Profiles of online racism exposure and mental health among Asian, Black, and Latinx emerging adults in the United States

Figures & data

Table 1. List of model indicators.

Exposure Domain	Indicator Description
Perceived Exposure to Racial Cyberaggression	Received posts with racist comments.
	Been tagged in (or shared) racist content (e.g. websites, photos, videos, posts) insulting my race/ethnicity.
	Received a racist meme (e.g. someone taking your online photos and writing racist phrases on it, racist #hashtags, etc.).
	Been harassed by someone (e.g. ‘troll’) who started a racist argument about me for no reason.
	Received racist insults regarding my online profile (e.g. profile pictures, user ID, name).
Online Mediated Exposure to Racist Reality	Unfairness in financial gains for racial/ethnic minorities (e.g. racial/ethnic minority members earning less money than whites for doing the same work, less housing and loan opportunities).
	A viral/trending online racist content (e.g. many likes, stars).
	A viral/trending racist event happening elsewhere (e.g. in a different location).
	Online resources (e.g. urban dictionary) promoting racial/ethnic stereotypes as if they are true.
	Online videos (e.g. YouTube) that portray my racial/ethnic group negatively.
Vicarious Exposure to Racial Cyberaggression	Seen other racial/ethnic minority users being threatened to be harmed or killed.
	Seen other racial/ethnic minority users being treated like a criminal.
	Seen other racial/ethnic minority users receive racist insults regarding their online profile (e.g. profile pictures, user ID, name).
	Seen other racial/ethnic minority users being treated like a second-class citizen.
	Seen other racial/ethnic minority users receive racist comments.

See Keum (2021).

Table 2. Fit statistics: Unconditional latent class analysis models.

K	LL	BIC	SABIC	CAIC	AWE	BLRT p	VLMR LRT p	Entropy	BF	cmP
Asian (n = 310)
1	−2689.617	5465.283	5417.709	5480.283	5596.331	–	–	–	0	0
2	−2242.075	4661.983	4563.663	4515.150	4577.150	<.001	<.001	.900	0	0
3	−2173.070	4615.759	4466.693	4393.140	4487.140	<.001	.747	.816	0	0
4	−2117.445	4596.295	4396.482	4297.890	4423.890	<.001	.008	.832	<99	1
5	−2088.855	4630.899	4380.341	4730.710	5362.710	<.001	.237	.861	–	0
Black (n = 306)
1	−2881.550	5848.955	5801.381	5979.808	5979.808	–	–	–	0	0
2	−2385.096	4947.623	4849.306	5218.054	5218.054	<.001	<.001	.898	0	0
3	−2262.176	4793.361	4644.299	5203.369	5203.369	<.001	.033	.868	0	0
4	−2208.569	4777.724	4577.917	5327.310	5327.310	<.001	.096	.857	<99	1
5	−2173.717	4799.596	4549.045	5488.760	5488.760	<.001	.368	.837	–	0
Latinx (n = 163)
1	−1481.908	3040.223	2992.735	3055.222	3161.629	–	–	–	0	0
2	−1200.350	2558.607	2460.465	2589.606	2809.513	<.001	<.001	.908	0	0
3	−1138.099	2515.605	2366.810	2562.604	2896.011	<.001	.007	.893	<99	1
4	−1105.827	2532.559	2333.110	2595.560	3042.467	<.001	.119	.910	<99	0
5	−1088.125	2578.657	2328.554	2657.656	3218.063	.103	.343	.907	–	0

K: number of classes; LL: log-likelihood; BIC: Bayesian information criterion; SABIC: sample size adjusted BIC; CAIC: consistent Akaike information criterion; AWE: approximate weight of evidence criterion; BLRT: bootstrapped likelihood ratio test; VLMR LRT: Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test; BF: Bayes factor; cmP: correct model probability. Entropy is included for brevity but not used for determining the number of classes (Masyn, 2013). Bolded values under K reflect chosen solutions within race. Bolded values elsewhere indicate ‘best’ fit for each statistic within race.

