Sage Journals: Discover world-class research

Abstract

In this study, we examined the measurement invariance of the Everyday Discrimination Scale (EDS) across Black older men and women (ages 66–90). Participants were 141 Black men and 287 Black women drawn from the Kaiser Health Aging and Diverse Life Experiences study. An exploratory factor analysis of EDS items resulted in a unidimensional scale, and results were corroborated with each group using confirmatory factor analysis (CFA). Measurement invariance across groups was tested using multiple group CFA. Data fit was tested for configural, metric, and scalar invariance. There was measurement noninvariance at the metric level, suggesting nonequivalent factor loadings. The extent of factor score misestimation between the unadjusted and final model was assessed. Among Black women who reported more frequent experiences of discrimination, the unmodified EDS overestimated their total factor score. Administering the EDS without refinement may lead to incorrect measurement particularly among older Black women. Accounting for the gendered nature of interpersonal discrimination may improve estimates of discrimination prevalence and more accurately estimate the relationship between discrimination and health among Black older adults.

Keywords

measurement invariance intersectionality discrimination gender differences Black adults aging

Interpersonal discrimination describes the unjust and prejudicial action of one person towards another on the basis of difference (e.g., race, religion, sexual orientation). A number of studies have linked discrimination on the basis of race to poor mental health (Berger & Sarnyai, 2015; Jones et al., 2022; Loyd et al., 2024; Madubata et al., 2018; Pascoe & Smart Richman, 2009; Seblova et al., 2022; Williams, 2018; Wycoff et al., 2024) and physical health outcomes (Barnes et al., 2012; Gamba et al., 2024; Hill-Jarrett & Jones, 2022; Lewis et al., 2015; Lewis & Van Dyke, 2018; Majoka & Schimming, 2021; Robinson-Lane et al., 2022; Williams, 2012; Zahodne et al., 2023), leading to its recognition as a major public health crisis (Causadias & Korous, 2019; Vandiver, 2020). Converging evidence has also indicated that Black Americans report more experiences of discrimination and unjust treatment compared to any other racial or ethnic group (Bleich et al., 2019; Everson-Rose et al., 2015; Kessler et al., 1999; Lanier et al., 2016; Lewis et al., 2013).

A primary limitation of many studies that have examined the impact of discrimination on health is the application of a single-axis framework of investigation, for example, examining race without considering intersecting identities such as gender. Considering only one aspect of identity flattens researchers’ ability to understand the impact of interlocking systems of discrimination and oppression on those who exist with multiply marginalized identities. Further, viewing Black individuals as a monolithic group introduces bias as it limits understanding of within-group heterogeneity and substantial socio-cultural differences across Black inhabitants of the United States (Lewis & Van Dyke, 2018; Onwong’a et al., 2021).

According to intersectionality theory, which has origins rooted in Black feminist thought, systems of oppression, such as racism and sexism, are cross-cutting and mutually reinforcing and should therefore be analyzed and addressed simultaneously (Bowleg, 2008; Cole, 2009; Combahee River Collective, 1995; Crenshaw, 1990). Thus, measures that focus exclusively on racial discrimination may not adequately capture the experiences of Black women as they are frequently erased in studies speaking to the experience of racial discrimination or gender discrimination, which most often align with the experiences of Black men or White women, respectively (Bowleg, 2008; Crenshaw, 1990). Per intersectionality theory, people’s social identities (e.g., race and gender) are not experienced in isolation, and they navigate and engage in society as gendered-racialized individuals (Monnat, 2010). As discussed by Harnois and Ifatunji (2011), the experience of racial discrimination is a “gendered phenomenon” and the way it is enacted against Black men and women may differ.

A burgeoning body of evidence supports both quantitative and qualitative differences in the experience of racial discrimination between Black men and women (Brownlow et al., 2019; Hoggard et al., 2019; Kwate & Goodman, 2015; Lewis & Van Dyke, 2018) so much so that the term gendered racism has been used to describe the simultaneous experience of racism and sexism experienced by Black women (Essed, 1991; Jones et al., 2022; Lewis & Neville, 2015; Spates et al., 2020). Some studies have shown that Black women report interpersonal incivilities at higher rates compared to Black men (Kwate & Goodman, 2015), whereas others have documented higher rates reported by Black men (Broman, 1996; Ifatunji & Harnois, 2016), or no differences in the frequency of everyday discrimination (Sellers et al., 2013). These findings may be mixed due to a limited number of questions on existing measures of discrimination that fully account for the nuanced and simultaneous experience of both race and gender, but this hypothesis remains to be fully explored particularly among older Black adults.

Studies have also suggested that there are differences in the type of discrimination experienced by Black men and women. For instance, Black men are more likely to experience fear/suspicion-based discrimination (Evans, 2011). Qualitatively, Black men have described experiences of major discrimination such as police violence and racial profiling, whereas Black women described instances of microaggressions, invasive and inappropriate questions, and being treated as if they are invisible (Kwate & Goodman, 2015; Wong et al., 2025). The stereotyped imagery and faulty beliefs society maintains about Black men versus Black women also differ, which may influence the type of discrimination they experience. Black men are often presented as violent/criminal, lazy, athletes, and lacking intellect (Chavous et al., 2004; Evans, 2011; Taylor et al., 2019; Timberlake & Estes, 2007), whereas Black women have traditionally been portrayed, especially in the media, as a Mammy (i.e., homely, self-sacrificing, subordinate, meant to serve), Sapphire (i.e., loud, overbearing, domineering, unfeminine), or Jezebel (i.e., hypersexualized, temptress, lascivious; Collins, 2000; Gordon, 2015; West, 2008).

Lastly, interpretation of discrimination may differ across Black men and women. Gender differences in the perception of discrimination may shape self-reports of discrimination. For instance, one study found parental differences in racial socialization (i.e., the process through which parents provide explicit and implicit messages to a child about their race and racial discrimination) for Black boys versus girls (Brown et al., 2017; Holman, 2012). Other studies have found that Black girls generally received more parental socialization messages around racial pride and cultural identity (McHale et al., 2006; Smith-Bynum et al., 2016), and Black boys received more messages preparing discriminatory types of encounters and racial barriers (Bowman & Howard, 1985; Cooper et al., 2015; Smith-Bynum et al., 2016). If there are gender differences in the type of racial socialization messaging received in childhood, this may lead to differential awareness of discrimination enacted in adulthood. Additionally, there is evidence that a parent’s racial socialization messages to a child may be largely determined by the parent’s experience of racial discrimination (Holloway & Varner, 2021; McNeil Smith et al., 2016; Saleem et al., 2016). Thus, early childhood socialization practices may also contribute to gender differences in the perception of discrimination in adulthood depending on whether socialization was predominantly from a male parent, a female parent, or any combination of two parents. Taken collectively, this body of evidence has suggested the need for more nuanced approaches to examining the gendered and racialized experiences of discrimination faced by Black Americans.

Everyday Discrimination Scale

In considering the measurement of discrimination, one of the most widely used research measures of day-to-day discrimination is the Everyday Discrimination Scale (EDS; Williams et al., 1997), which was originally comprised of nine items and developed to examine the association between discrimination and self-reported physical and mental health. The EDS has been used for almost three decades with a variety of populations (Goldman, 2022; Gonzales et al., 2016; Keys et al., 2019; Pearl et al., 2018), across countries (Caqueo-Urizar et al., 2021; Dambrun, 2007; Faerstein et al., 2014; Siddiqi et al., 2017; Williams et al., 2017) and to examine complex relationships between discrimination and health outcomes (e.g., see Lawrence et al., 2022). Given the pervasive use of the EDS across varying demographic groups, and the importance of understanding how experiences of discrimination shape health and wellness, identifying the best approach to measure the construct of discrimination is critical, and ensuring measurement is adequate for populations of interest is equally important.

Psychometric evaluation of the original nine item EDS with Black American respondents has shown good internal consistency with Cronbach’s alphas ranging from .80-.87 (Clark et al., 2004; Taylor et al., 2004). Convergent validity has also been established as evidenced by significant correlational relationships of the scale with perceived stress (.39), depression (.35), negative affect (.37), and social conflict (.30) (Taylor et al., 2004).

Existing research examining the EDS dimensionality and factor structure has varied. Some have identified a unidimensional scale (Clark et al., 2004; Kessler et al., 1999; Kim et al., 2014; Krieger et al., 2005; Williams et al., 1997), or a two-factor scale (Barnes et al., 2004; Guyll et al., 2001). Others have indicated a four-factor scale and noted issues of local dependence potentially resulting from item order or wording (Stucky et al., 2011). As a result of local dependence, Stucky and colleagues (2011) reduced the scale to five items that consistently reflect the one-factor model. These inconsistent findings are further compounded by studies that suggest scale dimensionality is primarily driven by the first two items on the scale (“treated with less courtesy” and “treated with less respect”) and therefore use a combined two-item adapted EDS measure (Benner et al., 2024; Harris et al., 2019).

Ensuring measurement invariance is a psychometric prerequisite before use of scales like the EDS; however, invariance is rarely tested. Measurement invariance is achieved when a scale or psychological instrument measures the same latent construct equivalently across groups or time points. Two critical assumptions when using the EDS in studies are: (a) the latent, unobserved construct (discrimination) is the actual construct measured by the set of questions, and (b) this construct is measured with equivalent validity across all subpopulations the instrument is used with (Bastos & Harnois, 2020). Meeting these measurement assumptions is particularly critical for cross-group (e.g., Black male vs. Black female) comparisons if differences on the latent construct are interpreted. Thus, when assumptions of measurement invariance are violated, biased estimation may impact the calculation of mean differences in discrimination in Black men and women, or it may impact the calculated relationship between each group’s experience of discrimination and a health outcome (e.g., see Chen, 2008; Guenole & Brown, 2014; Meuleman et al., 2022).

