PCSI Study 1 Pilot Analyses

Descriptive Statistics

Descriptive stats for each question (continued below)
  vars n mean sd median trimmed
gender_cat_1 1 24 41.17 40.42 24 35.2
gender_label_1 2 24 45 36.35 36 39.2
gender_stereo_1 3 24 66.62 45.91 60 61.9
gender_identify_1 4 24 55.83 55.86 36 49
gender_disclose_1 5 24 56.33 47.75 42.5 50.2
race_cat_1 6 22 71.91 48.69 60 67.61
race_label_1 7 22 97.77 42.75 84 95.5
race_stereo_1 8 22 97.41 48.31 84 95.06
race_identify_1 9 22 87.5 53.7 72 84.61
race_disclose_1 10 22 86.45 47.61 72 81.78
sexor_cat_1 11 24 139.8 34.37 144 143.8
sexor_label_1 12 24 135.6 42.5 138.5 141.1
sexor_stereo_1 13 24 120.6 43.69 125.5 121.7
sexor_identify_1 14 24 133.9 32.45 132 136.1
sexor_disclose_1 15 24 138.2 40.33 144 142.4
religion_cat_1 16 23 120.2 53.32 144 124.5
religion_label_1 17 23 106.1 39.04 108 106.3
religion_stereo_1 18 23 112.9 47.55 120 114.3
religion_identify_1 19 23 87.39 45.31 84 84.79
religion_disclose_1 20 23 87.26 44.56 84 84.79
polor_cat_1 21 22 160.2 26.9 168 165.4
polor_label_1 22 22 150 31.86 156 154.6
polor_stereo_1 23 22 148 32.02 156 151.6
polor_identify_1 24 22 150.8 32.39 156 155.9
polor_disclose_1 25 22 148.1 29.71 152 152.1
socclass_cat_1 26 23 131.6 47.89 144 137.1
socclass_label_1 27 23 128.7 41.75 133 132.4
socclass_stereo_1 28 23 115.3 44.04 120 118.1
socclass_identify_1 29 23 126.8 37.31 129 128.8
socclass_disclose_1 30 23 136.2 38 144 140.2
nation_cat_1 31 23 65.83 28.89 60 64.89
nation_label_1 32 23 88.83 32.64 84 86.42
nation_stereo_1 33 23 96.65 40.87 96 95.68
nation_identify_1 34 23 66.3 37.95 60 64.42
nation_disclose_1 35 23 82.74 27.78 84 81.84
disability_cat_1 36 23 82.13 48.47 71 77.68
disability_label_1 37 23 105.5 41.14 99 104.3
disability_stereo_1 38 23 103.8 41.83 96 102.9
disability_identify_1 39 23 91.96 57.21 85 91.68
disability_disclose_1 40 23 99.17 48.95 96 97.95
  mad min max range skew kurtosis se
gender_cat_1 25.2 2 180 178 1.778 3.278 8.252
gender_label_1 17.79 1 180 179 2.223 5.37 7.421
gender_stereo_1 35.58 0 180 180 1.075 0.2817 9.371
gender_identify_1 34.1 0 180 180 1.273 0.3163 11.4
gender_disclose_1 27.43 0 180 180 1.202 0.4127 9.747
race_cat_1 45.22 12 180 168 0.6856 -0.5672 10.38
race_label_1 44.48 48 168 120 0.4169 -1.486 9.114
race_stereo_1 46.7 36 180 144 0.5504 -1.174 10.3
race_identify_1 44.48 18 180 162 0.5845 -1.243 11.45
race_disclose_1 35.58 34 180 146 0.8348 -0.8086 10.15
sexor_cat_1 35.58 60 180 120 -0.8004 -0.08622 7.016
