Descriptive Statistics

stable:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	172	0.07703	0.5416	0.05	0.06866
White	2	172	3.16	0.5696	3.125	3.156
Multiracial	3	172	3.083	0.5688	3.075	3.082
Black	4	172	3.147	0.6911	3.213	3.182
trust_survey	5	177	5.003	0.8495	5.1	5.017
trust_therm	6	177	72.42	20.05	75	73.34
atma	7	177	3.602	0.5185	3.565	3.582
feel_therm	8	177	75.59	19.2	77	76.53
authen	9	177	5.573	0.9506	5.75	5.617
race_ess	10	177	3.819	1.245	4	3.891
pol_or	11	177	3.073	1.742	3	2.93

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.3336	-1.625	2.075	3.7	0.3512
White	0.4448	1.1	5	3.9	0.002589
Multiracial	0.5004	1.6	5	3.4	0.09065
Black	0.5374	1.025	5	3.975	-0.4093
trust_survey	0.8896	2.1	7	4.9	-0.3613
trust_therm	23.72	0	100	100	-0.4798
atma	0.5157	1.913	5	3.087	0.2713
feel_therm	22.24	0	100	100	-0.5477
authen	1.112	2.25	7	4.75	-0.4685
race_ess	0.9266	1	6.75	5.75	-0.5099
pol_or	1.483	1	7	6	0.4717

	kurtosis	se
whitemulti_trust_dif	2.504	0.0413
White	1.205	0.04343
Multiracial	0.6047	0.04337
Black	0.8893	0.0527
trust_survey	0.01425	0.06385
trust_therm	-0.308	1.507
atma	0.2957	0.03897
feel_therm	-0.0686	1.443
authen	0.004316	0.07145
race_ess	-0.1948	0.09361
pol_or	-0.7958	0.1309

fluid:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	179	0.1111	0.4736	0.05	0.09078
White	2	179	3.065	0.5146	3.05	3.077
Multiracial	3	179	2.954	0.4961	3	2.978
Black	4	179	3.051	0.5911	3.075	3.072
trust_survey	5	181	4.99	0.8156	5	4.994
trust_therm	6	181	71.4	18.12	75	71.08
atma	7	181	3.5	0.4915	3.478	3.492
feel_therm	8	181	74.27	18.52	76	74.47
authen	9	181	5.421	0.9452	5.5	5.45
race_ess	10	181	3.791	1.222	4	3.844
pol_or	11	181	2.801	1.614	2	2.641

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.3336	-1.85	2.05	3.9	0.4136
White	0.4448	1.1	4.35	3.25	-0.5168
Multiracial	0.4077	1	4	3	-0.6924
Black	0.4818	1.025	4.7	3.675	-0.4769
trust_survey	0.8896	1.5	7	5.5	-0.4737
trust_therm	22.24	23	100	77	-0.09943
atma	0.5157	2.174	4.783	2.609	0.08524
feel_therm	22.24	26	100	74	-0.1666
authen	1.112	2.5	7	4.5	-0.4124
race_ess	1.112	1	6.875	5.875	-0.3603
pol_or	1.483	1	7	6	0.5273

	kurtosis	se
whitemulti_trust_dif	3.058	0.0354
White	1.625	0.03847
Multiracial	1.561	0.03708
Black	0.6772	0.04418
trust_survey	0.8097	0.06063
trust_therm	-0.9839	1.347
atma	-0.1394	0.03654
feel_therm	-1.183	1.377
authen	-0.08457	0.07026
race_ess	-0.3309	0.09086
pol_or	-0.7234	0.12

Demographics

participant gender
Man	Woman	Non-binary	Prefer not to say
178	170	7	3

participant race
White	Black	Latine	Asian	Other
271	35	18	22	9

## [1] 18

## [1] 72

## [1] 36.419

## [1] 12.2144

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), condition (between subjects), and their interactions as predictors.

Findings:

The comparison group is White faces in the stable condition. The intercept is 3.1 on a scale from 1 to 5 (3 midpoint) with a higher score indicating greater trust.
Main effects
- There is a main effect of condition such that participants in the fluid condition report slightly less trust overall than participants in the stable condition.
- There is also a main effect of the Multiracial dummy code such that participants in both conditions report slightly less trust for Multiracial faces relative to White faces.
- There is also a main effect of the Black dummy code such that participants in both conditions report slightly less trust for Black faces relative to White faces.
All of these main effects are qualified by significant interactions (the interpretation of the interactions changed a bit).
- There is an interaction between condition and the Multiracial dummy code such that, in the fluid condition, participants show significantly lower trust for Multiracial people than White people but there is no significant difference in the stable condition.
- There is an interaction between condition and the Black dummy code such that people in the fluid condition show less trust for faces that are not Black than participants in the stable condition. There is no significant difference between conditions when faces are Black. This one doesn’t really make much sense to me.

##           (Intercept)            condition1            race_multi 
##           3.114003831          -0.047059387          -0.093357280 
##            race_black condition1:race_multi condition1:race_black 
##          -0.013201628          -0.016920498          -0.000270594

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value	NA
(Intercept)	3.1140	0.0093	335.5444	0.0287	108.5113
condition1	-0.0471	0.0093	-5.0708	0.0287	-1.6398
race_multi	-0.0934	0.0131	-7.1132	0.0269	-3.4712
race_black	-0.0132	0.0131	-1.0059	0.0423	-0.3119
condition1:race_multi	-0.0169	0.0131	-1.2892	0.0269	-0.6291
condition1:race_black	-0.0003	0.0131	-0.0206	0.0423	-0.0064

##                           Estimate Naive S.E.     Naive z Robust S.E.
## (Intercept)            3.114003831 0.00928045 335.5444400   0.0286975
## condition1            -0.047059387 0.00928045  -5.0708080   0.0286975
## race_multi            -0.093357280 0.01312454  -7.1131849   0.0268949
## race_black            -0.013201628 0.01312454  -1.0058736   0.0423247
## condition1:race_multi -0.016920498 0.01312454  -1.2892260   0.0268949
## condition1:race_black -0.000270594 0.01312454  -0.0206174   0.0423247
##                           Robust z
## (Intercept)           108.51125464
## condition1             -1.63984163
## race_multi             -3.47118832
## race_black             -0.31191280
## condition1:race_multi  -0.62913396
## condition1:race_black  -0.00639328

##           (Intercept)            condition1            race_multi 
##            0.00000000            0.10103810            0.00051816 
##            race_black condition1:race_multi condition1:race_black 
##            0.75510679            0.52926136            0.99489894

Emmeans for condition * race_multi
condition	race_multi	emmean	SE	df	asymp.LCL	asymp.UCL
stable	0	3.1546	0.0342	Inf	3.0876	3.2216
fluid	0	3.0602	0.0316	Inf	2.9982	3.1222
stable	1	3.0782	0.0582	Inf	2.9641	3.1922
fluid	1	2.9499	0.0493	Inf	2.8534	3.0465

Post hoc tests for race_multi * condition
contrast	condition	estimate	SE	df	z.ratio	p.value
race_multi1 - race_multi0	stable	-0.0764	0.0407	Inf	-1.8760	0.0607
race_multi1 - race_multi0	fluid	-0.1103	0.0351	Inf	-3.1403	0.0017

Emmeans for condition * race_black
condition	race_black	emmean	SE	df	asymp.LCL	asymp.UCL
stable	0	3.1228	0.0376	Inf	3.0491	3.1966
fluid	0	3.0118	0.0332	Inf	2.9467	3.0769
stable	1	3.1099	0.0671	Inf	2.9784	3.2414
fluid	1	2.9983	0.0556	Inf	2.8893	3.1073

Post hoc tests for condition * condition
contrast	condition	estimate	SE	df	z.ratio	p.value
race_black1 - race_black0	stable	-0.0129	0.0663	Inf	-0.195	0.8454
race_black1 - race_black0	fluid	-0.0135	0.0526	Inf	-0.256	0.7980

T-tests for main dependent variables for differences between conditions

From preregistration:

For each of the five other dependent variables (listed below), we will run independent samples T-tests to determine if there is any difference between participants who read the fluidity article and participants who read the stability article.

Trust Survey

Findings:

There is not a significant difference in explicit trust on the trust survey between conditions.

Trust survey by condition (continued below)
Test statistic	df	P value	Alternative hypothesis
0.1513	354.6	0.8798	two.sided

mean in group stable	mean in group fluid
5.003	4.99

## [1] 0.0160015

Trust Thermometer

Findings:

There is not a significant difference in explicit trust on the trust thermometer between conditions.

Trust thermometer by condition (continued below)
Test statistic	df	P value	Alternative hypothesis
0.5076	350.7	0.6121	two.sided

mean in group stable	mean in group fluid
72.42	71.4

## [1] 0.0537177

Attitudes towards multiracial adults

Findings:

There is not a significant difference in attitudes towards multiracial adults between conditions. Though it is worth noting that there is a marginal difference in line with our hypothesis (more negative attitudes in fluid condition).

Attitudes towards multiracial adults by condition (continued below)
Test statistic	df	P value	Alternative hypothesis	mean in group stable
1.908	354	0.05716	two.sided	3.602

mean in group fluid
3.5

## [1] 0.201849

Feeling thermometer

Findings:

There is not a significant difference in feelings towards multiracial people on the feeling thermometer between conditions.

Feeling thermometer by condition (continued below)
Test statistic	df	P value	Alternative hypothesis
0.6658	354.8	0.506	two.sided

mean in group stable	mean in group fluid
75.59	74.27

## [1] 0.0704103

Authenticity

Findings:

There is not a significant difference in perceived authenticity of multiracial people between conditions.

Authenticity by condition (continued below)
Test statistic	df	P value	Alternative hypothesis
1.519	355.7	0.1297	two.sided

mean in group stable	mean in group fluid
5.573	5.421

## [1] 0.160542

Exploratory Analyses.

From Preregistration:

“We will run an exploratory independent samples T-test to determine if there is any difference in race essentialism between participants who read the fluidity article and participants who read the stability article. If we find that there are no differences in race essentialism between conditions, we will examine whether race essentialism moderates the relationship between which article participants read and each of the six dependent variables.”