Several studies have shown EDS measurement invariance across different racial/ethnic groups. Kim and colleagues (2014) determined the underlying construct of discrimination was equivalent across four racial/ethnic groups (non-Hispanic White, Black, Hispanic/Latino, and Asian), but found that the intercept for item 7 (“People act as if they’re better than you”) was lower for Hispanic/Latino and Asian groups. In a modified version of the EDS, measurement was invariant across racial/ethnic groups (Black/African Americans, non-Hispanic White, Asian American Native Hawaiian or Pacific Islander, American Indian/Alaska Native, Multiracial), but racial/ethnic differences were observed in the likelihood of endorsing a specific item (e.g., Black participants were more likely to endorse “they are afraid of you”; Shariff-Marco et al., 2011). Despite these findings, EDS measurement invariance across racial groups has not been consistently observed (Bastos & Harnois, 2020; Harnois et al., 2019). Some have called the EDS measurement properties into question particularly when comparing groups stratified by multiple aspects of identity including age, gender, and education level (Bastos & Harnois, 2020; Harnois et al., 2019). For example, in a sample of adults in Texas, Harnois and colleagues (2019) found EDS nonequivalence across race/ethnicity, gender, and educational groupings when the reason for discrimination was not asked as a follow-up. Additionally, a qualitative study examining participant patterns of interpretation of EDS questions revealed that the respondents of varied sociodemographic groups interpreted the survey and the purpose of the questions in substantively different ways: racial/ethnic minority women interpreted the EDS questions as being generally about social inequality, and racial/ethnic minority men described the survey as asking about experiences of racism (Harnois, 2022).

Results comparing the EDS measurement properties across Black men and women have not been uniform as findings have differed across samples. For example, using data from the 2001-2003 National Survey of American Life (NSAL; Jackson et al., 2004) and the 1995 Detroit Area Study (DAS; Jackson & Williams, 2002), Ifatunji and Harnois (2016) found EDS non invariance at the metric level for Black men and women across both samples using the two-factor model (i.e., separating the EDS into implicit and explicit discrimination). After allowing factor loadings to vary, they observed improvement in the model fit for the NSAL sample, but not the DAS sample. They then tested whether the observed noninvariance resulted in group mean differences in discrimination across Black men and women. They also examined whether accounting for this difference explains the “gender gap” in the rates of everyday discrimination (namely, higher rates of interpersonal discrimination). Findings were mixed; in the local sample, DAS, they found that adjusting for measurement error across Black men and women in the sample fully accounted for the gender differences in the factor mean scores for the implicit discrimination factor. Adjusting for the measurement error accounted for 25% of the explicit discrimination factor mean difference between Black men and women. However, these findings were not upheld with the national sample, NSAL. Additionally, older adults (i.e., individuals aged 65 and older) were underrepresented across both studies. In the NSAL, the age of participants ranged from 18 to 64 years, and in the DAS, the maximum age was 67, with only 3.78% of the sample reflecting individuals from age 65–67. To our knowledge, no additional psychometric studies of the EDS include information about distribution of the sample across axes of race, gender, and age therefore limiting our ability to make meaningful psychometric evaluations across older Black men and women.

Given the inconsistent prior results and limited information on older adults in existing psychometric studies of the EDS, we wanted to address an important gap in the literature by testing for measurement invariance of the EDS across Black men and women elders age 65 or older. Examination of the psychometric properties of the EDS among subgroups of older Black adults is particularly important as many older adults existed during the Jim Crow era within the United States. Under Jim Crow laws, de jure racial segregation was enforced in the United States South, and Black Americans of this time experienced blatant forms of racial discrimination that is likely distinct from the racial discrimination most prevalent today (Adkins-Jackson et al., 2022). This is supported by the fact that the implementation of racial discrimination shifts over time and space in subtle ways to uphold White supremacy and maintain social hierarchy (Desmond & Emirbayer, 2009; Hardeman et al., 2022; Seamster & Ray, 2018). Additionally, “Gender neutral” questionnaires developed to assess racial discrimination and experiences may not adequately capture the nuanced differences in stressors experienced by older Black men and women. While the consequences of misestimation could be costly, the degree of measurement bias remains unknown. It is thus important to evaluate the EDS psychometric properties in older Black adults. As a step towards incorporating intersectional methods into our understanding of the measurement and conceptualization of interpersonal discrimination, this study aims to (a) examine the measurement invariance of the EDS among community-dwelling older Black adults, and (b) measure the extent to which nonequivalence would impact estimates of discrimination and potentially comparisons of group means.

Method

Participants

Participants were obtained from the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study, a longitudinal cohort of community-dwelling older adults who live in the San Francisco Bay area and Sacramento, California and have been long term members of Kaiser Permanente Northern California, an integrated healthcare delivery system. Data came from Wave 1, which consisted of 1,712 older adults; a total of 443 participants from this wave were Black Americans. A demographic overview of the participants by gender is presented in Table 1. The final sample consisted of 141 Black men and 287 Black women, who identified as such, with an average age of 75 years for both groups (Black men: M = 75.1, SD = 6.33, range: 65.7–90.0; Black women: M = 75.4, SD = 6.72; range = 65.6–90.0). A majority of Black men participants were married (66.7%), followed by divorced (14.2%), widowed (10.6%), separated (2.84%), and never married (5.67%). About one-third of Black women participants were married (31.7%), one-third were widowed (34.5%), and the remainder were divorced (24.7%), never married (6.27%), and separated (2.79%). The sample had a high level of educational attainment with most participants having completed some college or more (Black men: 74.5%, Black women: 79.1%), some completing high school (Black men: 17.0%, Black women: 15.3%), and few having less than a high school education (Black men: 8.50%, Black women: 5.60%). About one-fourth of the sample was still working (Black men: 31.2%, Black women: 22.4%). Most participants reported a household income under $145,000 (Black men: 71.6%, Black women: 84.0%).

Table 1.

Participant Descriptive Characteristics

Demographics	Black men (n = 141)		Black women (n = 287)
Demographics	n	%	n	%
Marital status
Never married	8	5.67	18	6.27
Married/domestic partner	94	66.7	91	31.7
Separated	4	2.84	8	2.79
Divorced	20	14.2	71	24.7
Widowed	15	10.6	99	34.5
Educational attainment
Less than high school degree	12	8.50	16	5.60
High school graduate/GED	24	17.0	44	15.3
Some college	52	36.9	133	46.3
College	26	18.4	45	15.7
Graduate school	27	19.1	49	17.1
Occupational status
Disabled	4	2.84	4	1.39
Working	44	31.2	64	22.3
Not working	91	64.5	218	76.0
Unknown	2	1.42	1	0.34
Total income (participant + spouse)
$44,999 or less	14	9.93	100	34.8
$45,000 to $74,999	41	29.1	88	30.7
$75,000 to $144,999	46	32.6	53	18.5
$150,000 and over	21	14.9	19	6.62
Refused/Not ascertained	19	13.5	27	9.41

Procedures

This dataset was archival. Participants were recruited into the KHANDLE study if they were 65 years of age or older on January 1, 2017. They were excluded if they had a medical diagnosis of dementia or another medical condition listed in their medical record that may interfere with study participation. KHANDLE was approved by the institutional review boards of Kaiser Permanente and the University of California, Davis. All participants provided written informed consent, which granted permission to use and publish data in future analyses.

Measures

Everyday Discrimination Scale

The Everyday Discrimination Scale (EDS; Williams et al., 1997) is a 9-item self-report instrument designed to measure perceived experiences of interpersonal discrimination. Respondents are presented with the following: “In your day-to-day life, how often do any of the following things happen to you?” Then, nine general scenarios are presented on how others have perceived or treated the respondent, such as being treated with less courtesy or respect, receiving poorer service at restaurants/stores, or being called names or insulted. The specific list of items is documented elsewhere (Williams, nd). Using a 6-point Likert scale, respondents rate the frequency of occurrence in their day-to-day life (6 = Almost everyday, 5 = At least once a week, 4 = A few times a month, 3 = A few times a year, 2 = Less than once a year, 1 = Never). For those who respond to a question indicating that the specific form of discrimination happens a few times a year or more often, the respondent is prompted to select what they think the main reason is for discrimination. They are provided a list of options to select from (e.g., race, gender, ancestry or national origins, height, other) and have the option to provide a free response. The specific reason(s) selected were not part of these data analyses; therefore, the study focused on discrimination without attribution. Total EDS score is summed across the nine ratings and ranges from 9 to 54, with higher scores reflecting more frequent instances of perceived discrimination.

Demographic Information

The demographic information used from the original data was gender, race/ethnicity, age, educational attainment, employment status, marital status, and total income (participant income + spouse/significant other income). Gender information was collected during baseline interview and is limited by a lack of query about transgender and gender nonconforming identities. Participants were asked, Do you consider yourself to be male or female and this information was coded as male, female, other, or refused.

Analytic Procedures

All factor analyses were completed using MPlus Version 8 (Muthén & Muthén, 1998-2017). We were first interested in identifying the best-fitting factor structure given inconsistencies in the literature. An exploratory factor analysis was therefore conducted using maximum likelihood estimation for extraction as has been done with other EDS data (Kim et al., 2014). This approach allows for non-normality (Shi et al., 2021). Geomin rotation was used to allow for correlations among factors. Upon identifying a viable factor solution, we then completed a confirmatory factor analysis (CFA) separately in each group to confirm adequate fit. We allowed for minor dissimilarities (i.e., differences in what residuals were correlated) in the final best-fitting baseline model for each group as has been recommended (Byrne, 2013) and conducted in other work (Harnois et al., 2019).