sexor_label_1 45.22 25 180 155 -0.8786 0.1643 8.675
sexor_stereo_1 51.15 41 180 139 -0.2079 -1.329 8.919
sexor_identify_1 35.58 60 180 120 -0.4284 -0.5464 6.623
sexor_disclose_1 44.48 48 180 132 -0.6953 -0.6598 8.232
religion_cat_1 35.58 18 180 162 -0.7085 -1.008 11.12
religion_label_1 44.48 36 180 144 -0.01112 -1.076 8.141
religion_stereo_1 53.37 28 180 152 -0.2465 -1.32 9.914
religion_identify_1 51.89 12 180 168 0.4731 -0.7359 9.449
religion_disclose_1 53.37 12 180 168 0.382 -0.817 9.291
polor_cat_1 17.79 66 180 114 -1.967 4.192 5.736
polor_label_1 35.58 60 180 120 -1.082 0.6365 6.792
polor_stereo_1 35.58 72 180 108 -0.7059 -0.6959 6.826
polor_identify_1 35.58 65 180 115 -1.04 0.3181 6.905
polor_disclose_1 25.2 72 180 108 -0.9572 0.1896 6.334
socclass_cat_1 53.37 24 180 156 -0.7986 -0.4901 9.985
socclass_label_1 34.1 36 180 144 -0.624 -0.5749 8.706
socclass_stereo_1 34.1 12 180 168 -0.4534 -0.3594 9.182
socclass_identify_1 40.03 48 180 132 -0.3427 -0.7599 7.781
socclass_disclose_1 35.58 48 180 132 -0.6749 -0.4441 7.924
nation_cat_1 17.79 11 120 109 0.4607 -0.4925 6.025
nation_label_1 35.58 47 168 121 0.55 -0.5873 6.806
nation_stereo_1 51.89 30 180 150 0.1496 -1.146 8.522
nation_identify_1 35.58 1 144 143 0.5977 -0.3562 7.913
nation_disclose_1 35.58 35 145 110 0.1132 -0.7684 5.792
disability_cat_1 51.89 24 180 156 0.6738 -0.8099 10.11
disability_label_1 31.13 36 180 144 0.2509 -0.8616 8.579
disability_stereo_1 37.06 36 180 144 0.2247 -0.8727 8.721
disability_identify_1 72.65 1 180 179 0.2008 -1.36 11.93
disability_disclose_1 53.37 24 180 156 0.3154 -1.169 10.21
Descriptive stats for averages of each social category (continued below)
  vars n mean sd median trimmed mad
gender_mean 1 24 52.99 38.34 38.9 48.42 26.98
race_mean 2 22 88.21 44.25 69.6 84.98 37.21
sexor_mean 3 24 133.6 29.75 136 136.1 33.21
religion_mean 4 23 102.8 36.46 103.2 103.2 36.18
polor_mean 5 22 151.4 26.72 156.4 155.3 24.31
socclass_mean 6 23 127.7 33.49 130.4 130.1 29.36
nation_mean 7 23 80.07 24.74 79.2 79.45 31.73
disability_mean 8 23 96.5 41.99 83.8 94.21 35.29
  min max range skew kurtosis se
gender_mean 4.6 180 175.4 1.511 2.506 7.826
race_mean 41 172.8 131.8 0.6153 -1.277 9.434
sexor_mean 57.6 180 122.4 -0.7279 -0.1849 6.072
religion_mean 38.2 165.6 127.4 0.02605 -1.132 7.603
polor_mean 67 180 113 -1.408 2.111 5.696
socclass_mean 50.4 180 129.6 -0.6879 -0.353 6.982
nation_mean 39.4 125 85.6 0.2152 -1.219 5.158
disability_mean 35.6 180 144.4 0.4866 -0.962 8.755

