Discrimination and Psychological Distress: Examining ...

Asian American Journal of Psychology 2012, Vol. 3, No. 2, 104 –120

© 2011 American Psychological Association 1948-1985/11/$12.00 DOI: 10.1037/a0025275

Discrimination and Psychological Distress: Examining the Moderating Role of Social Context in a Nationally Representative Sample of Asian American Adults Moin Syed and Mary Joyce D. Juan University of Minnesota Research on perceived discrimination among Asian Americans has consistently demonstrated a negative association to psychological distress and other mental health outcomes. Drawing from Bronfenbrenner’s (1979) ecological theory, this study examined the social context as a moderator of the association between perceived discrimination and psychological distress. Objective (ethnic density) and subjective (social cohesion) measures of social context were examined among Vietnamese (n ⫽ 478), Chinese (n ⫽ 566), and Filipino American (n ⫽ 493) adults from the National Latino and Asian American Study. Perceived discrimination was positively associated with psychological distress across all three groups. In addition, ethnic density and social cohesion moderated the association between discrimination and psychological distress with notable variation by ethnic group and operationalization of ethnic density. Overall, the findings highlight the need for further research regarding individuals’ subjective experiences with ethnic density. Keywords: discrimination, psychological distress, social context, ethnic density, social cohesion

Ahn, 2011; Lee, 2003; Ying, Lee, & Tsai, 2000). In the current study, we seek to identify factors that may mitigate the negative impact of discrimination on the mental health of Asian Americans. Specifically, we investigate how aspects of individuals’ social context (i.e., ethnic density and social cohesion) moderate the association between perceived discrimination and psychological distress among a nationally representative sample of Asian American adults in the United States.

At approximately 15 million people (United States Census Bureau, 2008), Asian Americans are one of the fastest growing minority groups in the United States. Identified as a “model minority,” Asian Americans are often believed to experience less intense discrimination and fairer treatment compared to other racial/ethnic minority groups (Gee, Spencer, Chen, & Takeuchi, 2007; Lee, 2003). Recent research, however, has revealed that Asian Americans do indeed face discrimination, and that discriminatory experiences adversely affect their mental health and well-being (e.g., Alvarez, Juang, & Liang, 2006; Juang & Cookston, 2009; Lee &

Discrimination and Mental Health

This article was published Online First September 26, 2011. Moin Syed and Mary Joyce D. Juan, Department of Psychology, University of Minnesota. The NLAAS was supported by NIH Research Grant U01 MH62209 funded by the National Institute of Mental Health as well as the Substance Abuse and Mental Health Services Administration Center for Mental Health Services (SAMHSA/CMHS) and the Office of Behavioral and Social Sciences Research (OBSSR). Thanks to Linda Juang for helpful comments on an earlier version of this article. Correspondence concerning this article should be addressed to Moin Syed, Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455. E-mail: [email protected]

Since the initial arrival of Chinese immigrants in the 1800s, Asian American immigration history has been marked by antimiscegenation, antinaturalization and other exclusionary laws, as well as overall anti-Asian sentiment (Alvarez, 2009). And yet, despite American retribution for this unfair treatment and the now established presence of the Asian American community, Asian Americans still face treatment as “forever foreigners” and “non-Americans” (Sue, Bucceri, Lin, Nadal, & Torino, 2007; Tuan, 1998). Additionally, in more recent years, the community has witnessed an increase in reports of anti-Asian violence, including rob-

104

DISCRIMINATION AND PSYCHOLOGICAL DISTRESS

bery, vandalism, intimidation, and threats (National Asian Pacific American Legal Consortium, 2003). Furthermore, research with Asian American middle school and high school students shows that experiences with discrimination increase over time (Greene, Way, & Pahl, 2006; Juang & Cookston, 2009; Wong, Eccles, & Sameroff, 2003), indicating that discrimination is a serious challenge for Asian Americans of all ages. Beyond withstanding the act of prejudice itself, Asian Americans must also deal with its psychological consequences (Craig-Henderson & Sloan, 2003; for reviews, see Lee & Anh, 2011; Okazaki, 2009; and Paradies, 2006). For example, Lee (2003) found that perceived discrimination was associated with increased reports of depressive symptoms and decreased self-esteem for Asian American college students. In addition, Gee (2002) found that reports of discrimination predicted poorer mental health and higher levels of psychological symptomatology among Chinese Americans. Similar findings have also been demonstrated in Filipino American and Vietnamese American samples (Lam, 2007; Lee & Anh, 2011; Mossakowski, 2003), showing that discrimination is a negative experience shared across many Asian ethnic groups. Thus, researchers are called upon to identify ways in which Asian Americans can counter the deleterious effects of discrimination. In sum, the fact that Asian Americans’ experiences with discrimination are related to poorer mental health has been firmly documented. Accordingly, researchers are beginning to probe the complexity of this association by testing for moderators that may serve as protective or exacerbating factors, including family conflict and cohesion, ethnic identity, and ethnic socialization (e.g., Huynh & Fuligni, 2010; Juang & Alvarez, 2010; Yoo & Lee, 2008). The present study follows this zeitgeist by examining how the social context that Asian Americans inhabit moderates the association between perceived discrimination and mental health. The Role of Social Context for Discrimination and Mental Health In prior research focusing on the relation between discrimination and mental health, most of the moderators that have been examined were

105

individual-level factors, such as ethnic identity and family relations (Juang & Alvarez, 2010; Yoo & Lee, 2008). Less attention has been placed on factors beyond the individual, such as one’s social context. Bronfenbrenner’s (1979) ecological theory, however, highlights the potential role that social context may play in individuals’ lives. The theory posits that the individual experience is shaped not only by her or his immediate surroundings but also by the larger social context in which she or he lives. These environments are conceptualized as a “nested arrangement of structures” (p. 514), with each environment contained within the next. The most immediate level of influence begins with the place in which the person lives and interacts with directly, such as her or his home, school, neighborhoods, or work environment (microsystem). The next level of influence expands to interrelations among microsystems, such as linkages between home and school (mesosystem). The systems become progressively larger, encompassing environments with direct or indirect impact on the individuals, such as the parents’ workplace and local government agencies (exosystem). At the broadest level is the macrosystem, which refers to cultural, social, political, educational, and legal systems in which information is carried and transmitted. Although discrimination can potentially be experienced at all ecological levels (Garcıá Coll et al., 1996), in the present study we focus on the microsystem because we are interested in more proximal factors associated with experiences of discrimination. Indeed, the notion of proximal processes, that the most immediate contextual factors will be the most important, is a core element of Bronfenbrenner’s theory. One aspect of the microsystem that we assess is the ethnic density of the county in which individuals live. Many have suggested that ethnic density is important to consider in the context of discrimination (e.g., Welch, Sigelman, Bledsoe, & Combs, 2001). Individuals are presumed to thrive in areas with a high concentration of the same racial/ethnic group members because they afford greater levels of social support, social cohesion, and social connection (Halpern, 1993). At the same time, People of Color are more likely to be clustered together in povertystricken neighborhoods, which in turn are associated with increased psychological distress among its residents (Murry, Berkel, Gaylord-

