MIFT Study 5 Analyses

About the study

From preregistration:

Hypothesis: This study will use a 2 (mutability: fluid vs stable) x 2 (construct: identity vs behavior) design. We have three main hypotheses: 

Hypothesis 1: We anticipate a main effect of mutability such that participants who learn about multiracial fluidity for Black/White biracial people will show reduced trust for multiracial people compared to participants who learn about multiracial stability.

Hypothesis 2: We anticipate a main effect of mutability such that participants who learn about multiracial fluidity for Black/White biracial people will view multiracial people as less authentic compared to participants who learn about multiracial stability. 

Hypothesis 3: We anticipate these effects will be qualified by an interaction with construct (identity vs behavior) such that the effect of fluidity on both trust and authenticity will be found in the behavior condition but not in the identity condition.

Dependent Variables:

  1. Trust
  • Speeded Trust Task
  • Trust Survey
  1. Authenticity
  • Authenticity Scale
  1. Perceptions Identity Fluidity
  • Perceptions of Multiracial Identity Fluidity Scale
  • Multiracial Identity Fluidity Scale – Should
  1. Additional Moderators and Exploratory Variables
  • Race Essentialism
  • Participant Race
  • Participant Political Orientation
  • Post-race Ideology

Conditions:

As we are using a between-subjects, 2 (mutability: fluid, stable) x 2 (construct: identity, behavior) design, participants will be randomly assigned to one of four conditions. In the identity condition, participants will be randomly assigned to either read an article indicating that multiracial identity is fluid or to read an article indicating multiracial identity is stable. In the racial behavior condition, participants will be randomly assigned to either read an article indicating that multiracial behavior is fluid or to read an article indicating multiracial behavior is stable.

Descriptive Statistics

Descriptives for whole sample (across all conditions)

Descriptive stats for main dependent variables (continued below)
  vars n mean sd median trimmed
whitemulti_trust_dif 1 551 0.07703 0.5143 0.025 0.06359
White 2 551 3.096 0.624 3.05 3.098
Multiracial 3 551 3.019 0.5997 3 3.016
Black 4 551 3.108 0.688 3.1 3.137
trust_survey 5 555 5.019 0.9269 5.1 5.009
authen 6 555 5.325 1.067 5.5 5.355
fluidity 7 555 4.332 1.102 4.429 4.357
fluidity_should 8 555 4.604 1.375 4.571 4.661
postrace 9 555 3.475 1.495 3.4 3.452
race_ess 10 555 3.884 1.208 4 3.922
pol_or 11 555 3.272 1.724 3 3.162
Table continues below
  mad min max range skew
whitemulti_trust_dif 0.3336 -2.225 2.975 5.2 0.7919
White 0.4818 1 5 4 -0.01065
Multiracial 0.4818 1 5 4 0.0587
Black 0.556 1 5 4 -0.3885
trust_survey 1.038 1.6 7 5.4 -0.2193
authen 1.112 1 7 6 -0.4946
fluidity 1.059 1 7 6 -0.2268
fluidity_should 1.271 1 7 6 -0.3442
postrace 1.779 1 7 6 0.1384
race_ess 1.112 1 7 6 -0.2704
pol_or 1.483 1 7 6 0.3323
  kurtosis se
whitemulti_trust_dif 6.448 0.02191
White 1.12 0.02658
Multiracial 1.316 0.02555
Black 0.977 0.02931
trust_survey -0.2597 0.03934
authen 0.3569 0.04528
fluidity -0.07107 0.04677
fluidity_should -0.1777 0.05835
postrace -0.7467 0.06344
race_ess -0.1071 0.0513
pol_or -0.8009 0.07316

Descriptives by condition

  • fluidbehav:

    Table continues below
      vars n mean sd median trimmed
    whitemulti_trust_dif 1 137 0.1141 0.5799 0.025 0.07122
    White 2 137 3.027 0.6297 3 3.04
    Multiracial 3 137 2.913 0.6262 3 2.933
    Black 4 137 2.992 0.7452 3 3.034
    trust_survey 5 138 4.894 1.005 4.9 4.904
    authen 6 138 5.04 1.138 5 5.089
    fluidity 7 138 4.646 0.9743 4.714 4.689
    fluidity_should 8 138 4.683 1.339 4.643 4.751
    postrace 9 138 3.359 1.567 3.4 3.311
    race_ess 10 138 3.908 1.193 4 3.938
    pol_or 11 138 3.275 1.716 3 3.152
    Table continues below
      mad min max range skew kurtosis
    whitemulti_trust_dif 0.2965 -1.6 2.675 4.275 1.186 5.279
    White 0.4448 1 4.875 3.875 -0.1481 1.082
    Multiracial 0.3336 1 4.925 3.925 -0.3291 1.757
    Black 0.5189 1 4.975 3.975 -0.4982 0.7212
    trust_survey 1.334 1.6 7 5.4 -0.2848 -0.03678
    authen 1.112 1 7 6 -0.5088 0.4185
    fluidity 0.9531 1.857 6.857 5 -0.4418 0.1076
    fluidity_should 1.165 1 7 6 -0.4357 0.03562
    postrace 2.076 1 7 6 0.1499 -0.8836
    race_ess 1.112 1 7 6 -0.1942 0.09905
    pol_or 1.483 1 7 6 0.4928 -0.6288
      se
    whitemulti_trust_dif 0.04954
    White 0.0538
    Multiracial 0.0535
    Black 0.06367
    trust_survey 0.08555
    authen 0.09683
    fluidity 0.08294
    fluidity_should 0.114
    postrace 0.1334
    race_ess 0.1016
    pol_or 0.1461

  • fluidident:

    Table continues below
      vars n mean sd median trimmed
    whitemulti_trust_dif 1 144 0.05937 0.4929 0.025 0.05172
    White 2 144 3.111 0.675 3.075 3.116
    Multiracial 3 144 3.051 0.5828 3 3.033
    Black 4 144 3.122 0.6723 3.05 3.136
    trust_survey 5 144 5.085 0.8853 5.25 5.083
    authen 6 144 5.281 0.9671 5.25 5.267
    fluidity 7 144 5.009 0.9694 5.143 5.053
    fluidity_should 8 144 4.928 1.434 5 5.017
    postrace 9 144 3.714 1.51 3.8 3.712
    race_ess 10 144 3.719 1.32 3.875 3.746
    pol_or 11 144 3.201 1.724 3 3.095
    Table continues below
      mad min max range skew
    whitemulti_trust_dif 0.4448 -1.275 1.6 2.875 0.196
    White 0.5745 1.1 5 3.9 0.01825
    Multiracial 0.4077 1.45 5 3.55 0.3982
    Black 0.556 1.225 5 3.775 -0.1851
    trust_survey 0.9637 2.7 6.7 4 -0.2203
    authen 1.112 2.75 7 4.25 -0.02288
    fluidity 0.8472 2.143 7 4.857 -0.4689
    fluidity_should 1.483 1 7 6 -0.4895
    postrace 1.779 1 7 6 0.03889
    race_ess 1.297 1 6.75 5.75 -0.1674
    pol_or 1.483 1 7 6 0.3074
      kurtosis se
    whitemulti_trust_dif 0.744 0.04107
    White 0.6465 0.05625
    Multiracial 0.997 0.04856
    Black 0.7422 0.05602
    trust_survey -0.9402 0.07378
    authen -0.5723 0.08059
    fluidity 0.1351 0.08078
    fluidity_should -0.3629 0.1195
    postrace -0.749 0.1258
    race_ess -0.5228 0.11
    pol_or -0.9822 0.1437