Race Essentialism

T-test

Findings:

There is not a significant difference in race essentialism between conditions Thus, we will move forward to examine whether race essentialism moderates the relationship between condition and the dependent variables.

Race essentialism by condition (continued below)
Test statistic	df	P value	Alternative hypothesis
0.2182	355.4	0.8274	two.sided

mean in group stable	mean in group fluid
3.819	3.791

Speeded trust task by condition + race essentialism as moderator

From preregistration:

“For the speeded trust task, we will first calculate a White/Multiracial trust difference score by subtracting participant’s average trust ratings for all multiracial targets from participant’s average trust ratings for all White targets, with a higher number indicating greater trust towards White targets relative to multiracial targets. We then will fit a linear regression model with the White/Multiracial trust difference score as the outcome and condition, race essentialism, and the interaction term between the two as predictors. If we find that the interactions is significant, we will conduct simple slopes analysis to probe the interaction.”

Findings:

Main Effects
- There is no main effect of condition. Confused by this as there is when analyzed with the GEE.
- There is a main effect of race essentialism such that participants who are more essentialist about race show greater trust towards White targets relative to Multiracial targets.
Interaction
- There is no significant interaction. In other words, race essentialism does not moderate the relationship between condition and trust.

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.2416	0.0857	-2.8201	0.0051
condition1	0.0606	0.0857	0.7074	0.4798
race_ess	0.0885	0.0215	4.1201	0.0000
condition1:race_ess	-0.0113	0.0215	-0.5258	0.5993

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0484	0.0402	0.4973	5.8884	0.0006	3	-250.859	511.717	531.021	85.8243	347	351

Other main DVs by condition + race essentialism as moderator

From preregistration:

“For each of the five other dependent variables, we will fit an individual linear regression model including condition, race essentialism, and the interaction term between the two as predictors. If we find a significant interaction between condition and race essentialism for any of the models, we will conduct simple slopes analysis for that model to probe the interaction.”

Trust survey

Findings:

Main Effects
- There is no main effect of condition.
- There is a main effect of race essentialism such that participants who are more essentialist about race show less trust towards multiracial people.
Interaction
- There is no significant interaction. In other words, race essentialism does not moderate the relationship between condition and trust.

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.4749	0.1408	38.8855	0.0000
condition1	0.0921	0.1408	0.6541	0.5135
race_ess	-0.1259	0.0352	-3.5762	0.0004
condition1:race_ess	-0.0264	0.0352	-0.7505	0.4535

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0363	0.0281	0.8196	4.4455	0.0044	3	-434.762	879.524	898.927	237.815	354	358

Trust Thermometer

Findings:

Main Effects
- There is no main effect of condition.
- There is a main effect of race essentialism such that participants who are more essentialist about race show less trust towards multiracial people.
Interaction
- There is no significant interaction. In other words, race essentialism does not moderate the relationship between condition and trust.

Estimates for the trust thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	80.5490	3.2544	24.7508	0.0000
condition1	-0.2977	3.2544	-0.0915	0.9272
race_ess	-2.2705	0.8138	-2.7899	0.0056
condition1:race_ess	-0.0651	0.8138	-0.0800	0.9363

Summary for the trust thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0222	0.0139	18.9453	2.6831	0.0466	3	-1559.05	3128.09	3147.5	127060	354	358

Attitudes towards multiracial adults

Findings:

Main Effects
- There is no main effect of condition.
- There is a main effect of race essentialism such that participants who are more essentialist about race show show more negative attitudes towards multiracial people.
Interaction
- There is no significant interaction. In other words, race essentialism does not moderate the relationship between condition and attitudes towards multiracial adults.

Estimates for the attitudes towards multiracial adults model
term	estimate	std.error	statistic	p.value
(Intercept)	3.7421	0.0863	43.3493	0.0000
condition1	-0.0834	0.0863	-0.9661	0.3346
race_ess	-0.0502	0.0216	-2.3243	0.0207
condition1:race_ess	0.0083	0.0216	0.3861	0.6996

Summary for the attitudes towards multiracial adults model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0255	0.0172	0.5025	3.0826	0.0275	3	-259.633	529.267	548.669	89.4001	354	358

Feeling thermometer

Findings:

Main Effects
- There is no main effect of condition.
- There is a main effect of race essentialism such that participants who are more essentialist about race show more negative attitudes towards multiracial people.
Interaction
- There is no significant interaction. In other words, race essentialism does not moderate the relationship between condition and attitudes.

Estimates for the feeling thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	82.4914	3.2175	25.6380	0.0000
condition1	2.2008	3.2175	0.6840	0.4944
race_ess	-1.9903	0.8046	-2.4736	0.0138
condition1:race_ess	-0.7604	0.8046	-0.9450	0.3453

Summary for the feeling thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0206	0.0122	18.7308	2.4758	0.0612	3	-1554.97	3119.94	3139.34	124198	354	358

Authenticity

Findings:

Main Effects
- There is no main effect of condition.
- There is a main effect of race essentialism such that participants who are more essentialist about race perceive multiracial people as less authentic.
Interaction
- There is no significant interaction. In other words, race essentialism does not moderate the relationship between condition and authenticity.

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	6.1840	0.1587	38.9668	0.0000
condition1	-0.1199	0.1587	-0.7557	0.4503
race_ess	-0.1804	0.0397	-4.5460	0.0000
condition1:race_ess	0.0108	0.0397	0.2733	0.7848

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0615	0.0535	0.9239	7.7295	0.0001	3	-477.615	965.23	984.633	302.142	354	358

Race & political orientation

From preregistration:

Additionally, we will conduct exploratory analyses to examine whether participants’ responses vary by participant race and political orientation..

Personally I would not read into the participant race effects too much due to the small Ns in some cells.

Participant Race
White	Black	Latine	Asian	Other
271	35	18	22	9

Speeded trust task by condition + participant race and political orientation as moderators

From preregistration:

For the speeded trust task, we will again use the White/Multiracial trust difference score as described above. We then will fit a linear regression model with the White/Multiracial trust difference score as the outcome and condition, participant race, political orientation, and the two two-way interaction terms between condition, and participant race/political orientation (i.e., condition X participant race, condition X political orientation) as predictors. If we find that any of the interactions are significant, we will conduct simple slopes analysis to probe the interaction.

Findings:

Main Effects
- There is no main effect of condition
- There are no main effects of participant race
- There is a main effect of political orientation such that participants that are more conservative show greater trust for white faces relative to multiracial faces.
Interactions
- There are no interactions between participant race and condition
- There is no interaction between condition and political orientation.

White, Black, Latine, Asian and Other

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.1724	0.0681	-2.5301	0.0119
condition1	0.1030	0.0681	1.5109	0.1318
race_factor1	-0.0685	0.0440	-1.5558	0.1207
race_factor2	0.0162	0.0421	0.3837	0.7014
race_factor3	0.0440	0.0292	1.5044	0.1334
race_factor4	-0.0295	0.0360	-0.8193	0.4132
pol_or	0.0828	0.0159	5.2209	0.0000
condition1:race_factor1	0.0248	0.0440	0.5644	0.5729
condition1:race_factor2	0.0452	0.0421	1.0744	0.2834
condition1:race_factor3	-0.0428	0.0292	-1.4658	0.1436
condition1:race_factor4	-0.0160	0.0360	-0.4445	0.6570
condition1:pol_or	-0.0235	0.0159	-1.4831	0.1390

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1076	0.0784	0.4883	3.6841	0.0001	11	-238.248	502.495	552.574	80.1231	336	348

Other main DVs by condition + participant race and political orientation as moderators

From preregistration:

For each of the five other dependent variables, we will fit an individual linear regression model including condition, participant race, political orientation, and the two-way interaction terms between condition and each demographic variable (i.e., condition X participant race, condition X political orientation) as predictors. If we find a significant effect for either of the interaction terms for any of the models, we will conduct simple slopes analysis for that model to probe the interaction.

Trust survey

Findings:

Main Effects
- There is no main effect of condition
- There are no main effects of participant race aside from Black participants. Black participants show lower trust for multiracial people.
- There is a main effect of political orientation such that participants that are more conservative show lower trust for multiracial people.
Interactions
- There are no interactions between participant race and condition
- There is no interaction between condition and political orientation

White, Black, Latine, Asian and Other

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.1043	0.1129	45.2268	0.0000
condition1	-0.0379	0.1129	-0.3361	0.7370
race_factor1	-0.1914	0.0732	-2.6160	0.0093
race_factor2	0.0123	0.0701	0.1752	0.8610
race_factor3	-0.0673	0.0486	-1.3837	0.1673
race_factor4	-0.0066	0.0598	-0.1106	0.9120
pol_or	-0.0950	0.0261	-3.6423	0.0003
condition1:race_factor1	-0.1017	0.0732	-1.3898	0.1655
condition1:race_factor2	0.0248	0.0701	0.3536	0.7238
condition1:race_factor3	0.0754	0.0486	1.5503	0.1220
condition1:race_factor4	0.0178	0.0598	0.2982	0.7657
condition1:pol_or	0.0145	0.0261	0.5573	0.5777

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0798	0.0503	0.8127	2.705	0.0023	11	-423.989	873.978	924.316	226.538	343	355

Trust Thermometer

Findings:

Main Effects
- There is no main effect of condition
- There are no main effects of participant race aside from Asian participants. Asian participants show lower trust for multiracial people.
- There is a main effect of political orientation such that participants that are more conservative show less trust for multiracial people.
Interactions
- There are no interactions between participant race and condition
- There is no interaction between condition and political orientation

