MIFT Study 5 Analyses

About the study

From preregistration:

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

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

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

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

Dependent Variables:

Trust

Speeded Trust Task
Trust Survey

Authenticity

Authenticity Scale

Perceptions Identity Fluidity

Perceptions of Multiracial Identity Fluidity Scale
Multiracial Identity Fluidity Scale – Should

Additional Moderators and Exploratory Variables

Race Essentialism
Participant Race
Participant Political Orientation
Post-race Ideology

Conditions:

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

Descriptive Statistics

Descriptives for whole sample (across all conditions)

Descriptive stats for main dependent variables (continued below)
whitemulti_trust_dif	White	Multiracial	Black

Table continues below
trust_survey	authen	fluidity	fluidity_should

postrace

race_ess

pol_or

Descriptives by condition

fluidbehav:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	137	0.1141	0.5799	0.025	0.07122
White	2	137	3.027	0.6297	3	3.04
Multiracial	3	137	2.913	0.6262	3	2.933
Black	4	137	2.992	0.7452	3	3.034
trust_survey	5	138	4.894	1.005	4.9	4.904
authen	6	138	5.04	1.138	5	5.089
fluidity	7	138	4.646	0.9743	4.714	4.689
fluidity_should	8	138	4.683	1.339	4.643	4.751
postrace	9	138	3.359	1.567	3.4	3.311
race_ess	10	138	3.908	1.193	4	3.938
pol_or	11	138	3.275	1.716	3	3.152

Table continues below
	mad	min	max	range	skew	kurtosis
whitemulti_trust_dif	0.2965	-1.6	2.675	4.275	1.186	5.279
White	0.4448	1	4.875	3.875	-0.1481	1.082
Multiracial	0.3336	1	4.925	3.925	-0.3291	1.757
Black	0.5189	1	4.975	3.975	-0.4982	0.7212
trust_survey	1.334	1.6	7	5.4	-0.2848	-0.03678
authen	1.112	1	7	6	-0.5088	0.4185
fluidity	0.9531	1.857	6.857	5	-0.4418	0.1076
fluidity_should	1.165	1	7	6	-0.4357	0.03562
postrace	2.076	1	7	6	0.1499	-0.8836
race_ess	1.112	1	7	6	-0.1942	0.09905
pol_or	1.483	1	7	6	0.4928	-0.6288

	se
whitemulti_trust_dif	0.04954
White	0.0538
Multiracial	0.0535
Black	0.06367
trust_survey	0.08555
authen	0.09683
fluidity	0.08294
fluidity_should	0.114
postrace	0.1334
race_ess	0.1016
pol_or	0.1461

fluidident:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	144	0.05937	0.4929	0.025	0.05172
White	2	144	3.111	0.675	3.075	3.116
Multiracial	3	144	3.051	0.5828	3	3.033
Black	4	144	3.122	0.6723	3.05	3.136
trust_survey	5	144	5.085	0.8853	5.25	5.083
authen	6	144	5.281	0.9671	5.25	5.267
fluidity	7	144	5.009	0.9694	5.143	5.053
fluidity_should	8	144	4.928	1.434	5	5.017
postrace	9	144	3.714	1.51	3.8	3.712
race_ess	10	144	3.719	1.32	3.875	3.746
pol_or	11	144	3.201	1.724	3	3.095

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.4448	-1.275	1.6	2.875	0.196
White	0.5745	1.1	5	3.9	0.01825
Multiracial	0.4077	1.45	5	3.55	0.3982
Black	0.556	1.225	5	3.775	-0.1851
trust_survey	0.9637	2.7	6.7	4	-0.2203
authen	1.112	2.75	7	4.25	-0.02288
fluidity	0.8472	2.143	7	4.857	-0.4689
fluidity_should	1.483	1	7	6	-0.4895
postrace	1.779	1	7	6	0.03889
race_ess	1.297	1	6.75	5.75	-0.1674
pol_or	1.483	1	7	6	0.3074

	kurtosis	se
whitemulti_trust_dif	0.744	0.04107
White	0.6465	0.05625
Multiracial	0.997	0.04856
Black	0.7422	0.05602
trust_survey	-0.9402	0.07378
authen	-0.5723	0.08059
fluidity	0.1351	0.08078
fluidity_should	-0.3629	0.1195
postrace	-0.749	0.1258
race_ess	-0.5228	0.11
pol_or	-0.9822	0.1437

stablebehav:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	140	0.07625	0.5445	0.05	0.08772
White	2	140	3.094	0.5813	3.025	3.092
Multiracial	3	140	3.017	0.5887	2.987	3.007
Black	4	140	3.104	0.6802	3.088	3.115
trust_survey	5	142	4.943	0.8485	5	4.925
authen	6	142	5.479	1.032	5.5	5.533
fluidity	7	142	3.909	0.9109	4	3.927
fluidity_should	8	142	4.439	1.285	4.286	4.49
postrace	9	142	3.366	1.339	3.4	3.368
race_ess	10	142	4.018	1.147	4	4.071
pol_or	11	142	3.331	1.67	3.5	3.246

Table continues below
	mad	min	max	range	skew
whitemulti_trust_dif	0.3706	-2.225	2.875	5.1	0.03116
White	0.4448	1	5	4	0.02031
Multiracial	0.5189	1	5	4	0.2306
Black	0.5004	1	5	4	-0.1629
trust_survey	0.8896	2.4	6.7	4.3	-0.04088
authen	0.9266	2.25	7	4.75	-0.5737
fluidity	0.8472	1.429	6.143	4.714	-0.1883
fluidity_should	1.165	1	7	6	-0.2762
postrace	1.483	1	6.6	5.6	0.04804
race_ess	1.112	1	6.875	5.875	-0.4059
pol_or	2.224	1	7	6	0.2435

	kurtosis	se
whitemulti_trust_dif	6.648	0.04602
White	2.073	0.04913
Multiracial	1.405	0.04976
Black	1.132	0.05749
trust_survey	-0.5178	0.0712
authen	0.06864	0.08664
fluidity	-0.001226	0.07644
fluidity_should	-0.1213	0.1078
postrace	-0.7785	0.1123
race_ess	0.03305	0.09622
pol_or	-0.8119	0.1402

stableident:

Table continues below
	vars	n	mean	sd	median	trimmed
whitemulti_trust_dif	1	130	0.05837	0.4271	0	0.04339
White	2	130	3.156	0.6034	3.112	3.152
Multiracial	3	130	3.097	0.5922	3.05	3.095
Black	4	130	3.217	0.6379	3.275	3.254
trust_survey	5	131	5.161	0.9516	5.3	5.146
authen	6	131	5.508	1.073	5.75	5.562
fluidity	7	131	3.714	1.023	3.857	3.755
fluidity_should	8	131	4.345	1.375	4.286	4.39
postrace	9	131	3.45	1.546	3.2	3.411
race_ess	10	131	3.896	1.154	4	3.924
pol_or	11	131	3.282	1.803	4	3.162

Table continues below
	mad	min	max	range	skew	kurtosis
whitemulti_trust_dif	0.2595	-1.25	2.975	4.225	2.149	15.99
White	0.5004	1.525	5	3.475	0.09147	0.5662
Multiracial	0.5374	1.425	5	3.575	0.07993	0.4085
Black	0.6301	1	5	4	-0.5742	0.9891
trust_survey	1.038	1.7	7	5.3	-0.2545	-0.1357
authen	1.112	1	7	6	-0.7224	1.04
fluidity	0.8472	1	7	6	-0.2343	0.4854
fluidity_should	1.271	1	7	6	-0.2923	-0.1181
postrace	1.779	1	7	6	0.2616	-0.7794
race_ess	1.112	1	6.875	5.875	-0.2366	-0.0447
pol_or	2.965	1	7	6	0.2814	-0.8773

	se
whitemulti_trust_dif	0.03746
White	0.05292
Multiracial	0.05194
Black	0.05595
trust_survey	0.08315
authen	0.09377
fluidity	0.08939
fluidity_should	0.1201
postrace	0.135
race_ess	0.1008
pol_or	0.1575

Demographics

participant gender
Man	Woman	Non-binary
209	335	11

participant race
White	Black	Latine	Asian	Native	Other
366	83	33	54	2	17

## [1] 18

## [1] 90

## [1] 38.4721

## [1] 13.1567

GEE Model for Speeded Trust Task

From preregistration:

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

Findings:

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

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

THESE ARE THE P-VALUES

##                                     (Intercept) 
##                                     0.000000000 
##                              mutability_factor1 
##                                     0.276062324 
##                               construct_factor1 
##                                     0.248511124 
##                                      race_multi 
##                                     0.000432936 
##                                      race_black 
##                                     0.706890200 
##            mutability_factor1:construct_factor1 
##                                     0.769966860 
##                   mutability_factor1:race_multi 
##                                     0.628558396 
##                    construct_factor1:race_multi 
##                                     0.389848264 
##                   mutability_factor1:race_black 
##                                     0.448046766 
##                    construct_factor1:race_black 
##                                     0.437207988 
## mutability_factor1:construct_factor1:race_multi 
##                                     0.682906503 
## mutability_factor1:construct_factor1:race_black 
##                                     0.969367440

Emmeans for condition * race_multi
mutability_factor	construct_factor	race_multi	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	behavior	0	3.0144	0.0475	Inf	2.9213	3.1076
stable	behavior	0	3.1122	0.0478	Inf	3.0185	3.2059
fluid	identity	0	3.1163	0.0438	Inf	3.0305	3.2022
stable	identity	0	3.1850	0.0469	Inf	3.0932	3.2769
fluid	behavior	1	2.9000	0.0719	Inf	2.7591	3.0408
stable	behavior	1	3.0365	0.0702	Inf	2.8990	3.1740
fluid	identity	1	3.0569	0.0590	Inf	2.9412	3.1727
stable	identity	1	3.1289	0.0587	Inf	3.0139	3.2440

Emmeans for condition * race_black
mutability_factor	construct_factor	race_black	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	behavior	0	2.9752	0.0475	Inf	2.8822	3.0683
stable	behavior	0	3.0692	0.0494	Inf	2.9725	3.1660
fluid	identity	0	3.0809	0.0482	Inf	2.9864	3.1754
stable	identity	0	3.1256	0.0484	Inf	3.0306	3.2205
fluid	behavior	1	2.9391	0.0795	Inf	2.7834	3.0949
stable	behavior	1	3.0795	0.0764	Inf	2.9298	3.2293
fluid	identity	1	3.0924	0.0683	Inf	2.9585	3.2263
stable	identity	1	3.1884	0.0634	Inf	3.0641	3.3126