  • stablebehav:

    Table continues below
      vars n mean sd median trimmed
    whitemulti_trust_dif 1 140 0.07625 0.5445 0.05 0.08772
    White 2 140 3.094 0.5813 3.025 3.092
    Multiracial 3 140 3.017 0.5887 2.987 3.007
    Black 4 140 3.104 0.6802 3.088 3.115
    trust_survey 5 142 4.943 0.8485 5 4.925
    authen 6 142 5.479 1.032 5.5 5.533
    fluidity 7 142 3.909 0.9109 4 3.927
    fluidity_should 8 142 4.439 1.285 4.286 4.49
    postrace 9 142 3.366 1.339 3.4 3.368
    race_ess 10 142 4.018 1.147 4 4.071
    pol_or 11 142 3.331 1.67 3.5 3.246
    Table continues below
      mad min max range skew
    whitemulti_trust_dif 0.3706 -2.225 2.875 5.1 0.03116
    White 0.4448 1 5 4 0.02031
    Multiracial 0.5189 1 5 4 0.2306
    Black 0.5004 1 5 4 -0.1629
    trust_survey 0.8896 2.4 6.7 4.3 -0.04088
    authen 0.9266 2.25 7 4.75 -0.5737
    fluidity 0.8472 1.429 6.143 4.714 -0.1883
    fluidity_should 1.165 1 7 6 -0.2762
    postrace 1.483 1 6.6 5.6 0.04804
    race_ess 1.112 1 6.875 5.875 -0.4059
    pol_or 2.224 1 7 6 0.2435
      kurtosis se
    whitemulti_trust_dif 6.648 0.04602
    White 2.073 0.04913
    Multiracial 1.405 0.04976
    Black 1.132 0.05749
    trust_survey -0.5178 0.0712
    authen 0.06864 0.08664
    fluidity -0.001226 0.07644
    fluidity_should -0.1213 0.1078
    postrace -0.7785 0.1123
    race_ess 0.03305 0.09622
    pol_or -0.8119 0.1402

  • stableident:

    Table continues below
      vars n mean sd median trimmed
    whitemulti_trust_dif 1 130 0.05837 0.4271 0 0.04339
    White 2 130 3.156 0.6034 3.112 3.152
    Multiracial 3 130 3.097 0.5922 3.05 3.095
    Black 4 130 3.217 0.6379 3.275 3.254
    trust_survey 5 131 5.161 0.9516 5.3 5.146
    authen 6 131 5.508 1.073 5.75 5.562
    fluidity 7 131 3.714 1.023 3.857 3.755
    fluidity_should 8 131 4.345 1.375 4.286 4.39
    postrace 9 131 3.45 1.546 3.2 3.411
    race_ess 10 131 3.896 1.154 4 3.924
    pol_or 11 131 3.282 1.803 4 3.162
    Table continues below
      mad min max range skew kurtosis
    whitemulti_trust_dif 0.2595 -1.25 2.975 4.225 2.149 15.99
    White 0.5004 1.525 5 3.475 0.09147 0.5662
    Multiracial 0.5374 1.425 5 3.575 0.07993 0.4085
    Black 0.6301 1 5 4 -0.5742 0.9891
    trust_survey 1.038 1.7 7 5.3 -0.2545 -0.1357
    authen 1.112 1 7 6 -0.7224 1.04
    fluidity 0.8472 1 7 6 -0.2343 0.4854
    fluidity_should 1.271 1 7 6 -0.2923 -0.1181
    postrace 1.779 1 7 6 0.2616 -0.7794
    race_ess 1.112 1 6.875 5.875 -0.2366 -0.0447
    pol_or 2.965 1 7 6 0.2814 -0.8773
      se
    whitemulti_trust_dif 0.03746
    White 0.05292
    Multiracial 0.05194
    Black 0.05595
    trust_survey 0.08315
    authen 0.09377
    fluidity 0.08939
    fluidity_should 0.1201
    postrace 0.135
    race_ess 0.1008
    pol_or 0.1575

Demographics

participant gender
Man Woman Non-binary
209 335 11
participant race
White Black Latine Asian Native Other
366 83 33 54 2 17 
## [1] 18
## [1] 90
## [1] 38.4721
## [1] 13.1567

GEE Model for Speeded Trust Task

From preregistration:

For the speeded trust task, we will fit a GEE regression model with trustworthiness ratings for each target face as the outcome and target race (within subjects—will be dummy coded with White as the comparison group), mutability (fluidity vs stability), and construct (identity vs behavior), and each of the interaction terms between the three as predictors.  

Findings:

NOTE: Don’t look at these p-values! They are not p-values

Estimates for the speeded trust model
term estimate std.error statistic p.value NA
(Intercept) 3.1009 0.0076 406.3328 0.0270 114.8256
mutability_factor1 0.0294 0.0076 3.8544 0.0270 1.0892
construct_factor1 0.0312 0.0076 4.0836 0.0270 1.1540
race_multi -0.0764 0.0108 -7.0800 0.0217 -3.5191
race_black 0.0121 0.0108 1.1218 0.0322 0.3760
mutability_factor1:construct_factor1 -0.0079 0.0076 -1.0348 0.0270 -0.2924
mutability_factor1:race_multi 0.0105 0.0108 0.9732 0.0217 0.4838
construct_factor1:race_multi 0.0187 0.0108 1.7300 0.0217 0.8599
mutability_factor1:race_black 0.0244 0.0108 2.2633 0.0322 0.7587
construct_factor1:race_black 0.0250 0.0108 2.3177 0.0322 0.7769
mutability_factor1:construct_factor1:race_multi -0.0089 0.0108 -0.8218 0.0217 -0.4085
mutability_factor1:construct_factor1:race_black 0.0012 0.0108 0.1146 0.0322 0.0384