106

SYED AND JUAN

Harden, Copeland-Linder, & Nation, 2011). Few studies, however, have moved forward with examining empirically the relationship between neighborhood ethnic density and discrimination. Available research suggests that those who live in a more ethnically homogeneous community are likely to report fewer instances of discrimination. For example, Hunt, Wise, Jipguep, Cozier, and Rosenberg (2007) found that in neighborhoods with a high percentage of Black residents, Black women reported the lowest levels of discrimination, relative to their counterparts in more multiracial/ethnic neighborhoods. However, another study with Black adolescents found no association between neighborhood density and personal experiences of discrimination (Seaton & Yip, 2009). In terms of mental health, a study of Latino and Chinese immigrants found that ethnic density was negatively associated with depression (Mair et al., 2010). However, in this same study, ethnic density was associated with increased depression for African Americans. In contrast, Bećares, Nazroo, and Stafford (2009) found support for the buffering effects of ethnic density. Their study found that among Caribbean, Indian, Pakistani, and Bangladeshi communities in the United Kingdom, the association between racism and psychotic symptoms weakened as ethnic density increased. However, another study did not find any interactions between density and personal discrimination for self-esteem, depressive symptoms, or life satisfaction (Seaton & Yip, 2009). Taken together, these studies provide some evidence that ethnic density would mitigate the association between discrimination and psychological outcomes, but the results are mixed. Moreover, although ethnic density may benefit communities of color, it remains to be seen how implications from this research apply specifically to Asian Americans. Objective and Subjective Components of Social Contexts A frequently overlooked aspect of Bronfenbrenner’s (1979) ecological theory is that individuals can experience the same context in different ways. Indeed, he suggested that social contexts can be understood both in terms of their objective properties and how they are subjectively experienced by individuals. That is, ethnic density of a particular environment can

be objectively measured, but that same percentage of ethnic density will potentially have a differential impact among residents of that environment. On its own, objective measures of ethnic density assume cohesion among all similar racial/ethnic members in a given geographic area. However, there are a number of reasons for why this may not apply to all people. Although an Asian American may live in an ethnically homogeneous area, it is possible that she or he may not feel socially connected to other residents and therefore would not be linked to the available resources and social and moral support provided by the community. Thus, for such an individual, ethnic density may not attenuate the association between discrimination and psychological distress as it might for a person who feels more attached to their community. In contrast, ethnic density may not matter much at all for those who feel connected to their community, even if there are small numbers of their same racial/ethnic group members in the area. To date, very few studies have examined the subjective dimension of social contexts. A recent study by Juang and Alvarez (2011) demonstrated that for Chinese Americans, the influence of ethnic density varies according to the conceptualization of ethnic density. That is, among Chinese American adolescents, an objective measure of ethnic density (i.e., percentage of Asian residents within zip code area) was not associated with perceptions of discrimination. However, those who reported higher subjective ethnic density (i.e., perceived percentages of Asian ethnic population in neighborhood) also reported greater discrimination. These findings lend credence to the value of examining subjective assessments of social contexts. However, the question of whether the social context might moderate the association between experiences of discrimination and mental health among Asian Americans has not been addressed. In the present study we address this limitation by assessing subjective social context in terms of how socially connected people feel to their neighborhoods. Furthermore, by suggesting that social contexts have two dimensions, we also examined the three-way interaction between objective context, subjective context, and discrimination for psychological distress. Such a test allowed us to understand how the


objective and subjective aspect interact in the context of discrimination. Finally, it is important to consider that inconsistencies in findings regarding social context may also be a product of inconsistencies in the measurement of the context itself. For instance, although most studies rely on an objective measure of ethnic density using U.S. census data, the geographic area of interest has ranged from census block groups (Hunt et al., 2007) to zip code areas (Juang & Alvarez, 2011) to entire U.S. states (Reidpath, 2003). Furthermore, it is important to consider ethnic density in relation to whom. Most studies on ethnic density have conceptualized density at a pan-ethnic level (e.g., “Black” in Hunt et al., 2007 and Reidpath, 2003), but it is worthwhile to consider other levels, such as density of one’s ethnic group or density of People of Color more broadly. Together, these variations in the definition of ethnic density in the current literature have limited our understanding of the true potential of social context as a protective factor against discrimination. In the current study, we address limitations in prior research by first using a consistent, objective, and reliable measure of ethnic density that is drawn from U.S. census county-level data. Second, we explore ethnic density in a nationally representative sample of Asian Americans in the United States and explore both ethnic-specific density (e.g., Chinese, Vietnamese, and Filipino Americans), pan-ethnic density (i.e., Asian Americans), and People of Color density. Comparing models using three different conceptualizations of ethnic density afforded an opportunity for a deeper understanding of the conditions under which ethnic density matters for peoples’ lives. The Present Study Facing and managing the effects of discrimination remains a challenge for Asian Americans today. The present study explores how aspects of the social context may serve as a moderator of the association between perceived discrimination and psychological distress among a nationally representative sample of Asian American adults. In consideration of the multiple levels of ethnic density, we extend research on this topic by examining objective and subjective context using three different forms of density (ethnic-specific, pan-ethnic,

107

and People of Color) among three ethnic groups (Vietnamese, Chinese, and Filipino Americans). Given the minimal and inconsistent findings of past research, we advanced the following openended research questions in lieu of targeted hypotheses: 1) Do ethnic density (an objective measure of social context) and/or social cohesion (a subjective measure of social context) moderate the association between perceived discrimination and psychological distress? 2) Does the pattern of results vary by how ethnic density is conceptualized (i.e., ethnic-specific, Asian American, and People of Color)? Method Participants Conducted in 2002 and 2003, the National Latino and Asian American Study (NLAAS) is a household survey of Latinos and Asian Americans living in the U.S. The goals of the study were to examine the prevalence of psychiatric symptoms and mental health service use among Latinos and Asian Americans and to understand how disorders and utilization rates are related to cultural and psychosocial factors (see Alegrıá et al., 2004 for more information). The NLAAS used a three-tiered stratified probability sample design (see Heeringa et al., 2004 for details). Sample weights were determined to account for unequal probability of selection, characteristic of nonresponders, and poststratification to ensure that final estimates could be interpreted as representative of the population. Sample weights were calculated separately for the Asian American and Latino samples and are valid for ethnic-specific analyses such as the ones carried out in the current analysis. Participants included in the current analysis were 478 Vietnamese Americans, 566 Chinese Americans, and 493 Filipino Americans (total N ⫽ 1537; Table 1) who participated in the NLAAS study and had complete data on the measures of interest (see below for details on how missing data were handled). Although the full Asian American sample in the NLAAS study was 2,095, the remaining participants

108

SYED AND JUAN

comprised various ethnic groups (e.g., South Asian, Japanese, Korean) in relatively small numbers (i.e., ⬍150). As a result, these participants were not included in the current study. The current analysis makes use of the restricted-access NLAAS dataset. The publicly available NLAAS dataset contains a crude geographic variable that divides the U.S. into four broad regions. The restricted-access dataset, however, provides the county of residence as the lowest level of geography for the respondents. The first author applied for and received approval to use the restricted-access dataset from the Inter-University Consortium for Political and Social Research, custodians of the NLAAS. Approval for secondary analysis was obtained from the Institutional Review Board of the authors’ university prior to conducting the current analysis. Measures Perceived race-related discrimination. Participants’ perceptions of race-related discrimination were measured by three items developed by Gil and Vega (1996): “How often have you been treated unfairly because you are (ethnic label)?”, “How often do people dislike you because you are (ethnic label)?”, and “How often have you seen friends or family be treated unfairly because they are (ethnic label)?” Respondents indicated their level of agreement on a 4-point Likert-type scale ranging from 1 (often) to 4 (never). The three items were reversecoded and averaged together so that higher values correspond to greater perceived race-related discrimination. Cronbach’s alpha was acceptable for each ethnic group: ␣s ⫽ .90 for Vietnamese, .85 for Chinese, and .84 for Filipino. Social cohesion. This 4-item measure of social cohesion assesses the degree of cohesiveness and trust the respondents feel in their neighborhoods. As described by Alegrıá et al. (2004), the scale was created by taking items from three existing sources: the Social Cohesion and Trust scale (Sampson, Raudenbush, & Earls, 1997), the multisite study of Mental Health Service Use, Needs, Outcomes, and Costs in Child and Adolescent Populations (UNOCCAP; National Institute of Mental Health, 1994), and the National Longitudinal Study of Adolescent Health (Bearman, Jones, & Udry, 1997). A sample item includes, “People