White, Black, Latine, Asian and Other

Estimates for the trust thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	74.0191	2.6147	28.3085	0.0000
condition1	-1.4210	2.6147	-0.5435	0.5872
race_factor1	-1.3787	1.6948	-0.8135	0.4165
race_factor2	0.0106	1.6232	0.0065	0.9948
race_factor3	-2.4172	1.1268	-2.1453	0.0326
race_factor4	-0.4964	1.3857	-0.3582	0.7204
pol_or	-1.8237	0.6042	-3.0181	0.0027
condition1:race_factor1	-1.7756	1.6948	-1.0477	0.2955
condition1:race_factor2	-0.4378	1.6232	-0.2697	0.7876
condition1:race_factor3	1.6171	1.1268	1.4352	0.1521
condition1:race_factor4	1.1699	1.3857	0.8443	0.3991
condition1:pol_or	0.4852	0.6042	0.8030	0.4225

Summary for the trust thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0585	0.0283	18.8281	1.9362	0.0341	11	-1539.67	3105.34	3155.67	121592	343	355

Attitudes towards multiracial adults

Findings:

Main Effects
- There is no main effect of condition
- There are no main effects of participant race aside from Black participants. Black participants show more negative attitudes towards multiracial people than White participants.
- There is no main effect of political orientation.
Interactions
- There are no interactions between participant race and condition
- There is no interaction between condition and political orientation

White, Black, Latine, Asian and Other

Estimates for the attitudes towards multiracial adults model
term	estimate	std.error	statistic	p.value
(Intercept)	3.4702	0.0697	49.8159	0.0000
condition1	-0.0400	0.0697	-0.5742	0.5662
race_factor1	-0.1200	0.0452	-2.6578	0.0082
race_factor2	-0.0312	0.0432	-0.7208	0.4715
race_factor3	0.0095	0.0300	0.3167	0.7517
race_factor4	-0.0341	0.0369	-0.9234	0.3565
pol_or	-0.0166	0.0161	-1.0334	0.3021
condition1:race_factor1	-0.0049	0.0452	-0.1087	0.9135
condition1:race_factor2	-0.0495	0.0432	-1.1440	0.2534
condition1:race_factor3	0.0432	0.0300	1.4381	0.1513
condition1:race_factor4	0.0470	0.0369	1.2744	0.2034
condition1:pol_or	0.0076	0.0161	0.4734	0.6362

Summary for the attitudes towards multiracial adults model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0538	0.0235	0.5016	1.7741	0.0571	11	-252.696	531.391	581.729	86.3042	343	355

Feeling thermometer

Findings:

Main Effects
- There is no main effect of condition
- There are no main effects of participant race aside from Asian participants. Asian participants show more negative attitudes towards multiracial people than White participants.
- There is a main effect of political orientation such that participants that are more conservative show more negative feelings towards multiracial people.
Interactions
- There are no interactions between participant race and condition.
- There is no interaction between condition and political orientation

White, Black, Latine, Asian and Other

Estimates for the feeling thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	77.7497	2.5667	30.2916	0.0000
condition1	-1.5771	2.5667	-0.6144	0.5393
race_factor1	-2.0650	1.6636	-1.2413	0.2154
race_factor2	0.5355	1.5934	0.3361	0.7370
race_factor3	-2.6335	1.1061	-2.3809	0.0178
race_factor4	-0.6549	1.3602	-0.4814	0.6305
pol_or	-2.1199	0.5931	-3.5740	0.0004
condition1:race_factor1	-1.0773	1.6636	-0.6475	0.5177
condition1:race_factor2	-0.5358	1.5934	-0.3363	0.7369
condition1:race_factor3	1.1686	1.1061	1.0565	0.2915
condition1:race_factor4	0.9056	1.3602	0.6658	0.5060
condition1:pol_or	0.4697	0.5931	0.7920	0.4289

Summary for the feeling thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.071	0.0412	18.4823	2.3829	0.0075	11	-1533.09	3092.18	3142.51	117167	343	355

Authenticity

Findings:

Main Effects
- There is no main effect of condition
- There are no main effects of participant race aside from Black participants. Black participants perceive multiracial people as less authentic than White participants.
- There is a main effect of political orientation such that participants that are more conservative perceive multiracial people as less authentic.
Interactions
- There are no interactions between participant race and condition.
- There is no interaction between condition and political orientation

White, Black, Latine, Asian and Other

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.6123	0.1261	44.5152	0.0000
condition1	-0.1258	0.1261	-0.9979	0.3191
race_factor1	-0.3267	0.0817	-3.9975	0.0001
race_factor2	0.0343	0.0783	0.4383	0.6614
race_factor3	-0.0173	0.0543	-0.3187	0.7501
race_factor4	-0.0201	0.0668	-0.3011	0.7635
pol_or	-0.1176	0.0291	-4.0371	0.0001
condition1:race_factor1	-0.0456	0.0817	-0.5585	0.5769
condition1:race_factor2	-0.0669	0.0783	-0.8545	0.3934
condition1:race_factor3	0.0479	0.0543	0.8815	0.3786
condition1:race_factor4	0.1162	0.0668	1.7396	0.0828
condition1:pol_or	0.0308	0.0291	1.0577	0.2909

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1149	0.0866	0.9078	4.0495	0	11	-463.298	952.595	1002.93	282.695	343	355

Additional Exploratory Descriptives looking at main Dvs by participant race

White:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	264	0.1132	0.519	0.075	0.09298
White	2	264	3.168	0.5267	3.138	3.164
Multiracial	3	264	3.055	0.5296	3.025	3.053
Black	4	264	3.127	0.6449	3.175	3.156
trust_survey	5	271	5.071	0.8156	5.2	5.08
trust_therm	6	271	72.76	18.97	75	73.1
atma	7	271	3.595	0.516	3.565	3.582
feel_therm	8	271	76.15	18.64	80	76.98
authen	9	271	5.585	0.9213	5.75	5.624
race_ess	10	271	3.78	1.282	4	3.851
pol_or	11	271	2.989	1.714	3	2.839

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.3336	-1.85	2.075	3.925	0.4582
White	0.4262	1.1	5	3.9	-0.01588
Multiracial	0.4448	1.2	5	3.8	0.02322
Black	0.556	1.025	5	3.975	-0.4322
trust_survey	0.8896	2.1	7	4.9	-0.3306
trust_therm	22.24	0	100	100	-0.3757
atma	0.5157	1.913	5	3.087	0.1061
feel_therm	22.24	0	100	100	-0.5009
authen	0.7413	2.75	7	4.25	-0.4259
race_ess	1.112	1	6.875	5.875	-0.4329
pol_or	1.483	1	7	6	0.4614

	kurtosis	se
whitemulti_trust_dif	2.98	0.03194
White	1.279	0.03241
Multiracial	0.9763	0.03259
Black	0.6574	0.03969
trust_survey	-0.01961	0.04955
trust_therm	-0.4341	1.152
atma	0.2125	0.03134
feel_therm	-0.2696	1.132
authen	-0.224	0.05596
race_ess	-0.3173	0.07788
pol_or	-0.8882	0.1041

Black:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	35	-0.03	0.4438	0	-0.005172
White	2	35	2.888	0.6321	3	2.93
Multiracial	3	35	2.918	0.5757	2.975	2.955
Black	4	35	3.162	0.6532	3.1	3.157
trust_survey	5	35	4.691	0.9147	5	4.759
trust_therm	6	35	70.14	20.16	71	70.52
atma	7	35	3.357	0.4269	3.348	3.325
feel_therm	8	35	72.2	20.43	70	72.31
authen	9	35	4.943	1.047	5	5.009
race_ess	10	35	4.261	0.9579	4.375	4.302
pol_or	11	35	2.943	1.697	3	2.759

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.2965	-1.425	1.05	2.475	-0.6684
White	0.4448	1.1	4.4	3.3	-0.8013
Multiracial	0.3336	1	4.2	3.2	-1.01
Black	0.4077	1.025	5	3.975	-0.1933
trust_survey	0.8896	1.5	6.2	4.7	-1.182
trust_therm	28.17	30	100	70	-0.1753
atma	0.4512	2.565	4.304	1.739	0.6286
feel_therm	29.65	35	100	65	0.02912
authen	0.7413	2.25	6.75	4.5	-0.5817
race_ess	0.556	2	6	4	-0.5097
pol_or	1.483	1	7	6	0.648

	kurtosis	se
whitemulti_trust_dif	1.857	0.07502
White	1.998	0.1068
Multiracial	2.636	0.09731
Black	2.778	0.1104
trust_survey	2.311	0.1546
trust_therm	-1.124	3.407
atma	-0.244	0.07216
feel_therm	-1.505	3.453
authen	0.3079	0.177
race_ess	0.004889	0.1619
pol_or	-0.2651	0.2868

Latine:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	18	-0.009722	0.5842	-0.0125	-0.02187
White	2	18	2.861	0.5004	2.975	2.883
Multiracial	3	18	2.871	0.4184	2.9	2.866
Black	4	18	2.986	0.6347	3	2.998
trust_survey	5	18	4.994	0.7787	5.1	5.019
trust_therm	6	18	73.67	19.42	75.5	74.5
atma	7	18	3.442	0.562	3.413	3.418
feel_therm	8	18	78.06	17.84	80	78.44
authen	9	18	5.528	0.9621	5.5	5.547
race_ess	10	18	3.597	1.044	3.562	3.602
pol_or	11	18	2.333	1.455	2	2.188

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.3521	-1.275	1.45	2.725	0.1618
White	0.4262	1.825	3.55	1.725	-0.6074
Multiracial	0.4077	2.1	3.725	1.625	0.0933
Black	0.5004	1.375	4.4	3.025	-0.2561
trust_survey	0.9637	3.6	6	2.4	-0.3004
trust_therm	23.72	34	100	66	-0.3494
atma	0.4512	2.435	4.826	2.391	0.3272
feel_therm	22.24	50	100	50	-0.2701
authen	0.556	3.75	7	3.25	-0.1793
race_ess	0.834	1.5	5.625	4.125	0.06359
pol_or	1.483	1	6	5	0.8533

	kurtosis	se
whitemulti_trust_dif	0.8556	0.1377
White	-0.8959	0.1179
Multiracial	-0.8162	0.09863
Black	0.912	0.1496
trust_survey	-1.362	0.1836
trust_therm	-1.048	4.578
atma	0.1102	0.1325
feel_therm	-1.335	4.205
authen	-0.7046	0.2268
race_ess	-0.2793	0.246
pol_or	-0.1919	0.343

