 0.030 0.061
## 13 gender_comp ~ autonomy_geniden 0.340 -3.153 -3.153 -0.175 -0.175
## 16 autonomy_geniden ~ condition 0.340 -0.162 -0.162 -0.044 -0.088Secondary Analyses
Additional Moderation Models
“In addition to our planned serial mediation model, we will also test two moderation models for each of our three main outcome variables (behavioral intentions to support the rights of transgender youth, monetary behavioral support for transgender youth, and support for anti-trans legislation). For our first model, we will test whether trust in scientists moderates the relation between condition and each of our three main outcome variables. Participants’ trust in scientists will be measured with 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).”
Moderation by trust in scientists
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 38.6185 | 10.6132 | 3.6387 | 0.0004 |
| condition1 | -14.1737 | 10.6132 | -1.3355 | 0.1835 |
| trust_scientists | -3.2280 | 1.9190 | -1.6821 | 0.0944 |
| condition1:trust_scientists | 0.9913 | 1.9190 | 0.5166 | 0.6061 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0781 | 0.062 | 32.2374 | 4.8568 | 0.0029 | 3 | -858.98 | 1727.96 | 1743.81 | 178750 | 172 | 176 |
Moderation by political ideology and education
“For our second model, we will test whether political ideology and education level together moderate the relation between condition and each of our three main outcome variables.”
A higher score in political ideology is more conservative
1 Strongly liberal 2
3
4 Moderate 5 6 7 Strongly conservative
“Some high school or high school diploma” is the reference level for education
##
## Some high school or high school diploma
## 16
## Some college, 2-year degree, or technical/trade school
## 63
## Bachelor's degree
## 64
## Graduate or professional school
## 32| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 14.9963 | 10.9441 | 1.3703 | 0.1726 |
| condition1 | -6.2802 | 10.9441 | -0.5738 | 0.5669 |
| pol_or | 1.9711 | 2.6645 | 0.7397 | 0.4606 |
| education_simplified1 | -10.0197 | 20.5179 | -0.4883 | 0.6260 |
| education_simplified2 | -1.7195 | 7.3437 | -0.2341 | 0.8152 |
| education_simplified3 | -1.3690 | 4.7586 | -0.2877 | 0.7740 |
| condition1:pol_or | -1.8574 | 2.6645 | -0.6971 | 0.4868 |
| condition1:education_simplified1 | 18.0947 | 20.5179 | 0.8819 | 0.3792 |
| condition1:education_simplified2 | 2.6948 | 7.3437 | 0.3670 | 0.7141 |
| condition1:education_simplified3 | 6.7757 | 4.7586 | 1.4239 | 0.1565 |
| pol_or:education_simplified1 | 2.1644 | 4.8184 | 0.4492 | 0.6539 |
| pol_or:education_simplified2 | 0.4450 | 1.8150 | 0.2452 | 0.8066 |
| pol_or:education_simplified3 | -0.1782 | 1.2582 | -0.1416 | 0.8876 |
| condition1:pol_or:education_simplified1 | -2.7408 | 4.8184 | -0.5688 | 0.5703 |
| condition1:pol_or:education_simplified2 | 0.0971 | 1.8150 | 0.0535 | 0.9574 |
| condition1:pol_or:education_simplified3 | -1.8285 | 1.2582 | -1.4533 | 0.1481 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1472 | 0.0657 | 32.2783 | 1.806 | 0.038 | 15 | -838.152 | 1710.3 | 1763.91 | 163576 | 157 | 173 |
Testing for direct effects of condition
“We will also conduct four separate independent samples t-test to determine whether there is a direct effect of condition on support for youth’s gender identity autonomy, behavioral intentions to support the rights of transgender youth, monetary behavioral support for transgender youth, and support for anti-trans legislation, respectively.”