in my neighborhood look out for each other.” Respondents indicated their level of agreement on a 4-point Likert-type scale ranging from 1 (very true) to 4 (not at all true). The four items were reverse-coded and averaged together so that higher values correspond to greater social cohesion. Cronbach’s alpha was acceptable for each ethnic group: ␣s ⫽ .84 for Vietnamese, .81 for Chinese, and .81 for Filipino. Ethnic density. The measures of ethnic density were derived from the U.S. Census (2000). Data collection for the NLAAS was conducted in 2002, rendering the 2000 Census data a reasonable approximation of ethnic density at the time of the study. The raw data provided estimates of the percentage of people from different ethnic groups at various levels of geography (e.g., state, county). Because the lowest level of geography available in the restricted-access NLAAS data set is the county, ethnic density measures were created at the county level. The 2000 Census was the first year that respondents were permitted to select more than one ethnic label. The measures of ethnic density used in this study were based on the number of respondents who selected a given ethnic group regardless of whether they also selected another group. For example, both individuals who selected only “Vietnamese” and those that selected “Vietnamese” in combination with any other ethnic label (e.g., “White,” “African American”) were included in the measures of Vietnamese density. The number of individuals who selected the label in a given country was divided by the total population for the county to get a county-level proportion of ethnic density. Five density measures were created: (a) any Vietnamese, (b) any Chinese, (c) any Filipino, (d) any Asian American (not limited to three aforementioned ethnic groups), and (e) any Person of Color (all respondents except those who selected only “White”). Psychological distress. Psychological distress was measured using the Kessler Psychological Distress Scale (K10; Kessler et al., 2002). The K10 comprises 10 questions about the respondent’s psychological functioning within the preceding 30 days. Sample items include, “about how often did you feel restless or fidgety?” and “about how often did you feel worthless?” Respondents indicated their level of agreement on a 5-point Likert-type scale ranging from 1 (all of the time) to 5 (none of the


time). The 10 items were reverse-coded and summed to create a total score with a possible range of 10 to 50. Cronbach’s alpha was acceptable for each ethnic group: ␣s ⫽ .87 for Vietnamese, .85 for Chinese, and .83 for Filipino. This scale has been widely used in nationally representative surveys in the U.S. (e.g., National Comorbidity Survey Replication), Australia (e.g., National Mental Health Survey), and as part of the World Health Organization’s World Mental Health Initiative. Results Descriptive Statistics of Discrimination and Distress Table 2 provides descriptive data on the participants’ responses to the discrimination and distress measures. Overall, reporting of discrimination was low, with the modal value being “none” for Vietnamese and Filipino Americans and between “none” and “rarely” for Chinese Americans. There was, however, still substantial reporting between “rarely” and “often.” Meaningful cut-off scores have been developed for the K10 psychological distress measure that correspond with other screening instruments and service utilization (Andrews & Slade, 2001; Australian Department of Human Services, 2002). A score of 10 –15 is considered “low or no risk,” a score of 16 –29 indicates “medium risk,” and a score of 30 –50 suggests “high risk.” Most participants in the present study were in the range of low or no risk, with scores indicating at least medium risk reported by 19.39% of Vietnamese Americans, 26.64% of Chinese Americans, and 19.43% of Filipino Americans (see Table 2). Because the NLAAS is the first nationwide study of Asian American mental health, there is no basis for comparison with other studies. Analysis Plan The primary analysis was conducted through a four-step hierarchical multiple regression analysis predicting the sum scores of the K10 psychological distress measure. Hierarchical multiple regression was particularly useful for the current analysis because it allowed for us to estimate the association between discrimination and distress, test for whether the association still

109

remains after entering the social context variables, and then test for interaction between discrimination and the social context variables. All dichotomous variables were dummy coded (i.e., 0, 1) and all continuous variables except psychological distress, the dependent variable, were converted to z-scores. Rescaling the variables in this way provided a meaningful zeropoint for all variables, which allowed the intercept to remain meaningful and facilitated interpretation of the interactions (Aiken & West, 1991). We conducted a log transformation of the K10 sum score because the variable was positively skewed for all three ethnic groups. The transformed score was used for all analyses, but we converted back to the original units when interpreting the findings. Due to the number of variables included in each analysis, we set our nominal alpha to .01 to better protect against Type I errors. In Step 1 of the regression, age (z-score), gender (0 ⫽ male, 1 ⫽ female), immigrant generation status (0 ⫽ U.S. born, 1 ⫽ foreignborn; 96% of Vietnamese Americans were foreign-born, so this control was not included in the Vietnamese models), English fluency (z-score) and schooling (z-score) were entered as control variables. These control variables were selected because either they have been previously shown to be related to psychological distress (i.e., gender) or represent proxies for individual differences in acculturation and social status (i.e., generation status, language, schooling). In Step 2, perceived race-based discrimination (z-score) was entered. In Step 3, social cohesion and ethnic density (both zscores) were entered as a set. These two steps allowed us to test for whether discrimination is a significant predictor of psychological distress, and whether it remains so after accounting for objective and subjective social context. In Step 4, we entered three two-way interaction terms: discrimination ⫻ ethnic density, social cohesion ⫻ ethnic density, and discrimination ⫻ social cohesion, and the three-way interaction among discrimination ⫻ ethnic density ⫻ social cohesion. All interactions were entered in a single step because we were not interested in interpreting how the coefficients for the two-way interactions changed upon entering the three-way interaction. All interactions terms were computed by multiplying the z-

110

SYED AND JUAN

scores of the main effects as recommend by Aiken and West (1991). The analysis described above was conducted nine times: three models each for Vietnamese American, Chinese American, and Filipino American participants. The only difference between the three models within each ethnic group was the measure of ethnic density: ethnicspecific density, Asian American density, and People of Color density. The models were run separately for each ethnic groups because we were interested in ethnic-specific findings and not differences between ethnic groups. All analyses were conducted using the SAS PROC SURVEYREG procedure which can accommodate the complex survey design used in the NLAAS. All reported regression coefficients are unstandardized. Weighted descriptive statistics for all study variables, separated by ethnic group, are reported in Table 1. Missing data was very low (⬍1%) for all variables except social cohesion (6% for Vietnamese, 5% for Chinese, 2% for Filipino). The results of Little’s MCAR test were not significant for any of the groups, indicating that using listwise deletion was appropriate. Thus, the final Ns for analysis were 478 for Vietnamese, 566 for Chinese, and 493 for Filipino. Vietnamese Americans Vietnamese American density. In Step 1 none of the control variables were predictive of distress. Discrimination, added in Step 2, was a

significant predictor, b ⫽ .031, SE ⫽ .007, p ⬍ .001, indicating that higher levels of perceived discrimination were associated with greater psychological distress. In Step 3, neither social cohesion nor ethnic density were significant predictors, and discrimination remained a significant predictor of distress with these other variables in the model, b ⫽ .028, SE ⫽ .007, p ⬍ .001. The next step examined the two- and three-way interactions among discrimination, ethnic density, and social cohesion. None of the interactions were significant, and the main effect of discrimination was still significant after adding the interactions. Estimates for all predictors in Step 4 of the model are reported in Table 3, Model A. Asian American density. Results from the model using Asian American density instead of Vietnamese density were similar until Step 4. In addition to the main effect for discrimination, b ⫽ .031, SE ⫽ .006, p ⬍ .001, the interaction between social cohesion ⫻ ethnic density was significant, b ⫽ .017, SE ⫽ .005, p ⬍ .01. Estimates for all predictors in Step 4 of the model are reported in Table 3, Model B. The significant interaction between social cohesion and ethnic density is plotted in Figure 1. Plotted values are the minimum and maximum values for social cohesion and the minimum, maximum, and average values for Asian American density. Superimposed on the figure is the K10 cutoff score for “medium risk,” which is a score of 16 in the original metric and 1.19 in the log-transformed metric. As can be seen in the