ANOVAs for Explicit Trust and Authenticity

From preregistration:

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

Trust Survey

Findings:

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1.6	4	5.1	4.991	5.7	7
stable	1.7	4	5.1	5.048	5.6	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1.6	4	4.9	4.919	5.6	7
identity	1.7	4.05	5.3	5.121	5.8	7

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1.6	4	4.9	4.894	5.7	7
fluid	identity	2.7	4.075	5.25	5.085	5.7	6.7
stable	behavior	2.4	4.025	5	4.943	5.6	6.7
stable	identity	1.7	4.1	5.3	5.161	5.8	7

ANOVA – explicit trust by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	13971.9977	1	16390.3593	0.0000
mutability_factor	0.5422	1	0.6360	0.4255
construct_factor	5.7845	1	6.7857	0.0094
mutability_factor:construct_factor	0.0264	1	0.0309	0.8604
Residuals	469.7012	551	NA	NA

ANOVA – explicit trust by condition
	eta.sq	eta.sq.part
mutability_factor	0.0011	0.0012
construct_factor	0.0122	0.0122
mutability_factor:construct_factor	0.0001	0.0001

Authenticity

Findings:

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1	4.5	5.25	5.163	6	7
stable	1	5	5.75	5.493	6.25	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1	4.5	5.25	5.263	6	7
identity	1	4.75	5.5	5.389	6	7

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1	4.25	5	5.04	6	7
fluid	identity	2.75	4.688	5.25	5.281	6	7
stable	behavior	2.25	5	5.5	5.479	6.188	7
stable	identity	1	5	5.75	5.508	6.125	7

ANOVA – authenticity by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	15727.9812	1	14181.8933	0.0000
mutability_factor	15.3381	1	13.8303	0.0002
construct_factor	2.5283	1	2.2798	0.1316
mutability_factor:construct_factor	1.5663	1	1.4123	0.2352
Residuals	611.0692	551	NA	NA

ANOVA – authenticity by condition
	eta.sq	eta.sq.part
mutability_factor	0.0243	0.0245
construct_factor	0.0040	0.0041
mutability_factor:construct_factor	0.0025	0.0026

Exploratory Analyses

Identity Fluidity

From preregistration:

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

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1.857	4.286	5	4.831	5.429	7
stable	1	3.143	4	3.816	4.429	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1.429	3.714	4.286	4.272	5	6.857
identity	1	3.571	4.429	4.392	5.286	7

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	min	q1	median	mean	q3
fluid	behavior	1.857	4	4.714	4.646	5.286
fluid	identity	2.143	4.393	5.143	5.009	5.607
stable	behavior	1.429	3.286	4	3.909	4.536
stable	identity	1	3.143	3.857	3.714	4.429

max
6.857
7
6.143
7

ANOVA – fluidity by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	10342.4213	1	11011.7873	0.0000
mutability_factor	142.9180	1	152.1677	0.0000
construct_factor	0.9753	1	1.0385	0.3086
mutability_factor:construct_factor	10.7916	1	11.4900	0.0007
Residuals	517.5067	551	NA	NA

Effect size – fluidity by condition
	eta.sq	eta.sq.part
mutability_factor	0.2125	0.2164
construct_factor	0.0015	0.0019
mutability_factor:construct_factor	0.0160	0.0204

Fluidity Should

From preregistration:

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

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1	4	4.857	4.808	6	7
stable	1	3.857	4.286	4.394	5.429	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1	4	4.429	4.559	5.571	7
identity	1	4	4.714	4.65	5.714	7

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1	4	4.643	4.683	5.571	7
fluid	identity	1	4	5	4.928	6.036	7
stable	behavior	1	3.857	4.286	4.439	5.536	7
stable	identity	1	3.857	4.286	4.345	5.286	7

ANOVA – fluidity should by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	11720.8151	1	6343.6586	0.0000
mutability_factor	23.7257	1	12.8411	0.0004
construct_factor	0.7828	1	0.4237	0.5154
mutability_factor:construct_factor	3.9665	1	2.1468	0.1434
Residuals	1018.0512	551	NA	NA

Effect size – fluidity should by condition
	eta.sq	eta.sq.part
mutability_factor	0.0227	0.0228
construct_factor	0.0007	0.0008
mutability_factor:construct_factor	0.0038	0.0039

Essentialism

From preregistration:

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

Checking for differences in essentialism by condition

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1	3	3.875	3.811	4.625	7
stable	1	3.375	4	3.959	4.75	6.875

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1	3.25	4	3.963	4.75	7
identity	1	3	3.875	3.803	4.625	6.875

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1	3.25	4	3.908	4.625	7
fluid	identity	1	2.844	3.875	3.719	4.625	6.75
stable	behavior	1	3.375	4	4.018	4.844	6.875
stable	identity	1	3.188	4	3.896	4.625	6.875

ANOVA – essentialism by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	8365.7144	1	5744.0370	0.0000
mutability_factor	2.8582	1	1.9625	0.1618
construct_factor	3.3392	1	2.2928	0.1306
mutability_factor:construct_factor	0.1566	1	0.1076	0.7431
Residuals	802.4859	551	NA	NA

Effect size – essentialism by condition
	eta.sq	eta.sq.part
mutability_factor	0.0035	0.0035
construct_factor	0.0041	0.0041
mutability_factor:construct_factor	0.0002	0.0002

Checking for moderation by essentialism

From preregistration:

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

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.3588	0.0731	-4.9089	0.0000
mutability_factor1	-0.1405	0.0731	-1.9216	0.0552
construct_factor1	0.1245	0.0731	1.7038	0.0890
race_ess	0.1107	0.0179	6.1786	0.0000
mutability_factor1:construct_factor1	-0.0182	0.0731	-0.2487	0.8037
mutability_factor1:race_ess	0.0316	0.0179	1.7632	0.0784
construct_factor1:race_ess	-0.0347	0.0179	-1.9392	0.0530
mutability_factor1:construct_factor1:race_ess	0.0080	0.0179	0.4489	0.6537

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0742	0.0623	0.4981	6.2177	0	7	-393.745	805.489	844.295	134.701	543	551

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of race_ess when mutability_factor = stable: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.14   0.03     5.32   0.00
## 
## Slope of race_ess when mutability_factor = fluid: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.08   0.02     3.31   0.00

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of race_ess when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.08   0.02     3.07   0.00
## 
## Slope of race_ess when construct_factor = behavior: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.15   0.03     5.61   0.00

Trust survey

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.4968	0.1323	41.5345	0.0000
mutability_factor1	-0.1096	0.1323	-0.8281	0.4080
construct_factor1	-0.1186	0.1323	-0.8959	0.3707
race_ess	-0.1221	0.0325	-3.7605	0.0002
mutability_factor1:construct_factor1	0.0585	0.1323	0.4420	0.6586
mutability_factor1:race_ess	0.0380	0.0325	1.1709	0.2421
construct_factor1:race_ess	0.0545	0.0325	1.6769	0.0941
mutability_factor1:construct_factor1:race_ess	-0.0130	0.0325	-0.4004	0.6890

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0456	0.0334	0.9113	3.7349	0.0006	7	-731.908	1481.82	1520.69	454.227	547	555

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	6.0320	0.1497	40.2865	0.0000
mutability_factor1	0.1270	0.1497	0.8483	0.3967
construct_factor1	-0.2134	0.1497	-1.4255	0.1546
race_ess	-0.1802	0.0367	-4.9052	0.0000
mutability_factor1:construct_factor1	0.0718	0.1497	0.4793	0.6319
mutability_factor1:race_ess	0.0125	0.0367	0.3405	0.7336
construct_factor1:race_ess	0.0693	0.0367	1.8855	0.0599
mutability_factor1:construct_factor1:race_ess	-0.0324	0.0367	-0.8821	0.3781

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0776	0.0658	1.031	6.5734	0	7	-800.402	1618.8	1657.67	581.39	547	555

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of race_ess when construct_factor = identity: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.11   0.05    -2.17   0.03
## 
## Slope of race_ess when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.25   0.05    -4.72   0.00

Participant race

Speeded trust task

From preregistration:

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

Descriptive stats by participant race
race_factor	min	q1	median	mean	q3	max
White	-1.675	-0.15	0.025	0.09375	0.325	2.975
Black	-1.6	-0.2187	0	-0.03171	0.175	1.225
Latine	-2.225	-0.025	0.075	0.1053	0.4	0.85
Asian	-1.25	-0.05	0.0375	0.08634	0.2938	0.925
Native	0.525	0.5375	0.55	0.55	0.5625	0.575
Other	-0.775	-0.25	0	0.1044	0.4	1.1

Descriptive stats by mutability (continued below)
mutability_factor	race_factor	min	q1	median
fluid	White	-1.575	-0.175	0.025
fluid	Black	-1.6	-0.3188	0
fluid	Latine	-0.85	0	0.075
fluid	Asian	-0.55	-0.0375	0.0375
fluid	Native	0.525	0.5375	0.55
fluid	Other	-0.375	-0.2438	-0.000000000000000222
stable	White	-1.675	-0.125	0
stable	Black	-0.85	-0.2	0.0375
stable	Latine	-2.225	-0.0625	0.1625
stable	Asian	-1.25	-0.1156	0.05
stable	Other	-0.775	-0.1375	0

mean	q3	max
0.1075	0.3313	2.675
-0.1066	0.1687	1.225
0.169	0.4	0.85
0.1175	0.25	0.925
0.55	0.5625	0.575
0.04583	0.1125	0.825
0.07961	0.3	2.975
0.03295	0.1813	0.925
-0.00625	0.3313	0.825
0.0474	0.3	0.85
0.1364	0.4125	1.1

Descriptive stats by construct (continued below)
construct_factor	race_factor	min	q1	median
behavior	White	-1.675	-0.175	0
behavior	Black	-1.6	-0.2	0.075
behavior	Latine	-2.225	-0.025	0.2
behavior	Asian	-0.375	0.01875	0.1125
behavior	Other	-0.325	-0.000000000000000111	0.0625
identity	White	-1.25	-0.125	0.05
identity	Black	-1.275	-0.225	0
identity	Latine	-0.85	-0.0125	0.025
identity	Asian	-1.25	-0.2656	0
identity	Native	0.525	0.5375	0.55
identity	Other	-0.775	-0.375	-0.025

mean	q3	max
0.09333	0.325	2.875
0.01556	0.3	0.925
0.1221	0.45	0.85
0.1906	0.3	0.85
0.2344	0.3187	1.1
0.09417	0.3062	2.975
-0.08919	0.1	1.225
0.0875	0.325	0.85
0.002917	0.2312	0.925
0.55	0.5625	0.575
-0.01111	0.4	0.925