THESE ARE THE P-VALUES

##                                     (Intercept) 
##                                     0.000000000 
##                              mutability_factor1 
##                                     0.276062324 
##                               construct_factor1 
##                                     0.248511124 
##                                      race_multi 
##                                     0.000432936 
##                                      race_black 
##                                     0.706890200 
##            mutability_factor1:construct_factor1 
##                                     0.769966860 
##                   mutability_factor1:race_multi 
##                                     0.628558396 
##                    construct_factor1:race_multi 
##                                     0.389848264 
##                   mutability_factor1:race_black 
##                                     0.448046766 
##                    construct_factor1:race_black 
##                                     0.437207988 
## mutability_factor1:construct_factor1:race_multi 
##                                     0.682906503 
## mutability_factor1:construct_factor1:race_black 
##                                     0.969367440
Emmeans for condition * race_multi
mutability_factor construct_factor race_multi emmean SE df asymp.LCL asymp.UCL
fluid behavior 0 3.0144 0.0475 Inf 2.9213 3.1076
stable behavior 0 3.1122 0.0478 Inf 3.0185 3.2059
fluid identity 0 3.1163 0.0438 Inf 3.0305 3.2022
stable identity 0 3.1850 0.0469 Inf 3.0932 3.2769
fluid behavior 1 2.9000 0.0719 Inf 2.7591 3.0408
stable behavior 1 3.0365 0.0702 Inf 2.8990 3.1740
fluid identity 1 3.0569 0.0590 Inf 2.9412 3.1727
stable identity 1 3.1289 0.0587 Inf 3.0139 3.2440
Emmeans for condition * race_black
mutability_factor construct_factor race_black emmean SE df asymp.LCL asymp.UCL
fluid behavior 0 2.9752 0.0475 Inf 2.8822 3.0683
stable behavior 0 3.0692 0.0494 Inf 2.9725 3.1660
fluid identity 0 3.0809 0.0482 Inf 2.9864 3.1754
stable identity 0 3.1256 0.0484 Inf 3.0306 3.2205
fluid behavior 1 2.9391 0.0795 Inf 2.7834 3.0949
stable behavior 1 3.0795 0.0764 Inf 2.9298 3.2293
fluid identity 1 3.0924 0.0683 Inf 2.9585 3.2263
stable identity 1 3.1884 0.0634 Inf 3.0641 3.3126

ANOVAs for Explicit Trust and Authenticity

From preregistration:

For each of the two other dependent variables (trust survey, authenticity scale), we will run a two-way analysis of variance to determine if there is any difference between participants who read the fluidity article and participants who read the stability article in the behavior vs identity conditions. We will use pairwise comparisons to test for differences by condition.   

Trust Survey

Findings:

Descriptive stats by mutability
mutability_factor min q1 median mean q3 max
fluid 1.6 4 5.1 4.991 5.7 7
stable 1.7 4 5.1 5.048 5.6 7
Descriptive stats by construct
construct_factor min q1 median mean q3 max
behavior 1.6 4 4.9 4.919 5.6 7
identity 1.7 4.05 5.3 5.121 5.8 7
Descriptive stats by mutability & construct
mutability_factor construct_factor min q1 median mean q3 max
fluid behavior 1.6 4 4.9 4.894 5.7 7
fluid identity 2.7 4.075 5.25 5.085 5.7 6.7
stable behavior 2.4 4.025 5 4.943 5.6 6.7
stable identity 1.7 4.1 5.3 5.161 5.8 7
ANOVA – explicit trust by condition
Sum Sq Df F value Pr(>F)
(Intercept) 13971.9977 1 16390.3593 0.0000
mutability_factor 0.5422 1 0.6360 0.4255
construct_factor 5.7845 1 6.7857 0.0094
mutability_factor:construct_factor 0.0264 1 0.0309 0.8604
Residuals 469.7012 551 NA NA
ANOVA – explicit trust by condition
eta.sq eta.sq.part
mutability_factor 0.0011 0.0012
construct_factor 0.0122 0.0122
mutability_factor:construct_factor 0.0001 0.0001

Authenticity

Findings:

Descriptive stats by mutability
mutability_factor min q1 median mean q3 max
fluid 1 4.5 5.25 5.163 6 7
stable 1 5 5.75 5.493 6.25 7
Descriptive stats by construct
construct_factor min q1 median mean q3 max
behavior 1 4.5 5.25 5.263 6 7
identity 1 4.75 5.5 5.389 6 7
Descriptive stats by mutability & construct
mutability_factor construct_factor min q1 median mean q3 max
fluid behavior 1 4.25 5 5.04 6 7
fluid identity 2.75 4.688 5.25 5.281 6 7
stable behavior 2.25 5 5.5 5.479 6.188 7
stable identity 1 5 5.75 5.508 6.125 7
ANOVA – authenticity by condition
Sum Sq Df F value Pr(>F)
(Intercept) 15727.9812 1 14181.8933 0.0000
mutability_factor 15.3381 1 13.8303 0.0002
construct_factor 2.5283 1 2.2798 0.1316
mutability_factor:construct_factor 1.5663 1 1.4123 0.2352
Residuals 611.0692 551 NA NA
ANOVA – authenticity by condition
eta.sq eta.sq.part
mutability_factor 0.0243 0.0245
construct_factor 0.0040 0.0041
mutability_factor:construct_factor 0.0025 0.0026

Exploratory Analyses

Identity Fluidity

From preregistration:

“To determine if there is any difference in participants’ belief that multiracial identity is fluid based on condition, we will run a two-way analysis of variance to determine if there is any difference between participants who read the fluidity article and participants who read the stability article in the behavior vs identity conditions. We will use pairwise comparisons to test for differences by condition.”

Descriptive stats by mutability
mutability_factor min q1 median mean q3 max
fluid 1.857 4.286 5 4.831 5.429 7
stable 1 3.143 4 3.816 4.429 7
Descriptive stats by construct
construct_factor min q1 median mean q3 max
behavior 1.429 3.714 4.286 4.272 5 6.857
identity 1 3.571 4.429 4.392 5.286 7
Descriptive stats by mutability & construct (continued below)
mutability_factor construct_factor min q1 median mean q3
fluid behavior 1.857 4 4.714 4.646 5.286
fluid identity 2.143 4.393 5.143 5.009 5.607
stable behavior 1.429 3.286 4 3.909 4.536
stable identity 1 3.143 3.857 3.714 4.429
max
6.857
7
6.143
7
ANOVA – fluidity by condition
Sum Sq Df F value Pr(>F)
(Intercept) 10342.4213 1 11011.7873 0.0000
mutability_factor 142.9180 1 152.1677 0.0000
construct_factor 0.9753 1 1.0385 0.3086
mutability_factor:construct_factor 10.7916 1 11.4900 0.0007
Residuals 517.5067 551 NA NA
Effect size – fluidity by condition
eta.sq eta.sq.part
mutability_factor 0.2125 0.2164
construct_factor 0.0015 0.0019
mutability_factor:construct_factor 0.0160 0.0204

Fluidity Should

From preregistration:

“To determine if there is any difference in participants’ belief that multiracial identity should be fluid based on condition, we will run a two-way analysis of variance to determine if there is any difference between participants who read the fluidity article and participants who read the stability article in the behavior vs identity conditions. We will use pairwise comparisons to test for differences by condition.”