We next used multiple group CFA to test for measurement invariance of the EDS across gender for older Black men and women. Configural, metric, and scalar invariance tests were conducted by sequentially constraining parameters across groups, starting with the least restrictive (configural) model. With each step, additional parameters were constrained to equivalency across groups. Briefly, the baseline configural invariance of the EDS was first tested to analyze the factor loading pattern and the number of dimensions. Constraints consisted of fixing the first item loading and first item intercept to equivalence across groups. All remaining parameters, including means and variances, were allowed to vary freely across groups. Then, metric invariance was evaluated to test the strength of association between items and the latent construct. In this model, the factor loadings were constrained to equivalence across groups. Lastly, scalar invariance was examined to test for the equivalence of item intercepts and whether the comparison of mean values of discrimination across groups is appropriate. This step included constraining intercepts to equivalence across the two groups and testing measurement invariance (Putnick & Bornstein, 2016; Vandenberg & Lance, 2000; Widaman & Reise, 1997).

At each step of invariance testing, modification indices were examined and used to determine if freeing some item parameters from constraint would improve model fit with theoretical relevance in mind. Specifically, we examined what made the most sense based on the change in chi-square modification index. This index reflects the potential change in the chi-square statistics if an association between parameters is added to the model and if the association is allowed to differ across groups (Whittaker, 2012). Given that modification indices are completely data-driven, we only freed parameters or allowed for correlation of parameters where reasonable and if the adjustment fit within the broader theoretical context (Whittaker, 2012).

Per Hu and Benter’s (1999) recommendation to examine at minimum three goodness-of-fit indices, we considered: Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMSR), and chi-square fit index with corresponding degrees of freedom. While there are no universally accepted criteria to denote adequate fit, we use the recommended fit criteria: CFI and TLI ≥.95; RMSEA ≤.05; and SRMR ≤.06. Differences in nested models were examined to determine whether imposing additional equality constraints across the groups reduced fit, which would indicate a difference in measurement between the groups. While we reported chi-square change (∆χ²), it is sensitive to large sample size (Cheung et al., 2002; La Du & Tanaka, 1990). We therefore also included change in the CFI (∆ CFI). Specifically, a CFI reduction equal to or less than .01 was used to indicate measurement invariance (Cheung & Rensvold, 2002). R² was calculated for each item as the square of the factor loading and reflected the proportion of variance in each item explained by the latent factor.

Additional Analyses

Descriptive data were analyzed using IBM Statistical Package for Social Sciences (SPSS) version 28 (IBM Corp, 2021). Bias is the systematic error related to a scale and was calculated as the average difference between baseline (configural model) and adjusted (final model) factor scores across all participants. Upper and lower limits of agreement were calculated as Bias +/− (1.96 x standard deviation_{Factor difference}) to reflect the upper and lower limits of agreement, respectively. It is recommended that 95% of data fall within the limits of agreement (Giavarina, 2015). Because a measure that over-estimates at one end of the distribution but under-estimates at another may appear unbiased on average, a Bland-Altman plot (Altman & Bland, 1983; Bland & Altman, 1986) was used to examine the degree of agreement between the factor score estimates for the baseline configural model and the factor scores of the final adjusted model across the range of the measures. The Bland-Altman plot is a scatter plot with the y-axis representing the difference between factor scores estimated by different models (baseline factor score - adjusted factor score) and the x-axis reflecting the average of the factor score values ((baseline factor score + adjusted factor score)/2).

Results

Preliminary Analyses

As part of preliminary analyses, data were cleaned and checked for missing values. Of the 443 Black American participants in the dataset, 12 were missing gender information and were eliminated from subsequent analyses. Analysis of the patterns of missing the EDS data indicated that of the 431 remaining participants, three had completely missing data, and these cases were removed. Across the entire dataset, 1.11% of the data were missing. Most participants (96.0%) had no missing data. The total missingness within each variable ranged from 0.90% to 1.20%. Little’s MCAR test was significant, indicating the data were not missing completely at random, χ² = 146.23 (76), p < .001. The data were not systematically missing by gender, χ² = 2.76 (5), p = .737 or education level χ² = 26.1 (25), p = .404. As a result, we used maximum likelihood with robust standard error (MLR) parameter estimates in our analyses as this has been identified as the optimal choice for latent models with missing data due to its yield of more accurate estimates (Shi et al., 2021). Participants who were 90 years of age or older were recoded as age 90 in the study as part of the study protocol (Black men: 2.84% were recoded; Black women: 5.23% were recoded).

Skewness and kurtosis of each item of the EDS was evaluated using cutoffs of skewness <2 and kurtosis <7 (Curran et al., 1996). Minor violations were observed for item 6 (skewness = 2.71, kurtosis = 7.49), item 8 (skewness = 2.41, kurtosis = 6.76), and item 9 (skewness = 3.46, kurtosis = 14.5). To address this, we elected to use MLR for parameter estimates as they are robust to non-normality and non-independence of observations and does not require data transformation (Shi et al., 2021).

Descriptive Statistics

The EDS item correlations and mean-level comparisons are shown in Table 2 for each group. Item correlations ranged from .31 to .79 and from .20 to .77 for Black men and women, respectively. Based on an independent t-test of total EDS scores, Black men (M = 17.1 [of 54], SD = 8.03) and women (M = 16.6 [of 54], SD = 6.80) did not significantly differ on total EDS mean scores, t (409) = 0.67, p = .506. A significantly higher percentage of Black men (41.8%) reported having at least one instance where people acted afraid of them compared to Black women (30.5%), χ² (1) = 5.36, p = .021. The percentage of participants who had at least one discriminatory encounter did not differ between men and women (84.1% of Black men and 84.6% of Black women; χ² (1) = 0.02, p = .897).

Table 2.

Item-Level Means, Standard Deviations, and Intercorrelations of Everyday Discrimination Scale by Gender (Black Men n = 141; Black Women n = 287)

	Black men											Black women
Item	M	SD	1	2	3	4	5	6	7	8	9	M	SD	1	2	3	4	5	6	7	8	9
1. Treated with less courtesy	2.33	1.36	--									2.42	1.40	--
2. Treated with less respect	2.14	1.33	.79	--								2.19	1.24	.77	--
3. Receive poorer service	2.01	1.15	.51	.45	--							1.95	0.98	.44	.50	--
4. People act as if you are not smart	1.96	1.30	.52	.55	.54	--						2.08	1.27	.56	.54	.52	--
5. People act as if they are afraid of you	1.97	1.41	.57	.49	.50	.48	--					1.62	1.16	.32	.34	.38	.48	--
6. People act as if you are dishonest	1.53	1.13	.44	.43	.58	.56	.63	--				1.39	0.84	.35	.36	.36	.48	.45	--
7. People act as if they are better than you	2.22	1.36	.57	.55	.57	.53	.54	.63	--			2.27	1.20	.55	.52	.44	.61	.42	.46	--
8. You are called names or insulted	1.52	0.92	.44	.44	.43	.42	.31	.52	.50	--		1.45	0.89	.42	.43	.29	.29	.35	.39	.43	--
9. You are threatened or harassed	1.28	0.76	.53	.44	.53	.50	.38	.55	.50	.64	--	1.32	0.79	.25	.29	.20	.25	.24	.29	.23	.52	--

Note. Frequency scores from 6-point Likert scale ranging from 1 = never to 6 = almost everyday.

Exploratory Factor Analysis

The Kaiser-Meyer-Olkin (KMO) measure was 0.878, which indicated the data were suitable for factor analysis. This was further supported by Bartlett’s test of sphericity which was significant, (χ² (36) = 1,614.50, p < .001) and indicated there are sufficient correlations between variables to proceed with factor analysis. Results from the factor analysis indicated three eigenvalues of: 4.54, .971, and .875. Visual inspection of the scree plot of eigenvalues showed a clear decline after the first factor, further supporting a one-factor solution/unidimensionality. Based on these data as well the Kaiser criterion (1960) indicating an eigenvalue greater than one is needed to be considered a tenable factor, we proceeded with a one-factor solution.

Confirmatory Factor Analysis: One-Factor Baseline Model

We next conducted a CFA separately in Black men and women participants using the one-factor solution to identify a model that fit well for both groups. As shown in Table 3, the model fit statistics for each group indicated moderate to poor model fit. Examination of modification indices suggested model improvement in both groups with correlation of residuals for items 1 & 2 as this allowed for the greatest decrement in chi-square statistic. Upon model re-specification, we found significant improvement in model fit for Black men only (Table 3). For Black women, significant improvement was observed with the additional correlation of residuals for items 8 and 9. The internal consistency of the EDS was good across both groups and included Cronbach’s alpha values of .90 and .86 for Black men and women, respectively.

Table 3.

CFA Model Fit Statistics for One-Factor Model (Black Men n = 141; Black Women n = 287)

Group	Model	χ ²	df	Δ χ²	Δdf	CFI	ΔCFI	TLI	RMSEA	SRMR
Black men	A	104.692	36	--	--	.848	--	.810	.120	.062
	B	55.308	35	−49.384	−1	.955	+.107	.942	.066	.050
Black women	A	131.289	36	--	--	.829	--	.787	.097	.068
	B	81.031	35	−50.258	−1	.918	+.089	.894	.069	.059
	C	54.751	34	−26.280	−1	.963	+.045	.951	.047	.046

Note. Model A: baseline; Model B: correlated residuals for items 1 and 2; Model C: correlated residuals for items 8 and 9.