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	race_factor	min
fluid	behavior	White	-1.575
fluid	behavior	Black	-1.6
fluid	behavior	Latine	-0.15
fluid	behavior	Asian	-0.25
fluid	behavior	Other	-0.325
fluid	identity	White	-1.05
fluid	identity	Black	-1.275
fluid	identity	Latine	-0.85
fluid	identity	Asian	-0.55
fluid	identity	Native	0.525
fluid	identity	Other	-0.375
stable	behavior	White	-1.675
stable	behavior	Black	-0.85
stable	behavior	Latine	-2.225
stable	behavior	Asian	-0.375
stable	behavior	Other	0
stable	identity	White	-1.25
stable	identity	Black	-0.4
stable	identity	Latine	-0.4
stable	identity	Asian	-1.25
stable	identity	Other	-0.775

q1	median	mean	q3	max
-0.1625	0.025	0.1398	0.2625	2.675
-0.25	0.025	-0.05341	0.175	0.475
-0.025	0.175	0.2188	0.3188	0.85
0	0.025	0.1283	0.15	0.825
-0.0000000000000004441	0	0.13	0.15	0.825
-0.175	0.05	0.08094	0.375	1.6
-0.5563	-0.0625	-0.1797	0.075	1.225
0	0.025	0.1028	0.4	0.85
-0.1625	0.125	0.1067	0.3375	0.925
0.5375	0.55	0.55	0.5625	0.575
-0.375	-0.375	-0.375	-0.375	-0.375
-0.175	0	0.05475	0.35	2.875
-0.175	0.125	0.08152	0.3125	0.925
0.05	0.275	-0.11	0.525	0.825
0.15	0.3	0.2944	0.4	0.85
0.0625	0.125	0.4083	0.6125	1.1
-0.075	0.05	0.1111	0.2625	2.975
-0.2	0	-0.02024	0.1	0.425
-0.075	0	0.06786	0.3	0.425
-0.2562	-0.025	-0.1008	0.05	0.475
-0.2938	-0.0125	0.03437	0.4063	0.925

White	Black	Latine	Asian	Native	Other
366	83	33	54	2	17

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.4720	0.3272	1.4429	0.1496
mutability_factor1	0.1719	0.1661	1.0353	0.3010
construct_factor1	-0.2197	0.1661	-1.3232	0.1863
race_factor1	-0.0698	0.0318	-2.1931	0.0287
race_factor2	0.0143	0.0332	0.4311	0.6666
race_factor3	0.0165	0.0203	0.8111	0.4177
race_factor4	0.4989	0.4100	1.2169	0.2242
race_factor5	-0.0845	0.0872	-0.9688	0.3331
mutability_factor1:construct_factor1	0.0328	0.1661	0.1973	0.8437
mutability_factor1:race_factor1	0.0437	0.0318	1.3719	0.1707
mutability_factor1:race_factor2	-0.0403	0.0332	-1.2116	0.2262
mutability_factor1:race_factor3	0.0000	0.0203	0.0001	0.9999
mutability_factor1:race_factor4	0.1823	0.1694	1.0757	0.2825
mutability_factor1:race_factor5	NA	NA	NA	NA
construct_factor1:race_factor1	-0.0282	0.0318	-0.8856	0.3762
construct_factor1:race_factor2	0.0148	0.0332	0.4441	0.6571
construct_factor1:race_factor3	-0.0225	0.0203	-1.1086	0.2681
construct_factor1:race_factor4	-0.1831	0.1694	-1.0808	0.2803
construct_factor1:race_factor5	NA	NA	NA	NA
mutability_factor1:construct_factor1:race_factor1	-0.0113	0.0318	-0.3562	0.7218
mutability_factor1:construct_factor1:race_factor2	0.0187	0.0332	0.5614	0.5748
mutability_factor1:construct_factor1:race_factor3	-0.0324	0.0203	-1.5924	0.1119
mutability_factor1:construct_factor1:race_factor4	0.0290	0.1694	0.1712	0.8641
mutability_factor1:construct_factor1:race_factor5	NA	NA	NA	NA

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0308	-0.0058	0.5158	0.8424	0.6617	20	-406.366	856.731	951.589	141.015	530	551

Trust survey

From preregistration:

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

Descriptive stats by participant race
race_factor	min	q1	median	mean	q3	max
White	1.6	4.125	5.2	5.107	5.8	7
Black	3.4	4	4.7	4.78	5.55	7
Latine	2.6	4.3	5.3	5.1	5.6	6.7
Asian	2.4	4	4.8	4.737	5.175	6.7
Native	5.4	5.45	5.5	5.5	5.55	5.6
Other	3.3	4	5	4.982	5.6	6.8

Descriptive stats by mutability
mutability_factor	race_factor	min	q1	median	mean	q3	max
fluid	White	1.6	4	5.3	5.097	5.8	6.8
fluid	Black	3.4	4	4.45	4.708	5.275	7
fluid	Latine	2.6	4.3	5.4	5.176	6	6.7
fluid	Asian	3	4	4.7	4.693	5.175	6.5
fluid	Native	5.4	5.45	5.5	5.5	5.55	5.6
fluid	Other	3.3	4	4.1	4.217	4.5	5.2
stable	White	1.7	4.3	5.2	5.117	5.8	7
stable	Black	3.4	4	4.8	4.84	5.6	6.5
stable	Latine	4	4.75	5.15	4.967	5.4	5.6
stable	Asian	2.4	4.075	4.8	4.792	5.15	6.7
stable	Other	4	4.7	5.4	5.4	6.25	6.8

Descriptive stats by construct
construct_factor	race_factor	min	q1	median	mean	q3	max
behavior	White	1.6	4	5.1	5.032	5.7	6.8
behavior	Black	3.4	4	4.65	4.707	5.2	7
behavior	Latine	2.6	4	5.4	4.976	5.6	6.7
behavior	Asian	2.4	4	4.25	4.433	4.925	6.5
behavior	Other	3.3	4	4.8	4.862	5.425	6.7
identity	White	1.7	4.4	5.4	5.183	5.8	7
identity	Black	3.4	4	4.7	4.87	5.6	6.5
identity	Latine	4	4.825	5.3	5.231	5.7	6.7
identity	Asian	3.2	4.525	4.95	4.98	5.55	6.7
identity	Native	5.4	5.45	5.5	5.5	5.55	5.6
identity	Other	4	4.2	5	5.089	5.6	6.8

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	race_factor	min	q1	median
fluid	behavior	White	1.6	4	5.2
fluid	behavior	Black	3.5	4	4.55
fluid	behavior	Latine	2.6	4	5.4
fluid	behavior	Asian	3	4	4.4
fluid	behavior	Other	3.3	4	4
fluid	identity	White	2.7	4.4	5.4
fluid	identity	Black	3.4	4	4.35
fluid	identity	Latine	4	5.1	5.3
fluid	identity	Asian	3.2	4	4.7
fluid	identity	Native	5.4	5.45	5.5
fluid	identity	Other	4.2	4.2	4.2
stable	behavior	White	3.2	4.3	5.1
stable	behavior	Black	3.4	4	4.7
stable	behavior	Latine	4	5	5
stable	behavior	Asian	2.4	4	4.1
stable	behavior	Other	5	5.55	6.1
stable	identity	White	1.7	4.375	5.35
stable	identity	Black	3.7	4	4.9
stable	identity	Latine	4	4.5	5.3
stable	identity	Asian	3.9	4.8	5.1
stable	identity	Other	4	4.3	5.2

mean	q3	max
5.019	5.8	6.8
4.709	5.125	7
4.983	5.85	6.7
4.62	5.15	6.5
4.22	4.6	5.2
5.161	5.8	6.6
4.706	5.375	6.4
5.433	6	6.7
4.767	5.35	6.4
5.5	5.55	5.6
4.2	4.2	4.2
5.043	5.6	6.7
4.704	5.2	5.8
4.96	5.4	5.4
4.122	4.8	5
5.933	6.4	6.7
5.211	6	7
4.995	5.7	6.5
4.971	5.45	5.6
5.193	5.6	6.7
5.2	5.8	6.8

White	Black	Latine	Asian	Native	Other
366	83	33	54	2	17

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.6496	0.5790	9.7570	0.0000
mutability_factor1	0.6783	0.2939	2.3079	0.0214
construct_factor1	-0.1883	0.2939	-0.6408	0.5219
race_factor1	-0.1649	0.0561	-2.9414	0.0034
race_factor2	0.0478	0.0588	0.8127	0.4168
race_factor3	-0.0790	0.0360	-2.1947	0.0286
race_factor4	0.8894	0.7256	1.2258	0.2208
race_factor5	-0.1523	0.1544	-0.9859	0.3246
mutability_factor1:construct_factor1	-0.1783	0.2939	-0.6067	0.5443
mutability_factor1:race_factor1	0.0263	0.0561	0.4696	0.6388
mutability_factor1:race_factor2	-0.0553	0.0588	-0.9407	0.3473
mutability_factor1:race_factor3	-0.0018	0.0360	-0.0495	0.9605
mutability_factor1:race_factor4	0.6908	0.2999	2.3034	0.0216
mutability_factor1:race_factor5	NA	NA	NA	NA
construct_factor1:race_factor1	-0.0028	0.0561	-0.0508	0.9595
construct_factor1:race_factor2	0.0135	0.0588	0.2293	0.8188
construct_factor1:race_factor3	0.0540	0.0360	1.5012	0.1339
construct_factor1:race_factor4	-0.3307	0.2999	-1.1028	0.2706
construct_factor1:race_factor5	NA	NA	NA	NA
mutability_factor1:construct_factor1:race_factor1	0.0334	0.0561	0.5965	0.5511
mutability_factor1:construct_factor1:race_factor2	-0.0499	0.0588	-0.8483	0.3967
mutability_factor1:construct_factor1:race_factor3	0.0602	0.0360	1.6743	0.0947
mutability_factor1:construct_factor1:race_factor4	-0.2287	0.2999	-0.7627	0.4460
mutability_factor1:construct_factor1:race_factor5	NA	NA	NA	NA

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0648	0.0298	0.913	1.851	0.0138	20	-726.264	1496.53	1591.55	445.082	534	555