Accuracy

“We will run one-sample T-tests to determine if there is any difference between participants’ estimates of the age that children meet various milestones and the actual age at which children meet these milestones by testing whether participant’s age difference scores are significantly different than zero. We will conduct a test for each question across each social category resulting in a total of 40 one-sample t-tests. Due to the large number of analyses proposed, we will use a more conservative p-value of p less-than .001 as a cut-off for determining significance.”

Gender

Categorization
Test statistic df P value Alternative hypothesis mean of x
4.625 23 0.0001184 * * * two.sided 38.17
Labeling
Test statistic df P value Alternative hypothesis mean of x
2.83 23 0.009492 * * two.sided 21
Stereotypes
Test statistic df P value Alternative hypothesis mean of x
3.268 23 0.00338 * * two.sided 30.62
Identify
Test statistic df P value Alternative hypothesis mean of x
2.792 23 0.01037 * two.sided 31.83
Disclose
Test statistic df P value Alternative hypothesis mean of x
2.086 23 0.04824 * two.sided 20.33

Race

Categorization
Test statistic df P value Alternative hypothesis mean of x
6.06 21 0.000005161 * * * two.sided 62.91
Labeling
Test statistic df P value Alternative hypothesis mean of x
5.461 21 0.00002033 * * * two.sided 49.77
Stereotypes
Test statistic df P value Alternative hypothesis mean of x
5.963 21 0.000006432 * * * two.sided 61.41
Identify
Test statistic df P value Alternative hypothesis mean of x
3.45 21 0.002398 * * two.sided 39.5
Disclose
Test statistic df P value Alternative hypothesis mean of x
3.789 21 0.001076 * * two.sided 38.45

Factor Analysis

“We will additionally conduct exploratory analyses examining whether there are differences in how much participants incorrectly estimate the age at which children reach various developmental milestones both within social categories and between social categories. We do not have specific hypotheses as to whether participant’s responses will differ either within or between social categories. To examine responses within social categories, we plan to conduct an exploratory factor analysis with oblimin rotation for each social category to determine the underlying factor structure for each social category. This will allow us to determine whether participant’s responses to questions within a social category hang together (i.e., show similar response patterns across questions for the same social categories). We will consider factor loadings greater than or equal to .40.”

Corplot to look at correlations overall between all items for all categories

Gender

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = genderData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      gender_cat_1    gender_label_1   gender_stereo_1 gender_identify_1 
##             0.005             0.264             0.558             0.448 
## gender_disclose_1 
##             0.476 
## 
## Loadings:
##                   Factor1
## gender_cat_1      0.998  
## gender_label_1    0.858  
## gender_stereo_1   0.665  
## gender_identify_1 0.743  
## gender_disclose_1 0.724  
## 
##                Factor1
## SS loadings      3.248
## Proportion Var   0.650
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 10.77 on 5 degrees of freedom.
## The p-value is 0.056

Race

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = raceData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      race_cat_1    race_label_1   race_stereo_1 race_identify_1 race_disclose_1 
##           0.213           0.381           0.296           0.035           0.126 
## 
## Loadings:
##                 Factor1
## race_cat_1      0.887  
## race_label_1    0.787  
## race_stereo_1   0.839  
## race_identify_1 0.982  
## race_disclose_1 0.935  
## 
##                Factor1
## SS loadings      3.948
## Proportion Var   0.790
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 10.44 on 5 degrees of freedom.
## The p-value is 0.0637

Sexual Orientation

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = sexorData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      sexor_cat_1    sexor_label_1   sexor_stereo_1 sexor_identify_1 
##            0.675            0.289            0.674            0.464 
## sexor_disclose_1 
##            0.390 
## 
## Loadings:
##                  Factor1
## sexor_cat_1      0.570  
## sexor_label_1    0.843  
## sexor_stereo_1   0.571  
## sexor_identify_1 0.732  
## sexor_disclose_1 0.781  
## 
##                Factor1
## SS loadings      2.508
## Proportion Var   0.502
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 4.33 on 5 degrees of freedom.
## The p-value is 0.503

Religion

## Parallel analysis suggests that the number of factors =  2  and the number of components =  1
## 
## Call:
## factanal(x = religionData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      religion_cat_1    religion_label_1   religion_stereo_1 religion_identify_1 
##               0.201               0.465               0.220               0.771 
## religion_disclose_1 
##               0.712 
## 
## Loadings:
##                     Factor1
## religion_cat_1      0.894  
## religion_label_1    0.731  
## religion_stereo_1   0.883  
## religion_identify_1 0.479  
## religion_disclose_1 0.537  
## 
##                Factor1
## SS loadings      2.631
## Proportion Var   0.526
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 17.85 on 5 degrees of freedom.
## The p-value is 0.00314