Asian:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	22	0.1909	0.4674	0.15	0.1708
White	2	22	3.09	0.5096	3.05	3.079
Multiracial	3	22	2.899	0.5839	2.925	2.938
Black	4	22	2.815	0.5883	2.912	2.878
trust_survey	5	22	4.705	0.7371	4.65	4.733
trust_therm	6	22	63.18	17.42	63	63.11
atma	7	22	3.5	0.4474	3.478	3.495
feel_therm	8	22	65.45	17.28	66	65.28
authen	9	22	5.273	0.8342	5.125	5.292
race_ess	10	22	3.483	1.165	3.688	3.493
pol_or	11	22	2.5	1.371	2	2.389

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.5189	-0.6	1.275	1.875	0.3943
White	0.5189	2.025	4.15	2.125	0.1512
Multiracial	0.7598	1.675	3.65	1.975	-0.4719
Black	0.6116	1.45	3.675	2.225	-0.7905
trust_survey	0.9637	3.1	5.6	2.5	-0.2579
trust_therm	19.27	26	100	74	0.01099
atma	0.3868	2.696	4.391	1.696	0.1335
feel_therm	21.5	33	100	67	0.09723
authen	0.9266	3.75	6.5	2.75	-0.06825
race_ess	1.297	1.625	5.625	4	-0.08792
pol_or	1.483	1	5	4	0.37

	kurtosis	se
whitemulti_trust_dif	-0.5158	0.09966
White	-0.6262	0.1086
Multiracial	-0.9505	0.1245
Black	0.009586	0.1254
trust_survey	-1.201	0.1571
trust_therm	-0.4796	3.715
atma	-0.7839	0.09538
feel_therm	-1.009	3.684
authen	-1.296	0.1779
race_ess	-1.346	0.2484
pol_or	-1.274	0.2924

Other:

Table continues below
	vars	n	mean	sd
whitemulti_trust_dif	1	9	-0.00000000000000006759	0.3034
White	2	9	2.819	0.5135
Multiracial	3	9	2.819	0.6048
Black	4	9	2.886	0.6531
trust_survey	5	9	4.767	1.078
trust_therm	6	9	67	19.92
atma	7	9	3.329	0.354
feel_therm	8	9	68.67	20.2
authen	9	9	5.25	1.008
race_ess	10	9	3.917	1.077
pol_or	11	9	3.444	1.81

Table continues below
	median	trimmed	mad	min
whitemulti_trust_dif	0.05	-0.00000000000000004934	0.1483	-0.625
White	2.725	2.819	0.2965	1.95
Multiracial	3	2.819	0.556	1.6
Black	3.15	2.886	0.4448	1.85
trust_survey	4.6	4.767	0.8896	3.3
trust_therm	65	67	22.24	39
atma	3.435	3.329	0.3868	2.739
feel_therm	65	68.67	22.24	45
authen	5.5	5.25	1.112	4
race_ess	3.75	3.917	0.3706	1.75
pol_or	4	3.444	1.483	1

	max	range	skew	kurtosis	se
whitemulti_trust_dif	0.35	0.975	-0.7574	-0.542	0.1011
White	3.6	1.65	0.1395	-1.019	0.1712
Multiracial	3.5	1.9	-0.694	-0.7509	0.2016
Black	3.675	1.825	-0.568	-1.339	0.2177
trust_survey	6.4	3.1	0.3205	-1.488	0.3594
trust_therm	100	61	0.2009	-1.473	6.64
atma	3.826	1.087	-0.201	-1.439	0.118
feel_therm	100	55	0.2088	-1.796	6.733
authen	7	3	0.173	-1.375	0.3359
race_ess	5.5	3.75	-0.3997	-0.4772	0.359
pol_or	7	6	0.3042	-0.6039	0.6035

Non-preregistered Analyses

Examining the Attitudes Towards Multiracial Adults scale by subscale

It appears that it is the self-esteem subscale that is driving any marginal by-condition differens as this is the only significant t-test between conditions

Self-esteem subscale

Attitudes towards multiracial adults by condition SE (continued below)
Test statistic	df	P value	Alternative hypothesis
2.345	350.5	0.01961 *	two.sided

mean in group stable	mean in group fluid
3.402	3.238

Multiracial Heritage subscale

Attitudes towards multiracial adults by condition MH (continued below)
Test statistic	df	P value	Alternative hypothesis
0.7131	354.6	0.4763	two.sided

mean in group stable	mean in group fluid
3.506	3.461

Psychological Adjustment subscale

Attitudes towards multiracial adults by condition PA (continued below)
Test statistic	df	P value	Alternative hypothesis
1.499	355.9	0.1346	two.sided

mean in group stable	mean in group fluid
3.702	3.613

Multiracial Identity subscale

Attitudes towards multiracial adults by condition MI (continued below)
Test statistic	df	P value	Alternative hypothesis
1.365	353.2	0.1732	two.sided

mean in group stable	mean in group fluid
3.798	3.702

Exploring moderation in the GEE model

Essentialism

Strangely, when essentialism is included in the GEE model, all of the effects disappear, including the effect of race essentialism.

##                    (Intercept)                     condition1 
##                      3.4502463                     -1.0550662 
##                     race_multi                       race_ess 
##                     -0.5925969                     -0.1059355 
##                     race_black          condition1:race_multi 
##                     -0.3081812                     -0.2701861 
##            condition1:race_ess            race_multi:race_ess 
##                      0.2142430                      0.1506636 
##          condition1:race_black            race_ess:race_black 
##                      0.4187384                      0.1231109 
## condition1:race_multi:race_ess condition1:race_ess:race_black 
##                      0.0598983                     -0.0918636

Estimates for the speeded trust model with essentialism
term	estimate	std.error	statistic	p.value	NA
(Intercept)	3.4502	0.1795	19.2257	0.5704	6.0484
condition1	-1.0551	0.1795	-5.8791	0.5704	-1.8496
race_multi	-0.5926	0.2538	-2.3349	0.4355	-1.3608
race_ess	-0.1059	0.0395	-2.6836	0.1288	-0.8223
race_black	-0.3082	0.2538	-1.2143	0.8425	-0.3658
condition1:race_multi	-0.2702	0.2538	-1.0646	0.4355	-0.6204
condition1:race_ess	0.2142	0.0395	5.4273	0.1288	1.6631
race_multi:race_ess	0.1507	0.0558	2.6988	0.1015	1.4840
condition1:race_black	0.4187	0.2538	1.6499	0.8425	0.4970
race_ess:race_black	0.1231	0.0558	2.2052	0.1975	0.6234
condition1:race_multi:race_ess	0.0599	0.0558	1.0729	0.1015	0.5900
condition1:race_ess:race_black	-0.0919	0.0558	-1.6455	0.1975	-0.4651

Political Orientation

Two-way interaction between multiracial dummy code and political orientation qualified by three way interaction between the multiracial dummy code, political orientation, and condition. It appears that in the fluid condition for non-white faces, the link between coversative political orientation and trust is strongest. There might be a better way to look at a three-way interaction like this.

##                  (Intercept)                   condition1 
##                   2.49375921                  -0.24014079 
##                   race_multi                       pol_or 
##                   0.00737895                   0.13578704 
##                   race_black        condition1:race_multi 
##                   0.50557632                  -0.15672105 
##            condition1:pol_or            race_multi:pol_or 
##                   0.04340704                   0.00490605 
##        condition1:race_black            pol_or:race_black 
##                  -0.12202368                  -0.08412882 
## condition1:race_multi:pol_or condition1:pol_or:race_black 
##                   0.02568605                   0.03495118

Estimates for the speeded trust model with essentialism
term	estimate	std.error	statistic	p.value	NA
(Intercept)	2.4938	0.0621	40.1565	0.2408	10.3547
condition1	-0.2401	0.0621	-3.8669	0.2408	-0.9971
race_multi	0.0074	0.0878	0.0840	0.1900	0.0388
pol_or	0.1358	0.0182	7.4640	0.0574	2.3674
race_black	0.5056	0.0878	5.7567	0.2739	1.8458
condition1:race_multi	-0.1567	0.0878	-1.7845	0.1900	-0.8249
condition1:pol_or	0.0434	0.0182	2.3860	0.0574	0.7568
race_multi:pol_or	0.0049	0.0257	0.1907	0.0478	0.1027
condition1:race_black	-0.1220	0.0878	-1.3894	0.2739	-0.4455
pol_or:race_black	-0.0841	0.0257	-3.2700	0.0606	-1.3879
condition1:race_multi:pol_or	0.0257	0.0257	0.9984	0.0478	0.5379
condition1:pol_or:race_black	0.0350	0.0257	1.3585	0.0606	0.5766

##  condition race_multi pol_or.trend     SE  df asymp.LCL asymp.UCL
##  stable             0       0.0328 0.0610 Inf  -0.08678     0.152
##  fluid              0       0.1546 0.0779 Inf   0.00197     0.307
##  stable             1       0.0121 0.0692 Inf  -0.12363     0.148
##  fluid              1       0.1852 0.1002 Inf  -0.01115     0.382
## 
## Results are averaged over the levels of: race_black 
## Covariance estimate used: robust.variance 
## Confidence level used: 0.95

Examining implicit attitudes as a mediator for explicit attitudes

It appears that the white/multiracial trust difference score independently predicts both the trust thermometer and the feeling thermometer ### Trust Survey