Authenticity

Descriptive stats by participant race
race_factor	min	q1	median	mean	q3	max
White	1	4.75	5.5	5.426	6	7
Black	2.25	4.5	5	5.039	5.75	7
Latine	2.75	4.75	5.5	5.311	6	7
Asian	2	4.5	5.125	5.13	6	7
Native	4.75	4.875	5	5	5.125	5.25
Other	3	4.25	5.25	5.25	6	7

Descriptive stats by mutability
mutability_factor	race_factor	min	q1	median	mean	q3	max
fluid	White	1	4.5	5.25	5.304	6	7
fluid	Black	2.25	4.5	5	4.783	5.438	6.5
fluid	Latine	2.75	4	5.25	5.167	6	7
fluid	Asian	2	4.312	5	4.95	5.688	6.5
fluid	Native	4.75	4.875	5	5	5.125	5.25
fluid	Other	3	4	4.25	4.333	4.875	5.5
stable	White	1	5	5.75	5.55	6.25	7
stable	Black	2.75	4.5	5.5	5.256	6	7
stable	Latine	4.75	5.438	5.5	5.562	5.812	6
stable	Asian	2.25	4.5	5.625	5.354	6.062	7
stable	Other	4	5.125	6	5.75	6.375	7

Descriptive stats by construct
construct_factor	race_factor	min	q1	median	mean	q3	max
behavior	White	1	4.75	5.5	5.384	6	7
behavior	Black	2.25	4.5	5	5.098	6	7
behavior	Latine	2.75	4.25	5.25	5.029	5.75	7
behavior	Asian	2	4	5	4.896	6	6.5
behavior	Other	3	4	5	5	6	7
identity	White	1	4.75	5.5	5.468	6.25	7
identity	Black	3.25	4	5	4.966	5.75	7
identity	Latine	3	5.25	5.75	5.609	6	7
identity	Asian	3.5	4.562	5.375	5.317	6	7
identity	Native	4.75	4.875	5	5	5.125	5.25
identity	Other	4	5	5.25	5.472	6.25	7

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	race_factor	min	q1	median
fluid	behavior	White	1	4.5	5.25
fluid	behavior	Black	2.25	4.5	5
fluid	behavior	Latine	2.75	3.938	4.625
fluid	behavior	Asian	2	4.125	5
fluid	behavior	Other	3	4	4
fluid	identity	White	2.75	4.75	5.25
fluid	identity	Black	3.25	4	4.875
fluid	identity	Latine	3	5.25	5.75
fluid	identity	Asian	3.5	4.5	5
fluid	identity	Native	4.75	4.875	5
fluid	identity	Other	5	5	5
stable	behavior	White	2.5	5	5.75
stable	behavior	Black	2.75	4.688	5.25
stable	behavior	Latine	4.75	5.25	5.5
stable	behavior	Asian	2.25	4	5
stable	behavior	Other	6	6	6
stable	identity	White	1	5	5.75
stable	identity	Black	4	4	5.5
stable	identity	Latine	5.25	5.5	5.75
stable	identity	Asian	4	4.875	5.75
stable	identity	Other	4	4.812	5.625

mean	q3	max
5.199	6	7
4.807	5.5	6.5
4.875	5.812	7
4.9	5.875	6.25
4.2	4.5	5.5
5.391	6	7
4.75	5.25	6
5.556	6.75	7
5	5.5	6.5
5	5.125	5.25
5	5	5
5.537	6	7
5.365	6.25	7
5.4	5.5	6
4.889	6	6.5
6.333	6.5	7
5.566	6.562	7
5.131	5.75	7
5.679	5.875	6
5.633	6.125	7
5.531	6.312	7

White	Black	Latine	Asian	Native	Other
366	83	33	54	2	17

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.3823	0.6644	8.1005	0.0000
mutability_factor1	0.6661	0.3373	1.9751	0.0488
construct_factor1	-0.0005	0.3373	-0.0015	0.9988
race_factor1	-0.2051	0.0643	-3.1877	0.0015
race_factor2	0.0530	0.0675	0.7857	0.4324
race_factor3	-0.0414	0.0413	-1.0032	0.3162
race_factor4	0.1757	0.8326	0.2110	0.8330
race_factor5	-0.0232	0.1772	-0.1310	0.8958
mutability_factor1:construct_factor1	-0.4005	0.3373	-1.1876	0.2355
mutability_factor1:race_factor1	0.0533	0.0643	0.8285	0.4078
mutability_factor1:race_factor2	-0.0065	0.0675	-0.0957	0.9238
mutability_factor1:race_factor3	-0.0048	0.0413	-0.1172	0.9067
mutability_factor1:race_factor4	0.4961	0.3441	1.4415	0.1500
mutability_factor1:race_factor5	NA	NA	NA	NA
construct_factor1:race_factor1	-0.0638	0.0643	-0.9920	0.3217
construct_factor1:race_factor2	0.0829	0.0675	1.2277	0.2201
construct_factor1:race_factor3	0.0343	0.0413	0.8298	0.4070
construct_factor1:race_factor4	-0.1089	0.3441	-0.3163	0.7519
construct_factor1:race_factor5	NA	NA	NA	NA
mutability_factor1:construct_factor1:race_factor1	-0.0017	0.0643	-0.0265	0.9789
mutability_factor1:construct_factor1:race_factor2	-0.0193	0.0675	-0.2864	0.7747
mutability_factor1:construct_factor1:race_factor3	0.0557	0.0413	1.3499	0.1776
mutability_factor1:construct_factor1:race_factor4	-0.3944	0.3441	-1.1462	0.2522
mutability_factor1:construct_factor1:race_factor5	NA	NA	NA	NA

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0702	0.0354	1.0476	2.0159	0.0057	20	-802.617	1649.23	1744.25	586.05	534	555

Political orientation

Speeded trust task

From preregistration:

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

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	-0.1903	0.0452	-4.2090	0.0000
mutability_factor1	0.0263	0.0452	0.5823	0.5606
construct_factor1	0.0779	0.0452	1.7223	0.0856
pol_or	0.0814	0.0122	6.6735	0.0000
mutability_factor1:construct_factor1	0.0873	0.0452	1.9309	0.0540
mutability_factor1:pol_or	-0.0120	0.0122	-0.9798	0.3276
construct_factor1:pol_or	-0.0281	0.0122	-2.3065	0.0215
mutability_factor1:construct_factor1:pol_or	-0.0239	0.0122	-1.9606	0.0504

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0918	0.0801	0.4933	7.8405	0	7	-388.46	794.92	833.726	132.142	543	551

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.05   0.02     3.14   0.00
## 
## Slope of pol_or when construct_factor = behavior: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.11   0.02     6.24   0.00

##  mutability_factor construct_factor pol_or.trend     SE  df lower.CL upper.CL
##  fluid             behavior               0.0976 0.0247 543   0.0490   0.1461
##  stable            behavior               0.1215 0.0249 543   0.0725   0.1705
##  fluid             identity               0.0892 0.0239 543   0.0422   0.1361
##  stable            identity               0.0174 0.0240 543  -0.0297   0.0646
## 
## Confidence level used: 0.95

Trust survey

From preregistration:

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

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.2150	0.0835	62.4533	0.0000
mutability_factor1	0.0133	0.0835	0.1597	0.8732
construct_factor1	-0.0950	0.0835	-1.1371	0.2560
pol_or	-0.0588	0.0226	-2.6061	0.0094
mutability_factor1:construct_factor1	0.0585	0.0835	0.7000	0.4842
mutability_factor1:pol_or	0.0058	0.0226	0.2582	0.7964
construct_factor1:pol_or	0.0598	0.0226	2.6509	0.0083
mutability_factor1:construct_factor1:pol_or	-0.0162	0.0226	-0.7181	0.4730

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0378	0.0255	0.915	3.0701	0.0035	7	-734.171	1486.34	1525.21	457.946	547	555

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.00   0.03     0.03   0.97
## 
## Slope of pol_or when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.12   0.03    -3.66   0.00

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.6732	0.0941	60.2856	0.0000
mutability_factor1	0.1655	0.0941	1.7591	0.0791
construct_factor1	-0.1706	0.0941	-1.8129	0.0704
pol_or	-0.1051	0.0254	-4.1295	0.0000
mutability_factor1:construct_factor1	0.0922	0.0941	0.9803	0.3274
mutability_factor1:pol_or	0.0008	0.0254	0.0308	0.9755
construct_factor1:pol_or	0.0723	0.0254	2.8402	0.0047
mutability_factor1:construct_factor1:pol_or	-0.0450	0.0254	-1.7679	0.0776

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0772	0.0654	1.0312	6.5397	0	7	-800.512	1619.02	1657.9	581.622	547	555

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of pol_or when construct_factor = identity: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.03   0.04    -0.93   0.35
## 
## Slope of pol_or when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.18   0.04    -4.85   0.00

Post-race Ideology

From preregistration:

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

Are there differences by condition?

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1	2.25	3.6	3.54	4.6	7
stable	1	2.2	3.4	3.407	4.4	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1	2.2	3.4	3.363	4.4	7
identity	1	2.4	3.6	3.588	4.6	7

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1	2	3.4	3.359	4.4	7
fluid	identity	1	2.6	3.8	3.714	4.8	7
stable	behavior	1	2.25	3.4	3.366	4.2	6.6
stable	identity	1	2.2	3.2	3.45	4.6	7

ANOVA – explicit trust by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	6683.4533	1	3004.4972	0.0000
mutability_factor	2.2833	1	1.0264	0.3114
construct_factor	6.6657	1	2.9965	0.0840
mutability_factor:construct_factor	2.5307	1	1.1377	0.2866
Residuals	1225.6902	551	NA	NA

ANOVA – explicit trust by condition
	eta.sq	eta.sq.part
mutability_factor	0.0018	0.0019
construct_factor	0.0054	0.0054
mutability_factor:construct_factor	0.0020	0.0021

Is Post race ideology a moderator?