Political Orientation

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = polorData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      polor_cat_1    polor_label_1   polor_stereo_1 polor_identify_1 
##            0.647            0.142            0.276            0.005 
## polor_disclose_1 
##            0.410 
## 
## Loadings:
##                  Factor1
## polor_cat_1      0.594  
## polor_label_1    0.926  
## polor_stereo_1   0.851  
## polor_identify_1 0.998  
## polor_disclose_1 0.768  
## 
##                Factor1
## SS loadings      3.520
## Proportion Var   0.704
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 4.53 on 5 degrees of freedom.
## The p-value is 0.476

Social Class

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = socclassData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      socclass_cat_1    socclass_label_1   socclass_stereo_1 socclass_identify_1 
##               0.393               0.005               0.500               0.659 
## socclass_disclose_1 
##               0.380 
## 
## Loadings:
##                     Factor1
## socclass_cat_1      0.779  
## socclass_label_1    0.998  
## socclass_stereo_1   0.707  
## socclass_identify_1 0.584  
## socclass_disclose_1 0.787  
## 
##                Factor1
## SS loadings      3.063
## Proportion Var   0.613
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 6.6 on 5 degrees of freedom.
## The p-value is 0.252

Nationality

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = nationData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      nation_cat_1    nation_label_1   nation_stereo_1 nation_identify_1 
##             0.915             0.238             0.610             0.720 
## nation_disclose_1 
##             0.311 
## 
## Loadings:
##                   Factor1
## nation_cat_1      0.291  
## nation_label_1    0.873  
## nation_stereo_1   0.624  
## nation_identify_1 0.529  
## nation_disclose_1 0.830  
## 
##                Factor1
## SS loadings      2.205
## Proportion Var   0.441
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 7.55 on 5 degrees of freedom.
## The p-value is 0.183

Disability

## Parallel analysis suggests that the number of factors =  1  and the number of components =  1
## 
## Call:
## factanal(x = disabilityData, factors = 1, scores = c("regression"),     rotation = "varimax")
## 
## Uniquenesses:
##      disability_cat_1    disability_label_1   disability_stereo_1 
##                 0.465                 0.274                 0.256 
## disability_identify_1 disability_disclose_1 
##                 0.196                 0.158 
## 
## Loadings:
##                       Factor1
## disability_cat_1      0.732  
## disability_label_1    0.852  
## disability_stereo_1   0.862  
## disability_identify_1 0.897  
## disability_disclose_1 0.918  
## 
##                Factor1
## SS loadings       3.65
## Proportion Var    0.73
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 4.01 on 5 degrees of freedom.
## The p-value is 0.548

Examining responses for each question type

“To examine responses between social categories, we plan to assess participant’s responses in two separate ways. First, we plan to examine whether participant’s responses differ across question type. We will fit a separate multilevel linear regression model for each of the five milestone question types with social category as a fixed effect, participant as a random effect (to control for the differing categories that participants will complete), and experience with children as a covariate to determine whether age difference scores for each milestone vary by social category.”

Categorization

## [1] "disability_cat_1" "gender_cat_1"     "nation_cat_1"     "polor_cat_1"     
## [5] "race_cat_1"       "religion_cat_1"   "sexor_cat_1"      "socclass_cat_1"
Estimates for the categorization model
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 91.0952 8.1121 11.2296 45.2517 0.0000
fixed NA category1 -18.7158 5.6156 -3.3328 161.6005 0.0011
fixed NA category2 0.5071 3.2525 0.1559 159.2480 0.8763
fixed NA category3 24.3923 2.3449 10.4022 159.6889 0.0000
fixed NA category4 -2.9079 1.8182 -1.5993 158.5663 0.1117
fixed NA category5 6.0048 1.4683 4.0895 162.4211 0.0001
fixed NA category6 7.2223 1.2105 5.9662 158.3545 0.0000
fixed NA category7 4.1508 1.0718 3.8727 160.7523 0.0002
fixed NA demo_expchildren 2.0042 1.3744 1.4582 45.2815 0.1517
ran_pars id sd__(Intercept) 17.2512 NA NA NA NA
ran_pars Residual sd__Observation 37.0717 NA NA NA NA
Summary for the categorization model
nobs sigma logLik AIC BIC deviance df.residual
184 37.0717 -940.2 1902.4 1937.76 1880.4 173

