This study introduces new measures of ethnicity‐related stress and a newly adapted measure of ethnic identity. Ethnicity‐related stressors assessed in this study were perceived discrimination, stereotype confirmation concern, and own‐group conformity pressure. Ethnic identity refers to the subjective sense of ethnic group membership and, following Luhtanen and Crocker (1992), was assessed as public regard, identity centrality, and private feelings. Data for 333 undergraduates from diverse ethnic groups indicated that the measures are psychometrically sound. Ethnic group differences for mean scores demonstrated the measures’ known‐groups validity. Cross‐sectional analyses indicated that ethnicity‐related stress and identity constructs captured by the instruments are related to measures of psychological and physical well‐being. The new measures may be useful in the investigation of psychological aspects of ethnicity and their adaptive consequences.

Early research on ethnicity focused on the stereotyped thinking, prejudiced attitudes, and discriminatory actions of Euro-Americans. Minoritygroup members were viewed largely as passive targets of these negative reactions, with low self-esteem studied as the main psychological outcome. By contrast, recent research has increasingly made explicit use of stress theory in emphasizing the perspectives and experiences of minority-group members. Several ethnicity-related stressors have been identified, and it has been found that individuals cope with these threats in an active, purposeful manner. In this article, we focus on ethnicity-related stress stemming from discrimination, from stereotypes, and from conformity pressure arising from one's own ethnic group. We discuss theory and review research in which examination of ethnicity-related outcomes has extended beyond self-esteem to include psychological and physical well-being.

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Across many case studies, the Descriptive to Executable Simulation Modeling (DESIM) method has demonstrated the ability to capture and model qualitative knowledge from multiple subject-matter experts (SMEs), convert those models to an executable form using a crowdsourcing approach, and interactively visualize the outputs. This method helps decision makers leverage collective expertise to perform complex “What if?” analysis. This paper takes advantage of a large-scale multiple-model application of DESIM to illustrate the nature and interpretation of the data produced throughout its multiple phases. Lessons learned from this study provide direction toward future evaluation and improvements to this method.