Descriptive stats by mutability
mutability_factor min q1 median mean q3 max
fluid 1 4 4.857 4.808 6 7
stable 1 3.857 4.286 4.394 5.429 7
Descriptive stats by construct
construct_factor min q1 median mean q3 max
behavior 1 4 4.429 4.559 5.571 7
identity 1 4 4.714 4.65 5.714 7
Descriptive stats by mutability & construct
mutability_factor construct_factor min q1 median mean q3 max
fluid behavior 1 4 4.643 4.683 5.571 7
fluid identity 1 4 5 4.928 6.036 7
stable behavior 1 3.857 4.286 4.439 5.536 7
stable identity 1 3.857 4.286 4.345 5.286 7
ANOVA – fluidity should by condition
Sum Sq Df F value Pr(>F)
(Intercept) 11720.8151 1 6343.6586 0.0000
mutability_factor 23.7257 1 12.8411 0.0004
construct_factor 0.7828 1 0.4237 0.5154
mutability_factor:construct_factor 3.9665 1 2.1468 0.1434
Residuals 1018.0512 551 NA NA
Effect size – fluidity should by condition
eta.sq eta.sq.part
mutability_factor 0.0227 0.0228
construct_factor 0.0007 0.0008
mutability_factor:construct_factor 0.0038 0.0039

Essentialism

From preregistration:

“We will run a two-way analysis of variance to determine if race essentialism varies by mutability (fluid vs stable) and construct (identity vs behavior). If we find that there are no differences in race essentialism based on mutability and construct, we will examine whether race essentialism moderates the relationship between which article participants read and each of the two main dependent variables.”

Checking for differences in essentialism by condition

Descriptive stats by mutability
mutability_factor min q1 median mean q3 max
fluid 1 3 3.875 3.811 4.625 7
stable 1 3.375 4 3.959 4.75 6.875
Descriptive stats by construct
construct_factor min q1 median mean q3 max
behavior 1 3.25 4 3.963 4.75 7
identity 1 3 3.875 3.803 4.625 6.875
Descriptive stats by mutability & construct
mutability_factor construct_factor min q1 median mean q3 max
fluid behavior 1 3.25 4 3.908 4.625 7
fluid identity 1 2.844 3.875 3.719 4.625 6.75
stable behavior 1 3.375 4 4.018 4.844 6.875
stable identity 1 3.188 4 3.896 4.625 6.875
ANOVA – essentialism by condition
Sum Sq Df F value Pr(>F)
(Intercept) 8365.7144 1 5744.0370 0.0000
mutability_factor 2.8582 1 1.9625 0.1618
construct_factor 3.3392 1 2.2928 0.1306
mutability_factor:construct_factor 0.1566 1 0.1076 0.7431
Residuals 802.4859 551 NA NA
Effect size – essentialism by condition
eta.sq eta.sq.part
mutability_factor 0.0035 0.0035
construct_factor 0.0041 0.0041
mutability_factor:construct_factor 0.0002 0.0002

Checking for moderation by essentialism

From preregistration:

“For the speeded trust task, we will fit a linear regression model with the White/Multiracial trust difference score as the outcome and conditions (mutability: fluidity vs stability and construct: identity vs behavior), race essentialism, and each of the interaction terms between the three as predictors. If we find that any of the interactions are significant, we will conduct analyses to probe the interaction. For each of the two other dependent variables, we will fit an individual linear regression model including both conditions of mutability (fluidity vs stability) and construct (identity vs behavior), race essentialism, and each of the interaction terms between the three as predictors. If we find that any of interactions are significant for any of the models, we will conduct analyses for that model to probe the interaction.”

Speeded trust task

Estimates for the speeded trust model
term estimate std.error statistic p.value
(Intercept) -0.3588 0.0731 -4.9089 0.0000
mutability_factor1 -0.1405 0.0731 -1.9216 0.0552
construct_factor1 0.1245 0.0731 1.7038 0.0890
race_ess 0.1107 0.0179 6.1786 0.0000
mutability_factor1:construct_factor1 -0.0182 0.0731 -0.2487 0.8037
mutability_factor1:race_ess 0.0316 0.0179 1.7632 0.0784
construct_factor1:race_ess -0.0347 0.0179 -1.9392 0.0530
mutability_factor1:construct_factor1:race_ess 0.0080 0.0179 0.4489 0.6537
Summary for the speeded trust model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0742 0.0623 0.4981 6.2177 0 7 -393.745 805.489 844.295 134.701 543 551 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of race_ess when mutability_factor = stable: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.14   0.03     5.32   0.00
## 
## Slope of race_ess when mutability_factor = fluid: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.08   0.02     3.31   0.00
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of race_ess when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.08   0.02     3.07   0.00
## 
## Slope of race_ess when construct_factor = behavior: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.15   0.03     5.61   0.00

Trust survey

Estimates for the trust survey model
term estimate std.error statistic p.value
(Intercept) 5.4968 0.1323 41.5345 0.0000
mutability_factor1 -0.1096 0.1323 -0.8281 0.4080
construct_factor1 -0.1186 0.1323 -0.8959 0.3707
race_ess -0.1221 0.0325 -3.7605 0.0002
mutability_factor1:construct_factor1 0.0585 0.1323 0.4420 0.6586
mutability_factor1:race_ess 0.0380 0.0325 1.1709 0.2421
construct_factor1:race_ess 0.0545 0.0325 1.6769 0.0941
mutability_factor1:construct_factor1:race_ess -0.0130 0.0325 -0.4004 0.6890
Summary for the trust survey model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0456 0.0334 0.9113 3.7349 0.0006 7 -731.908 1481.82 1520.69 454.227 547 555

Authenticity

Estimates for the authenticity model
term estimate std.error statistic p.value
(Intercept) 6.0320 0.1497 40.2865 0.0000
mutability_factor1 0.1270 0.1497 0.8483 0.3967
construct_factor1 -0.2134 0.1497 -1.4255 0.1546
race_ess -0.1802 0.0367 -4.9052 0.0000
mutability_factor1:construct_factor1 0.0718 0.1497 0.4793 0.6319
mutability_factor1:race_ess 0.0125 0.0367 0.3405 0.7336
construct_factor1:race_ess 0.0693 0.0367 1.8855 0.0599
mutability_factor1:construct_factor1:race_ess -0.0324 0.0367 -0.8821 0.3781
Summary for the authenticity model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0776 0.0658 1.031 6.5734 0 7 -800.402 1618.8 1657.67 581.39 547 555 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of race_ess when construct_factor = identity: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.11   0.05    -2.17   0.03
## 
## Slope of race_ess when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.25   0.05    -4.72   0.00

Participant race

Speeded trust task

From preregistration:

“We then will fit a linear regression model with the White/Multiracial trust difference score as the outcome and mutability (fluidity vs stability), construct (identity vs behavior), participant race, and each of the interaction terms between the three as predictors.”