Labeling

## [1] "disability_label_1" "gender_label_1"     "nation_label_1"    
## [4] "polor_label_1"      "race_label_1"       "religion_label_1"  
## [7] "sexor_label_1"      "socclass_label_1"
Estimates for the labeling model
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 108.1880 8.6038 12.5744 45.7701 0.0000
fixed NA category1 -28.8659 4.7675 -6.0547 153.7321 0.0000
fixed NA category2 3.6571 2.7564 1.3268 152.1641 0.1866
fixed NA category3 17.6865 1.9878 8.8976 152.3635 0.0000
fixed NA category4 0.2797 1.5402 0.1816 151.8585 0.8561
fixed NA category5 1.1922 1.2475 0.9557 154.3620 0.3407
fixed NA category6 5.6472 1.0251 5.5087 151.4862 0.0000
fixed NA category7 2.6820 0.9096 2.9487 153.5584 0.0037
fixed NA demo_expchildren -0.1970 1.4577 -0.1352 45.7909 0.8931
ran_pars id sd__(Intercept) 21.9750 NA NA NA NA
ran_pars Residual sd__Observation 30.9183 NA NA NA NA
Summary for the labeling model
nobs sigma logLik AIC BIC deviance df.residual
184 30.9183 -917.879 1857.76 1893.12 1835.76 173

Stereotypes

## [1] "disability_stereo_1" "gender_stereo_1"     "nation_stereo_1"    
## [4] "polor_stereo_1"      "race_stereo_1"       "religion_stereo_1"  
## [7] "sexor_stereo_1"      "socclass_stereo_1"
Estimates for the stereotype model
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 100.9677 9.6002 10.5173 45.8309 0.0000
fixed NA category1 -16.1289 5.3083 -3.0384 153.7119 0.0028
fixed NA category2 2.7951 3.0690 0.9108 152.1522 0.3639
fixed NA category3 15.0677 2.2132 6.8081 152.3497 0.0000
fixed NA category4 -1.0177 1.7148 -0.5935 151.8492 0.5537
fixed NA category5 1.7738 1.3890 1.2771 154.3392 0.2035
fixed NA category6 2.5089 1.1414 2.1982 151.4770 0.0295
fixed NA category7 0.9365 1.0127 0.9247 153.5423 0.3566
fixed NA demo_expchildren 1.2746 1.6265 0.7837 45.8516 0.4373
ran_pars id sd__(Intercept) 24.5473 NA NA NA NA
ran_pars Residual sd__Observation 34.4206 NA NA NA NA
Summary for the stereotype model
nobs sigma logLik AIC BIC deviance df.residual
184 34.4206 -937.727 1897.46 1932.82 1875.46 173

Identification

## [1] "disability_identify_1" "gender_identify_1"     "nation_identify_1"    
## [4] "polor_identify_1"      "race_identify_1"       "religion_identify_1"  
## [7] "sexor_identify_1"      "socclass_identify_1"
Estimates for the identification model
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 99.2853 9.9226 10.0060 45.7857 0.0000
fixed NA category1 -16.8778 5.5948 -3.0167 154.2811 0.0030
fixed NA category2 -2.5998 3.2350 -0.8036 152.6610 0.4229
fixed NA category3 20.0629 2.3329 8.5998 152.8737 0.0000
fixed NA category4 -0.3149 1.8077 -0.1742 152.3354 0.8619
fixed NA category5 -0.8079 1.4639 -0.5519 154.9263 0.5818
fixed NA category6 6.5102 1.2032 5.4107 151.9666 0.0000
fixed NA category7 3.2435 1.0674 3.0387 154.0746 0.0028
fixed NA demo_expchildren 0.1473 1.6811 0.0876 45.8071 0.9306
ran_pars id sd__(Intercept) 25.1045 NA NA NA NA
ran_pars Residual sd__Observation 36.3238 NA NA NA NA
Summary for the identification model
nobs sigma logLik AIC BIC deviance df.residual
184 36.3238 -946.672 1915.34 1950.71 1893.34 173