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.1857	0.0554	3.3518	0.0009
mutability_factor1	0.0105	0.0554	0.1896	0.8497
construct_factor1	-0.1563	0.0554	-2.8200	0.0050
postrace	-0.0331	0.0147	-2.2502	0.0248
mutability_factor1:construct_factor1	0.0824	0.0554	1.4874	0.1375
mutability_factor1:postrace	-0.0049	0.0147	-0.3351	0.7377
construct_factor1:postrace	0.0403	0.0147	2.7417	0.0063
mutability_factor1:construct_factor1:postrace	-0.0209	0.0147	-1.4238	0.1551

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0292	0.0167	0.51	2.3335	0.0236	7	-406.821	831.643	870.448	141.249	543	551

## SIMPLE SLOPES ANALYSIS 
## 
## Slope of postrace when construct_factor = identity: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.01   0.02     0.36   0.72
## 
## Slope of postrace when construct_factor = behavior: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.07   0.02    -3.45   0.00

Trust survey

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	4.6358	0.0986	47.0329	0.0000
mutability_factor1	-0.0091	0.0986	-0.0919	0.9268
construct_factor1	0.1707	0.0986	1.7317	0.0839
postrace	0.1123	0.0261	4.2964	0.0000
mutability_factor1:construct_factor1	-0.0823	0.0986	-0.8347	0.4042
mutability_factor1:postrace	0.0124	0.0261	0.4742	0.6355
construct_factor1:postrace	-0.0225	0.0261	-0.8623	0.3889
mutability_factor1:construct_factor1:postrace	0.0271	0.0261	1.0350	0.3011

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0492	0.037	0.9095	4.0439	0.0002	7	-730.863	1479.73	1518.6	452.52	547	555

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	4.9458	0.113	43.7561	0.0000
mutability_factor1	0.1659	0.113	1.4679	0.1427
construct_factor1	0.1140	0.113	1.0086	0.3136
postrace	0.1107	0.030	3.6913	0.0002
mutability_factor1:construct_factor1	-0.1147	0.113	-1.0150	0.3106
mutability_factor1:postrace	0.0012	0.030	0.0411	0.9672
construct_factor1:postrace	-0.0165	0.030	-0.5501	0.5825
mutability_factor1:construct_factor1:postrace	0.0195	0.030	0.6518	0.5148

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0558	0.0438	1.043	4.6218	0	7	-806.869	1631.74	1670.61	595.099	547	555

Non-preregistered Analyses

Correlations between main variables

row	column	n	cor	p
whitemulti_trust_dif	White	551	0.45848184	0.00000000000000000000
whitemulti_trust_dif	Multiracial	551	-0.38058336	0.00000000000000000000
White	Multiracial	551	0.64733525	0.00000000000000000000
whitemulti_trust_dif	Black	551	-0.50337912	0.00000000000000000000
White	Black	551	0.32218904	0.00000000000000888178
Multiracial	Black	551	0.76698392	0.00000000000000000000
whitemulti_trust_dif	trust_survey	551	-0.16887654	0.00006783619507322314
White	trust_survey	551	0.21411268	0.00000039058487977250
Multiracial	trust_survey	551	0.36763497	0.00000000000000000000
Black	trust_survey	551	0.43095887	0.00000000000000000000
whitemulti_trust_dif	authen	551	-0.16658616	0.00008531841267611640
White	authen	551	0.16824080	0.00007231570869992154
Multiracial	authen	551	0.31793832	0.00000000000002087219
Black	authen	551	0.37689700	0.00000000000000000000
trust_survey	authen	555	0.64834522	0.00000000000000000000
whitemulti_trust_dif	fluidity	551	-0.06770445	0.11240720349471611250
White	fluidity	551	-0.05309913	0.21332886096330305747
Multiracial	fluidity	551	0.00281473	0.94744044788595638273
Black	fluidity	551	0.00387784	0.92763596858187602479
trust_survey	fluidity	555	-0.10336268	0.01484652689285281824
authen	fluidity	555	-0.17378437	0.00003851379890695839
whitemulti_trust_dif	fluidity_should	551	-0.23197327	0.00000003626398226153
White	fluidity_should	551	0.00922448	0.82895453319591383412
Multiracial	fluidity_should	551	0.20855271	0.00000078633463695965
Black	fluidity_should	551	0.27425132	0.00000000005793654445
trust_survey	fluidity_should	555	0.24085464	0.00000000912876796377
authen	fluidity_should	555	0.22459107	0.00000008915283800093
fluidity	fluidity_should	555	0.46754538	0.00000000000000000000
whitemulti_trust_dif	postrace	551	-0.09554920	0.02490301824898422112
White	postrace	551	0.13964797	0.00101374048500124658
Multiracial	postrace	551	0.22726032	0.00000006922125539432
Black	postrace	551	0.17971238	0.00002200852398681619
trust_survey	postrace	555	0.18524912	0.00001120987564107168
authen	postrace	555	0.15414332	0.00026739668534769834
fluidity	postrace	555	0.09393844	0.02690117808265179988
fluidity_should	postrace	555	0.21222232	0.00000045128981129849
whitemulti_trust_dif	race_ess	551	0.24257521	0.00000000804598787596
White	race_ess	551	0.01049450	0.80584572191431513843
Multiracial	race_ess	551	-0.19712686	0.00000312309128425170
Black	race_ess	551	-0.27370662	0.00000000006342326664
trust_survey	race_ess	555	-0.16324046	0.00011202081478001169
authen	race_ess	555	-0.19228869	0.00000505595695354799
fluidity	race_ess	555	-0.17503792	0.00003377794652226207
fluidity_should	race_ess	555	-0.40120265	0.00000000000000000000
postrace	race_ess	555	-0.17966455	0.00002064787275091184
whitemulti_trust_dif	pol_or	551	0.27016736	0.00000000011361933616
White	pol_or	551	0.15737114	0.00020826089977843409
Multiracial	pol_or	551	-0.06795806	0.11106469505924843055
Black	pol_or	551	-0.25294587	0.00000000172043512769
trust_survey	pol_or	555	-0.10743068	0.01132440191734174206
authen	pol_or	555	-0.16455869	0.00009836644044103338
fluidity	pol_or	555	-0.09854414	0.02023477496743963933
fluidity_should	pol_or	555	-0.32266186	0.00000000000000666134
postrace	pol_or	555	-0.03017405	0.47806870203444407075
race_ess	pol_or	555	0.27276876	0.00000000006324629709
whitemulti_trust_dif	age	551	-0.10171739	0.01692065638597539667
White	age	551	0.12638395	0.00296013849776466387
Multiracial	age	551	0.21874857	0.00000021480468914703
Black	age	551	0.09644342	0.02357458215136709612
trust_survey	age	555	0.03678269	0.38710367946484636192
authen	age	555	0.10541372	0.01296571063067974983
fluidity	age	555	-0.05375253	0.20609197680683877252
fluidity_should	age	555	0.08169092	0.05443144973533886599
postrace	age	555	0.08038185	0.05842911319980825802
race_ess	age	555	-0.11426656	0.00704512572134730064
pol_or	age	555	-0.00105727	0.98017336051678816133

Main Analyses Again with White Participants ONLY

GEE Model for Speeded Trust Task

From preregistration:

Findings:

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

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value	NA
(Intercept)	3.1655	0.0094	335.3228	0.0327	96.7600
mutability_factor1	0.0201	0.0094	2.1242	0.0327	0.6129
construct_factor1	0.0490	0.0094	5.1918	0.0327	1.4981
race_multi	-0.0967	0.0134	-7.2436	0.0289	-3.3504
race_black	-0.0191	0.0134	-1.4271	0.0421	-0.4525
mutability_factor1:construct_factor1	0.0113	0.0094	1.1977	0.0327	0.3456
mutability_factor1:race_multi	0.0138	0.0134	1.0331	0.0289	0.4778
construct_factor1:race_multi	0.0007	0.0134	0.0522	0.0289	0.0241
mutability_factor1:race_black	0.0316	0.0134	2.3644	0.0421	0.7496
construct_factor1:race_black	-0.0058	0.0134	-0.4323	0.0421	-0.1371
mutability_factor1:construct_factor1:race_multi	-0.0289	0.0134	-2.1617	0.0289	-0.9999
mutability_factor1:construct_factor1:race_black	0.0005	0.0134	0.0402	0.0421	0.0127

THESE ARE THE P-VALUES

##                                     (Intercept) 
##                                     0.000000000 
##                              mutability_factor1 
##                                     0.539911898 
##                               construct_factor1 
##                                     0.134097381 
##                                      race_multi 
##                                     0.000807063 
##                                      race_black 
##                                     0.650939729 
##            mutability_factor1:construct_factor1 
##                                     0.729645095 
##                   mutability_factor1:race_multi 
##                                     0.632768676 
##                    construct_factor1:race_multi 
##                                     0.980736317 
##                   mutability_factor1:race_black 
##                                     0.453481782 
##                    construct_factor1:race_black 
##                                     0.890976147 
## mutability_factor1:construct_factor1:race_multi 
##                                     0.317380866 
## mutability_factor1:construct_factor1:race_black 
##                                     0.989835521

Emmeans for condition * race_multi
mutability_factor	construct_factor	race_multi	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	behavior	0	3.0856	0.0621	Inf	2.9639	3.2073
stable	behavior	0	3.1341	0.0476	Inf	3.0409	3.2274
fluid	identity	0	3.1547	0.0523	Inf	3.0523	3.2571
stable	identity	0	3.2495	0.0617	Inf	3.1286	3.3704
fluid	behavior	1	2.9455	0.1032	Inf	2.7432	3.1479
stable	behavior	1	3.0794	0.0778	Inf	2.9268	3.2319
fluid	identity	1	3.0738	0.0699	Inf	2.9367	3.2109
stable	identity	1	3.1384	0.0805	Inf	2.9806	3.2963

Emmeans for condition * race_black
mutability_factor	construct_factor	race_black	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	behavior	0	3.0377	0.0595	Inf	2.9210	3.1544
stable	behavior	0	3.0979	0.0496	Inf	3.0006	3.1951
fluid	identity	0	3.1427	0.0566	Inf	3.0318	3.2536
stable	identity	0	3.1903	0.0640	Inf	3.0649	3.3158
fluid	behavior	1	2.9934	0.1154	Inf	2.7673	3.2196
stable	behavior	1	3.1156	0.0862	Inf	2.9467	3.2845
fluid	identity	1	3.0858	0.0809	Inf	2.9273	3.2442
stable	identity	1	3.1976	0.0877	Inf	3.0257	3.3695

ANOVAs for Explicit Trust and Authenticity

From preregistration:

Trust Survey

Findings:

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1.6	4	5.3	5.097	5.8	6.8
stable	1.7	4.3	5.2	5.117	5.8	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1.6	4	5.1	5.032	5.7	6.8
identity	1.7	4.4	5.4	5.183	5.8	7

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1.6	4	5.2	5.019	5.8	6.8
fluid	identity	2.7	4.4	5.4	5.161	5.8	6.6
stable	behavior	3.2	4.3	5.1	5.043	5.6	6.7
stable	identity	1.7	4.375	5.35	5.211	6	7