Gender identity autonomy
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.2594 | 175 | 0.7957 | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 5.065 | 5.137 |
Behavioral Intentions
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.5692 | 174.9 | 0.57 | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 2.785 | 2.92 |
Monetary Behavioral Support
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.01602 | 175 | 0.9872 | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 0.9195 | 0.9222 |
Support for anti-trans legislation
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.2679 | 169.9 | 0.7891 | two.sided |
| mean in group Control | mean in group Experiment |
|---|---|
| 4.048 | 4.122 |
Testing for order effects
“Next, we will conduct an independent samples t-test to test for order effects on participants’ age gap in their perceptions of gender development for participants who are first asked about gender development of cisgender people compared to gender development of transgender people. If there is an order effect, order will be included as a covariate in the confirmatory models.”
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| 0.1056 | 147.3 | 0.916 | two.sided |
| mean in group Cis first | mean in group Trans first |
|---|---|
| 21.28 | 20.74 |
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.3111 | 160.2 | 0.7561 | two.sided |
| mean in group Cis first | mean in group Trans first |
|---|---|
| 41.02 | 42.18 |
| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -0.08194 | 167 | 0.9348 | two.sided |
| mean in group Cis first | mean in group Trans first |
|---|---|
| 62.3 | 62.8 |
Exploratory Factor Analysis on Behavioral Intentions Scale
“We will additionally conduct an exploratory factor analysis on the measure of behavioral intentions to support the rights of transgender youth to determine the underlying factor structure of this scale. We will consider factor loadings greater than or equal to .40, and we will only include questions in the average that load onto a factor. We will also conduct exploratory analyses to determine how condition relates to the individual factor(s) that emerge based on the exploratory factor analysis.”
##
## Cronbach's alpha for the 'pcsi3Data[, c("behav_intent_1", "behav_intent_2", "behav_intent_3", ' ' "behav_intent_4", "behav_intent_5", "behav_intent_6", "behav_intent_7", ' ' "behav_intent_8", "behav_intent_9", "behav_intent_10", "behav_intent_11", ' ' "behav_intent_12", "behav_intent_13", "behav_intent_14", ' ' "behav_intent_15", "behav_intent_16", "behav_intent_17", ' ' "behav_intent_18")]' data-set
##
## Items: 18
## Sample units: 177
## alpha: 0.97
##
## Bootstrap 95% CI based on 1000 samples
## 2.5% 97.5%
## 0.962 0.976
## Parallel analysis suggests that the number of factors = 2 and the number of components = 1##
## Call:
## factanal(x = behavintentData, factors = 1, scores = c("regression"), rotation = "varimax")
##
## Uniquenesses:
## behav_intent_1 behav_intent_2 behav_intent_3 behav_intent_4 behav_intent_5
## 0.229 0.269 0.302 0.215 0.231
## behav_intent_6 behav_intent_7 behav_intent_8 behav_intent_9 behav_intent_10
## 0.326 0.284 0.294 0.492 0.396
## behav_intent_11 behav_intent_12 behav_intent_13 behav_intent_14 behav_intent_15
## 0.327 0.346 0.261 0.281 0.438
## behav_intent_16 behav_intent_17 behav_intent_18
## 0.562 0.526 0.444
##
## Loadings:
## Factor1
## behav_intent_1 0.878
## behav_intent_2 0.855
## behav_intent_3 0.835
## behav_intent_4 0.886
## behav_intent_5 0.877
## behav_intent_6 0.821
## behav_intent_7 0.846
## behav_intent_8 0.840
## behav_intent_9 0.713
## behav_intent_10 0.777
## behav_intent_11 0.820
## behav_intent_12 0.809
## behav_intent_13 0.859
## behav_intent_14 0.848
## behav_intent_15 0.750
## behav_intent_16 0.661
## behav_intent_17 0.689
## behav_intent_18 0.745
##
## Factor1
## SS loadings 11.776
## Proportion Var 0.654
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 554.19 on 135 degrees of freedom.
## The p-value is 3.67e-52