Disclosure

## [1] "disability_disclose_1" "gender_disclose_1"     "nation_disclose_1"    
## [4] "polor_disclose_1"      "race_disclose_1"       "religion_disclose_1"  
## [7] "sexor_disclose_1"      "socclass_disclose_1"
Estimates for the disclosure model
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 99.7901 9.2073 10.8382 45.2604 0.0000
fixed NA category1 -20.4055 5.0772 -4.0190 153.2050 0.0001
fixed NA category2 0.6920 2.9353 0.2357 151.6363 0.8139
fixed NA category3 16.3968 2.1168 7.7459 151.8339 0.0000
fixed NA category4 -1.3698 1.6401 -0.8352 151.3331 0.4049
fixed NA category5 -1.3399 1.3285 -1.0086 153.8369 0.3147
fixed NA category6 7.0390 1.0917 6.4479 150.9564 0.0000
fixed NA category7 3.8933 0.9686 4.0194 153.0385 0.0001
fixed NA demo_expchildren 0.8547 1.5599 0.5479 45.2808 0.5865
ran_pars id sd__(Intercept) 23.5763 NA NA NA NA
ran_pars Residual sd__Observation 32.9165 NA NA NA NA
Summary for the disclosure model
nobs sigma logLik AIC BIC deviance df.residual
184 32.9165 -929.64 1881.28 1916.64 1859.28 173

Examining responses between social categories

“Next, we plan to examine whether participant’s responses differ across social categories as a whole. Specifically, we will calculate a composite variable for each social category. We will average participant’s age difference scores across all question types for a social category to calculate a single variable that represents participants average age difference score for a given social category. We will fit a single multilevel linear regression model with social category as a fixed effect, participant as a random effect, and experience with children as a covariate to determine whether the average age difference score varies by social category.”

## [1] "disability_mean" "gender_mean"     "nation_mean"     "polor_mean"     
## [5] "race_mean"       "religion_mean"   "sexor_mean"      "socclass_mean"
Estimates for the between category model
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 99.9051 8.2477 12.1131 45.6985 0.0000
fixed NA category1 -19.7925 4.0575 -4.8780 150.4091 0.0000
fixed NA category2 0.7853 2.3443 0.3350 149.1632 0.7381
fixed NA category3 18.7397 1.6908 11.0836 149.2893 0.0000
fixed NA category4 -0.9715 1.3098 -0.7418 148.9660 0.4594
fixed NA category5 1.3501 1.0620 1.2714 150.9367 0.2056
fixed NA category6 5.8477 0.8716 6.7089 148.5945 0.0000
fixed NA category7 2.8900 0.7741 3.7336 150.3993 0.0003
fixed NA demo_expchildren 0.8085 1.3973 0.5786 45.7153 0.5657
ran_pars id sd__(Intercept) 22.1979 NA NA NA NA
ran_pars Residual sd__Observation 26.1405 NA NA NA NA
Summary for the between category model
nobs sigma logLik AIC BIC deviance df.residual
184 26.1405 -892.776 1807.55 1842.92 1785.55 173