## lavaan 0.6.15 ended normally after 21 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           351         358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                 4.247
##   Degrees of freedom                                 3
##   P-value                                        0.236
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -688.370
##   Loglikelihood unrestricted model (H1)       -688.370
##                                                       
##   Akaike (AIC)                                1390.740
##   Bayesian (BIC)                              1417.766
##   Sample-size adjusted Bayesian (SABIC)       1395.559
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   trust_survey ~                                                              
##     condition  (c)         -0.016    0.093   -0.171    0.864   -0.194    0.187
##   whitemulti_trust_dif ~                                                      
##     condition  (a)          0.034    0.053    0.637    0.524   -0.075    0.133
##   trust_survey ~                                                              
##     whtmlt_tr_ (b)         -0.169    0.118   -1.434    0.152   -0.439    0.047
##    Std.lv  Std.all
##                   
##    -0.016   -0.010
##                   
##     0.034    0.034
##                   
##    -0.169   -0.104
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_survey      5.042    0.149   33.943    0.000    4.740    5.326
##    .whtmlt_trst_df    0.043    0.087    0.494    0.622   -0.112    0.227
##    Std.lv  Std.all
##     5.042    6.105
##     0.043    0.085
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_survey      0.675    0.055   12.281    0.000    0.566    0.783
##    .whtmlt_trst_df    0.257    0.030    8.611    0.000    0.203    0.318
##    Std.lv  Std.all
##     0.675    0.989
##     0.257    0.999
## 
## R-Square:
##                    Estimate
##     trust_survey      0.011
##     whtmlt_trst_df    0.001
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.006    0.011   -0.505    0.614   -0.029    0.022
##     total            -0.022    0.093   -0.234    0.815   -0.198    0.183
##    Std.lv  Std.all
##    -0.006   -0.003
##    -0.022   -0.013

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

Trust Thermometer

## lavaan 0.6.15 ended normally after 30 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           351         358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                13.759
##   Degrees of freedom                                 3
##   P-value                                        0.003
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1785.314
##   Loglikelihood unrestricted model (H1)      -1785.314
##                                                       
##   Akaike (AIC)                                3584.629
##   Bayesian (BIC)                              3611.654
##   Sample-size adjusted Bayesian (SABIC)       3589.448
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   trust_therm ~                                                               
##     condition  (c)         -0.954    2.101   -0.454    0.650   -4.812    3.361
##   whitemulti_trust_dif ~                                                      
##     condition  (a)          0.034    0.055    0.623    0.534   -0.068    0.148
##   trust_therm ~                                                               
##     whtmlt_tr_ (b)         -7.174    2.310   -3.106    0.002  -11.841   -2.902
##    Std.lv  Std.all
##                   
##    -0.954   -0.025
##                   
##     0.034    0.034
##                   
##    -7.174   -0.191
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_therm      74.140    3.372   21.990    0.000   67.120   80.620
##    .whtmlt_trst_df    0.043    0.089    0.485    0.628   -0.145    0.207
##    Std.lv  Std.all
##    74.140    3.890
##     0.043    0.085
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_therm     349.651   20.158   17.346    0.000  307.516  385.155
##    .whtmlt_trst_df    0.257    0.031    8.349    0.000    0.202    0.318
##    Std.lv  Std.all
##   349.651    0.963
##     0.257    0.999
## 
## R-Square:
##                    Estimate
##     trust_therm       0.037
##     whtmlt_trst_df    0.001
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.244    0.395   -0.619    0.536   -1.048    0.554
##     total            -1.198    2.143   -0.559    0.576   -4.993    3.631
##    Std.lv  Std.all
##    -0.244   -0.006
##    -1.198   -0.031

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

Attitudes towards multiracial adults

## lavaan 0.6.15 ended normally after 16 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           351         358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                 4.338
##   Degrees of freedom                                 3
##   P-value                                        0.227
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -513.462
##   Loglikelihood unrestricted model (H1)       -513.462
##                                                       
##   Akaike (AIC)                                1040.923
##   Bayesian (BIC)                              1067.949
##   Sample-size adjusted Bayesian (SABIC)       1045.742
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   atma ~                                                                      
##     condition  (c)         -0.104    0.053   -1.987    0.047   -0.221   -0.012
##   whitemulti_trust_dif ~                                                      
##     condition  (a)          0.034    0.054    0.627    0.530   -0.069    0.137
##   atma ~                                                                      
##     whtmlt_tr_ (b)         -0.015    0.070   -0.219    0.827   -0.144    0.120
##    Std.lv  Std.all
##                   
##    -0.104   -0.104
##                   
##     0.034    0.034
##                   
##    -0.015   -0.015
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .atma              3.709    0.085   43.662    0.000    3.550    3.894
##    .whtmlt_trst_df    0.043    0.089    0.483    0.629   -0.151    0.216
##    Std.lv  Std.all
##     3.709    7.390
##     0.043    0.085
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .atma              0.249    0.019   12.805    0.000    0.212    0.283
##    .whtmlt_trst_df    0.257    0.030    8.635    0.000    0.197    0.313
##    Std.lv  Std.all
##     0.249    0.989
##     0.257    0.999
## 
## R-Square:
##                    Estimate
##     atma              0.011
##     whtmlt_trst_df    0.001
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.001    0.005   -0.111    0.911   -0.011    0.009
##     total            -0.105    0.053   -1.996    0.046   -0.215   -0.015
##    Std.lv  Std.all
##    -0.001   -0.001
##    -0.105   -0.105

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

Feeling thermometer

## lavaan 0.6.15 ended normally after 30 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           351         358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                15.177
##   Degrees of freedom                                 3
##   P-value                                        0.002
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1779.466
##   Loglikelihood unrestricted model (H1)      -1779.466
##                                                       
##   Akaike (AIC)                                3572.932
##   Bayesian (BIC)                              3599.958
##   Sample-size adjusted Bayesian (SABIC)       3577.751
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   feel_therm ~                                                                
##     condition  (c)         -1.385    1.974   -0.702    0.483   -5.167    2.857
##   whitemulti_trust_dif ~                                                      
##     condition  (a)          0.034    0.054    0.633    0.526   -0.083    0.141
##   feel_therm ~                                                                
##     whtmlt_tr_ (b)         -7.353    2.340   -3.143    0.002  -11.839   -2.625
##    Std.lv  Std.all
##                   
##    -1.385   -0.037
##                   
##     0.034    0.034
##                   
##    -7.353   -0.198
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .feel_therm       77.911    3.205   24.310    0.000   71.429   84.054
##    .whtmlt_trst_df    0.043    0.091    0.474    0.635   -0.135    0.233
##    Std.lv  Std.all
##    77.911    4.148
##     0.043    0.085
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .feel_therm      338.191   19.767   17.109    0.000  299.358  374.170
##    .whtmlt_trst_df    0.257    0.030    8.517    0.000    0.203    0.321
##    Std.lv  Std.all
##   338.191    0.959
##     0.257    0.999
## 
## R-Square:
##                    Estimate
##     feel_therm        0.041
##     whtmlt_trst_df    0.001
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.250    0.402   -0.624    0.533   -1.009    0.735
##     total            -1.635    2.016   -0.811    0.417   -5.475    2.608
##    Std.lv  Std.all
##    -0.250   -0.007
##    -1.635   -0.044

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

Authenticity

## lavaan 0.6.15 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           351         358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                 5.728
##   Degrees of freedom                                 3
##   P-value                                        0.126
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -732.563
##   Loglikelihood unrestricted model (H1)       -732.563
##                                                       
##   Akaike (AIC)                                1479.125
##   Bayesian (BIC)                              1506.151
##   Sample-size adjusted Bayesian (SABIC)       1483.944
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws              500
##   Number of successful bootstrap draws             500
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   authen ~                                                                    
##     condition  (c)         -0.176    0.103   -1.714    0.086   -0.373    0.026
##   whitemulti_trust_dif ~                                                      
##     condition  (a)          0.034    0.053    0.642    0.521   -0.068    0.140
##   authen ~                                                                    
##     whtmlt_tr_ (b)         -0.141    0.105   -1.340    0.180   -0.334    0.070
##    Std.lv  Std.all
##                   
##    -0.176   -0.094
##                   
##     0.034    0.034
##                   
##    -0.141   -0.076
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .authen            5.785    0.158   36.602    0.000    5.476    6.112
##    .whtmlt_trst_df    0.043    0.088    0.487    0.626   -0.130    0.214
##    Std.lv  Std.all
##     5.785    6.162
##     0.043    0.085
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .authen            0.868    0.066   13.108    0.000    0.740    0.994
##    .whtmlt_trst_df    0.257    0.031    8.388    0.000    0.199    0.318
##    Std.lv  Std.all
##     0.868    0.985
##     0.257    0.999
## 
## R-Square:
##                    Estimate
##     authen            0.015
##     whtmlt_trst_df    0.001
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.005    0.010   -0.479    0.632   -0.029    0.013
##     total            -0.181    0.103   -1.758    0.079   -0.378    0.022
##    Std.lv  Std.all
##    -0.005   -0.003
##    -0.181   -0.096

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

Analyses including only White participants

Note: All analyses NOT presented here did not have notable differences from the main analyses

Exploratory Analyses.

Race Essentialism as moderator

Attitudes towards multiracial adults

Findings:

The difference between this analysis and the main analysis is that there is no significant main effect of race essentialism in this analysis while there was in the analysis with the full sample.

Estimates for the attitudes towards multiracial adults model
term	estimate	std.error	statistic	p.value
(Intercept)	3.7703	0.0972	38.8056	0.0000
condition1	-0.0801	0.0972	-0.8240	0.4107
race_ess	-0.0464	0.0243	-1.9045	0.0579
condition1:race_ess	0.0078	0.0243	0.3208	0.7486

Summary for the attitudes towards multiracial adults model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0232	0.0122	0.5128	2.1138	0.0988	3	-201.536	413.072	431.083	70.2171	267	271

Political orientation as a moderator

Speeded trust task by condition + political orientation as moderator

Findings:

Interesting, in contrast with the analyses with the main sample, here we see a main effect of condition in our predicted direction. Further, there is an interaction between condition and political orientation such that the relationship between conservative political orientation and the bias against multiracial faces is stronger in the stable condition.