Measurement Invariance

The results of the measurement invariance tests between groups are shown in Table 4. Results from the baseline configural model indicated good to acceptable fit of the data for both groups (CFI = .97, TLI = .95, RMSEA = .06, SRMR = .04). Imposing additional model constraints across the groups with the metric model resulted in a significant decrement in fit (∆CFI = −.02), and the overall fit of the data to this model was mixed, ranging from acceptable to fair fit (CFI = .95, TLI = .94, RMSEA = .06, SRMR = .07). Partial metric invariance was determined by constraining all factor loadings and iteratively freeing each loading using a stepwise backward process. Comparison of ∆χ² during this iterative process was used to identify the item that would result in the most improved fit. Upon review of ∆χ² and the modification indices, the factor loading for item four was freed to vary across gender, which resulted in significantly improved fit of the data in the partial metric model (∆CFI = +.01). This partial metric model was then compared to the scalar model which suggested a significant decrement in model fit with the inclusion of additional constraints across the group models (∆CFI = −.01). Using the stepwise backward process, each item intercept was iteratively freed and fit indices, namely ∆CFI and ∆ χ ², were compared for improvement in model fit. The effect of freeing each intercept on model fit was negligible (i.e., no ∆CFI was greater +.005 than and the largest ∆χ² was −5.47, ∆ df = −1) suggesting that no additional adjustments to the model were warranted. Comparison of fit indices for this final scalar model suggested overall reasonable fit of the data to the model (CFI =. 0.95, TLI = 0.94, RMSEA = 0.06, SRMR = 0.06). The final adjusted models with standardized parameters are shown in Figure 1 (Black men) and Figure 2 (Black women). As shown in Table 5, the final R² values ranged from .38 to .62 (median = .51) and .16 to .65 (median = .40) for Black men and women, respectively. Comparison of R² values showed that for eight of the nine EDS items, the amount of variance explained by the final modified scale was greater for Black men than women.

Table 4.

Stepwise Measurement Invariance Tests Across Gender Groups (Black Men n = 141; Black Women n = 287)

Model	χ ²	df	χ ²	Δdf	CFI	ΔCFI	TLI	RMSEA	SRMR
a. Configural	83.7	51	--	--	.965	--	.951	.056	.041
b. Metric	107.1	59	+23.331	+8	.949	−.016	.937	.063	.070
c. Partial metric (free item 4 factor loading)	95.9	58	−11.216	−1	.959	+.010	.950	.057	.060
d. Scalar	115.2	66	+19.320	+8	.947	−.012	.943	.061	.063

Figure 1.

Final Scalar EDS Model With Standardized Results for Black Men (n = 141)

Figure 2.

Final Scalar EDS Model With Standardized Results for Black Women (n = 287)

Table 5.

Factor Coefficients of the Everyday Discrimination Scale Across Measurement Invariance Test Models for Black Men (n = 141) and Black Women (n = 287)

Item	Configural			Metric			Partial metric			Scalar
Item	Factor coefficients	Residual variance	R ²	Factor coefficients	Residual variance	R ²	Factor coefficients	Residual variance	R ²	Factor coefficients	Residual variance	R ²
Black men
1	0.685	0.530	0.470	0.743	0.449	0.551	0.753	0.433	0.567	0.754	0.432	0.568
2	0.654	0.572	0.428	0.705	0.503	0.497	0.715	0.489	0.511	0.716	0.487	0.513
3	0.713	0.492	0.508	0.694	0.519	0.481	0.714	0.490	0.510	0.716	0.487	0.513
4	0.646	0.582	0.418	0.750*	0.438	0.562	0.655	0.571	0.429	0.648	0.58	0.420
5	0.680	0.538	0.462	0.655	0.571	0.429	0.678	0.541	0.459	0.672	0.548	0.452
6	0.820	0.328	0.672	0.702	0.507	0.493	0.739	0.453	0.547	0.734	0.461	0.539
7	0.762	0.419	0.581	0.774	0.401	0.599	0.789	0.378	0.622	0.785	0.384	0.616
8	0.672	0.548	0.452	0.634	0.598	0.402	0.650	0.577	0.423	0.651	0.576	0.424
9	0.662	0.562	0.438	0.605	0.634	0.366	0.619	0.617	0.383	0.618	0.618	0.382
1 with 2^a	0.666	--	--	0.644	--	--	0.649	--	--	0.649	--	--
Black women
1	0.694	0.518	0.482	0.657	0.568	0.432	0.633	0.599	0.401	0.633	0.600	0.400
2	0.680	0.538	0.462	0.657	0.568	0.432	0.634	0.599	0.401	0.633	0.599	0.401
3	0.640	0.590	0.410	0.650	0.577	0.423	0.634	0.598	0.402	0.635	0.597	0.403
4	0.807	0.348	0.652	0.750*	0.437	0.563	0.808	0.347	0.653	0.808	0.348	0.652
5	0.598	0.642	0.358	0.625	0.609	0.391	0.612	0.625	0.375	0.611	0.626	0.374
6	0.597	0.644	0.356	0.670	0.551	0.449	0.664	0.559	0.441	0.663	0.561	0.439
7	0.728	0.470	0.530	0.716	0.487	0.513	0.699	0.512	0.488	0.696	0.515	0.485
8	0.489	0.761	0.239	0.543	0.706	0.294	0.519	0.731	0.269	0.519	0.730	0.270
9	0.332	0.890	0.110	0.419	0.824	0.176	0.400	0.840	0.160	0.400	0.840	0.160
1 with 2^a	0.583	--	--	0.594	--	--	0.604	--	--	0.604	--	--
8 with 9^b	0.405	--	--	0.397	--	--	0.401	--	--	0.400	--	--

Note. Item four was noninvariant across groups and factor loadings were free to vary for partial metric and scalar models.

^aResidual correlation for items 1 and 2.

^bResidual correlation for items 8 and 9.

Measurement Bias

Given the adjustments made to the original baseline EDS model and the final scalar model to achieve measurement invariance, our final step of analysis was to examine the extent to which measurement bias had an impact on factor score estimates. We were also interested in whether the degree of measurement bias differed across groups and might be greater for Black women. We examined the degree of measurement bias with the Bland-Altman plot shown in Figure 3. The plot provides a visual indication of agreement between the factor scores resulting from the baseline unadjusted model and the final scalar model. Factor score differences on the y-axis were plotted against the average of the factor score values on the x-axis.

Figure 3.

Bland-Altman Plot Comparing Everyday Discrimination Scale Bias Between Black Men (n = 141) and Women (n = 287). Note. Solid Black Line Represents Bias. Dashed Gray Lines Represent the Upper and Lower Limits of Agreement, Respectively

Upon initial examination, the average discrepancy in factor score estimates between the two models, or the bias (Figure 3 solid black line), is close to zero. This suggests that, when collapsed across the two groups, there does not appear to be a meaningful difference in the estimates of the latent factors between adjusted and unadjusted models. However, in comparison across groups, the magnitude of difference in factor score estimates tends to increase for Black women only and exceeds the limits of agreement for Black women with higher factor scores. Figure 3 suggests that for Black women who report more frequent experiences of discrimination and have higher scores on the EDS, there may be an overestimation of the total score when using the scale as originally intended. Additionally, while still within the broad limits of agreement, Black women who reported less frequent experiences of discrimination had unadjusted total EDS scores that underestimated exposure; the lack of significance of this finding may represent a floor effect.

Discussion

The current study examined the measurement invariance of the EDS in a sample of 141 older Black men and 287 older Black women. Based on intersectionality theory, race and gender are co-constructed and reinforced in ways that guide perceptions and the enactment of prejudicial behavior, and this may contribute to differences in how discrimination is experienced across Black men and women. Whereas Black men are typically viewed as the target of racial discrimination (Goff & Kahn, 2013), this belief may lead to narrow constructions of racial discrimination that are reflected in the questions included in measures like the EDS.

In our initial analyses of the EDS structure, we found a one-factor model was best-fitting across the entire sample, which is consistent with some of the existing psychometric literature (Clark et al., 2004; Kessler et al., 1999; Kim et al., 2014; Krieger et al., 2005; Williams et al., 1997). We then sought to confirm this unidimensional factor structure using CFA. Post-hoc adjustments, namely correlating the residual error from item 1 (You are treated with less courtesy than other people are) with item 2 (You are treated with less respect than other people are) resulted in improved fit for Black men, which is consistent with other psychometric investigations of the EDS with Black Americans (Kim et al., 2014; Reeve et al., 2011). For Black women, correlating the residual error from item 8 (You are called names or insulted) and item 9 (You are threated or harassed) was additionally required for improved model fit and has been a modification made in other samples (Kim et al., 2014; Reeve et al., 2011; Stucky et al., 2011). The need to adjust residual error terms suggests there is shared variability across these items that is not captured by the latent discrimination construct. This shared variability is thought to reflect local dependence. The local dependence of EDS items 8 and 9 has been discussed by others (Harnois et al., 2019; Kim et al., 2014; Stucky et al., 2011). In a sample of Black Americans, Stucky and colleagues (2011) found that local dependence was due to the adjacent order of the EDS items on the scale as well as the overlap in the content captured by the items that extend beyond the underlying construct of discrimination (Steinberg, 1994; Stucky et al., 2011; Tourangeau & Rasinski, 1988). These findings provide further evidence that adjustments to the scale may be needed before use with older Black adults.

Next, we tested the adjusted baseline models to determine whether measurement using the EDS was invariant by gender. Findings were notable for invariance at the configural level. This suggests that the latent construct of discrimination has the same pattern of free and fixed loadings across gender groups, and the patterns of EDS item loadings on the latent discrimination factor is equivalent across gender (Putnick & Bornstein, 2016).

In the subsequent step of analyses, we found gender noninvariance at the metric level. Further examination of what contributed to metric noninvariance revealed item 4 (People act as if they think you are not smart), differentially related to the construct of discrimination across older Black men and women. This pattern of results has been confirmed in other Black American samples (Stucky et al., 2011). Our findings indicate that item 4 more strongly relates to the discrimination construct for older Black women than Black men. While this is contrary to existing literature on the racial gender stereotypes of intellectual ability and academic performance (Evans, 2011; Jaxon et al., 2019), these findings may be contextualized by the demographic background of our KHANDLE cohort. Most of the Black women in our sample were born in the U.S. South, and all were born in 1952 or earlier, therefore making them primary school aged prior to the passing of Brown v. Board. Thus, not only did the Black women in the sample possibly experience racially segregated education, but they were educated during a tumultuous period of education integration and may have been subjected to more overt stereotypes and prejudices based on race and gender (Hutson, 2022; Jones, 2019; Keels et al., 2017). Additionally, most Black women in the sample attended college and worked during a period where employment in “nontraditional” high paying roles outside of domestic service or low wage jobs was uncommon. Thus, given their high educational attainment, Black women in our sample may have been one of few (or one of one) Black women in their work setting. This may have placed them in environments where insidious forms of discrimination, such as having their intellect questioned on the job, was common. Further investigation is needed to examine how the intersections of race, gender, and age impact perceptions and reports of interpersonal discrimination.