Examining variance by key demographic factors

“We will additionally conduct exploratory analyses to examine whether participant responses vary by key demographic factors. For each of the eight social categories, we will fit a linear regression with the average age difference score for that social category as the outcome, the relevant demographic factors as predictor(s), and experience with children as a covariate. The relevant demographic factor(s) for each category are outline below. 1. Outcome: average age difference score for gender; Predictor: participant gender & transgender status 2. Outcome: average age difference score for race/ethnicity; Predictor: participant race/ethnicity 3. Outcome: average age difference score for sexual orientation; Predictor: participant sexual orientation 4. Outcome: average age difference score for religion; Predictor: participant religiosity 5. Outcome: average age difference score for political orientation; Predictor: participant political orientation 6. Outcome: average age difference score for social class; Predictor: participant income 7. Outcome: average age difference score for disability status; Predictor: participant disability status 8. Outcome: average age difference score for nationality; Predictor: whether participant was born in US or not”

Gender

Estimates for the gender demo model
term estimate std.error statistic p.value
(Intercept) 62.6543 24.3192 2.5763 0.0176
demo_gender -33.1962 14.5099 -2.2878 0.0326
demo_trans NA NA NA NA
demo_expchildren 8.6980 2.5815 3.3694 0.0029
Summary for the gender demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.3801 0.3211 31.5899 6.4386 0.0066 2 -115.32 238.641 243.353 20956.3 21 24

Race

Estimates for the race demo model
term estimate std.error statistic p.value
(Intercept) 82.9503 21.1778 3.9168 0.0009
demo_race 0.3797 0.3502 1.0841 0.2919
demo_expchildren 0.1810 3.6662 0.0494 0.9611
Summary for the race demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0717 -0.026 44.8219 0.7335 0.4933 2 -113.263 234.527 238.891 38171 19 22

Sexual Orientation

Estimates for the sexor demo model
term estimate std.error statistic p.value
(Intercept) 128.4295 18.4356 6.9664 0.0000
demo_sexor 2.5145 5.9577 0.4221 0.6773
demo_expchildren 0.3054 2.3757 0.1286 0.8989
Summary for the sexor demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0084 -0.086 31 0.0891 0.9151 2 -114.868 237.736 242.448 20181 21 24

Religion

Estimates for the religion demo model
term estimate std.error statistic p.value
(Intercept) 111.4616 25.7891 4.3220 0.0003
demo_relig -2.1152 3.7489 -0.5642 0.5789
demo_expchildren -0.1988 3.5841 -0.0555 0.9563
Summary for the religion demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0157 -0.0828 37.9431 0.1592 0.8539 2 -114.658 237.317 241.859 28793.6 20 23

Political Orientation

Estimates for the polor demo model
term estimate std.error statistic p.value
(Intercept) 129.3728 22.4969 5.7507 0.0000
demo_polor 3.8720 5.6423 0.6862 0.5008
demo_expchildren 2.0945 2.1726 0.9641 0.3471
Summary for the polor demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0576 -0.0416 27.2676 0.581 0.5689 2 -102.329 212.659 217.023 14127 19 22

Social Class

Estimates for the socclass demo model
term estimate std.error statistic p.value
(Intercept) 135.7111 26.7827 5.0671 0.0001
demo_income 3.0308 3.4319 0.8831 0.3877
demo_expchildren -5.0175 2.7574 -1.8197 0.0838
Summary for the socclass demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.2245 0.147 30.9266 2.8957 0.0786 2 -109.956 227.911 232.453 19129.1 20 23

Nationality

Estimates for the nation demo model
term estimate std.error statistic p.value
(Intercept) 96.5032 19.1016 5.0521 0.0001
demo_bornUS -7.7953 10.8754 -0.7168 0.4818
demo_expchildren -1.0892 1.9154 -0.5686 0.5759
Summary for the nation demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0388 -0.0573 25.4366 0.4038 0.6731 2 -105.461 218.921 223.463 12940.4 20 23

Disability

Estimates for the disability demo model
term estimate std.error statistic p.value
(Intercept) 62.5444 44.4139 1.4082 0.1744
demo_disability 13.2295 22.2598 0.5943 0.5590
demo_expchildren 1.6445 2.9194 0.5633 0.5795
Summary for the disability demo model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0367 -0.0596 43.2225 0.3812 0.6879 2 -117.655 243.309 247.851 37363.8 20 23