Descriptive stats by participant race
race_factor min q1 median mean q3 max
White -1.675 -0.15 0.025 0.09375 0.325 2.975
Black -1.6 -0.2187 0 -0.03171 0.175 1.225
Latine -2.225 -0.025 0.075 0.1053 0.4 0.85
Asian -1.25 -0.05 0.0375 0.08634 0.2938 0.925
Native 0.525 0.5375 0.55 0.55 0.5625 0.575
Other -0.775 -0.25 0 0.1044 0.4 1.1
Descriptive stats by mutability (continued below)
mutability_factor race_factor min q1 median
fluid White -1.575 -0.175 0.025
fluid Black -1.6 -0.3188 0
fluid Latine -0.85 0 0.075
fluid Asian -0.55 -0.0375 0.0375
fluid Native 0.525 0.5375 0.55
fluid Other -0.375 -0.2438 -0.000000000000000222
stable White -1.675 -0.125 0
stable Black -0.85 -0.2 0.0375
stable Latine -2.225 -0.0625 0.1625
stable Asian -1.25 -0.1156 0.05
stable Other -0.775 -0.1375 0
mean q3 max
0.1075 0.3313 2.675
-0.1066 0.1687 1.225
0.169 0.4 0.85
0.1175 0.25 0.925
0.55 0.5625 0.575
0.04583 0.1125 0.825
0.07961 0.3 2.975
0.03295 0.1813 0.925
-0.00625 0.3313 0.825
0.0474 0.3 0.85
0.1364 0.4125 1.1
Descriptive stats by construct (continued below)
construct_factor race_factor min q1 median
behavior White -1.675 -0.175 0
behavior Black -1.6 -0.2 0.075
behavior Latine -2.225 -0.025 0.2
behavior Asian -0.375 0.01875 0.1125
behavior Other -0.325 -0.000000000000000111 0.0625
identity White -1.25 -0.125 0.05
identity Black -1.275 -0.225 0
identity Latine -0.85 -0.0125 0.025
identity Asian -1.25 -0.2656 0
identity Native 0.525 0.5375 0.55
identity Other -0.775 -0.375 -0.025
mean q3 max
0.09333 0.325 2.875
0.01556 0.3 0.925
0.1221 0.45 0.85
0.1906 0.3 0.85
0.2344 0.3187 1.1
0.09417 0.3062 2.975
-0.08919 0.1 1.225
0.0875 0.325 0.85
0.002917 0.2312 0.925
0.55 0.5625 0.575
-0.01111 0.4 0.925
Descriptive stats by mutability & construct (continued below)
mutability_factor construct_factor race_factor min
fluid behavior White -1.575
fluid behavior Black -1.6
fluid behavior Latine -0.15
fluid behavior Asian -0.25
fluid behavior Other -0.325
fluid identity White -1.05
fluid identity Black -1.275
fluid identity Latine -0.85
fluid identity Asian -0.55
fluid identity Native 0.525
fluid identity Other -0.375
stable behavior White -1.675
stable behavior Black -0.85
stable behavior Latine -2.225
stable behavior Asian -0.375
stable behavior Other 0
stable identity White -1.25
stable identity Black -0.4
stable identity Latine -0.4
stable identity Asian -1.25
stable identity Other -0.775
q1 median mean q3 max
-0.1625 0.025 0.1398 0.2625 2.675
-0.25 0.025 -0.05341 0.175 0.475
-0.025 0.175 0.2188 0.3188 0.85
0 0.025 0.1283 0.15 0.825
-0.0000000000000004441 0 0.13 0.15 0.825
-0.175 0.05 0.08094 0.375 1.6
-0.5563 -0.0625 -0.1797 0.075 1.225
0 0.025 0.1028 0.4 0.85
-0.1625 0.125 0.1067 0.3375 0.925
0.5375 0.55 0.55 0.5625 0.575
-0.375 -0.375 -0.375 -0.375 -0.375
-0.175 0 0.05475 0.35 2.875
-0.175 0.125 0.08152 0.3125 0.925
0.05 0.275 -0.11 0.525 0.825
0.15 0.3 0.2944 0.4 0.85
0.0625 0.125 0.4083 0.6125 1.1
-0.075 0.05 0.1111 0.2625 2.975
-0.2 0 -0.02024 0.1 0.425
-0.075 0 0.06786 0.3 0.425
-0.2562 -0.025 -0.1008 0.05 0.475
-0.2938 -0.0125 0.03437 0.4063 0.925
White Black Latine Asian Native Other
366 83 33 54 2 17
Estimates for the speeded trust model
term estimate std.error statistic p.value
(Intercept) 0.4720 0.3272 1.4429 0.1496
mutability_factor1 0.1719 0.1661 1.0353 0.3010
construct_factor1 -0.2197 0.1661 -1.3232 0.1863
race_factor1 -0.0698 0.0318 -2.1931 0.0287
race_factor2 0.0143 0.0332 0.4311 0.6666
race_factor3 0.0165 0.0203 0.8111 0.4177
race_factor4 0.4989 0.4100 1.2169 0.2242
race_factor5 -0.0845 0.0872 -0.9688 0.3331
mutability_factor1:construct_factor1 0.0328 0.1661 0.1973 0.8437
mutability_factor1:race_factor1 0.0437 0.0318 1.3719 0.1707
mutability_factor1:race_factor2 -0.0403 0.0332 -1.2116 0.2262
mutability_factor1:race_factor3 0.0000 0.0203 0.0001 0.9999
mutability_factor1:race_factor4 0.1823 0.1694 1.0757 0.2825
mutability_factor1:race_factor5 NA NA NA NA
construct_factor1:race_factor1 -0.0282 0.0318 -0.8856 0.3762
construct_factor1:race_factor2 0.0148 0.0332 0.4441 0.6571
construct_factor1:race_factor3 -0.0225 0.0203 -1.1086 0.2681
construct_factor1:race_factor4 -0.1831 0.1694 -1.0808 0.2803
construct_factor1:race_factor5 NA NA NA NA
mutability_factor1:construct_factor1:race_factor1 -0.0113 0.0318 -0.3562 0.7218
mutability_factor1:construct_factor1:race_factor2 0.0187 0.0332 0.5614 0.5748
mutability_factor1:construct_factor1:race_factor3 -0.0324 0.0203 -1.5924 0.1119
mutability_factor1:construct_factor1:race_factor4 0.0290 0.1694 0.1712 0.8641
mutability_factor1:construct_factor1:race_factor5 NA NA NA NA
Summary for the speeded trust model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0308 -0.0058 0.5158 0.8424 0.6617 20 -406.366 856.731 951.589 141.015 530 551

Trust survey

From preregistration:

“For each of the other two dependent variables, we will fit an individual linear regression model including mutability (fluidity vs stability), construct (identity vs behavior), participant race, and each of the interaction terms between the three as predictors.”