There were differences in how the EDS operates across Black men and women resulting in measurement bias. First, we found that the total variance in discrimination explained by the EDS items was greater for Black men than Black women, which suggests the original scale may more accurately capture discrimination experienced by Black men. Additionally, there was evidence of biased measurement in that the unadjusted EDS poorly estimated scores for Black women at the extremes ends of the scale. That is, exposure was significantly overestimated for Black women who reported more frequent encounters of discrimination and trended toward underestimating scores for Black women who reported less frequent experiences of discrimination. With the scale adjustments, invariance was achieved across gender; however, use of the EDS as originally developed may result in poor estimates of interpersonal discrimination, particularly for Black women. Our findings indicated measurement bias would impact the comparison of mean EDS scores across gender groups, leading to faulty conclusions about group differences. This measurement bias could also potentially impact our understanding of the relationship between discrimination and other outcomes, such as health, specifically for Black women. Further scale refinement would benefit from the inclusion of discrimination experiences that more closely aligned with those of Black women. Some experiences of discrimination that are relevant to Black women may include silencing, invisibility, the projection of specific stereotypes and tropes (e.g., Angry Black woman), comments about appearance, and tone policing (Lewis et al., 2016; Lewis & Neville, 2015; Williams, 2023).

Overall, our finding that there are differences in the types of racial discrimination experienced between older Black men and women fits with an intersectional understanding of how our perception of race is shaped by gender. Further, our findings support one of the central tenants of intersectionality theory—that identity-based discrimination and its associated social inequalities are interdependent and mutually constitutive (Bowleg, 2008). We show how considering race and gender as mutually exclusive constructs within a racial discrimination instrument development, like the EDS, can lead to faulty measurement. Additionally, the use of such faulty logic reinforces existing social hierarchies by prioritizing the experience of Black men and contributing to the intersectional invisibility of Black women (Purdie-Vaughns & Eibach, 2008). Greater emphasis in research and clinical contexts should be given to the distinct forms of interpersonal discrimination experienced by Black men and Black women as opposed to discrimination due to race alone. We caution against androcentrism, whereby society centers men as a “gender neutral” standard, and all things health are measured in relation to men (Bailey et al., 2019, 2024; Gilman, 1911; Hibbs, 2014). Fortunately, instruments such as the Gender Racial Microaggressions Scale (Lewis & Neville, 2015) and the African American Men’s Gendered Racism Stress Inventory (Schwing et al., 2013) have been developed to capture the intersectional discrimination experienced by Black women and men, respectively. We encourage continued development measures that capture the distinct, nuanced experiences of racial gender groups.

Overall, the strengths of this paper and its findings should be considered with some limitations in mind. First, our data were limited to self-reported experiences of discrimination which relies on the individual’s perception of the incident and accurate self-report. Further, there is no timeframe indicated in the EDS to cue participants to a specific stage of life, so it is unclear whether participants responded with their entire life history in mind or only more recent encounters of discrimination. Additionally, there may be qualitative differences in individuals who are able to recall instances of discrimination more accurately compared those who do not, which likely impacts the approach to self-report. While the EDS offers the option to select the main reason for discrimination, we included all data in analyses irrespective of the main reason selected for discrimination. Perhaps selecting only responses that indicate discrimination was because of race would have led to a different pattern or results; however, there is also evidence that individuals with multiple marginalized identities may be unable to identify the sole reason for the act of discrimination, and this may impact the validity of scales that contain this line of questioning (Bowleg, 2008; Harnois et al., 2022).

The KHANDLE cohort has a relatively high socioeconomic status and is drawn from a single region in Northern California, although many older Black participants immigrated to California from the U.S. South. Additionally, our sample generally had a high level of education, which may influence generalizability as well as exposure to discrimination. Results may differ in other regions of the country and across social classes and education levels. However, the robust evidence that interpersonal discrimination is manifested differently towards Black men and Black women suggests our findings are likely to be broadly relevant.

Implications for Clinical Practice and Research

This study has implications for clinical practice. Awareness of intersectional experiences of discrimination and practicing cultural responsivity is paramount within a therapy and assessment setting. Not only is having the language to discuss and affirm lived experiences important to maintaining a therapeutic alliance (Haas et al., 2024) and providing adequate care (Mendoza et al., 2020), but having measures that accurately measure and reflect lived experiences is critical for identifying areas for potential intervention. For instance, assessing both the form and frequency of discrimination may aid a psychologist in the development of mitigation and stress coping strategies within a therapeutic setting (e.g., see Lewis et al., 2013). Additionally, understanding how discrimination is enacted across race and gender can aid in the assessment and conceptualization of an older adult presenting for cognitive changes.

With regards to research, there is currently no objective criterion for adequate measurement of the EDS. The implications of mismeasurement, therefore, depend on the specific use of the measure. For instance, in studies that attempt to understand how experiences of interpersonal discrimination shape health and contribute to health disparities, measurement nonequivalence may result in misestimation and limit our ability to make inferences about the nature of this relationship. Thus, not only are Black men’s and women’s experiences of discrimination different, but how discrimination relates to health outcomes may also differ and warrants future investigation. Rigorous measures of discrimination introduced by Williams et al. (1997) were a major step forward in research on racial health disparities, but it is essential that we take as much care for valid and reliable measures of discrimination as for any other domain. Our findings are thus an important step towards ensuring measurement of interpersonal discrimination is valid for both Black men and Black women.

Footnotes

ORCID iD

Tanisha G. Hill-Jarrett

Ethical Considerations

KHANDLE was approved by the institutional review boards of Kaiser Permanente and the University of California, Davis. All participants provided written informed consent, which granted permission to use and publish data in future analyses.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by National Institute on Aging K23AG084871 (PI: Hill-Jarrett); National Institute on Aging R13AG030995-15 (PI: Mungas); National Institute on Aging R01AG052132 (PI: Whitmer, Glymour, Mayeda, Gilsanz); The Alzheimer’s Association/The Judy Fund [2019-AARGD-644788 (PI: Gilsanz)].

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Adkins-Jackson

P. B.

Chantarat

Bailey

Z. D.

Ponce

N. A.

(2022). Measuring structural racism: A guide for epidemiologists and other health researchers. American Journal of Epidemiology, 191(4), 539–547. https://doi.org/10.1093/aje/kwab239

Altman

D. G.

Bland

J. M.

(1983). Measurement in medicine: The analysis of method comparison studies. The Statistician, 32(3), 307–317. https://doi.org/10.2307/2987937

Bailey

A. H.

LaFrance

Dovidio

J. F.

(2019). Is man the measure of all things? A social cognitive account of androcentrism. Personality and Social Psychology Review: An Official Journal of the Society for Personality and Social Psychology, Inc, 23(4), 307–331. https://doi.org/10.1177/1088868318782848

Bailey

A. H.

Williams

Poddar

Cimpian

(2024). Intersectional male-centric and white-centric biases in collective concepts. Personality & Social Psychology Bulletin, 51(10), 1461672241232114. https://doi.org/10.1177/01461672241232114

Barnes

L. L.

Lewis

T. T.

Begeny

C. T.

Bennett

D. A.

Wilson

R. S.

(2012). Perceived discrimination and cognition in older African Americans. Journal of the International Neuropsychological Society: JINS, 18(5), 856–865. https://doi.org/10.1017/S1355617712000628

Barnes

L. L.

Mendes De Leon

C. F.

Wilson

R. S.

Bienias

J. L.

Bennett

D. A.

Evans

D. A.

(2004). Racial differences in perceived discrimination in a community population of older Blacks and whites. Journal of Aging and Health, 16(3), 315–337. https://doi.org/10.1177/0898264304264202

Bastos

J. L.

Harnois

C. E.

(2020). Does the everyday discrimination scale generate meaningful cross-group estimates? A psychometric evaluation. Social Science & Medicine (1982), 265, 113321. https://doi.org/10.1016/j.socscimed.2020.113321

Benner

A. D.

Chen

Fernandez

C. C.

Hayward

M. D.

(2024). The potential for using a shortened version of the everyday discrimination scale in population research with young adults: A construct validation investigation. Sociological Methods & Research, 53(2), 804–838. https://doi.org/10.1177/00491241211067512

Berger

Sarnyai

(2015). “More than skin deep”: Stress neurobiology and mental health consequences of racial discrimination. Stress (Amsterdam, Netherlands), 18(1), 1–10. https://doi.org/10.3109/10253890.2014.989204

10.

Bland

J. M.

Altman

D. G.

(1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (London, England), 1(8476), 307–310. https://doi.org/10.1016/S0140-6736(86)90837-8

11.

Bleich

S. N.

Findling

M. G.

Casey

L. S.

Blendon

R. J.

Benson

J. M.

SteelFisher

G. K.

Sayde

J. M.

Miller

(2019). Discrimination in the United States: Experiences of Black Americans. Health Services Research, 54(S2), 1399–1408. https://doi.org/10.1111/1475-6773.13220

12.

Bowleg

(2008). When Black+ lesbian+ woman≠ Black lesbian woman: The methodological challenges of qualitative and quantitative intersectionality research. Sex Roles, 59(5–6), 312–325. https://doi.org/10.1007/s11199-008-9400-z

13.

Bowman

P. J.

Howard

(1985). Race-related socialization, motivation, and academic achievement: A study of Black youths in three-generation families. Journal of the American Academy of Child Psychiatry, 24(2), 134–141. https://doi.org/10.1016/s0002-7138(09)60438-6

14.