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.1904	0.0605	-3.1483	0.0018
condition1	0.1290	0.0605	2.1340	0.0338
pol_or	0.1011	0.0177	5.7025	0.0000
condition1:pol_or	-0.0353	0.0177	-1.9906	0.0476

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1224	0.1123	0.489	12.0916	0	3	-183.724	377.448	395.327	62.1729	260	264

##  condition pol_or.trend     SE  df lower.CL upper.CL
##  stable          0.1364 0.0253 260   0.0866    0.186
##  fluid           0.0658 0.0249 260   0.0168    0.115
## 
## Confidence level used: 0.95

Attitudes towards multiracial adults

Findings:

Here, of note there is a marginal effect of condition in the expected direction (there was not in analyses with main sample).

White, Black, Latine, Asian and Other

Estimates for the attitudes towards multiracial adults model
term	estimate	std.error	statistic	p.value
(Intercept)	3.6555	0.0632	57.8778	0.0000
condition1	-0.1209	0.0632	-1.9140	0.0567
pol_or	-0.0189	0.0183	-1.0328	0.3026
condition1:pol_or	0.0226	0.0183	1.2295	0.2200

Summary for the attitudes towards multiracial adults model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0191	0.008	0.5139	1.729	0.1614	3	-202.109	414.219	432.229	70.5149	267	271

Authenticity

Findings:

Here, of note there is also a marginal effect of condition in the expected direction (there was not in analyses with main sample).

White, Black, Latine, Asian and Other

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.9257	0.1108	53.4700	0.0000
condition1	-0.1934	0.1108	-1.7456	0.0820
pol_or	-0.1118	0.0322	-3.4735	0.0006
condition1:pol_or	0.0365	0.0322	1.1344	0.2576

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0526	0.042	0.9017	4.9458	0.0023	3	-354.489	718.978	736.988	217.108	267	271

Analyses including only Black participants

GEE Model for Speeded Trust Task

Findings:

Interestingly, the condition main effect is not significant here, though it was in the main analyses. There are also no main effects of the dummy coded race variables nor interactions, though there were with the main sample.

##           (Intercept)            condition1            race_multi 
##             2.9062135            -0.0951023             0.0248904 
##            race_black condition1:race_multi condition1:race_black 
##             0.2567251            -0.0790570            -0.0261696

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value	NA
(Intercept)	2.9062	0.0287	101.3055	0.0992	29.2892
condition1	-0.0951	0.0287	-3.3151	0.0992	-0.9585
race_multi	0.0249	0.0406	0.6135	0.0694	0.3588
race_black	0.2567	0.0406	6.3279	0.1161	2.2121
condition1:race_multi	-0.0791	0.0406	-1.9486	0.0694	-1.1397
condition1:race_black	-0.0262	0.0406	-0.6450	0.1161	-0.2255

Emmeans for condition * race_multi
condition	race_multi	emmean	SE	df	asymp.LCL	asymp.UCL
stable	0	3.1428	0.1036	Inf	2.9396	3.3459
fluid	0	2.9264	0.1255	Inf	2.6804	3.1724
stable	1	3.2467	0.1310	Inf	2.9899	3.5035
fluid	1	2.8722	0.1532	Inf	2.5719	3.1725

Post hoc tests for race_multi * condition
contrast	condition	estimate	SE	df	z.ratio	p.value
race_multi1 - race_multi0	stable	0.1039	0.0951	Inf	1.0927	0.2745
race_multi1 - race_multi0	fluid	-0.0542	0.1010	Inf	-0.5364	0.5917

Emmeans for condition * race_black
condition	race_black	emmean	SE	df	asymp.LCL	asymp.UCL
stable	0	3.0533	0.1171	Inf	2.8238	3.2828
fluid	0	2.7840	0.1288	Inf	2.5315	3.0365
stable	1	3.3362	0.1811	Inf	2.9813	3.6910
fluid	1	3.0146	0.1462	Inf	2.7281	3.3011

Post hoc tests for condition * condition
contrast	race_black	estimate	SE	df	z.ratio	p.value
fluid - stable	0	-0.2693	0.1741	Inf	-1.5467	0.1219
fluid - stable	1	-0.3216	0.2327	Inf	-1.3820	0.1670

Exploratory Analyses.

Race Essentialism

T-test

Findings:

Interesting, there is actually quite a large difference in race essentialism here. Black participants in the fluid condition report less race essentialism that Black participants in the stable condition.

Race essentialism by condition (continued below)
Test statistic	df	P value	Alternative hypothesis
3.001	28.43	0.005547 * *	two.sided

mean in group stable	mean in group fluid
4.706	3.84

Race essentialism as moderator

Of note, there is no main effect of race essentialism for Black participants for any of the following DVs, though there were for the full sample.

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.5782	0.4431	1.3048	0.2016
condition1	0.2846	0.4431	0.6423	0.5254
race_ess	-0.1491	0.0973	-1.5333	0.1353
condition1:race_ess	-0.0614	0.0973	-0.6317	0.5322

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1672	0.0866	0.4242	2.074	0.1239	3	-17.523	45.046	52.8228	5.5778	31	35

Trust survey

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.1741	0.9179	5.6371	0.0000
condition1	0.7361	0.9179	0.8020	0.4287
race_ess	-0.1357	0.2015	-0.6737	0.5055
condition1:race_ess	-0.2402	0.2015	-1.1921	0.2423

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1587	0.0773	0.8786	1.9497	0.1421	3	-43.0105	96.0209	103.798	23.9319	31	35

Trust Thermometer

Estimates for the trust thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	46.3571	20.4028	2.2721	0.0302
condition1	32.7295	20.4028	1.6042	0.1188
race_ess	4.7640	4.4782	1.0638	0.2956
condition1:race_ess	-8.2151	4.4782	-1.8345	0.0762

Summary for the trust thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.144	0.0612	19.5308	1.7384	0.1796	3	-151.559	313.117	320.894	11825	31	35

Attitudes towards multiracial adults

Estimates for the attitudes towards multiracial adults model
term	estimate	std.error	statistic	p.value
(Intercept)	3.8289	0.4452	8.6011	0.0000
condition1	0.0093	0.4452	0.0209	0.9835
race_ess	-0.1131	0.0977	-1.1571	0.2561
condition1:race_ess	-0.0291	0.0977	-0.2979	0.7678

Summary for the attitudes towards multiracial adults model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0914	0.0035	0.4261	1.0397	0.3887	3	-17.6844	45.3689	53.1456	5.6294	31	35

Feeling thermometer

Estimates for the feeling thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	63.8742	21.4541	2.9773	0.0056
condition1	23.3033	21.4541	1.0862	0.2858
race_ess	1.3559	4.7089	0.2879	0.7753
condition1:race_ess	-6.0603	4.7089	-1.2870	0.2076

Summary for the feeling thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0783	-0.0109	20.5371	0.8777	0.4633	3	-153.317	316.635	324.411	13075	31	35

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.6394	1.0844	5.2005	0.0000
condition1	0.5273	1.0844	0.4863	0.6302
race_ess	-0.1807	0.2380	-0.7594	0.4534
condition1:race_ess	-0.1882	0.2380	-0.7906	0.4352

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.1035	0.0168	1.038	1.1933	0.3285	3	-48.8458	107.692	115.468	33.4034	31	35

Political orientation as moderator

Of note, there is no main effect of political orientation for Black participants for any of the following DVs, though there were for the full sample.

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.0079	0.1640	-0.0480	0.9620
condition1	0.1572	0.1640	0.9588	0.3451
pol_or	-0.0070	0.0484	-0.1443	0.8862
condition1:pol_or	-0.0236	0.0484	-0.4879	0.6290

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0465	-0.0457	0.4539	0.5042	0.6822	3	-19.8903	49.7805	57.5573	6.3857	31	35

Trust Thermometer

White, Black, Latine, Asian and Other

Estimates for the trust thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	73.9851	7.3570	10.0564	0.0000
condition1	-6.3404	7.3570	-0.8618	0.3954
pol_or	-1.3074	2.1709	-0.6023	0.5514
condition1:pol_or	0.7252	2.1709	0.3341	0.7406

Summary for the trust thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0695	-0.0205	20.3629	0.7719	0.5185	3	-153.019	316.038	323.815	12854	31	35

Feeling thermometer

White, Black, Latine, Asian and Other

Estimates for the feeling thermometer model
term	estimate	std.error	statistic	p.value
(Intercept)	81.1048	7.3926	10.9710	0.0000
condition1	-2.3943	7.3926	-0.3239	0.7482
pol_or	-2.9947	2.1814	-1.3728	0.1797
condition1:pol_or	-0.0908	2.1814	-0.0416	0.9671

Summary for the feeling thermometer model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0851	-0.0035	20.4614	0.9609	0.4235	3	-153.188	316.376	324.152	12978.7	31	35

Authenticity

White, Black, Latine, Asian and Other

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.3939	0.3780	14.2706	0.0000
condition1	-0.0452	0.3780	-0.1196	0.9055
pol_or	-0.1490	0.1115	-1.3363	0.1912
condition1:pol_or	-0.0434	0.1115	-0.3891	0.6999

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0894	0.0013	1.0462	1.0151	0.3993	3	-49.1183	108.237	116.013	33.9278	31	35

Figures

Graphs

Additional Non-preregistered analyses added 072424

Moderation by Political orientation alone

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.1499	0.0523	-2.8671	0.0044
condition1	0.1048	0.0523	2.0044	0.0458
pol_or	0.0829	0.0156	5.3100	0.0000
condition1:pol_or	-0.0271	0.0156	-1.7339	0.0838

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0857	0.0778	0.4875	10.8429	0	3	-243.847	497.695	516.999	82.4632	347	351

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when condition = fluid: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.06   0.02     2.47   0.01
## 
## Slope of pol_or when condition = stable: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.11   0.02     5.10   0.00

##  condition pol_or.trend     SE  df lower.CL upper.CL
##  stable          0.1099 0.0216 347   0.0675    0.152
##  fluid           0.0558 0.0226 347   0.0114    0.100
## 
## Confidence level used: 0.95

Trust survey

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.2750	0.0875	60.3108	0.0000
condition1	-0.0514	0.0875	-0.5882	0.5568
pol_or	-0.0944	0.0259	-3.6429	0.0003
condition1:pol_or	0.0109	0.0259	0.4193	0.6752