Table 1 Weighted Estimates for Demographic and Study Variables Vietnamese American (n ⫽ 478)

Age Female Foreign-born Years of Schooling English Fluency Psychological Distress Psych. Distress Log Trans. Race-based Discrimination Social Cohesion Ethnic-specific Density Asian American Density People of Color Density

M

SE

Range

40.99 51% 96% 12.23 2.11 13.05 1.09 1.63 3.19 0.030 0.140 0.380

0.77

18–95

Chinese American (n ⫽ 566) M

41.13 52% 80% 0.25 4–17 13.76 0.06 1–4 2.63 0.19 10–44 13.90 0.01 1.00–1.64 1.12 0.04 1.00–4.00 1.83 0.03 1.00–4.00 3.11 0.004 0.002–0.062 0.062 0.018 0.012–0.616 0.185 0.009 0.086–0.787 0.467

SE

Range

1.00

18–85

Filipino American (n ⫽ 493) M

41.67 53% 69% 0.22 4–17 13.75 0.09 1⫺4 3.36 0.17 10–37 12.90 0.01 1.00–1.57 1.10 0.03 1.00–4.00 1.64 0.04 1.00–4.00 3.25 0.007 0.003–0.207 0.077 0.017 0.012–0.616 0.216 0.010 0.090–0.787 0.468

SE

Range

0.80

18–89

0.23 4–17 0.05 1–4 0.12 10–34 0.01 1.00–1.53 0.03 1.00–4.00 0.05 1.00–4.00 0.021 0.001–0.269 0.044 0.015–0.616 0.035 0.090–0.787


111

Table 2 Frequency of Responses for Discrimination and Psychological Distress Vietnamese American (n ⫽ 478)

Chinese American (n ⫽ 566)

Filipino American (n ⫽ 493)

47.14% 31.23% 17.57% 4.06%

25.83% 45.18% 27.43% 1.56%

41.96% 19.93% 36.31% 1.80%

80.61% 17.36% 2.03%

73.36% 25.31% 1.33%

80.57% 18.88% 0.55%

Perceived Discriminationa 1.0 1.01–2.0 2.01–3.0 3.01–4.0 Psychological Distress 10–15 – “no or minimal risk” 16–29 – “medium risk” 30–50 – “high risk” a

1 ⫽ never, 2 ⫽ rarely, 3 ⫽ sometime, 4 ⫽ often.

figure, the combination of high social cohesion in areas highly dense with Asian Americans puts Vietnamese Americans within the risk zone. In contrast, feeling minimal levels of social cohesion in areas with very few Asian Americans places Vietnamese Americans right on the brink of potential risk. People of Color density. Results from the model using People of Color density were nearly identical to the previous model. The interaction between social cohesion ⫻ ethnic density was significant, b ⫽ .017, SE ⫽ .006, p ⬍ .01, and of the same magnitude as with Asian American density, and follow-up analyses of the interactions indicated the same patterns. Es-

timates for all predictors in Step 4 of the model are reported in Table 3, Model C. Chinese Americans Chinese American density. In Step 1 none of the control variables were predictive of distress. Discrimination, added in Step 2, was a significant and positive predictor, b ⫽ .017, SE ⫽ .006, p ⬍ .01. In Step 3, ethnic density and social cohesion were not significant predictors, and discrimination remained significant, b ⫽ .017, SE ⫽ .006, p ⬍ .01. Adding the two- and three-way interactions in Step 4 indicated two significant interactions.

Table 3 Weighted Regression Coefficients for Final Models Predicting Psychological Distress for Vietnamese American Sample

Intercept Age Female Schooling English Fluency Discrimination Social Cohesion Ethnic Density Discrimination ⫻ Density Social Cohesion ⫻ Discrimination Social Cohesion ⫻ Density Social Coh. ⫻ Discrim. ⫻ Density ⴱⴱ

p ⬍ .01.

ⴱⴱⴱ

p ⬍ .001.

Model A:

Model B:

Model C:

Vietnamese density

Asian American density

People of Color density

B

SE

B

SE

B

SE

1.086 .010 .014 .004 .002 .029ⴱⴱⴱ ⫺.007 .002 .003 .002 .006 .001

.009 .008 .017 .008 .008 .006 .007 .007 .007 .006 .006 .006

1.089 .010 .013 .003 .002 .031ⴱⴱⴱ ⫺.004 .010 .012 .002 .017ⴱⴱ .003

.010 .008 .017 .007 .008 .006 .006 .007 .006 .006 .005 .005

1.085 .011 .017 .005 .002 .026ⴱⴱⴱ ⫺.005 .004 .014 .001 .017ⴱⴱ ⫺.006

.009 .008 .017 .008 .008 .006 .006 .008 .007 .006 .006 .007

112

SYED AND JUAN

Figure 1. Interaction between Asian American ethnic density and social cohesion for Vietnamese Americans.

The two-way interaction between discrimination and ethnic density was significant, b ⫽ .013, SE ⫽ .004, p ⬍ .01, but so was the three-way interaction between discrimination,

ethnic density, and social cohesion, b ⫽ .013, SE ⫽ .005, p ⬍ .01. Estimates for all predictors in Step 4 of the model are reported in Table 4, Model A.

Table 4 Weighted Regression Coefficients for Final Models Predicting Psychological Distress for Chinese American Sample

Intercept Age Female Immigrant Schooling English Fluency Discrimination Social Cohesion Ethnic Density Discrimination ⫻ Density Social Cohesion ⫻ Discrimination Social Cohesion ⫻ Density Social Coh. ⫻ Discrim. ⫻ Density ⴱⴱ

p ⬍ .01.

Model A:

Model B:

Model C:

Chinese density



B

SE

B

SE

B

SE

1.105 ⫺.007 .031 .003 .001 ⫺.005 .017ⴱⴱ ⫺.001 ⫺.003 .013ⴱⴱ ⫺.002 .002 .013ⴱⴱ

.019 .007 .017 .017 .007 .009 .005 .004 .007 .004 .006 .005 .005

1.108 ⫺.006 .031 ⫺.001 .001 ⫺.004 .017ⴱⴱ ⫺.001 ⫺.003 .008 ⫺.002 .001 .013

.018 .007 .017 .016 .007 .009 .006 .004 .004 .006 .005 .004 .005

1.110 ⫺.006 .027 ⫺.002 ⫺.001 ⫺.004 .018ⴱⴱ ⫺.003 ⫺.010 .008 ⫺.005 .003 .002

.019 .006 .018 .017 .007 .009 .006 .004 .007 .006 .006 .006 .008


113

Figure 2. Conditional three-way interaction among Chinese density, discrimination, and social cohesion for Chinese Americans.