Descriptive stats by participant race
race_factor min q1 median mean q3 max
White 1.6 4.125 5.2 5.107 5.8 7
Black 3.4 4 4.7 4.78 5.55 7
Latine 2.6 4.3 5.3 5.1 5.6 6.7
Asian 2.4 4 4.8 4.737 5.175 6.7
Native 5.4 5.45 5.5 5.5 5.55 5.6
Other 3.3 4 5 4.982 5.6 6.8
Descriptive stats by mutability
mutability_factor race_factor min q1 median mean q3 max
fluid White 1.6 4 5.3 5.097 5.8 6.8
fluid Black 3.4 4 4.45 4.708 5.275 7
fluid Latine 2.6 4.3 5.4 5.176 6 6.7
fluid Asian 3 4 4.7 4.693 5.175 6.5
fluid Native 5.4 5.45 5.5 5.5 5.55 5.6
fluid Other 3.3 4 4.1 4.217 4.5 5.2
stable White 1.7 4.3 5.2 5.117 5.8 7
stable Black 3.4 4 4.8 4.84 5.6 6.5
stable Latine 4 4.75 5.15 4.967 5.4 5.6
stable Asian 2.4 4.075 4.8 4.792 5.15 6.7
stable Other 4 4.7 5.4 5.4 6.25 6.8
Descriptive stats by construct
construct_factor race_factor min q1 median mean q3 max
behavior White 1.6 4 5.1 5.032 5.7 6.8
behavior Black 3.4 4 4.65 4.707 5.2 7
behavior Latine 2.6 4 5.4 4.976 5.6 6.7
behavior Asian 2.4 4 4.25 4.433 4.925 6.5
behavior Other 3.3 4 4.8 4.862 5.425 6.7
identity White 1.7 4.4 5.4 5.183 5.8 7
identity Black 3.4 4 4.7 4.87 5.6 6.5
identity Latine 4 4.825 5.3 5.231 5.7 6.7
identity Asian 3.2 4.525 4.95 4.98 5.55 6.7
identity Native 5.4 5.45 5.5 5.5 5.55 5.6
identity Other 4 4.2 5 5.089 5.6 6.8
Descriptive stats by mutability & construct (continued below)
mutability_factor construct_factor race_factor min q1 median
fluid behavior White 1.6 4 5.2
fluid behavior Black 3.5 4 4.55
fluid behavior Latine 2.6 4 5.4
fluid behavior Asian 3 4 4.4
fluid behavior Other 3.3 4 4
fluid identity White 2.7 4.4 5.4
fluid identity Black 3.4 4 4.35
fluid identity Latine 4 5.1 5.3
fluid identity Asian 3.2 4 4.7
fluid identity Native 5.4 5.45 5.5
fluid identity Other 4.2 4.2 4.2
stable behavior White 3.2 4.3 5.1
stable behavior Black 3.4 4 4.7
stable behavior Latine 4 5 5
stable behavior Asian 2.4 4 4.1
stable behavior Other 5 5.55 6.1
stable identity White 1.7 4.375 5.35
stable identity Black 3.7 4 4.9
stable identity Latine 4 4.5 5.3
stable identity Asian 3.9 4.8 5.1
stable identity Other 4 4.3 5.2
mean q3 max
5.019 5.8 6.8
4.709 5.125 7
4.983 5.85 6.7
4.62 5.15 6.5
4.22 4.6 5.2
5.161 5.8 6.6
4.706 5.375 6.4
5.433 6 6.7
4.767 5.35 6.4
5.5 5.55 5.6
4.2 4.2 4.2
5.043 5.6 6.7
4.704 5.2 5.8
4.96 5.4 5.4
4.122 4.8 5
5.933 6.4 6.7
5.211 6 7
4.995 5.7 6.5
4.971 5.45 5.6
5.193 5.6 6.7
5.2 5.8 6.8
White Black Latine Asian Native Other
366 83 33 54 2 17
Estimates for the trust survey model
term estimate std.error statistic p.value
(Intercept) 5.6496 0.5790 9.7570 0.0000
mutability_factor1 0.6783 0.2939 2.3079 0.0214
construct_factor1 -0.1883 0.2939 -0.6408 0.5219
race_factor1 -0.1649 0.0561 -2.9414 0.0034
race_factor2 0.0478 0.0588 0.8127 0.4168
race_factor3 -0.0790 0.0360 -2.1947 0.0286
race_factor4 0.8894 0.7256 1.2258 0.2208
race_factor5 -0.1523 0.1544 -0.9859 0.3246
mutability_factor1:construct_factor1 -0.1783 0.2939 -0.6067 0.5443
mutability_factor1:race_factor1 0.0263 0.0561 0.4696 0.6388
mutability_factor1:race_factor2 -0.0553 0.0588 -0.9407 0.3473
mutability_factor1:race_factor3 -0.0018 0.0360 -0.0495 0.9605
mutability_factor1:race_factor4 0.6908 0.2999 2.3034 0.0216
mutability_factor1:race_factor5 NA NA NA NA
construct_factor1:race_factor1 -0.0028 0.0561 -0.0508 0.9595
construct_factor1:race_factor2 0.0135 0.0588 0.2293 0.8188
construct_factor1:race_factor3 0.0540 0.0360 1.5012 0.1339
construct_factor1:race_factor4 -0.3307 0.2999 -1.1028 0.2706
construct_factor1:race_factor5 NA NA NA NA
mutability_factor1:construct_factor1:race_factor1 0.0334 0.0561 0.5965 0.5511
mutability_factor1:construct_factor1:race_factor2 -0.0499 0.0588 -0.8483 0.3967
mutability_factor1:construct_factor1:race_factor3 0.0602 0.0360 1.6743 0.0947
mutability_factor1:construct_factor1:race_factor4 -0.2287 0.2999 -0.7627 0.4460
mutability_factor1:construct_factor1:race_factor5 NA NA NA NA
Summary for the trust survey model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0648 0.0298 0.913 1.851 0.0138 20 -726.264 1496.53 1591.55 445.082 534 555

