Poster #177 - A Latent Class Approach to Discrimination: Attributes and Adjustment among Ethnically-Racially Diverse Adolescents

• In Event: 1-005 - Poster Session 01
  In Poster Session: PS 01 Section - Race, Ethnicity, Culture, Context

Integrative Statement

The co-occurrence of multiple attributes of daily discrimination among U.S. adolescents is a critical yet understudied phenomenon. Although prior work with adolescents documents links between discrimination and maladjustment (e.g., depressive symptoms; Seaton et al., 2010; Smetana et al., 2006), limited empirical work has considered the interactive role of discrimination experiences based on multiple attributions. Furthermore, much discrimination work has largely centered on racial discrimination, with narrow attention to other reasons for discrimination. Given that less is known about the co-occurrence of multiple discrimination experiences among adolescents, a focus on identifying patterns of discrimination attributes and links to adjustment for this at-risk population is warranted.

Scholars have used intersectionality as a theoretical model to understand the complexities of discrimination experiences. Intersectionality perspectives underscore collective and simultaneous interactions based on multiple marginalized domains or social identities (e.g., ancestry; Andersen et al., 2010; Bowleg, 2008). Because adolescence is a developmental period when youth are particularly vulnerable to meaning making, an understanding of youths’ perceptions of discrimination attributes may shed light on consequences of patterns of daily discrimination experiences. Guided by intersectionality perspectives, the current study expands prior work by using a person-centered approach (via latent class analyses) to simultaneously examine different sub-groups of individuals who differ based on discrimination attributes. The current study was guided by two goals: 1) identify clusters across multiple discrimination attributes based on education/income, physical disability, skin color/race, ancestry/national origins, gender, sexual orientation/gender presentation, age, religion, height, weight, physical appearance; 2) examine links between unique patterns of discrimination attributes and adolescents’ adjustment over time.

The current study included 385 ethnically-racially diverse adolescents (67% Euro-American, 11% African American, 6% Asian American, 5% Latinos. 11% other) in grades 9th through 11th grade attending a large public high school in the Northeastern region of the United States. Adolescents (55% Female; Mage = 15.36 years; SD = .87) completed surveys at the end of the academic year and every year thereafter for three years. Based on parental education level (22.4% master’s or doctorate degree, 38.6% associate or bachelor’s degree, and 12.1% less than high school degree) and free lunch status (36.4% free lunch eligible), the sample represented a range of socioeconomic backgrounds.

Latent class analyses (LCA) via Mplus v7.4 with FIML revealed that the best fitting model suggested five clusters based on discrimination attributes (Table 1). The 5-classes were characterized as low overall discrimination (50%), high gender and age discrimination (19%), high race and skin color discrimination (13%), high weight discrimination (11%), and high overall discrimination (6%; Figure 1). Results revealed links between class membership probably and sociodemographic factors (i.e., ethnicity/race, gender) and adjustment. High skin color-race discrimination and high overall discrimination clusters rated school importance the highest, despite having the lowest GPAs. The low overall discrimination cluster reported the highest levels of school belongingness; the high overall discrimination cluster reported the lowest levels of school equal status. Finally, the gender-age discrimination cluster reported the highest levels of depression and lowest wellbeing. Discussion will center on study implications for educators, practitioners, and families.

Authors

• Diamond Yvonne Bravo, Harvard University
  Presenting Author

• Nancy Hill, Harvard University
  Non-Presenting Author