The significant three-way interaction is plotted in the three-panel Figure 2. Plotted values are minimum and maximum levels of discrimination and minimum, maximum, and average levels of social cohesion are plotted separately at the minimum, maximum, and average levels of Chinese American density. Using the superimposed cutoff line for “medium risk” as a guide, the first two panels, which display the findings for minimum and average levels of Chinese American density, indicate that distress is low and not dramatically different according to the interaction of discrimination and social cohesion. The final panel illustrating the results for maximum density indicates a very different situation. First, maximum discrimination places Chinese Americans at “medium risk” in a highly dense context regardless of social cohesion. Taking social cohesion into account, it can be seen that maximum cohesion paired with maximum discrimination is associated with the highest levels of psychological distress, 1.37 on the log transformed scale, which corresponds with a score of 24 on the original scale. Asian American density. Results from the model using Asian American density instead of Chinese density were similar, although gener-

ally the effects were weaker. Indeed, discrimination continued to be a significant predictor of distress, but the discrimination ⫻ ethnic density ⫻ social cohesion interaction, despite being of similar magnitude as to previous model (b ⫽ .013), failed to reach significance at the .01 level. Estimates for all predictors in Step 4 of the model are reported in Table 4, Model B. People of Color density. Results from the model using People of Color density were similar but even weaker than the Asian American density model. Discrimination was once again the sole predictor of psychological distress. None of the interactions were significant in Step 4, and the magnitude of the discrimination ⫻ ethnic density ⫻ social cohesion interaction was much lower than in the previous models. (b ⫽ .002). Estimates for all predictors in Step 4 of the model are reported in Table 4, Model C. Filipino Americans Filipino American density. In Step 1 age was a significant negative predictor of distress, b ⫽ ⫺.015, SE ⫽ .004, p ⬍ .01. Discrimination, added in Step 2, was a significant and positive

114

SYED AND JUAN

predictor, b ⫽ 0.021, SE ⫽ .005, p ⬍ .001. In Step 3, the main effects of discrimination was still significant, b ⫽ 0.019, SE ⫽ .006, p ⬍ .01, but ethnic density and social cohesion were not. The addition of the two- and three-way interactions in Step 4 did not yield any significant effects. Estimates for all predictors in Step 4 of the model are reported in Table 5, Model A. Asian American density. Results from the model using Asian American density instead of Filipino density were nearly identical, with only age and discrimination as significant predictors of distress. Estimates for all predictors in Step 4 of the model are reported in Table 5, Model B. People of Color density. Results from the model using People of Color density were the same as the previous two models. The only significant predictors of psychological distress were age and discrimination. Estimates for all predictors in Step 4 of the model are reported in Table 5, Model C. Discussion The purpose of the present study was to examine the role of the social context in the association between perceived discrimination and psychological distress in a nationally representative sample of Vietnamese, Chinese, and Filipino Americans. In general, the findings sug-

gest that considering the social context can be useful for understanding how discrimination is related to psychological distress (cf. Bronfenbrenner, 1979; Garcıá Coll et al., 1996). Although many researchers have suggested that the effects of discrimination may vary depending on the social context (e.g., Welch et al., 2001), few studies have empirically tested this idea. Below we highlight some of the key findings and ponder the role and measurement of ethnic density and social context for future research. As captured by the title of Huynh and Fuligni’s (2010) recent article, “discrimination hurts.” Perceived discrimination was a significant predictor of psychological distress for all three ethnic groups. This finding is consistent with a growing body of literature documenting the psychological ramifications of experiencing discrimination among Asian Americans (e.g., Gee, 2002; Lam, 2007; Lee & Ahn, 2011; Mossakowski, 2003). Moreover, the association remained significant even after controlling for ethnic density and social cohesion. Our primary interest in the current study, however, was to build on this past research by examining how ethnic density and social cohesion moderated the association between perceived discrimination and psychological distress. The short and unsatisfying answer to this question is, “it de-

Table 5 Weighted Regression Coefficients for Final Models Predicting Psychological Distress for Filipino American Sample

Intercept Age Female Immigrant Schooling English Fluency Discrimination Social Cohesion Ethnic Density Discrimination ⫻ Density Social Cohesion ⫻ Discrimination Social Cohesion ⫻ Density Social Coh. ⫻ Discrim. ⫻ Density ⴱⴱ

p ⬍ .01.

Model A:

Model B:

Model C:

Filipino density



B

SE

B

SE

B

SE

1.110 ⫺.013ⴱⴱ ⫺.001 ⫺.023 ⫺.017 ⫺.005 .020ⴱⴱ ⫺.015 .001 .007 ⫺.003 .001 .001

.013 .005 .012 .011 .0006 .006 .006 .006 .004 .006 .005 .006 .003

1.110 ⫺.013ⴱⴱ ⫺.001 ⫺.025 ⫺.017 ⫺.006 .020ⴱⴱ ⫺.015 ⫺.001 .009 ⫺.003 ⫺.001 .001

.013 .005 .012 .012 .006 .006 .007 .006 .003 .008 .005 .008 .004

1.120 ⫺.012ⴱⴱ ⫺.002 ⫺.027 ⫺.017 ⫺.006 .019ⴱⴱ ⫺.015 ⫺.004 .007 ⫺.002 .001 .001

.012 .004 .012 .010 .006 .006 .006 .006 .003 .006 .005 .008 .003


pends.” The findings suggest that the role of social context depends on the ethnic group and the operationalization of density. We discuss the findings for each ethnic group in turn. The only significant finding for Vietnamese Americans beyond the main effect of discrimination was the interaction between ethnic density and social cohesion, which indicated different associations between social cohesion and distress depending on ethnic density. In extremely dense areas, higher social cohesion is related to greater distress, to the point where it places them at medium risk for a psychological disorder. The opposite was observed for areas with very low density: high cohesion in these settings is related to very little distress, whereas low cohesion is related to levels right below the cutoff for medium risk. This interaction, however, was only significant for the Asian American and People of Color density models, not for the Vietnamese density model. These findings highlight the utility of examining different conceptualizations of ethnic density and suggest that Vietnamese Americans living in areas dense with other Asian Americans and People of Color can be potentially problematic. These findings may have to do with the unique contexts that Vietnamese Americans inhabit. Compared with the other two ethnic groups in the study, they are relatively more recent immigrants, came to the U.S. under different circumstances, and are generally from lower SES backgrounds (Gee & Ponce, 2010; Ngo & Lee, 2007). The finding that feeling very connected to their neighborhoods when living in areas that are denser with Asian Americans is related to greater psychological distress could be due to some Vietnamese individuals making comparisons between themselves and other Asian Americans that they perceive as “better off.” This is just speculation at this point but suggests that examining interethnic attitudes among Asian Americans could be a fruitful avenue for future research. Indeed, it is possible that in such contexts these individuals are treated poorly by other Asian Americans or People of Color, even if they do not perceive the treatment as race-based discrimination. Particularly because the majority of Vietnamese American adults are immigrants or refugees, other more established Asian Americans may be prone to act disparagingly toward them. It is also possible that the findings are associated with the fact