Authenticity

Descriptive stats by participant race
race_factor min q1 median mean q3 max
White 1 4.75 5.5 5.426 6 7
Black 2.25 4.5 5 5.039 5.75 7
Latine 2.75 4.75 5.5 5.311 6 7
Asian 2 4.5 5.125 5.13 6 7
Native 4.75 4.875 5 5 5.125 5.25
Other 3 4.25 5.25 5.25 6 7
Descriptive stats by mutability
mutability_factor race_factor min q1 median mean q3 max
fluid White 1 4.5 5.25 5.304 6 7
fluid Black 2.25 4.5 5 4.783 5.438 6.5
fluid Latine 2.75 4 5.25 5.167 6 7
fluid Asian 2 4.312 5 4.95 5.688 6.5
fluid Native 4.75 4.875 5 5 5.125 5.25
fluid Other 3 4 4.25 4.333 4.875 5.5
stable White 1 5 5.75 5.55 6.25 7
stable Black 2.75 4.5 5.5 5.256 6 7
stable Latine 4.75 5.438 5.5 5.562 5.812 6
stable Asian 2.25 4.5 5.625 5.354 6.062 7
stable Other 4 5.125 6 5.75 6.375 7
Descriptive stats by construct
construct_factor race_factor min q1 median mean q3 max
behavior White 1 4.75 5.5 5.384 6 7
behavior Black 2.25 4.5 5 5.098 6 7
behavior Latine 2.75 4.25 5.25 5.029 5.75 7
behavior Asian 2 4 5 4.896 6 6.5
behavior Other 3 4 5 5 6 7
identity White 1 4.75 5.5 5.468 6.25 7
identity Black 3.25 4 5 4.966 5.75 7
identity Latine 3 5.25 5.75 5.609 6 7
identity Asian 3.5 4.562 5.375 5.317 6 7
identity Native 4.75 4.875 5 5 5.125 5.25
identity Other 4 5 5.25 5.472 6.25 7
Descriptive stats by mutability & construct (continued below)
mutability_factor construct_factor race_factor min q1 median
fluid behavior White 1 4.5 5.25
fluid behavior Black 2.25 4.5 5
fluid behavior Latine 2.75 3.938 4.625
fluid behavior Asian 2 4.125 5
fluid behavior Other 3 4 4
fluid identity White 2.75 4.75 5.25
fluid identity Black 3.25 4 4.875
fluid identity Latine 3 5.25 5.75
fluid identity Asian 3.5 4.5 5
fluid identity Native 4.75 4.875 5
fluid identity Other 5 5 5
stable behavior White 2.5 5 5.75
stable behavior Black 2.75 4.688 5.25
stable behavior Latine 4.75 5.25 5.5
stable behavior Asian 2.25 4 5
stable behavior Other 6 6 6
stable identity White 1 5 5.75
stable identity Black 4 4 5.5
stable identity Latine 5.25 5.5 5.75
stable identity Asian 4 4.875 5.75
stable identity Other 4 4.812 5.625
mean q3 max
5.199 6 7
4.807 5.5 6.5
4.875 5.812 7
4.9 5.875 6.25
4.2 4.5 5.5
5.391 6 7
4.75 5.25 6
5.556 6.75 7
5 5.5 6.5
5 5.125 5.25
5 5 5
5.537 6 7
5.365 6.25 7
5.4 5.5 6
4.889 6 6.5
6.333 6.5 7
5.566 6.562 7
5.131 5.75 7
5.679 5.875 6
5.633 6.125 7
5.531 6.312 7
White Black Latine Asian Native Other
366 83 33 54 2 17
Estimates for the authenticity model
term estimate std.error statistic p.value
(Intercept) 5.3823 0.6644 8.1005 0.0000
mutability_factor1 0.6661 0.3373 1.9751 0.0488
construct_factor1 -0.0005 0.3373 -0.0015 0.9988
race_factor1 -0.2051 0.0643 -3.1877 0.0015
race_factor2 0.0530 0.0675 0.7857 0.4324
race_factor3 -0.0414 0.0413 -1.0032 0.3162
race_factor4 0.1757 0.8326 0.2110 0.8330
race_factor5 -0.0232 0.1772 -0.1310 0.8958
mutability_factor1:construct_factor1 -0.4005 0.3373 -1.1876 0.2355
mutability_factor1:race_factor1 0.0533 0.0643 0.8285 0.4078
mutability_factor1:race_factor2 -0.0065 0.0675 -0.0957 0.9238
mutability_factor1:race_factor3 -0.0048 0.0413 -0.1172 0.9067
mutability_factor1:race_factor4 0.4961 0.3441 1.4415 0.1500
mutability_factor1:race_factor5 NA NA NA NA
construct_factor1:race_factor1 -0.0638 0.0643 -0.9920 0.3217
construct_factor1:race_factor2 0.0829 0.0675 1.2277 0.2201
construct_factor1:race_factor3 0.0343 0.0413 0.8298 0.4070
construct_factor1:race_factor4 -0.1089 0.3441 -0.3163 0.7519
construct_factor1:race_factor5 NA NA NA NA
mutability_factor1:construct_factor1:race_factor1 -0.0017 0.0643 -0.0265 0.9789
mutability_factor1:construct_factor1:race_factor2 -0.0193 0.0675 -0.2864 0.7747
mutability_factor1:construct_factor1:race_factor3 0.0557 0.0413 1.3499 0.1776
mutability_factor1:construct_factor1:race_factor4 -0.3944 0.3441 -1.1462 0.2522
mutability_factor1:construct_factor1:race_factor5 NA NA NA NA
Summary for the authenticity model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0702 0.0354 1.0476 2.0159 0.0057 20 -802.617 1649.23 1744.25 586.05 534 555

Political orientation

Speeded trust task

From preregistration:

“We will also fit a linear regression model with the White/Multiracial trust difference score as the outcome and mutability (fluidity vs stability), construct (identity vs behavior), participant political orientation, and each of the interaction terms between the three as predictors. If we find that if any of the interactions are significant, we will conduct analyses to probe the interaction for that model.”

Estimates for the speeded trust model
term estimate std.error statistic p.value
(Intercept) -0.1903 0.0452 -4.2090 0.0000
mutability_factor1 0.0263 0.0452 0.5823 0.5606
construct_factor1 0.0779 0.0452 1.7223 0.0856
pol_or 0.0814 0.0122 6.6735 0.0000
mutability_factor1:construct_factor1 0.0873 0.0452 1.9309 0.0540
mutability_factor1:pol_or -0.0120 0.0122 -0.9798 0.3276
construct_factor1:pol_or -0.0281 0.0122 -2.3065 0.0215
mutability_factor1:construct_factor1:pol_or -0.0239 0.0122 -1.9606 0.0504
Summary for the speeded trust model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0918 0.0801 0.4933 7.8405 0 7 -388.46 794.92 833.726 132.142 543 551 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.05   0.02     3.14   0.00
## 
## Slope of pol_or when construct_factor = behavior: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.11   0.02     6.24   0.00
##  mutability_factor construct_factor pol_or.trend     SE  df lower.CL upper.CL
##  fluid             behavior               0.0976 0.0247 543   0.0490   0.1461
##  stable            behavior               0.1215 0.0249 543   0.0725   0.1705
##  fluid             identity               0.0892 0.0239 543   0.0422   0.1361
##  stable            identity               0.0174 0.0240 543  -0.0297   0.0646
## 
## Confidence level used: 0.95

Trust survey

From preregistration:

“Again, for each of the other dependent variables, we will also fit an individual linear regression model including mutability (fluidity vs stability), construct (identity vs behavior), participant political orientation, and each of the interaction terms between the three as predictors. If we find that any of the interaction terms are significant for any of the models, we will conduct analyses for that model to probe the interaction.”