ANOVA – explicit trust by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	9445.5932	1	11051.5574	0.0000
mutability_factor	0.1218	1	0.1426	0.7060
construct_factor	2.1881	1	2.5602	0.1105
mutability_factor:construct_factor	0.0157	1	0.0184	0.8923
Residuals	309.3957	362	NA	NA

ANOVA – explicit trust by condition
	eta.sq	eta.sq.part
mutability_factor	0.0004	0.0004
construct_factor	0.0070	0.0070
mutability_factor:construct_factor	0.0001	0.0001

Authenticity

Findings:

Descriptive stats by mutability
mutability_factor	min	q1	median	mean	q3	max
fluid	1	4.5	5.25	5.304	6	7
stable	1	5	5.75	5.55	6.25	7

Descriptive stats by construct
construct_factor	min	q1	median	mean	q3	max
behavior	1	4.75	5.5	5.384	6	7
identity	1	4.75	5.5	5.468	6.25	7

Descriptive stats by mutability & construct
mutability_factor	construct_factor	min	q1	median	mean	q3	max
fluid	behavior	1	4.5	5.25	5.199	6	7
fluid	identity	2.75	4.75	5.25	5.391	6	7
stable	behavior	2.5	5	5.75	5.537	6	7
stable	identity	1	5	5.75	5.566	6.562	7

ANOVA – authenticity by condition
	Sum Sq	Df	F value	Pr(>F)
(Intercept)	10645.3669	1	9742.6711	0.0000
mutability_factor	5.9360	1	5.4326	0.0203
construct_factor	1.0967	1	1.0037	0.3171
mutability_factor:construct_factor	0.6024	1	0.5513	0.4583
Residuals	395.5407	362	NA	NA

ANOVA – authenticity by condition
	eta.sq	eta.sq.part
mutability_factor	0.0147	0.0148
construct_factor	0.0027	0.0028
mutability_factor:construct_factor	0.0015	0.0015

Is participant age a moderator?

Speeded trust task

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.2232	0.0681	3.2791	0.0011
mutability_factor1	0.0498	0.0681	0.7316	0.4647
construct_factor1	-0.0738	0.0681	-1.0842	0.2787
age	-0.0038	0.0017	-2.2765	0.0232
mutability_factor1:construct_factor1	0.0123	0.0681	0.1809	0.8565
mutability_factor1:age	-0.0015	0.0017	-0.9062	0.3652
construct_factor1:age	0.0015	0.0017	0.8693	0.3851
mutability_factor1:construct_factor1:age	-0.0001	0.0017	-0.0828	0.9341

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0151	0.0024	0.5137	1.1926	0.305	7	-410.783	839.567	878.372	143.295	543	551

Trust survey

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	4.9349	0.1223	40.3387	0.0000
mutability_factor1	-0.1190	0.1223	-0.9727	0.3311
construct_factor1	0.1678	0.1223	1.3720	0.1706
age	0.0023	0.0030	0.7505	0.4533
mutability_factor1:construct_factor1	-0.0220	0.1223	-0.1796	0.8576
mutability_factor1:age	0.0039	0.0030	1.2806	0.2009
construct_factor1:age	-0.0017	0.0030	-0.5468	0.5847
mutability_factor1:construct_factor1:age	0.0008	0.0030	0.2505	0.8023

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0182	0.0056	0.9243	1.4487	0.1833	7	-739.767	1497.53	1536.4	467.275	547	555

Authenticity

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.0167	0.1391	36.0676	0.0000
mutability_factor1	0.1024	0.1391	0.7364	0.4618
construct_factor1	0.1332	0.1391	0.9579	0.3385
age	0.0081	0.0034	2.3546	0.0189
mutability_factor1:construct_factor1	-0.0443	0.1391	-0.3182	0.7504
mutability_factor1:age	0.0016	0.0034	0.4753	0.6348
construct_factor1:age	-0.0018	0.0034	-0.5119	0.6089
mutability_factor1:construct_factor1:age	-0.0001	0.0034	-0.0241	0.9808

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0417	0.0294	1.0508	3.3995	0.0015	7	-810.998	1640	1678.87	604.02	547	555

Is participant gender a moderator?

Participant gender
Man	Woman	Non-binary
209	335	11

Speeded trust task

Descriptive stats by participant race
gender_factor	min	q1	median	mean	q3	max
Man	-1.575	-0.05	0.125	0.2273	0.4625	2.975
Woman	-2.225	-0.175	0	-0.007754	0.225	2.675
Non-binary	-0.75	-0.375	-0.075	-0.1841	0	0.2

Descriptive stats by mutability (continued below)
mutability_factor	gender_factor	min	q1	median	mean
fluid	Man	-1.575	-0.05	0.125	0.2326
fluid	Woman	-1.6	-0.2125	0	0.002166
fluid	Non-binary	-0.75	-0.4312	-0.0375	-0.1906
stable	Man	-1.2	-0.05	0.125	0.2214
stable	Woman	-2.225	-0.175	0	-0.01738
stable	Non-binary	-0.35	-0.25	-0.15	-0.1667

q3	max
0.475	2.375
0.2313	2.675
0.00625	0.2
0.425	2.975
0.2	1.1
-0.075	0

Descriptive stats by construct (continued below)
construct_factor	gender_factor	min	q1	median	mean
behavior	Man	-1.575	0	0.15	0.2355
behavior	Woman	-2.225	-0.175	0	0.0009587
behavior	Non-binary	-0.15	-0.1125	-0.075	-0.075
identity	Man	-1.05	-0.1	0.075	0.2176
identity	Woman	-1.275	-0.2	0	-0.01601
identity	Non-binary	-0.75	-0.4312	-0.175	-0.225

q3	max
0.4313	2.875
0.2188	2.675
-0.0375	0
0.475	2.975
0.225	1.25
0.00625	0.2

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	gender_factor	min	q1
fluid	behavior	Man	-1.575	-0.025
fluid	behavior	Woman	-1.6	-0.175
fluid	behavior	Non-binary	-0.075	-0.075
fluid	identity	Man	-1.05	-0.075
fluid	identity	Woman	-1.275	-0.3062
fluid	identity	Non-binary	-0.75	-0.4625
stable	behavior	Man	-1.2	0
stable	behavior	Woman	-2.225	-0.175
stable	behavior	Non-binary	-0.15	-0.1125
stable	identity	Man	-0.4	-0.125
stable	identity	Woman	-1.25	-0.125
stable	identity	Non-binary	-0.35	-0.35

median	mean	q3	max
0.125	0.2263	0.3812	2.375
0	0.03788	0.1875	2.675
-0.075	-0.075	-0.075	-0.075
0.125	0.2392	0.525	1.6
0	-0.03185	0.2562	1.25
0	-0.2071	0.0125	0.2
0.175	0.2446	0.475	2.875
-0.025	-0.03506	0.2375	1.1
-0.075	-0.075	-0.0375	0
0	0.1905	0.4187	2.975
0	-0.0007184	0.175	0.7
-0.35	-0.35	-0.35	-0.35

Estimates for the speeded trust model
term	estimate	std.error	statistic	p.value
(Intercept)	0.0136	0.0700	0.1950	0.8455
mutability_factor1	-0.0179	0.0700	-0.2561	0.7980
construct_factor1	-0.0403	0.0700	-0.5762	0.5647
gender_factor1	-0.1163	0.0224	-5.1840	0.0000
gender_factor2	-0.0952	0.0688	-1.3845	0.1668
mutability_factor1:construct_factor1	-0.0088	0.0700	-0.1260	0.8998
mutability_factor1:gender_factor1	-0.0014	0.0224	-0.0638	0.9492
mutability_factor1:gender_factor2	-0.0089	0.0688	-0.1294	0.8971
construct_factor1:gender_factor1	0.0007	0.0224	0.0333	0.9735
construct_factor1:gender_factor2	-0.0307	0.0688	-0.4469	0.6552
mutability_factor1:construct_factor1:gender_factor1	0.0214	0.0224	0.9531	0.3410
mutability_factor1:construct_factor1:gender_factor2	-0.0135	0.0688	-0.1956	0.8450

Summary for the speeded trust model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0567	0.0374	0.5046	2.9442	0.0008	11	-398.912	823.823	879.876	137.251	539	551

Trust survey

Descriptive stats by participant race
gender_factor	min	q1	median	mean	q3	max
Man	1.7	4	5	4.909	5.6	7
Woman	1.6	4	5.1	5.081	5.7	7
Non-binary	4	4.4	5.1	5.218	6	6.7

Descriptive stats by mutability
mutability_factor	gender_factor	min	q1	median	mean	q3	max
fluid	Man	1.9	4	5	4.94	5.6	7
fluid	Woman	1.6	4	5.1	5.015	5.8	6.6
fluid	Non-binary	4	4.45	5.2	5.213	5.8	6.7
stable	Man	1.7	4	5	4.875	5.6	7
stable	Woman	3.4	4.4	5.3	5.144	5.7	7
stable	Non-binary	4.2	4.6	5	5.233	5.75	6.5

Descriptive stats by construct
construct_factor	gender_factor	min	q1	median	mean	q3	max
behavior	Man	1.9	4	4.7	4.748	5.4	7
behavior	Woman	1.6	4	5	5.016	5.7	6.7
behavior	Non-binary	5	5.75	6.5	6.067	6.6	6.7
identity	Man	1.7	4.15	5.2	5.099	5.8	7
identity	Woman	2.7	4.05	5.3	5.144	5.8	7
identity	Non-binary	4	4.15	4.85	4.9	5.375	6.4

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	gender_factor	min	q1	median
fluid	behavior	Man	1.9	4	4.7
fluid	behavior	Woman	1.6	4	4.9
fluid	behavior	Non-binary	6.7	6.7	6.7
fluid	identity	Man	3.4	4.2	5.3
fluid	identity	Woman	2.7	4.075	5.2
fluid	identity	Non-binary	4	4.3	5.1
stable	behavior	Man	2.4	4	4.75
stable	behavior	Woman	3.4	4.4	5.1
stable	behavior	Non-binary	5	5.375	5.75
stable	identity	Man	1.7	4.15	5.1
stable	identity	Woman	3.7	4.2	5.3
stable	identity	Non-binary	4.2	4.2	4.2

mean	q3	max
4.795	5.6	7
4.942	5.725	6.5
6.7	6.7	6.7
5.096	5.7	6.7
5.085	5.8	6.6
5	5.45	6.4
4.7	5.2	6.4
5.086	5.6	6.7
5.75	6.125	6.5
5.102	5.9	7
5.201	5.8	7
4.2	4.2	4.2