115

that Vietnamese Americans, on average, inhabit more challenging lives than do many other Asian Americans. Thus, when feeling especially connected to communities with many other Asian Americans they may be frequently reminded that they are not sharing in the relative successes of some other Asian Americans. For Chinese Americans there was a significant two-way interaction for discrimination ⫻ density, but this was qualified by a significant three-way interaction among discrimination, density, and social cohesion. This interaction, however, was only significant for the Chinese density model. At very low levels of Chinese density, there is some variation in the association among social cohesion, discrimination, and distress, but all combinations correspond with very low levels of distress, all well below the cut-off for medium risk. At average levels of density, greater discrimination is associated with higher distress, with social cohesion playing virtually no role whatsoever. However, at very high levels of Chinese density is where we observed implications for distress. Experiencing high discrimination in highly dense contexts places Chinese Americans at medium risk for a psychological disorder regardless of their levels of social cohesion. That said, the risk is even greater at high levels of social cohesion. Indeed, across all models tested in the current study, the combination of high discrimination, high Chinese density, and high social cohesion was by far related to the highest levels of psychological distress. This may be because when Chinese Americans are in areas with many other Chinese and are particularly embedded in the community, experiencing discrimination may be a more disruptive experience because a core aspect of their life is being threatened. Indeed, a study of peer victimization among sixth grade students found that victimization had the most pronounced negative psychological effects in classrooms where there were many others who shared their ethnicity (Bellmore, Witkow, Graham, & Juvonen, 2004). Although that study was conducted with a much younger age group than the current study, the same underlying phenomenon may be at work: victimization in presumably “safe” contexts may be particularly negative. Additionally, as discussed by Juang and Alvarez (2011), having a greater proportion of coethnics in a given area corresponds with greater within-group variability and the poten-

116

SYED AND JUAN

tial for heightened within-group discrimination (e.g., between first- and second-generation). Future research that assesses the source of the discrimination, in terms of the ethnic background of the perpetrator, would be useful to understand this phenomenon more deeply. Apart from discrimination, none of the main effects or interactions were significant for Filipino Americans for any of the three different operationalizations of density. The only other variable that was significant was age, which indicated that younger adults experienced lower levels of distress than older adults. Why did neither ethnic density nor social cohesion predict distress on their own or in conjunction with discrimination? The answer to this question is not immediately clear and may not be easily attainable using the NLAAS dataset. Furthermore, it is important to remember that, in terms of Bronfenbrenner’s ecological theory, we only examined a limited set of microsystem factors. It is possible that for Filipino Americans, other microsystem factors, such as family relations or work, or even broader factors aligned with the exo- or macrosystem, are more relevant for discrimination experiences. In sum, the story about how ethnic density and social cohesion moderate the association between perceived discrimination and psychological distress seems to vary depending on the ethnic group in question. For the most part, the role of ethnic density is better understood when also examining individuals’ subjective feelings about their social context, such as social cohesion. This finding indicates that future research should continue to investigate the role of dynamic social context variables that can vary among individuals located in the same context, rather than assume homogeneity of experiences due to residing in a particular location. This is not to say that we believe the study of ethnic density to be a lost cause. Indeed, there is much research to be done on the topic, to which we now turn. Conceptualizing Ethnic Density Three major strengths of the current analysis are that it is based on a nationally representative sample, relies on U.S. Census data to measure objective ethnic density, and includes a subjective assessment of social context. These aspects of the study allowed us to understand how, on

average, both objective and subjective social contexts moderate the association between discrimination and distress. An additional strength of the current analysis is that we conceptualized ethnic density at three levels: ethnic-specific, pan-ethnic, and People of Color, thereby allowing us to address questions about what “type” of density matters. Although the Census-based approach we used in the current study is a significant improvement over previous research, we do not believe it should be the gold standard for how studies examine ethnic density, nor does it offer the definitive word on how ethnic density matters for the association between discrimination and psychological distress. Two of the major analytic strengths of this study—a nationally representative sample and careful measurement of density— can simultaneously be considered as limitations. We explicate these limitations below, and propose that different conceptualizations of ethnic density are positioned to address different questions about how density matters for individuals’ psychological experiences. Our analysis is useful for understanding how, on average, ethnic density plays a role in the association between discrimination and psychological distress. The limitation of this approach is that it does not adequately acknowledge the local social context. That is, gains in generality come at the expense of specificity. For example, a qualitative study of two groups of Vietnamese Americans in two distinct parts of Southern California, one who lived in a culturally and politically active ethnic enclave and one who did not, found that youth who lived in the enclave demonstrated greater integration of their ethnicity with other aspects of their lives (e.g., career), than did those youth who lived outside of it (Vo-Jutabha, Dinh, McHale, & Valsiner, 2009). Thus, the local ethnic context might be particularly important for how individuals make meaning of their lives. Using more qualitative and interpretative methods would also permit a deeper understanding of how the association between discrimination and distress is contoured by the historical and economic contexts of different Asian American ethnic groups. As discussed in the Introduction, there are both objective and subjective aspects of individuals’ social contexts (Bronfenbrenner, 1979). Unfortunately, scant research has examined the subjective component. We attempted to address this gap by including a measure of


social cohesion, or the degree to which individuals feel connected to their neighborhoods. While this was a useful and informative measure, there are a variety of methods for assessing subjective social context. In particular, ethnic density itself can be understood in both objective and subjective terms. Defining ethnic density as the percentage of individuals from a given ethnic group as reported in the U.S. Census affords an excellent measure of the objective ethnic density. Considering density as objective implies that all individuals therein perceive the context in the same way. This may not, however, be an accurate portrayal of how contexts operate. Indeed, more research needs to be conducted on the subjective ethnic density, or how individuals uniquely perceive the ethnic representation of where they live (Juang & Alvarez, 2011). Conceptualizing density as subjective allows for the possibility that some individuals may perceive a county as comprising a very larger number of Vietnamese, whereas other individuals may perceive the same county as comprising a dearth of Vietnamese. The existence of these “dueling contexts” has been highlighted in the literature (Juang, Nguyen, & Lin, 2006; Syed, Azmitia, & Phinney, 2007), but seldom examined empirically. Taken together, the results suggest that when ethnic density does matter, it matters most when conceptualized as ethnic-specific density and the least when conceptualized as other People of Color. While it may certainly be the case that individuals from different ethnic minority groups can band together to enhance their social power, in the context of discriminatory experiences that are directed to one’s own ethnic group such alliances may make little difference (see also Suyemoto & Fox Tree, 2006, for a discussion about the challenges of alliances among People of Color). Researchers interested in the role of ethnic density should give serious consideration to how density is operationalized, be it ethnic-specific, pan-ethnic, People of Color, or some other classification system. Limitations and Future Research Despite the strengths of the current study, there are several limitations to consider when interpreting the results. First, we only examined a single measure of generalized psychological distress. Discrimination has been linked to nu-