Estimates for the trust survey model
term estimate std.error statistic p.value
(Intercept) 5.2150 0.0835 62.4533 0.0000
mutability_factor1 0.0133 0.0835 0.1597 0.8732
construct_factor1 -0.0950 0.0835 -1.1371 0.2560
pol_or -0.0588 0.0226 -2.6061 0.0094
mutability_factor1:construct_factor1 0.0585 0.0835 0.7000 0.4842
mutability_factor1:pol_or 0.0058 0.0226 0.2582 0.7964
construct_factor1:pol_or 0.0598 0.0226 2.6509 0.0083
mutability_factor1:construct_factor1:pol_or -0.0162 0.0226 -0.7181 0.4730
Summary for the trust survey model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0378 0.0255 0.915 3.0701 0.0035 7 -734.171 1486.34 1525.21 457.946 547 555 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.00   0.03     0.03   0.97
## 
## Slope of pol_or when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.12   0.03    -3.66   0.00

Authenticity

Estimates for the authenticity model
term estimate std.error statistic p.value
(Intercept) 5.6732 0.0941 60.2856 0.0000
mutability_factor1 0.1655 0.0941 1.7591 0.0791
construct_factor1 -0.1706 0.0941 -1.8129 0.0704
pol_or -0.1051 0.0254 -4.1295 0.0000
mutability_factor1:construct_factor1 0.0922 0.0941 0.9803 0.3274
mutability_factor1:pol_or 0.0008 0.0254 0.0308 0.9755
construct_factor1:pol_or 0.0723 0.0254 2.8402 0.0047
mutability_factor1:construct_factor1:pol_or -0.0450 0.0254 -1.7679 0.0776
Summary for the authenticity model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0772 0.0654 1.0312 6.5397 0 7 -800.512 1619.02 1657.9 581.622 547 555 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when construct_factor = identity: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.03   0.04    -0.93   0.35
## 
## Slope of pol_or when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.18   0.04    -4.85   0.00

Post-race Ideology

From preregistration:

“Lastly, we will collect data on an exploratory measure of participants’ endorsement of a Multiracial postrace ideology through five-items. Participants will respond to statements such as, “Race in the United States will stop mattering when everyone is mixed”, “In the future, everyone in the US will be Multiracial”, and “When most people are Multiracial, race will not matter” using a scale of 1 (strongly disagree) to 7 (strongly agree). This measure will be used to conduct exploratory correlational and moderation analyses.”

Are there differences by condition?

Descriptive stats by mutability
mutability_factor min q1 median mean q3 max
fluid 1 2.25 3.6 3.54 4.6 7
stable 1 2.2 3.4 3.407 4.4 7
Descriptive stats by construct
construct_factor min q1 median mean q3 max
behavior 1 2.2 3.4 3.363 4.4 7
identity 1 2.4 3.6 3.588 4.6 7
Descriptive stats by mutability & construct
mutability_factor construct_factor min q1 median mean q3 max
fluid behavior 1 2 3.4 3.359 4.4 7
fluid identity 1 2.6 3.8 3.714 4.8 7
stable behavior 1 2.25 3.4 3.366 4.2 6.6
stable identity 1 2.2 3.2 3.45 4.6 7
ANOVA – explicit trust by condition
Sum Sq Df F value Pr(>F)
(Intercept) 6683.4533 1 3004.4972 0.0000
mutability_factor 2.2833 1 1.0264 0.3114
construct_factor 6.6657 1 2.9965 0.0840
mutability_factor:construct_factor 2.5307 1 1.1377 0.2866
Residuals 1225.6902 551 NA NA
ANOVA – explicit trust by condition
eta.sq eta.sq.part
mutability_factor 0.0018 0.0019
construct_factor 0.0054 0.0054
mutability_factor:construct_factor 0.0020 0.0021

Is Post race ideology a moderator?

Speeded trust task

Estimates for the speeded trust model
term estimate std.error statistic p.value
(Intercept) 0.1857 0.0554 3.3518 0.0009
mutability_factor1 0.0105 0.0554 0.1896 0.8497
construct_factor1 -0.1563 0.0554 -2.8200 0.0050
postrace -0.0331 0.0147 -2.2502 0.0248
mutability_factor1:construct_factor1 0.0824 0.0554 1.4874 0.1375
mutability_factor1:postrace -0.0049 0.0147 -0.3351 0.7377
construct_factor1:postrace 0.0403 0.0147 2.7417 0.0063
mutability_factor1:construct_factor1:postrace -0.0209 0.0147 -1.4238 0.1551
Summary for the speeded trust model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0292 0.0167 0.51 2.3335 0.0236 7 -406.821 831.643 870.448 141.249 543 551 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of postrace when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.01   0.02     0.36   0.72
## 
## Slope of postrace when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.07   0.02    -3.45   0.00

Trust survey

Estimates for the trust survey model
term estimate std.error statistic p.value
(Intercept) 4.6358 0.0986 47.0329 0.0000
mutability_factor1 -0.0091 0.0986 -0.0919 0.9268
construct_factor1 0.1707 0.0986 1.7317 0.0839
postrace 0.1123 0.0261 4.2964 0.0000
mutability_factor1:construct_factor1 -0.0823 0.0986 -0.8347 0.4042
mutability_factor1:postrace 0.0124 0.0261 0.4742 0.6355
construct_factor1:postrace -0.0225 0.0261 -0.8623 0.3889
mutability_factor1:construct_factor1:postrace 0.0271 0.0261 1.0350 0.3011
Summary for the trust survey model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0492 0.037 0.9095 4.0439 0.0002 7 -730.863 1479.73 1518.6 452.52 547 555

Authenticity

Estimates for the authenticity model
term estimate std.error statistic p.value
(Intercept) 4.9458 0.113 43.7561 0.0000
mutability_factor1 0.1659 0.113 1.4679 0.1427
construct_factor1 0.1140 0.113 1.0086 0.3136
postrace 0.1107 0.030 3.6913 0.0002
mutability_factor1:construct_factor1 -0.1147 0.113 -1.0150 0.3106
mutability_factor1:postrace 0.0012 0.030 0.0411 0.9672
construct_factor1:postrace -0.0165 0.030 -0.5501 0.5825
mutability_factor1:construct_factor1:postrace 0.0195 0.030 0.6518 0.5148
Summary for the authenticity model
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
0.0558 0.0438 1.043 4.6218 0 7 -806.869 1631.74 1670.61 595.099 547 555