Broman

C. L.

(1996). The health consequences of racial discrimination: A study of African Americans. Ethnicity & Disease, 6(1-2), 148–153.

15.

Brown

D. L.

Blackmon

Rosnick

C. B.

Griffin-Fennell

F. D.

White-Johnson

R. L.

(2017). Initial development of a gendered-racial socialization scale for African American college women. Sex Roles, 77(3-4), 178–193. https://doi.org/10.1007/s11199-016-0707-x

16.

Brownlow

B. N.

Sosoo

E. E.

Long

R. N.

Hoggard

L. S.

Burford

T. I.

Hill

L. K.

(2019). Sex differences in the impact of racial discrimination on mental health among Black Americans. Current Psychiatry Reports, 21(11), 112. https://doi.org/10.1007/s11920-019-1098-9

17.

Byrne

B. M.

(2013). Structural equation modeling with Mplus. Routledge.

18.

Caqueo-Urízar

Urzúa

Mena-Chamorro

Flores

Irarrázaval

Graniffo

Williams

D. R.

(2021). Relationship between everyday discrimination and substance use among adolescents in Northern Chile. International Journal of Environmental Research and Public Health, 18(12), 6485. https://doi.org/10.3390/ijerph18126485

19.

Causadias

J. M.

Korous

K. M.

(2019). Racial discrimination in the United States: A national health crisis that demands a national health solution. The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 64(2), 147–148. https://doi.org/10.1016/j.jadohealth.2018.11.001

20.

Chavous

T. M.

Harris

Rivas

Helaire

Green

(2004). Racial stereotypes and gender in context: African Americans at predominantly Black and predominantly white colleges. Sex Roles, 51(1-2), 1–16. https://doi.org/10.1023/B:SERS.0000032305.48347.6d

21.

Chen

F. F.

(2008). What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research. Journal of Personality and Social Psychology, 95(5), 1005–1018. https://doi.org/10.1037/a0013193

22.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/s15328007sem0902_5

23.

Clark

Coleman

A. P.

Novak

J. D.

(2004). Brief report: Initial psychometric properties of the everyday discrimination scale in Black adolescents. Journal of Adolescence, 27(3), 363–368. https://doi.org/10.1016/j.adolescence.2003.09.004

24.

Cole

E. R.

(2009). Intersectionality and research in psychology. The American Psychologist, 64(3), 170–180. https://doi.org/10.1037/a0014564

25.

Collins

P. H.

(2000). Mammies, matriarchs, and other controlling images. In Collins

P. H.

(Ed.), Black feminist thought: Knowledge, consciousness, and the politics of empowerment (2nd ed., pp. 69–96). Routledge.

26.

Combahee River Collective . (1995). Combahee river collective statement. In Guy-Sheftall

(Ed.), Words of fire: An anthology of African American feminist thought (pp. 232–240). New Press.

27.

Cooper

S. M.

Smalls-Glover

Metzger

Griffin

(2015). African American fathers’ racial socialization patterns: Associations with racial identity beliefs and discrimination experiences. Family Relations, 64(2), 278–290. https://doi.org/10.1111/fare.12115

28.

Crenshaw

(1990). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299. https://doi.org/10.2307/1229039

29.

Curran

P. J.

West

S. G.

Finch

J. F.

(1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. https://doi.org/10.1037/1082-989X.1.1.16

30.

Dambrun

(2007). Gender differences in mental health: The mediating role of perceived personal discrimination. Journal of Applied Social Psychology, 37(5), 1118–1129. https://doi.org/10.1111/j.1559-1816.2007.00202.x

31.

Desmond

Mustafa

(2009). What is racial domination? Du Bois Review: Social Science Research on Race, 6(2), 335–355. https://doi.org/10.10170S1742058X09990166

32.

Essed

(1991). Understanding everyday racism: An interdisciplinary theory. Sage Publications, Inc.

33.

Evans

A. B.

(2011). The relation of gender to racial discrimination experiences and achievement among Black college students. [Doctoral dissertation, University of Michigan]. Deep Blue Documents. https://hdl.handle.net/2027.42/89778

34.

Everson-Rose

S. A.

Lutsey

P. L.

Roetker

N. S.

Lewis

T. T.

Kershaw

K. N.

Alonso

Diez Roux

A. V.

(2015). Perceived discrimination and incident cardiovascular events: The multi-ethnic study of atherosclerosis. American Journal of Epidemiology, 182(3), 225–234. https://doi.org/10.1093/aje/kwv035

35.

Faerstein

Chor

Werneck

G. L.

Lopes

C. D. S.

Kaplan

(2014). Race and perceived racism, education, and hypertension among Brazilian civil servants: The pró-saúde study. Revista Brasileira de Epidemiologia, 17(Suppl 2), 81–87. https://doi.org/10.1590/1809-4503201400060007

36.

Gamba

Toosi

Wood

Correia

Medina

Pritchard

Venerable

Lee

Santillan

J. K. A.

(2024). Racial discrimination is associated with food insecurity, stress, and worse physical health among college students. BMC Public Health, 24(1), 883. https://doi.org/10.1186/s12889-024-18240-3

37.

Giavarina

(2015). Understanding bland Altman analysis. Biochemia Medica, 25(2), 141–151. https://doi.org/10.11613/BM.2015.015

38.

Gilman

C. P.

(1911). The man-made world: Or, our androcentric culture (3rd ed.). Charlotte Company.

39.

Goff

P. A.

Kahn

K. B.

(2013). How psychological science impedes intersectional thinking. Du Bois Review: Social Science Research on Race, 10(2), 365–384. https://doi.org/10.1017/s1742058x13000313

40.

Goldman

A. W.

(2022). Everyday discrimination in later life: A social network approach. Social Science Research, 104, 102670. https://doi.org/10.1016/j.ssresearch.2021.102670

41.

Gonzales

K. L.

Noonan

Goins

R. T.

Henderson

W. G.

Beals

Manson

S. M.

Acton

K. J.

Roubideaux

(2016). Assessing the everyday discrimination scale among American Indians and Alaska natives. Psychological Assessment, 28(1), 51–58. https://doi.org/10.1037/a0039337

42.

Gordon

M. K.

(2015). Achievement scripts: Media influences on Black students’ academic performance, self-perceptions, and career interests. Journal of Black Psychology, 42(3), 195–220. https://doi.org/10.1177/0095798414566510

43.

Guenole

Brown

(2014). The consequences of ignoring measurement invariance for path coefficients in structural equation models. Frontiers in Psychology, 5, 980. https://doi.org/10.3389/fpsyg.2014.00980

44.

Guyll

Matthews

K. A.

Bromberger

J. T.

(2001). Discrimination and unfair treatment: Relationship to cardiovascular reactivity among African American and European American women. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 20(5), 315–325. https://doi.org/10.1037//0278-6133.20.5.315

45.

Haas

Walsh

Marroquin

(2024). Enhancing cultural competence in counselor education through sociolinguistic awareness. Teaching and Supervision in Counseling, 6(3), 4. https://doi.org/10.7290/tsc06vevm

46.

Hardeman

R. R.

Homan

P. A.

Chantarat

Davis

B. A.

Brown

T. H.

(2022). Improving the measurement of structural racism to achieve antiracist health policy. Health Affairs (Project Hope), 41(2), 179–186. https://doi.org/10.1377/hlthaff.2021.01489

47.

Harnois

C. E.

(2022). What do we measure when we measure perceptions of everyday discrimination? Social Science & Medicine (1982), 292, 114609. https://doi.org/10.1016/j.socscimed.2021.114609

48.

Harnois

C. E.

Bastos

J. L.

Campbell

M. E.

Keith

V. M.

(2019). Measuring perceived mistreatment across diverse social groups: An evaluation of the Everyday Discrimination Scale. Social Science & Medicine, 232, 298–306. https://doi.org/10.1016/j.socscimed.2019.05.011

49.

Harnois

C. E.

Bastos

J. L.

Shariff-Marco

(2022). Intersectionality, contextual specificity, and everyday discrimination: Assessing the difficulty associated with identifying a main reason for discrimination among racial/ethnic minority respondents. Sociological Methods & Research, 51(3), 983–1013. https://doi.org/10.1177/0049124120914929

50.

Harnois

C. E.

Ifatunji

(2011). Gendered measures, gendered models: Toward an intersectional analysis of interpersonal racial discrimination. Ethnic and Racial Studies, 34(6), 1006–1028. https://doi.org/10.1080/01419870.2010.516836

51.

Harris

K. M.

Halpern

C. T.

Whitsel

E. A.

Hussey

J. M.

Killeya-Jones

L. A.

Tabor

Dean

S. C.

(2019). Cohort profile: The national longitudinal study of adolescent to adult health (add health). International Journal of Epidemiology, 48(5), 1415–1415k. https://doi.org/10.1093/ije/dyz115

52.

Hibbs

(2014). Androcentrism. In Teo

(Ed.), Encyclopedia of critical psychology. Springer. https://doi.org/10.1007/978-1-4614-5583-7_16

53.

Hill-Jarrett

T. G.

Jones

M. K.

(2022). Gendered racism and subjective cognitive complaints among older Black women: The role of depression and coping. The Clinical Neuropsychologist, 36(2), 479–502. https://doi.org/10.1080/13854046.2021.1923804

54.

Hoggard

L. S.

Volpe

Thomas

Wallace

Ellis

(2019). The role of emotional eating in the links between racial discrimination and physical and mental health. Journal of Behavioral Medicine, 42(6), 1091–1103. https://doi.org/10.1007/s10865-019-00044-1

55.

Holloway

Varner

(2021). Maternal race-related stressors and African American adolescents’ academic and behavioral outcomes. Family Relations, 70(2), 603–618. https://doi.org/10.1111/fare.12540

56.