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0373	0.0292	0.8192	4.5755	0.0037	3	-434.572	879.144	898.547	237.563	354	358

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.8457	0.0990	59.0236	0.0000
condition1	-0.1850	0.0990	-1.8677	0.0626
pol_or	-0.1171	0.0294	-3.9905	0.0001
condition1:pol_or	0.0316	0.0294	1.0779	0.2818

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0538	0.0458	0.9276	6.7096	0.0002	3	-479.073	968.146	987.548	304.613	354	358

Correlations between main variables

row	column	n	cor	p
whitemulti_trust_dif	White	351	0.48145746	0.000000000000000000000
whitemulti_trust_dif	Multiracial	351	-0.45870892	0.000000000000000000000
White	Multiracial	351	0.55797017	0.000000000000000000000
whitemulti_trust_dif	Black	351	-0.61359168	0.000000000000000000000
White	Black	351	0.10342148	0.052882010038933113805
Multiracial	Black	351	0.68581368	0.000000000000000000000
whitemulti_trust_dif	trust_survey	351	-0.10399798	0.051566730451732212259
White	trust_survey	351	0.13212831	0.013231978167101088317
Multiracial	trust_survey	351	0.23242260	0.000010860326303596679
Black	trust_survey	351	0.29791517	0.000000012609467248126
whitemulti_trust_dif	trust_therm	351	-0.19164957	0.000304694016479478691
White	trust_therm	351	0.12515597	0.018993305372088320482
Multiracial	trust_therm	351	0.30834436	0.000000003621595912051
Black	trust_therm	351	0.40388119	0.000000000000003330669
trust_survey	trust_therm	358	0.63175913	0.000000000000000000000
whitemulti_trust_dif	atma	351	-0.01887048	0.724606818211798842100
White	atma	351	0.22998438	0.000013498781204157595
Multiracial	atma	351	0.25103089	0.000001909581565762153
Black	atma	351	0.20780520	0.000087729028861671665
trust_survey	atma	358	0.52044322	0.000000000000000000000
trust_therm	atma	358	0.40316020	0.000000000000001998401
whitemulti_trust_dif	feel_therm	351	-0.19969417	0.000165934912646381605
White	feel_therm	351	0.10799958	0.043168927453030470076
Multiracial	feel_therm	351	0.29856760	0.000000011679611056081
Black	feel_therm	351	0.41538628	0.000000000000000444089
trust_survey	feel_therm	358	0.60006043	0.000000000000000000000
trust_therm	feel_therm	358	0.75897699	0.000000000000000000000
atma	feel_therm	358	0.43834191	0.000000000000000000000
whitemulti_trust_dif	authen	351	-0.07912138	0.139041163742135598369
White	authen	351	0.13383995	0.012078313516783234505
Multiracial	authen	351	0.21060419	0.000070001795889451657
Black	authen	351	0.24023671	0.000005323747950480850
trust_survey	authen	358	0.60418394	0.000000000000000000000
trust_therm	authen	358	0.47404745	0.000000000000000000000
atma	authen	358	0.55516484	0.000000000000000000000
feel_therm	authen	358	0.47386758	0.000000000000000000000
whitemulti_trust_dif	race_ess	351	0.21553660	0.000046685441475791123
White	race_ess	351	0.07432298	0.164712385325974430828
Multiracial	race_ess	351	-0.12872441	0.015817658766597242348
Black	race_ess	351	-0.22261199	0.000025686039990091558
trust_survey	race_ess	358	-0.18619702	0.000397537933663594245
trust_therm	race_ess	358	-0.14627513	0.005555818187539474096
atma	race_ess	358	-0.12093483	0.022102733610235070216
feel_therm	race_ess	358	-0.12933101	0.014333605282907724288
authen	race_ess	358	-0.23324017	0.000008221577008749037
whitemulti_trust_dif	pol_or	351	0.27408523	0.000000181935844612013
White	pol_or	351	0.20460489	0.000113148058916046068
Multiracial	pol_or	351	-0.05207653	0.330636114958004823094
Black	pol_or	351	-0.26479303	0.000000482044999916553
trust_survey	pol_or	358	-0.19051381	0.000288578356427482419
trust_therm	pol_or	358	-0.15944120	0.002481430965600939231
atma	pol_or	358	-0.05568753	0.293355881883568203605
feel_therm	pol_or	358	-0.19062749	0.000286127110704414989
authen	pol_or	358	-0.20318021	0.000108229205120435878
race_ess	pol_or	358	0.34896126	0.000000000010881961998
whitemulti_trust_dif	age	351	0.04141622	0.439228479659273141067
White	age	351	0.19080814	0.000324238857120295876
Multiracial	age	351	0.15423224	0.003772499818371244373
Black	age	351	0.00122434	0.981765030040303354752
trust_survey	age	358	-0.01815344	0.732120476673239650722
trust_therm	age	358	0.03329867	0.529996561144927103015
atma	age	358	0.06628101	0.210903194210905642336
feel_therm	age	358	0.09671009	0.067590431787001747921
authen	age	358	-0.06574185	0.214647608591457483840
race_ess	age	358	0.05509514	0.298528981891974964924
pol_or	age	358	0.16101009	0.002244902400092119166

Is participant age a moderator?

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.0300	0.0861	0.3488	0.7274
condition1	0.0198	0.0861	0.2294	0.8187
age	0.0018	0.0023	0.7843	0.4334
condition1:age	-0.0001	0.0023	-0.0275	0.9781

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0029	-0.0057	0.5091	0.3375	0.7982	3	-259.062	528.124	547.427	89.9312	347	351

Trust survey

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.0470	0.1398	36.1093	0.0000
condition1	-0.0533	0.1398	-0.3811	0.7033
age	-0.0014	0.0036	-0.3804	0.7039
condition1:age	0.0013	0.0036	0.3483	0.7278

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0007	-0.0077	0.8346	0.0879	0.9666	3	-441.248	892.497	911.899	246.591	354	358

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.7016	0.1587	35.9243	0.0000
condition1	-0.1808	0.1587	-1.1394	0.2553
age	-0.0056	0.0041	-1.3511	0.1775
condition1:age	0.0028	0.0041	0.6839	0.4945

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0124	0.004	0.9477	1.4769	0.2205	3	-486.746	983.491	1002.89	317.954	354	358

Is participant gender a moderator?

Participant gender
Man	Woman	Non-binary	Prefer not to say
178	170	7	3

Participant gender ### Speeded trust task
Man	Woman	Non-binary	Prefer not to say
178	170	0	0

Descriptive stats
gender_minimal	min	q1	median	mean	q3	max
Man	-1.85	-0.05625	0.125	0.1962	0.4562	2.075
Woman	-1.625	-0.225	0	-0.007727	0.225	2.05
NA	-0.55	-0.3437	-0.075	-0.0125	0.3437	0.525

Descriptive stats
condition	gender_minimal	min	q1	median	mean	q3	max
stable	Man	-1.425	-0.05	0.175	0.2199	0.4125	2.075
stable	Woman	-1.625	-0.275	0	-0.08006	0.2	1.275
stable	NA	-0.35	-0.325	0.025	0.045	0.35	0.525
fluid	Man	-1.85	-0.075	0.0875	0.1726	0.4813	1.5
fluid	Woman	-0.75	-0.1938	0.0375	0.05872	0.225	2.05
fluid	NA	-0.55	-0.375	-0.175	-0.07	0.325	0.425

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.0928	0.0271	3.4232	0.0007
condition1	0.0229	0.0271	0.8438	0.3994
gender_minimal1	-0.1035	0.0271	-3.8169	0.0002
condition1:gender_minimal1	0.0465	0.0271	1.7164	0.0870

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.05	0.0415	0.5	5.912	0.0006	3	-245.496	500.991	520.151	84.2563	337	341

Trust survey

Descriptive stats
gender_minimal	min	q1	median	mean	q3	max
Man	1.5	4.2	4.95	4.872	5.5	6.7
Woman	2.8	4.5	5.2	5.121	5.6	7
NA	4	4.75	4.9	5.09	5.6	6.3

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	4.9957	0.0445	112.3831	0.0000
condition1	-0.0088	0.0445	-0.1970	0.8440
gender_minimal1	0.1242	0.0445	2.7940	0.0055
condition1:gender_minimal1	0.0286	0.0445	0.6443	0.5198

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0235	0.015	0.8287	2.7632	0.042	3	-426.407	862.813	882.074	236.261	344	348

Authenticity

Descriptive stats
gender_minimal	min	q1	median	mean	q3	max
Man	2.5	4.75	5.5	5.438	6	7
Woman	2.25	5	5.75	5.549	6.25	7
NA	3.75	5.312	5.75	5.65	6.25	7

Descriptive stats
condition	min	q1	median	mean	q3	max
stable	2.25	5	5.75	5.573	6.25	7
fluid	2.5	4.75	5.5	5.421	6	7

Descriptive stats
condition	gender_minimal	min	q1	median	mean	q3	max
stable	Man	4	5	5.75	5.619	6.25	7
stable	Woman	2.25	5	5.5	5.534	6	7
stable	NA	3.75	5.25	5.5	5.4	6	6.5
fluid	Man	2.5	4.75	5.25	5.253	6	7
fluid	Woman	3	4.938	5.75	5.562	6.25	7
fluid	NA	4.5	5.5	6.25	5.9	6.25	7

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.4921	0.0506	108.5459	0.0000
condition1	-0.0844	0.0506	-1.6683	0.0962
gender_minimal1	0.0559	0.0506	1.1056	0.2697
condition1:gender_minimal1	0.0989	0.0506	1.9545	0.0515

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0226	0.0141	0.9433	2.65	0.0488	3	-471.463	952.927	972.188	306.092	344	348

Is there an effect of target gender in the implicit trust task?