117

merous psychological and physical health outcomes (Lee & Ahn, 2011; Okazaki, 2009; Paradies, 2006). Moreover, a recent meta-analysis of the mental health implications of discrimination among Asian Americans indicated that effect sizes were larger for depression and anxiety than they were for overall distress (Lee & Ahn, 2011). Thus, the use of a general distress measure in the present study likely resulted in attenuated estimates. Also, although there are currently no available data to the contrary, it is possible that the particular measure of distress that we used, the K10, does not fully or accurately capture the nature of distress among Asian Americans. The findings are also limited to the adult population, as no children or adolescents were included in the NLAAS. Discrimination among Asian American adolescents has recently been highlighted in the literature and has been shown to have developmental consequences (Greene et al., 2006; Juang & Cookston, 2009). At this time it is unclear whether the findings in the current study can generalize to younger populations. In addition, the sample is cross-sectional in nature, which limits interpreting the directionality of effect between discrimination and psychological distress. That is, those who report high distress may be more susceptible to the negative effects of discrimination. However, longitudinal studies such as Greene et al. (2006) suggest the discrimination precedes distress. When considering these findings, it is also important to note that we did not model other aspects of the social environment that may affect conditions within one’s county. For example, previous research shows that socioeconomic status is also predictive of racial differences in mental health (e.g., Williams, Takeuchi, & Adair, 1992). In addition, other research finds that socioeconomic status may augment racial differences in psychological distress. In a study of African Americans and Whites, Schulz et al. (2000) found that higher levels of psychological distress reported by African Americans compared to Whites became insignificant when considering those who lived in low poverty areas. Asian Americans also vary in terms of socioeconomic status, and this may be reflected in their experiences with mental health and discrimination. Portes and Zhou (1993) suggest that upon immigration, Asian

118

SYED AND JUAN

Americans settle into different communities, ranging from lower-income ethnic enclaves to middle- and high-class, White neighborhoods. It may be that these variations in socioeconomic status differentially affect the association of perceived discrimination and psychological distress. Finally, in this study we were particularly interested in aspects of Asian Americans’ social context and did not consider individual factors. For example, a number of studies have explored how ethnic identity moderates the association between discrimination and mental health, including one using the NLAAS dataset (Yip, Gee, & Takeuchi, 2008), although the findings have been inconsistent. Other important personal factors to explore in the future would be levels of acculturation, coping skills, and personality traits that may be related to how individuals perceive and react to discrimination. Likewise, we did not have information regarding characteristics of the perpetrators of discrimination, such as ethnicity or generational status. Indeed, examining in detail the episodic, interactional nature of discrimination, through narrative methods for example, would be a useful area for future research. Conclusion Recent research has brought attention to the fact that Asian Americans do indeed experience discrimination and that these experiences have consequences for their mental and physical health (Lee & Anh, 2011). Thus, it is important to understand factors that protect Asian Americans from these deleterious consequences. In the present study, we investigated how ethnic density and perceptions of social context influence the association between perceived discrimination and psychological distress using a nationally representative sample of Asian American adults. The findings of the study suggest that how ethnic density matters depends on the ethnic group and how density is defined, and that density must be considered in tandem with other social context factors such as social cohesion. Thus, determining factors that can lead to improved lives for Asian Americans is a complex task that calls for careful research on individuals’ social contexts. The findings of this study contribute to this goal.

References Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage. Alegrıá, M., Vila, D., Woo, M., Canino, G., Takeuchi, D., Vera, M., . . . Shrout, P. (2004). Cultural relevance and equivalence in the NLAAS instrument: Integrating etic and emic in the development of cross-cultural measures for a psychiatric epidemiology and services study of Latinos. International Journal of Methods in Psychiatric Research, 13, 270 –288. doi:10.1002/mpr.181 Alvarez, A. N. (2009). Racism: “It isn’t fair.” In N. Tewari & A. N. Alvarez (Eds.), Asian American psychology: Current perspectives (pp. 399 – 420). New York, NY: Psychology Press. Alvarez, A. N., Juang, L., & Liang, C. T. H. (2006). Asian Americans and racism: When bad things happen to ‘model minorities.’ Cultural Diversity and Ethnic Minority Psychology, 12, 477– 492. doi:10.1037/1099-9809.12.3.477 Andrews, G., & Slade, T. (2001). Interpreting scores on the Kessler Psychological Distress Scale (K10). Australian and New Zealand Journal of Public Health, 25, 494 – 497. doi:10.1111/j.1467842X.2001.tb00310.x Australian Department of Human Services. (2002). The Kessler Psychological Distress Scale. Brief Reports, No. 2002–14. Centre for Population Studies in Epidemiology. Retrieved from http:// health.adelaide.edu.au/pros/ Bearman, J., Jones, J., & Udry, R. (1997). The National Longitudinal Study of Adolescent Health. Chapel Hill, NC: Carolina Population Center. Bećares, L., Nazroo, J., & Stafford, M. (2009). The buffering effects of ethnic density on experienced racism and health. Health & Place, 15, 700 –708. doi:10.1016/j.healthplace.2008.10.008 Bellmore, A. D., Witkow, M. R., Graham, S., & Juvonen, J. (2004). Beyond the individual: The impact of ethnic context and classroom behavioral norms on victims’ adjustment. Developmental Psychology, 40, 1159 –1172. doi:10.1037/00121649.40.6.1159 Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, MA: Harvard University Press. Craig-Henderson, K., & Sloan, L. R. (2003). After the hate: Helping psychologists help victims of racist hate crime. Clinical Psychology: Science and Practice, 10, 481– 490. doi:10.1093/clipsy .bpg048 Garcıá Coll, C., Lamberty, G., Jenkins, R., McAdoo, H. P., Crnic, K., Wasik, B. H., & Garcia, H. V. (1996). An integrative model for the study of developmental competencies in minority children.


Child Development, 67, 1891–1914. doi:10.2307/ 1131600 Gee, G. C. (2002). A multilevel analysis of the relationship between institutional and individual racial discrimination and health status. American Journal of Public Health, 92, 615– 623. doi:10.2105/ AJPH.92.4.615 Gee, G. C., & Ponce, N. (2010). Associations between racial discrimination, limited English proficiency, and health-related quality of life among 6 Asian ethnic groups in California. American Journal of Public Health, 100, 888 – 895. doi:10.2105/ AJPH.2009.178012 Gee, G. C., Spencer, M. S., Chen, J., & Takeuchi, D. (2007). A nationwide study of discrimination and chronic health conditions among Asian Americans. American Journal of Public Health, 97, 1275–1282. doi:10.2105/AJPH.2006.091827 Gil, A., & Vega, W. (1996). Two different worlds: Acculturation stress and adaptation among Cuban and Nicaraguan families. Journal of Social and Personal Relationships, 13, 435– 456. doi: 10.1177/0265407596133008 Greene, M. L., Way, N., & Pahl, K. (2006). Trajectories of perceived adult and peer discrimination among Black, Latino, and Asian American adolescents: Patterns and psychological correlates. Developmental Psychology, 42, 218 –238. doi: 10.1037/0012-1649.42.2.218 Halpern, D. (1993). Minorities and mental health. Social Science & Medicine, 36, 597– 607. doi: 10.1016/0277-9536(93)90056-A Heeringa, S., Wagner, J., Torres, M., Duan, N. H., Adams, T., & Berglund, P. (2004). Sample designs and sampling methods for the Collaborative Psychiatric Epidemiology Studies (CPES). International Journal of Methods in Psychiatric Research, 13, 221–240. doi:10.1002/mpr.179 Hunt, M. O., Wise, L. A., Jipguep, M.-C., Cozier, Y. C., & Rosenberg, L. (2007). Neighborhood racial composition and perceptions of racial discrimination: Evidence from the Black Women’s Health Study. Social Psychology Quarterly, 70, 272–289. doi:10.1177/019027250707000306 Huynh, V. W., & Fuligni, A. J. (2010). Discrimination hurts: The academic, psychological, and physical well-being of adolescents. Journal of Research on Adolescence, 20, 916 –941. doi:10.1111/ j.1532-7795.2010.00670.x Juang, L. P., & Alvarez, A. N. (2010). Discrimination and adjustment among Chinese American adolescents: Family conflict and family cohesion as vulnerability and protective factors. American Journal of Public Health, 100, 2403–2409. Juang, L. P., & Alvarez, A. N. (2011). Family, school, and neighborhood: Links to Chinese American adolescent perceptions of racial/ethnic