Holman

A. R.

(2012). Gendered racial socialization in Black families: Mothers’ beliefs, approaches, and advocacy. [Doctoral dissertation, University of California]. https://escholarship.org/uc/item/7xd4r8w8

57.

Bentler

(1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

58.

Hutson

T. M.

(2022). By any means necessary: A brief educational history of Black women and girls in the United States. Journal of Critical Thought and Praxis, 11(3), 1–14. https://doi.org/10.31274/jctp.12960

59.

IBM Corp . (2021). IBM SPSS statistics for Macintosh, version 28.0. IBM Corp.

60.

Ifatunji

M. A.

Harnois

C. E.

(2016). An explanation for the gender gap in perceptions of discrimination among African Americans: Considering the role of gender bias in measurement. Sociology of Race and Ethnicity, 2(3), 263–288. https://doi.org/10.1177/2332649215613

61.

Jackson

J. S.

Torres

Caldwell

C. H.

Neighbors

H. W.

Nesse

R. M.

Taylor

R. J.

Trierweiler

S. J.

Williams

D. R.

(2004). The national survey of American life: A study of racial, ethnic and cultural influences on mental disorders and mental health. International Journal of Methods in Psychiatric Research, 13(4), 196–207. https://doi.org/10.1002/mpr.177

62.

Jackson

J. S.

Williams

(2002). Detroit Area Study, 1995: Social influence on health: Stress, racism, and health Protective Resources. Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/ICPSR03272.v1

63.

Jaxon

Lei

R. F.

Shachnai

Chestnut

E. K.

Cimpian

(2019). The acquisition of gender stereotypes about intellectual ability: Intersections with race. Journal of Social Issues, 75(4), 1192–1215. https://doi.org/10.1111/josi.12352

64.

Jones

M. K.

Leath

Settles

I. H.

Doty

Conner

(2022). Gendered racism and depression among Black women: Examining the roles of social support and identity. Cultural Diversity & Ethnic Minority Psychology, 28(1), 39–48. https://doi.org/10.1037/cdp0000486

65.

Jones

Y. A.

(2019). “I was the only one in the building.” lived experiences of Black school counselors post-brown V. board of education in predominantly white schools (Order No. 13865736) [Doctoral dissertation, Indiana State University]. ProQuest Dissertation and Theses Database. https://www.proquest.com/dissertations-theses/i-was-only-one-building-lived-experiences-black/docview/2240068449/se-2

66.

Kaiser

H. F.

(1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116

67.

Keels

Durkee

Hope

(2017). The psychological and academic costs of school-based racial and ethnic microaggressions. American Educational Research Journal, 54(6), 1316–1344. https://doi.org/10.3102/0002831217722120. https://www.jstor.org/stable/26642039

68.

Kessler

R. C.

Mickelson

K. D.

Williams

D. R.

(1999). The prevalence, distribution, and mental health correlates of perceived discrimination in the United States. Journal of Health and Social Behavior, 40(3), 208–230. https://doi.org/10.2307/2676349

69.

Keys

H. M.

Noland

G. S.

De Rochars

M. B.

Taylor

T. H.

Blount

Gonzales

(2019). Perceived discrimination in bateyes of the Dominican Republic: Results from the everyday discrimination scale and implications for public health programs. BMC Public Health, 19(1), 1–13. https://doi.org/10.1186/s12889-019-7773-2

70.

Kim

Sellbom

Ford

K. L.

(2014). Race/ethnicity and measurement equivalence of the everyday discrimination scale. Psychological Assessment, 26(3), 892–900. https://doi.org/10.1037/a0036431

71.

Krieger

Smith

Naishadham

Hartman

Barbeau

E. M.

(2005). Experiences of discrimination: Validity and reliability of a self-report measure for population health research on racism and health. Social Science & Medicine (1982), 61(7), 1576–1596. https://doi.org/10.1016/j.socscimed.2005.03.006

72.

Kwate

N. O.

Goodman

M. S.

(2015). Racism at the intersections: Gender and socioeconomic differences in the experience of racism among African Americans. The American Journal of Orthopsychiatry, 85(5), 397–408. https://doi.org/10.1037/ort0000086

73.

La Du

T. J.

Tanaka

J. S.

(1990). “The influence of sample size, estimation methods, and model specification on goodness-of-fit assessments in structural equation models”: Correction to la Du and Tanaka (1989). Journal of Applied Psychology, 75(1), 20. https://doi.org/10.1037/h0090371

74.

Lanier

Sommers

M. S.

Fletcher

Sutton

M. Y.

Roberts

D. D.

(2016). Examining racial discrimination frequency, racial discrimination stress, and psychological well-being among Black early adolescents. Journal of Black Psychology, 43(3), 219–229. https://doi.org/10.1177/0095798416638189

75.

Lawrence

J. A.

Kawachi

White

Bassett

M. T.

Priest

Masunga

J. G.

Cory

H. J.

Mita

Williams

D. R.

(2022). A systematic review and meta-analysis of the everyday discrimination scale and biomarker outcomes. Psychoneuroendocrinology, 142, 105772. https://doi.org/10.1016/j.psyneuen.2022.105772

76.

Lewis

J. A.

Mendenhall

Harwood

S. A.

Browne Huntt

(2013). Coping with gendered racial microaggressions among Black women college students. Journal of African American Studies, 17(1), 51–73. https://doi.org/10.1007/s12111-012-9219-0

77.

Lewis

J. A.

Mendenhall

Harwood

S. A.

Browne Huntt

(2016). “Ain’t I a woman?” perceived gendered racial microaggressions experienced by Black women. The Counseling Psychologist, 44(5), 758–780. https://doi.org/10.1177/0011000016641193

78.

Lewis

J. A.

Neville

H. A.

(2015). Construction and initial validation of the gendered racial microaggressions scale for Black women. Journal of Counseling Psychology, 62(2), 289–302. https://doi.org/10.1037/cou0000062

79.

Lewis

T. T.

Cogburn

C. D.

Williams

D. R.

(2015). Self-reported experiences of discrimination and health: Scientific advances, ongoing controversies, and emerging issues. Annual Review of Clinical Psychology, 11, 407–440. https://doi.org/10.1146/annurev-clinpsy-032814-112728

80.

Lewis

T. T.

Van Dyke

M. E.

(2018). Discrimination and the health of African Americans: The potential importance of intersectionalities. Current Directions in Psychological Science, 27(3), 176–182. https://doi.org/10.1177/0963721418770442

81.

Loyd

A. B.

Humphries

M. L.

Moore

Owens

C. L.

Smith

A. M.

Williams

(2024). Identifying risk and protective factors in research on mental health and Black American adolescents: 1990 through 2022. Journal of Black Psychology, 50(4), 448–475. https://doi.org/10.1177/00957984241249360

82.

Madubata

I. J.

Odafe

M. O.

Talavera

D. C.

Hong

J. H.

Walker

R. L.

(2018). Helplessness mediates racial discrimination and depression for African American young adults. Journal of Black Psychology, 44(7), 626–643. https://doi.org/10.1177/0095798418811476

83.

Majoka

M. A.

Schimming

(2021). Effect of social determinants of health on cognition and risk of Alzheimer disease and related dementias. Clinical Therapeutics, 43(6), 922–929. https://doi.org/10.1016/j.clinthera.2021.05.005

84.

McHale

S. M.

Crouter

A. C.

Kim

J. Y.

Burton

L. M.

Davis

K. D.

Dotterer

A. M.

Swanson

D. P.

(2006). Mothers’ and fathers’ racial socialization in African American families: Implications for youth. Child Development, 77(5), 1387–1402. https://doi.org/10.1111/j.1467-8624.2006.00942.x

85.

McNeil Smith

Reynolds

J. E.

Fincham

F. D.

Beach

S. R.

(2016). Parental experiences of racial discrimination and youth racial socialization in two-parent African American families. Cultural Diversity & Ethnic Minority Psychology, 22(2), 268–276. https://doi.org/10.1037/cdp0000064

86.

Mendoza

N. S.

Moreno

F. A.

Hishaw

G. A.

Gaw

A. C.

Fortuna

L. R.

Skubel

Porche

M. V.

Roessel

M. H.

Shore

Gallegos

(2020). Affirmative care across cultures: Broadening application. Focus (American Psychiatric Publishing), 18(1), 31–39. https://doi.org/10.1176/appi.focus.20190030

87.

Meuleman

Żółtak

Pokropek

Davidov

Muthén

Oberski

D. L.

Billiet

Schmidt

(2022). Why measurement invariance is important in comparative research. A Response to Welzel et al. (2021). Sociological Methods & Research, 52(3), 004912412210917. https://doi.org/10.1177/00491241221091755

88.

Monnat

S. M.

(2010). Toward a critical understanding of gendered color-blind racism within the U.S. welfare institution. Journal of Black Studies, 40(4), 637–652. https://doi.org/10.1177/0021934708317739

89.

Muthén

L. K.

Muthén

B. O.

(1998-2017). Mplus user’s guide (8th ed.). Muthén & Muthén.

90.

Onwong’a

J. R.

Slaten

C. D.

McClain

(2021). “AmeriKenyan”: Lived acculturation and ethnic identification of Kenyan natives during their youth. Journal of Black Psychology, 48(5), 571–603. https://doi.org/10.1177/00957984211039861

91.

Pascoe

E. A.

Smart Richman

(2009). Perceived discrimination and health: A metanalytic review. Psychological Bulletin, 135(4), 531–554. https://doi.org/10.1037/a0016059

92.

Pearl

R. L.

Wadden

T. A.

Tronieri

J. S.

Chao

A. M.

Alamuddin

Berkowitz

R. I.

(2018). Everyday discrimination in a racially diverse sample of patients with obesity. Clinical Obesity, 8(2), 140–146. https://doi.org/10.1111/cob.12235

93.