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)	3.1140	0.0090	345.9875	0.0287	108.5113
condition1	-0.0471	0.0090	-5.2286	0.0287	-1.6398
race_multi	-0.0934	0.0127	-7.3346	0.0269	-3.4712
gender1	-0.3545	0.0090	-39.3850	0.0193	-18.4086
race_black	-0.0132	0.0127	-1.0372	0.0423	-0.3119
condition1:race_multi	-0.0169	0.0127	-1.3294	0.0269	-0.6291
condition1:gender1	-0.0150	0.0090	-1.6629	0.0193	-0.7772
race_multi:gender1	0.1614	0.0127	12.6783	0.0153	10.5281
condition1:race_black	-0.0003	0.0127	-0.0213	0.0423	-0.0064
gender1:race_black	0.1209	0.0127	9.4972	0.0156	7.7679
condition1:race_multi:gender1	0.0206	0.0127	1.6161	0.0153	1.3420
condition1:gender1:race_black	0.0137	0.0127	1.0763	0.0156	0.8803

THESE ARE THE P-VALUES

##                                                                       (Intercept) 
## 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                        condition1 
## 0.1010380995704386886124837019451661035418510437011718750000000000000000000000000 
##                                                                        race_multi 
## 0.0005181604749700874975454345516823195794131606817245483398437500000000000000000 
##                                                                           gender1 
## 0.0000000000000000000000000000000000000000000000000000000000000000000000000011218 
##                                                                        race_black 
## 0.7551067920699121582828183818492107093334197998046875000000000000000000000000000 
##                                                             condition1:race_multi 
## 0.5292613577706506422515531085082329809665679931640625000000000000000000000000000 
##                                                                condition1:gender1 
## 0.4370226925361014869153564177395310252904891967773437500000000000000000000000000 
##                                                                race_multi:gender1 
## 0.0000000000000000000000000641149845387159709157156088429418147914497525478786292 
##                                                             condition1:race_black 
## 0.9948989361965662192588411016913596540689468383789062500000000000000000000000000 
##                                                                gender1:race_black 
## 0.0000000000000079788348223620209731697848892361661348734480983524441910503810504 
##                                                     condition1:race_multi:gender1 
## 0.1796007791935943787020590889369486831128597259521484375000000000000000000000000 
##                                                     condition1:gender1:race_black 
## 0.3786772929581446178737280661152908578515052795410156250000000000000000000000000

Emmeans for gender
gender	emmean	SE	df	asymp.LCL	asymp.UCL
female	3.2741	0.0293	Inf	3.2167	3.3314
male	2.8474	0.0327	Inf	2.7833	2.9115

Emmeans for race multi
race_multi	emmean	SE	df	asymp.LCL	asymp.UCL
0	3.1074	0.0233	Inf	3.0618	3.1530
1	3.0140	0.0381	Inf	2.9393	3.0888

Emmeans
race_multi	gender	emmean	SE	df	asymp.LCL	asymp.UCL
0	female	3.4014	0.0259	Inf	3.3507	3.4521
1	female	3.1467	0.0381	Inf	3.0720	3.2214
0	male	2.8134	0.0289	Inf	2.7568	2.8699
1	male	2.8814	0.0431	Inf	2.7970	2.9658

Emmeans
race_black	gender	emmean	SE	df	asymp.LCL	asymp.UCL
0	female	3.3411	0.0270	Inf	3.2881	3.3941
1	female	3.2070	0.0429	Inf	3.1230	3.2910
0	male	2.7935	0.0313	Inf	2.7322	2.8548
1	male	2.9012	0.0484	Inf	2.8064	2.9960

Emmeans (this one is not significant)
condition	race_multi	gender	emmean	SE	df	asymp.LCL	asymp.UCL
stable	0	female	3.4405	0.0362	Inf	3.3696	3.5114
fluid	0	female	3.3624	0.0370	Inf	3.2899	3.4348
stable	1	female	3.2233	0.0578	Inf	3.1101	3.3365
fluid	1	female	3.0701	0.0498	Inf	2.9726	3.1677
stable	0	male	2.8687	0.0432	Inf	2.7840	2.9533
fluid	0	male	2.7581	0.0383	Inf	2.6830	2.8331
stable	1	male	2.9330	0.0651	Inf	2.8054	3.0607
fluid	1	male	2.8297	0.0563	Inf	2.7194	2.9401

Does authenticity mediate the relation between condition and trust

Implicit trust

## lavaan 0.6.15 ended normally after 24 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           351         358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                 5.728
##   Degrees of freedom                                 3
##   P-value                                        0.126
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -732.563
##   Loglikelihood unrestricted model (H1)       -732.563
##                                                       
##   Akaike (AIC)                                1479.125
##   Bayesian (BIC)                              1506.151
##   Sample-size adjusted Bayesian (SABIC)       1483.944
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             5000
##   Number of successful bootstrap draws            5000
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   whitemulti_trust_dif ~                                                      
##     condition  (c)          0.027    0.054    0.490    0.624   -0.078    0.135
##   authen ~                                                                    
##     condition  (a)         -0.181    0.101   -1.796    0.072   -0.378    0.020
##   whitemulti_trust_dif ~                                                      
##     authen     (b)         -0.041    0.031   -1.331    0.183   -0.101    0.019
##    Std.lv  Std.all
##                   
##     0.027    0.026
##                   
##    -0.181   -0.096
##                   
##    -0.041   -0.077
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .whtmlt_trst_df    0.282    0.203    1.388    0.165   -0.108    0.694
##    .authen            5.779    0.159   36.334    0.000    5.464    6.084
##    Std.lv  Std.all
##     0.282    0.556
##     5.779    6.156
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .whtmlt_trst_df    0.255    0.030    8.523    0.000    0.198    0.313
##    .authen            0.873    0.067   13.081    0.000    0.744    1.006
##    Std.lv  Std.all
##     0.255    0.993
##     0.873    0.991
## 
## R-Square:
##                    Estimate
##     whtmlt_trst_df    0.007
##     authen            0.009
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab                0.007    0.008    0.993    0.321   -0.004    0.025
##     total             0.034    0.054    0.628    0.530   -0.070    0.141
##    Std.lv  Std.all
##     0.007    0.007
##     0.034    0.034

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

Explicit trust

## lavaan 0.6.15 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##   Number of observations                           358
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                               165.842
##   Degrees of freedom                                 3
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)               -847.433
##   Loglikelihood unrestricted model (H1)       -847.433
##                                                       
##   Akaike (AIC)                                1708.866
##   Bayesian (BIC)                              1736.029
##   Sample-size adjusted Bayesian (SABIC)       1713.822
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.000
##   P-value H_0: RMSEA <= 0.050                       NA
##   P-value H_0: RMSEA >= 0.080                       NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             5000
##   Number of successful bootstrap draws            5000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   trust_survey ~                                                        
##     condition  (c)    0.068    0.070    0.965    0.335   -0.068    0.207
##   authen ~                                                              
##     condition  (a)   -0.152    0.101   -1.506    0.132   -0.352    0.049
##   trust_survey ~                                                        
##     authen     (b)    0.532    0.042   12.631    0.000    0.447    0.612
##    Std.lv  Std.all
##                   
##     0.068    0.041
##                   
##    -0.152   -0.080
##                   
##     0.532    0.607
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_survey      1.971    0.272    7.235    0.000    1.454    2.501
##    .authen            5.726    0.160   35.804    0.000    5.410    6.039
##    Std.lv  Std.all
##     1.971    2.374
##     5.726    6.038
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_survey      0.437    0.038   11.351    0.000    0.360    0.511
##    .authen            0.893    0.067   13.366    0.000    0.762    1.024
##    Std.lv  Std.all
##     0.437    0.633
##     0.893    0.994
## 
## R-Square:
##                    Estimate
##     trust_survey      0.367
##     authen            0.006
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.081    0.055   -1.481    0.139   -0.192    0.025
##     total            -0.013    0.088   -0.152    0.879   -0.185    0.161
##    Std.lv  Std.all
##    -0.081   -0.049
##    -0.013   -0.008

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

MIFT Study 1 Analyses

MIFT Study 1 Analyses

Descriptive Statistics

Demographics

GEE Model for Speeded Trust Task

T-tests for main dependent variables for differences between conditions

Trust Survey

Trust Thermometer

Attitudes towards multiracial adults

Feeling thermometer

Authenticity

Exploratory Analyses.

Race Essentialism

T-test

Speeded trust task by condition + race essentialism as moderator

Other main DVs by condition + race essentialism as moderator

Trust survey

Trust Thermometer

Attitudes towards multiracial adults

Feeling thermometer

Authenticity

Race & political orientation

Speeded trust task by condition + participant race and political orientation as moderators

Other main DVs by condition + participant race and political orientation as moderators

Trust survey

Trust Thermometer

Attitudes towards multiracial adults

Feeling thermometer

Authenticity

Additional Exploratory Descriptives looking at main Dvs by participant race

Non-preregistered Analyses

Examining the Attitudes Towards Multiracial Adults scale by subscale

Self-esteem subscale

Multiracial Heritage subscale

Psychological Adjustment subscale

Multiracial Identity subscale

Exploring moderation in the GEE model

Essentialism

Political Orientation

Examining implicit attitudes as a mediator for explicit attitudes

Trust Thermometer

Attitudes towards multiracial adults

Feeling thermometer

Authenticity

Analyses including only White participants

Exploratory Analyses.

Race Essentialism as moderator

Attitudes towards multiracial adults

Political orientation as a moderator

Speeded trust task by condition + political orientation as moderator

Attitudes towards multiracial adults

Authenticity

Analyses including only Black participants

GEE Model for Speeded Trust Task

Exploratory Analyses.

Race Essentialism

T-test

Race essentialism as moderator

Speeded trust task

Trust survey

Trust Thermometer

Attitudes towards multiracial adults

Feeling thermometer

Authenticity

Political orientation as moderator

Speeded trust task

Trust Thermometer

Feeling thermometer

Authenticity

Figures

Graphs

Additional Non-preregistered analyses added 072424

Moderation by Political orientation alone

Speeded trust task

Trust survey

Authenticity

Correlations between main variables

Is participant age a moderator?

Speeded trust task

Trust survey