119

discrimination. Asian American Journal of Psychology, 2, 1–12. doi:10.1037/a0023107 Juang, L. P., & Cookston, J. T. (2009). Acculturation, discrimination, and depressive symptoms among Chinese American adolescents: A longitudinal study. The Journal of Primary Prevention, 30, 475– 496. doi:10.1007/s10935-009-0177-9 Juang, L. P., Nguyen, H. H., & Lin, Y. (2006). The ethnic identity, other-group attitudes, and psychosocial functioning of Asian American emerging adults from two contexts. Journal of Adolescent Research, 21, 542–568. doi:10.1177/ 0743558406291691 Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S.L. T., . . . Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and trends in nonspecific psychological distress. Psychological Medicine, 32, 959 –976. doi: 10.1017/S0033291702006074 Lam, B. T. (2007). Impact of perceived racial discrimination and collective self-esteem on psychological distress among Vietnamese-American college students: Sense of coherence as mediator. American Journal of Orthopsychiatry, 77, 370 – 376. doi:10.1037/0002-9432.77.3.370 Lee, D. L., & Ahn, S. (2011). Racial discrimination and Asian mental health: A meta-analysis. The Counseling Psychologist, 39, 463– 489. doi: 10.1177/0011000010381791 Lee, R. M. (2003). Do ethnic identity and othergroup orientation protect against discrimination for Asian Americans? Journal of Counseling Psychology, 50, 133–141. doi:10.1037/0022-0167.50 .2.133 Mair, C., Roux, A. V. D., Osypuk, T. L., Rapp, S. R., Seeman, T., & Watson, K. E. (2010). Is neighborhood racial/ethnic composition associated with depressive symptoms? The multiethnic study of atherosclerosis. Social Science & Medicine, 71, 541–550. doi:10.1016/j.socscimed .2010.04.014 Mossakowski, K. N. (2003). Coping with perceived discrimination: Does ethnic identity protect mental health? Journal of Health and Social Behavior, 44, 318 –331. doi:10.2307/1519782 Murry, V. M., Berkel, C., Gaylord-Harden, N. K., Copeland-Linder, N., & Nation, M. (2011). Neighborhood poverty and adolescent development. Journal of Research on Adolescence, 21, 114 –128. doi:10.1111/j.1532-7795.2010.00718.x National Asian Pacific American Legal Consortium. (2003). Remembering: A ten year retrospective. Washington, DC: Author. National Institute of Mental Health [NIMH]. (1994). Cooperative agreement for a multi-site study of mental health service use, need, outcomes, and

120

SYED AND JUAN

costs in child and adolescent populations (UNOCAP). Rockville, MD: Author. Ngo, B., & Lee, S. J. (2007). Complicating the image of model minority success: A review of Southeast Asian American education. Review of Educational Research, 77, 415– 453. doi:10 .3102/0034654307309918 Okazaki, S. (2009). Impact of racism on ethnic minority mental health. Perspective on Psychological Science, 4, 103–107. doi:10.1111/j.1745-6924 .2009.01099.x Paradies, Y. (2006). A systematic review of empirical research on self-reported racism and health. International Journal of Epidemiology, 25, 888 –901. doi:10.1093/ije/dyl056 Portes, A., & Zhou, M. (1993). The new second generation: Segmented assimilation and its variants. Annals of American Academy of Political and Social Science, 530, 74 –96. doi:10.1177/ 0002716293530001006 Reidpath, D. D. (2003). “Love thy neighbour”–It’s good for your health: A study of racial homogeneity, mortality and social cohesion in the United States. Social Science & Medicine, 57, 253–261. doi:10.1016/S0277-9536(02)00344-1 Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918 –924. doi:10.1126/science.277.5328.918 Schulz, A., Williams, D., Israel, B., Becker, A., Parker, E., James, S. A., & Jackson, J. (2000). Unfair treatment, neighborhood effects, and mental health in the Detroit metropolitan area. Journal of Health and Social Behavior, 41, 314 –332. doi: 10.2307/2676323 Seaton, E. K., & Yip, T. (2009). School and neighborhood contexts, perceptions of racial discrimination, and psychological well-being among African American adolescents. Journal of Youth and Adolescence, 38, 153–163. doi:10.1007/s10964-0089356-x Sue, D. W., Bucceri, J., Lin, A. I., Nadal, K. L., & Torino, G. C. (2007). Racial microaggressions and the Asian American experience. Cultural Diversity and Ethnic Minority Psychology, 13, 72– 81. doi: 10.1037/1099-9809.13.1.72 Suyemoto, K. L., & Fox Tree, C. A. (2006). Building bridges across differences to meet social action goals: Being and creating allies among People of Color. American Journal of Community Psychology, 37, 237–246. doi:10.1007/s10464-006-9048-1 Syed, M., Azmitia, M., & Phinney, J. S. (2007). Stability and change in ethnic identity among La-

tino emerging adults in two contexts. Identity: An International Journal of Theory and Research, 7, 155–178. doi:10.1080/15283480701326117 Tuan, M. (1998). Forever foreigners or honorary Whites? New Brunswick, NJ: Routledge Press. United States Census Bureau. (2000). American FactFinder. Retrieved June 1, 2010, from http:// factfinder.census.gov/home/saff/main.html United States Census Bureau. (2008). Population estimates program. Available from http://www.census.gov/popest/estimates.php Vo-Jutabha, E. D., Dinh, K. T., McHale, J. P., & Valsiner, J. (2009). A qualitative analysis of Vietnamese adolescent identity exploration within and outside an ethnic enclave. Journal of Youth and Adolescence, 38, 672– 690. doi:10.1007/s10964008-9365-9 Welch, S., Sigelman, L., Bledsoe, T., & Combs, M. (2001). Race and place: Race relations in an American city. Cambridge, UK: Cambridge University Press. Williams, D. R., Takeuchi, D. T., & Adair, R. K. (1992). Socioeconomic status and psychiatric disorder among Blacks and Whites. Social Forces, 71, 179 –194. doi:10.2307/2579972 Wong, C. A., Eccles, J. S., & Sameroff, A. (2003). The influence of ethnic discrimination and ethnic identification on African American adolescents’ school and socioemotional adjustment. Journal of Personality, 71, 1197–1232. doi:10.1111/14676494.7106012 Ying, Y.-W., Lee, P. A., & Tsai, J. L. (2000). Cultural orientation and racial discrimination: Predictors of coherence in Chinese American young adults. Journal of Community Psychology, 28, 427– 442. doi:10.1002/1520-6629(200007)28:4⬍ 427::AID-JCOP5⬎3.0.CO;2-F Yip, T., Gee, G. C., & Takeuchi, D. T. (2008). Racial discrimination and psychological distress: The impact of ethnic identity and age among immigrant and United States-born Asian adults. Developmental Psychology, 44, 787– 800. doi:10.1037/00121649.44.3.787 Yoo, H. C., & Lee, R. M. (2008). Does ethnic identity buffer or exacerbate the effects of frequent racial discrimination on situational well-being of Asian Americans? Journal of Counseling Psychology, 55, 63–74. doi:10.1037/0022-0167.55.1.63 Received January 28, 2011 Revision received June 3, 2011 Accepted July 25, 2011 䡲