Purdie-Vaughns

Eibach

R. P.

(2008). Intersectional invisibility: The distinctive advantages and disadvantages of multiple subordinate-group identities. Sex Roles, 59(5-6), 377–391. https://doi.org/10.1007/s11199-008-9424-4

94.

Putnick

D. L.

Bornstein

M. H.

(2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review: DR, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

95.

Reeve

B. B.

Willis

Shariff-Marco

S. N.

Breen

Williams

D. R.

Gee

G. C.

Alegría

Takeuchi

D. T.

Kudela

M. S.

Levin

K. Y.

(2011). Comparing cognitive interviewing and psychometric methods to evaluate a racial/ethnic discrimination scale. Field Methods, 23(4), 397–419. https://doi.org/10.1177/1525822X11416564

96.

Robinson-Lane

S. G.

Hill-Jarrett

T. G.

Janevic

M. R.

(2022). “Ooh, you got to holler sometime:” pain meaning and experiences of Black older adults. In van Rysewyk

(Ed.), Meanings of pain (3, pp. 45–64). Springer. https://doi.org/10.1007/978-3-030-95825-1_4

97.

Saleem

F. T.

English

Busby

D. R.

Lambert

S. F.

Harrison

Stock

M. L.

Gibbons

F. X.

(2016). The impact of African American parents’ racial discrimination experiences and perceived neighborhood cohesion on their racial socialization practices. Journal of Youth and Adolescence, 45(7), 1338–1349. https://doi.org/10.1007/s10964-016-0499-x

98.

Schwing

A. E.

Wong

Y. J.

Fann

M. D.

(2013). Development and validation of the African American Men’s Gendered Racism Stress Inventory. Psychology of Men & Masculinity, 14(1), 16–24. https://doi.org/10.1037/a0028272

99.

Seamster

Ray

(2018). Against teleology in the study of race: Toward the abolition of the progress paradigm. Sociological Theory, 36(4), 315–342. https://doi.org/10.1177/07352751188136

100.

Seblova

Avila

J. F.

Kraal

A. Z.

Starks

T. M.

Adkins-Jackson

Brickman

A. M.

Manly

J. J.

(2022). Relationship of daily, institutional and structural racism with cognition in ethnically/racially diverse middle-age Americans. Alzheimer’s & Dementia, 18(S11), Article e067224. https://doi.org/10.1002/alz.067224

101.

Sellers

Cherepanav

Hanmer

Fryback

D. G.

Palta

(2013). Interpersonal discrimination and health-related quality of life among Black and white men and women in the United States. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 22(6), 1307–1312. https://doi.org/10.1007/s11136-012-0258-8

102.

Shariff-Marco

Breen

Landrine

Reeve

B. B.

Krieger

Gee

G. C.

Williams

D. R.

Mays

V. M.

Ponce

N. A.

Alegría

Liu

Willis

Johnson

T. P.

(2011). Measuring everyday racial/ethnic discrimination in health surveys: How best to ask the questions, in one or two stages, across multiple racial/ethnic groups? Du Bois Review: Social Science Research on Race, 8(1), 159–177. https://doi.org/10.1017/S1742058X11000129

103.

Shi

DiStefano

Zheng

Liu

Jiang

(2021). Fitting latent growth models with small sample sizes and non-normal missing data. International Journal of Behavioral Development, 45(2), 179–192. https://doi.org/10.1177/0165025420979365

104.

Siddiqi

Shahidi

F. V.

Ramraj

Williams

D. R.

(2017). Associations between race, discrimination and risk for chronic disease in a population-based sample from Canada. Social Science & Medicine (1982), 194, 135–141. https://doi.org/10.1016/j.socscimed.2017.10.009

105.

Smith-Bynum

M. A.

Anderson

R. E.

Davis

B. L.

Franco

M. G.

English

(2016). Observed racial socialization and maternal positive emotions in African American mother-adolescent discussions about racial discrimination. Child Development, 87(6), 1926–1939. https://doi.org/10.1111/cdev.12562

106.

Spates

Evans

James

T. A.

Martinez

(2020). Gendered racism in the lives of Black women: A qualitative exploration. Journal of Black Psychology, 46(8), 583–606. https://doi.org/10.1177/0095798420962257

107.

Steinberg

(1994). Context and serial-order effects in personality measurement: Limits on the generality of measuring changes the measure. Journal of Personality and Social Psychology, 66(2), 341–349. https://doi.org/10.1037/0022-3514.66.2.341

108.

Stucky

B. D.

Gottfredson

N. C.

Panter

A. T.

Daye

C. E.

Allen

W. R.

Wightman

L. F.

(2011). An item factor analysis and item response theory-based revision of the everyday discrimination scale. Cultural Diversity & Ethnic Minority Psychology, 17(2), 175–185. https://doi.org/10.1037/a0023356

109.

Taylor

Guy-Walls

Wilkerson

Addae

(2019). The historical perspectives of stereotypes on African-American males. Journal of Human Rights and Social Work, 4(3), 213–225. https://doi.org/10.1007/s41134-019-00096-y

110.

Taylor

T. R.

Kamarck

T. W.

Shiffman

(2004). Validation of the Detroit area study discrimination scale in a community sample of older African American adults: The Pittsburgh healthy heart project. International Journal of Behavioral Medicine, 11(2), 88–94. https://doi.org/10.1207/s15327558ijbm1102_4

111.

Timberlake

J. M.

Estes

S. B.

(2007). Do racial and ethnic stereotypes depend on the sex of the target group members? Evidence from a survey-based experiment. The Sociological Quarterly, 48(3), 399–433. https://doi.org/10.1111/j.1533-8525.2007.00083.x

112.

Tourangeau

Rasinski

K. A.

(1988). Cognitive processes underlying context effects in attitude measurement. Psychological Bulletin, 103(3), 299–314. https://doi.org/10.1037/0033-2909.103.3.299

113.

Vandenberg

R. J.

Lance

C. E.

(2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. https://doi.org/10.1177/109442810031002

114.

Vandiver

B. J.

(2020). Message from the editor-in-chief: Racism as public health crisis. Journal of Black Psychology, 46(5), 347–350. https://doi.org/10.1177/0095798420956806

115.

West

C. M.

(2008). Mammy, Jezebel, Sapphire, and their homegirls: Developing an “oppositional” gaze toward the images of Black women. In Chrisler

J. C.

Golden

Rozee

P. D.

(Eds.), Lectures on the psychology of women (4th ed., pp. 287–299). McGraw-Hill.

116.

Whittaker

T. A.

(2012). Using the modification index and standardized expected parameter change for model modification. The Journal of Experimental Education, 80(1), 26–44. https://doi.org/10.1080/00220973.2010.531299

117.

Widaman

K. F.

Reise

S. P.

(1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In Bryant

K. J.

Windle

West

S. G.

(Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). American Psychological Association. https://doi.org/10.1037/10222-009

118.

Williams

B. M.

(2023). “It’s Just My Face:” workplace policing of Black professional women in higher education. Journal of Women and Gender in Higher Education, 16(2), 67–89. https://doi.org/10.1080/26379112.2023.2172730

119.

Williams

C. C.

Curling

Steele

L. S.

Gibson

M. F.

Daley

Green

D. C.

Ross

L. E.

(2017). Depression and discrimination in the lives of women, transgender and gender liminal people in Ontario, Canada. Health & Social Care in the Community, 25(3), 1139–1150. https://doi.org/10.1111/hsc.12414

120.

Williams

D. R.

(n.d.). Everyday discrimination scale. https://scholar.harvard.edu/davidrwilliams/node/32397

121.

Williams

D. R.

(2012). Miles to go before we sleep: Racial inequities in health. Journal of Health and Social Behavior, 53(3), 279–295. https://doi.org/10.1177/0022146512455804

122.

Williams

D. R.

(2018). Stress and the mental health of populations of color: Advancing our understanding of race-related stressors. Journal of Health and Social Behavior, 59(4), 466–485. https://doi.org/10.1177/0022146518814251

123.

Williams

D. R.

Yan

Jackson

J. S.

Anderson

N. B.

(1997). Racial differences in physical and mental health: Socio-economic status, stress and discrimination. Journal of Health Psychology, 2(3), 335–351. https://doi.org/10.1177/135910539700200305

124.

Wong

K. L.

Jackson

M. A.

Holman

Ponterotto

J. G.

(2025). Experiences of Black women understanding racial microaggressions and identifying replenishing healing practices. Journal of Black Psychology, 51(2), 241–280. https://doi.org/10.1177/00957984251317119

125.

Wycoff

K. L.

Dunbar

A. S.

Felter

Bohn

Foleno

Wilson

Miller

(2024). “Just trying to make it through”: Understanding transitions into motherhood among young Black women amidst trauma, racism, and structural oppression. Journal of Black Psychology, 50(3), 293–325. https://doi.org/10.1177/00957984231223000

126.

Zahodne

L. B.

Sharifian

Kraal

A. Z.

Morris

E. P.

Sol

Zaheed

A. B.

Meister

Mayeux

Schupf

Manly

J. J.

Brickman

A. M.

(2023). Longitudinal associations between racial discrimination and hippocampal and white matter hyperintensity volumes among older Black adults. Social Science & Medicine (1982), 316, 114789. https://doi.org/10.1016/j.socscimed.2022.114789

Evaluating the Measurement Invariance of the Everyday Discrimination Scale Among Older Black Men and Women

Abstract

Keywords

Everyday Discrimination Scale

Method

Participants

Procedures

Measures

Everyday Discrimination Scale

Demographic Information

Analytic Procedures

Additional Analyses

Results

Preliminary Analyses

Descriptive Statistics

Exploratory Factor Analysis

Confirmatory Factor Analysis: One-Factor Baseline Model

Measurement Invariance

Measurement Bias

Discussion

Implications for Clinical Practice and Research

Footnotes

ORCID iD

Ethical Considerations

Funding

Declaration of Conflicting Interests

References