Estimates for the trust survey model
term	estimate	std.error	statistic	p.value
(Intercept)	5.1381	0.1273	40.3465	0.0000
mutability_factor1	-0.1316	0.1273	-1.0331	0.3020
construct_factor1	-0.1907	0.1273	-1.4977	0.1348
gender_factor1	0.0776	0.0406	1.9086	0.0568
gender_factor2	0.1372	0.1252	1.0961	0.2735
mutability_factor1:construct_factor1	0.0187	0.1273	0.1467	0.8834
mutability_factor1:gender_factor1	0.0436	0.0406	1.0717	0.2843
mutability_factor1:gender_factor2	-0.1530	0.1252	-1.2221	0.2222
construct_factor1:gender_factor1	-0.0558	0.0406	-1.3727	0.1704
construct_factor1:gender_factor2	-0.3109	0.1252	-2.4837	0.0133
mutability_factor1:construct_factor1:gender_factor1	-0.0159	0.0406	-0.3919	0.6953
mutability_factor1:construct_factor1:gender_factor2	0.0094	0.1252	0.0751	0.9401

Summary for the trust survey model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0375	0.018	0.9185	1.9221	0.0343	11	-734.264	1494.53	1550.68	458.101	543	555

Authenticity

Descriptive stats by participant race
gender_factor	min	q1	median	mean	q3	max
Man	1	4.5	5.25	5.169	6	7
Woman	2.25	4.75	5.5	5.428	6	7
Non-binary	3.75	4.625	5.25	5.182	5.875	6.5

Descriptive stats by mutability
mutability_factor	gender_factor	min	q1	median	mean	q3	max
fluid	Man	1	4.5	5	5.052	5.75	7
fluid	Woman	2.25	4.5	5.25	5.242	6	7
fluid	Non-binary	3.75	4.375	5	5.062	5.812	6.5
stable	Man	1	4.5	5.5	5.298	6	7
stable	Woman	2.75	5	5.75	5.605	6.25	7
stable	Non-binary	4.75	5.25	5.75	5.5	5.875	6

Descriptive stats by construct
construct_factor	gender_factor	min	q1	median	mean	q3	max
behavior	Man	1	4.5	5	5.077	6	7
behavior	Woman	2.25	4.688	5.5	5.386	6	7
behavior	Non-binary	4.75	5.25	5.75	5.5	5.875	6
identity	Man	1	4.688	5.25	5.276	6	7
identity	Woman	2.75	4.75	5.5	5.468	6.125	7
identity	Non-binary	3.75	4.375	5	5.062	5.812	6.5

Descriptive stats by mutability & construct (continued below)
mutability_factor	construct_factor	gender_factor	min	q1	median
fluid	behavior	Man	1	4.25	4.75
fluid	behavior	Woman	2.25	4.438	5.25
fluid	behavior	Non-binary	5.75	5.75	5.75
fluid	identity	Man	3.75	4.75	5.25
fluid	identity	Woman	2.75	4.688	5.25
fluid	identity	Non-binary	3.75	4.25	4.75
stable	behavior	Man	2.25	4.5	5.375
stable	behavior	Woman	2.75	5	5.875
stable	behavior	Non-binary	4.75	5.062	5.375
stable	identity	Man	1	4.5	5.5
stable	identity	Woman	3	5	5.75
stable	identity	Non-binary	5.75	5.75	5.75

mean	q3	max
4.886	5.75	7
5.141	6	7
5.75	5.75	5.75
5.231	5.75	7
5.339	6	7
4.964	5.625	6.5
5.272	6	7
5.619	6.25	7
5.375	5.688	6
5.331	6	7
5.592	6.25	7
5.75	5.75	5.75

Estimates for the authenticity model
term	estimate	std.error	statistic	p.value
(Intercept)	5.3543	0.1459	36.7003	0.0000
mutability_factor1	0.1357	0.1459	0.9302	0.3527
construct_factor1	0.0138	0.1459	0.0943	0.9249
gender_factor1	0.1213	0.0466	2.6042	0.0095
gender_factor2	0.0528	0.1434	0.3681	0.7129
mutability_factor1:construct_factor1	0.0541	0.1459	0.3706	0.7111
mutability_factor1:gender_factor1	0.0306	0.0466	0.6563	0.5119
mutability_factor1:gender_factor2	-0.0165	0.1434	-0.1152	0.9084
construct_factor1:gender_factor1	-0.0291	0.0466	-0.6246	0.5325
construct_factor1:gender_factor2	-0.0582	0.1434	-0.4060	0.6849
mutability_factor1:construct_factor1:gender_factor1	0.0075	0.0466	0.1620	0.8714
mutability_factor1:construct_factor1:gender_factor2	0.1181	0.1434	0.8233	0.4107

Summary for the authenticity model
r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual	nobs
0.0461	0.0268	1.0522	2.3883	0.0068	11	-809.704	1645.41	1701.55	601.209	543	555

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)	3.1009	0.0074	419.8940	0.0270	114.8250
mutability_factor1	0.0294	0.0074	3.9849	0.0270	1.0897
construct_factor1	0.0312	0.0074	4.2217	0.0270	1.1545
race_multi	-0.0764	0.0104	-7.3143	0.0217	-3.5181
gender1	-0.3681	0.0074	-49.8461	0.0156	-23.6720
race_black	0.0122	0.0104	1.1636	0.0322	0.3775
mutability_factor1:construct_factor1	-0.0079	0.0074	-1.0712	0.0270	-0.2929
mutability_factor1:race_multi	0.0105	0.0104	1.0037	0.0217	0.4828
construct_factor1:race_multi	0.0186	0.0104	1.7856	0.0217	0.8589
mutability_factor1:gender1	-0.0395	0.0074	-5.3432	0.0156	-2.5375
construct_factor1:gender1	-0.0010	0.0074	-0.1350	0.0156	-0.0641
race_multi:gender1	0.1481	0.0104	14.1798	0.0129	11.4958
mutability_factor1:race_black	0.0244	0.0104	2.3344	0.0322	0.7573
construct_factor1:race_black	0.0250	0.0104	2.3907	0.0322	0.7755
gender1:race_black	0.1278	0.0104	12.2399	0.0133	9.5918
mutability_factor1:construct_factor1:race_multi	-0.0088	0.0104	-0.8472	0.0217	-0.4075
mutability_factor1:construct_factor1:gender1	0.0275	0.0074	3.7190	0.0156	1.7662
mutability_factor1:race_multi:gender1	0.0065	0.0104	0.6250	0.0129	0.5067
construct_factor1:race_multi:gender1	-0.0004	0.0104	-0.0413	0.0129	-0.0335
mutability_factor1:construct_factor1:race_black	0.0013	0.0104	0.1228	0.0322	0.0398
mutability_factor1:gender1:race_black	0.0082	0.0104	0.7807	0.0133	0.6118
construct_factor1:gender1:race_black	-0.0183	0.0104	-1.7487	0.0133	-1.3704
mutability_factor1:construct_factor1:race_multi:gender1	-0.0214	0.0104	-2.0457	0.0129	-1.6585
mutability_factor1:construct_factor1:gender1:race_black	-0.0152	0.0104	-1.4598	0.0133	-1.1440

THESE ARE THE P-VALUES

##                                                                                                                         (Intercept) 
## 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                                  mutability_factor1 
## 0.275840851290069410950422934547532349824905395507812500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                                   construct_factor1 
## 0.248305211071214670948137381856213323771953582763671875000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                                          race_multi 
## 0.000434620337445140389204950581714115287468302994966506958007812500000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                                             gender1 
## 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000701234 
##                                                                                                                          race_black 
## 0.705829842123826045607870582898613065481185913085937500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                mutability_factor1:construct_factor1 
## 0.769581853883096966839616470679175108671188354492187500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                       mutability_factor1:race_multi 
## 0.629272461006990857512732873146887868642807006835937500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                        construct_factor1:race_multi 
## 0.390404636351615375033929922210518270730972290039062500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                          mutability_factor1:gender1 
## 0.011165619976099759938503730438696948112919926643371582031250000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                           construct_factor1:gender1 
## 0.948871036227960762410305051162140443921089172363281250000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                                  race_multi:gender1 
## 0.000000000000000000000000000001384461736154860818240066465440227760315654945915115446864319350716580188773348076419766883304873772 
##                                                                                                       mutability_factor1:race_black 
## 0.448894704744025541121033029412501491606235504150390625000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                        construct_factor1:race_black 
## 0.438044046084215876746270623698364943265914916992187500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                                  gender1:race_black 
## 0.000000000000000000000865382746886544167189056128375802996519080851188968943945309733600801394004520261660218238830566406250000000 
##                                                                                     mutability_factor1:construct_factor1:race_multi 
## 0.683644481126140401983093397575430572032928466796875000000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                        mutability_factor1:construct_factor1:gender1 
## 0.077368722699506980999828442691068630665540695190429687500000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                               mutability_factor1:race_multi:gender1 
## 0.612367565430561922035224142746301367878913879394531250000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                construct_factor1:race_multi:gender1 
## 0.973264484622566539151478082203539088368415832519531250000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                     mutability_factor1:construct_factor1:race_black 
## 0.968232414288818787895252171438187360763549804687500000000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                               mutability_factor1:gender1:race_black 
## 0.540654264840789933188602844893466681241989135742187500000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                                                construct_factor1:gender1:race_black 
## 0.170567804338139950015573731434415094554424285888671875000000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                             mutability_factor1:construct_factor1:race_multi:gender1 
## 0.097219918539417735092733607871195999905467033386230468750000000000000000000000000000000000000000000000000000000000000000000000000 
##                                                                             mutability_factor1:construct_factor1:gender1:race_black 
## 0.252619320617645304327680833011982031166553497314453125000000000000000000000000000000000000000000000000000000000000000000000000000

Emmeans for gender
gender	emmean	SE	df	asymp.LCL	asymp.UCL
female	3.2990	0.0275	Inf	3.2452	3.3528
male	2.8387	0.0287	Inf	2.7823	2.8950

Emmeans for race multi
race_multi	emmean	SE	df	asymp.LCL	asymp.UCL
0	3.1070	0.0233	Inf	3.0614	3.1526
1	3.0306	0.0326	Inf	2.9667	3.0945