Table 3. Conditional item probabilities across latent classes by race.

		Class
		Asian (n = 310)				Black (n = 306)			Latinx (n = 163)
Exposure Domain	Indicator	1 (33%)	2 (29%)	3 (24%)	4 (13%)	1 (48%)	2 (28%)	3 (24%)	1 (39%)	2 (34%)	3 (27%)
Perceived Exposure to Racial Cyberaggression	Received racist post	.019	.012	.148	.625	.257	.070	.848	.081	.037	.511
	Tagged in racist content	.000	.035	.124	.714	.164	.000	.817	.051	.018	.599
	Received racist meme	.010	.048	.092	.692	.103	.025	.787	.051	.073	.665
	Harassed by racist troll	.017	.042	.180	.675	.217	.015	.789	.029	.060	.378
	Racist insult of my profile	.010	.029	.039	.708	.091	.000	.706	.047	.019	.515
Online Mediated Exposure to Racist Reality	Systemic racism on financial gains	.159	.733	.792	.634	.801	.430	.923	.899	.173	.948
	Viral racist content	.186	.678	.872	.834	.851	.359	.959	.724	.192	.904
	Viral racist event elsewhere	.113	.774	.804	.735	.791	.270	.896	.629	.194	.880
	Online resources on racist stereotypes	.132	.690	.680	.778	.737	.231	.872	.757	.027	.927
	Racist videos	.102	.499	.556	.794	.781	.277	.869	.570	.169	.883
Vicarious Exposure to Racial Cyberaggression	Racist threats-others	.105	.208	.688	.612	.528	.105	.865	.369	.000	.772
	Criminalisation-others	.121	.220	.882	.756	.711	.148	.923	.540	.036	.798
	Racist profile insults-others	.025	.037	.630	.832	.512	.048	.908	.320	.045	.787
	Second-class citizen-others	.106	.189	.916	.872	.717	.021	.921	.521	.077	.931
	Racist comments-others	.204	.254	1.000	.817	.772	.106	.904	.637	.142	.889

Class labels are in Table 4. Percentages are rounded proportions and may not equal 100% within race. All values are from unconditional latent class analysis models.

Table 4. Distal outcomes by latent classes across race.

	Distal Outcomes
Class	MHI-5	PSS-10	UVS
Asian (n = 310)
1: Low Exposure (33%)	3.097	28.791	3.576
2: Systemic Exposure (29%)	3.153	29.528	3.541
3: Mediated Exposure (24%)	3.173	30.497	3.377
4: High Exposure (13%)	3.447	32.208	3.027
Black (n = 306)
1: Mediated Exposure (48%)	3.048	28.907	3.236
2: Low Exposure (28%)	2.931	26.939	3.601
3: High Exposure (24%)	3.363	31.053	3.069
Latinx (n = 163)
1: Systemic Exposure (39%)	3.098	29.659	3.650
2: Low Exposure (34%)	2.971	27.323	3.632
3: Mediated Exposure (27%)	3.417	31.322	3.302

MHI: Mental Health Inventory; PSS: Perceived Stress Scale; UVS: Unjust Views Scale (lower values reflect greater unjust views). Percentages are rounded proportions from unconditional latent class analysis models and may not equal 100% within race. Distal outcomes control for time spent on the Internet and extent of reliance on the Internet as a resource.

Figure 1. Conditional Item Probability Plot: Asian.

Figure 2. Conditional Item Probability Plot: Black.

Figure 3. Conditional Item Probability Plot: Latinx.

Keum, B. T. (2021). Development and validation of the Perceived Online Racism Scale short form (15 items) and very brief (six items). Computers in Human Behavior Reports, 3, 100082. https://doi.org/10.1016/j.chbr.2021.100082

 Google Scholar

Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In T. D. Little (Ed.), The Oxford handbook of quantitative methods: Vol. 2: Statistical analysis (pp. 551–611). Oxford University Press.

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Data availability statement

Not applicable as this study reports on secondary data analysis.