Emmeans
mutability_factor	gender	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	female	3.2200	0.0387	Inf	3.1441	3.2958
stable	female	3.3779	0.0390	Inf	3.3016	3.4543
fluid	male	2.8239	0.0395	Inf	2.7465	2.9014
stable	male	2.8534	0.0417	Inf	2.7716	2.9352

Emmeans
race_multi	gender	emmean	SE	df	asymp.LCL	asymp.UCL
0	female	3.4112	0.0258	Inf	3.3606	3.4618
1	female	3.1867	0.0332	Inf	3.1216	3.2518
0	male	2.8028	0.0265	Inf	2.7509	2.8547
1	male	2.8745	0.0364	Inf	2.8032	2.9458

Emmeans
race_black	gender	emmean	SE	df	asymp.LCL	asymp.UCL
0	female	3.3568	0.0259	Inf	3.3060	3.4076
1	female	3.2411	0.0368	Inf	3.1690	3.3132
0	male	2.7687	0.0280	Inf	2.7139	2.8235
1	male	2.9087	0.0396	Inf	2.8311	2.9862

Emmeans (this one is marginal)
mutability_factor	construct_factor	gender	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	behavior	female	3.1358	0.0591	Inf	3.0199	3.2517
stable	behavior	female	3.3355	0.0564	Inf	3.2248	3.4461
fluid	identity	female	3.3042	0.0499	Inf	3.2063	3.4020
stable	identity	female	3.4204	0.0537	Inf	3.3152	3.5257
fluid	behavior	male	2.7787	0.0592	Inf	2.6626	2.8949
stable	behavior	male	2.8133	0.0624	Inf	2.6910	2.9356
fluid	identity	male	2.8691	0.0523	Inf	2.7666	2.9716
stable	identity	male	2.8935	0.0554	Inf	2.7849	3.0021

Emmeans (this one is marginal)
mutability_factor	construct_factor	race_multi	gender	emmean	SE	df	asymp.LCL	asymp.UCL
fluid	behavior	0	female	3.2533	0.0543	Inf	3.1469	3.3597
stable	behavior	0	female	3.4615	0.0518	Inf	3.3599	3.5631
fluid	identity	0	female	3.4151	0.0478	Inf	3.3214	3.5088
stable	identity	0	female	3.5149	0.0526	Inf	3.4118	3.6180
fluid	behavior	1	female	3.0183	0.0732	Inf	2.8749	3.1617
stable	behavior	1	female	3.2094	0.0694	Inf	3.0734	3.3454
fluid	identity	1	female	3.1932	0.0604	Inf	3.0749	3.3116
stable	identity	1	female	3.3260	0.0619	Inf	3.2046	3.4473
fluid	behavior	0	male	2.7756	0.0516	Inf	2.6745	2.8768
stable	behavior	0	male	2.7629	0.0561	Inf	2.6529	2.8730
fluid	identity	0	male	2.8175	0.0494	Inf	2.7208	2.9143
stable	identity	0	male	2.8552	0.0545	Inf	2.7484	2.9620
fluid	behavior	1	male	2.7819	0.0787	Inf	2.6276	2.9362
stable	behavior	1	male	2.8637	0.0798	Inf	2.7073	3.0200
fluid	identity	1	male	2.9207	0.0662	Inf	2.7908	3.0505
stable	identity	1	male	2.9319	0.0651	Inf	2.8044	3.0594

Does authenticity mediate the relation between mutability and trust

Implicit trust

## lavaan 0.6.15 ended normally after 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##                                                   Used       Total
##   Number of observations                           551         555
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                                29.263
##   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)              -1216.013
##   Loglikelihood unrestricted model (H1)      -1216.013
##                                                       
##   Akaike (AIC)                                2446.026
##   Bayesian (BIC)                              2476.208
##   Sample-size adjusted Bayesian (SABIC)       2453.987
## 
## 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 ~                                                      
##     mtblty_fct (c)          0.009    0.045    0.191    0.849   -0.079    0.098
##   authen ~                                                                    
##     mtblty_fct (a)          0.334    0.088    3.774    0.000    0.163    0.512
##   whitemulti_trust_dif ~                                                      
##     authen     (b)         -0.081    0.033   -2.439    0.015   -0.146   -0.017
##    Std.lv  Std.all
##                   
##     0.009    0.008
##                   
##     0.334    0.157
##                   
##    -0.081   -0.168
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .whtmlt_trst_df    0.496    0.178    2.789    0.005    0.150    0.846
##    .authen            4.830    0.139   34.655    0.000    4.551    5.098
##    Std.lv  Std.all
##     0.496    0.966
##     4.830    4.543
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .whtmlt_trst_df    0.257    0.029    8.752    0.000    0.201    0.315
##    .authen            1.103    0.073   15.148    0.000    0.962    1.245
##    Std.lv  Std.all
##     0.257    0.972
##     1.103    0.975
## 
## R-Square:
##                    Estimate
##     whtmlt_trst_df    0.028
##     authen            0.025
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab               -0.027    0.013   -2.065    0.039   -0.056   -0.005
##     total            -0.018    0.044   -0.421    0.673   -0.104    0.068
##    Std.lv  Std.all
##    -0.027   -0.026
##    -0.018   -0.018

## [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 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         7
## 
##   Number of observations                           555
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Model Test Baseline Model:
## 
##   Test statistic                               320.905
##   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)              -1407.227
##   Loglikelihood unrestricted model (H1)      -1407.227
##                                                       
##   Akaike (AIC)                                2828.454
##   Bayesian (BIC)                              2858.687
##   Sample-size adjusted Bayesian (SABIC)       2836.466
## 
## 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 ~                                                        
##     mtblty_fct (c)   -0.133    0.059   -2.239    0.025   -0.249   -0.015
##   authen ~                                                              
##     mtblty_fct (a)    0.330    0.089    3.702    0.000    0.151    0.502
##   trust_survey ~                                                        
##     authen     (b)    0.573    0.030   19.107    0.000    0.513    0.630
##    Std.lv  Std.all
##                   
##    -0.133   -0.072
##                   
##     0.330    0.155
##                   
##     0.573    0.659
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_survey      2.166    0.179   12.095    0.000    1.828    2.532
##    .authen            4.834    0.140   34.438    0.000    4.556    5.114
##    Std.lv  Std.all
##     2.166    2.339
##     4.834    4.536
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .trust_survey      0.493    0.035   14.016    0.000    0.423    0.561
##    .authen            1.109    0.073   15.263    0.000    0.969    1.256
##    Std.lv  Std.all
##     0.493    0.575
##     1.109    0.976
## 
## R-Square:
##                    Estimate
##     trust_survey      0.425
##     authen            0.024
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab                0.189    0.052    3.602    0.000    0.086    0.292
##     total             0.056    0.078    0.720    0.472   -0.097    0.210
##    Std.lv  Std.all
##     0.189    0.102
##     0.056    0.030

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

Does fluidity mediate the relation between mutability/construct and trust? (in moderated mediation)

## lavaan 0.6.15 ended normally after 27 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##                                                   Used       Total
##   Number of observations                           551         555
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 1.720
##   Degrees of freedom                                 2
##   P-value (Chi-square)                           0.423
## 
## Model Test Baseline Model:
## 
##   Test statistic                               148.936
##   Degrees of freedom                                 7
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.007
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -1175.826
##   Loglikelihood unrestricted model (H1)      -1174.966
##                                                       
##   Akaike (AIC)                                2369.652
##   Bayesian (BIC)                              2408.458
##   Sample-size adjusted Bayesian (SABIC)       2379.888
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent confidence interval - lower         0.000
##   90 Percent confidence interval - upper         0.081
##   P-value H_0: RMSEA <= 0.050                    0.766
##   P-value H_0: RMSEA >= 0.080                    0.052
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.013
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   fluidity ~                                                                  
##     cnstrc_  (a11)          0.954    0.259    3.689    0.000    0.447    1.461
##     mtblty_ (a1m1)         -0.138    0.260   -0.532    0.595   -0.647    0.371
##     cnst_:_ (a111)         -0.581    0.165   -3.533    0.000   -0.904   -0.259
##   whitemulti_trust_dif ~                                                      
##     fluidty  (b11)         -0.031    0.020   -1.551    0.121   -0.070    0.008
##     cnstrc_  (c11)         -0.032    0.044   -0.741    0.459   -0.118    0.053
##    Std.lv  Std.all
##                   
##     0.954    0.434
##    -0.138   -0.063
##    -0.581   -0.565
##                   
##    -0.031   -0.066
##    -0.032   -0.032
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .fluidity          4.401    0.410   10.723    0.000    3.597    5.206
##    .whtmlt_trst_df    0.259    0.108    2.409    0.016    0.048    0.470
##    Std.lv  Std.all
##     4.401    4.001
##     0.259    0.504
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .fluidity          0.932    0.056   16.598    0.000    0.822    1.042
##    .whtmlt_trst_df    0.263    0.016   16.598    0.000    0.232    0.294
##    Std.lv  Std.all
##     0.932    0.770
##     0.263    0.994
## 
## R-Square:
##                    Estimate
##     fluidity          0.230
##     whtmlt_trst_df    0.006
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     a11_cond1         0.954    0.259    3.689    0.000    0.447    1.461
##     a11_cond2         0.373    0.115    3.236    0.001    0.147    0.599
##     i_y1m1x1mod1      0.018    0.013    1.420    0.156   -0.007    0.043
##     ind_b11_11_cn1   -0.029    0.021   -1.430    0.153   -0.070    0.011
##     ind_b11_11_cn2   -0.011    0.008   -1.399    0.162   -0.028    0.005
##    Std.lv  Std.all
##     0.954    0.434
##     0.373   -0.131
##     0.018    0.037
##    -0.029   -0.029
##    -0.011    0.009

##                     lhs op                                  rhs    mi    epc
## 25 whitemulti_trust_dif  ~                   mutability_numeric 1.701 -0.064
## 24             fluidity  ~                 whitemulti_trust_dif 1.438 -0.202
## 26 whitemulti_trust_dif  ~ construct_numeric:mutability_numeric 1.375 -0.037
## 32   mutability_numeric  ~                 whitemulti_trust_dif 0.327 -0.008
## 28    construct_numeric  ~                 whitemulti_trust_dif 0.053 -0.003
##    sepc.lv sepc.all sepc.nox
## 25  -0.064   -0.062   -0.125
## 24  -0.202   -0.094   -0.094
## 26  -0.037   -0.077   -0.072
## 32  -0.008   -0.008   -0.008
## 28  -0.003   -0.003   -0.003

```