UC Irvine

This dissertation examined the development and contextual correlates of Asian, Black, Latina/o, and White adolescents’ STEM motivational beliefs. This is an important topic because STEM is increasingly critical for our daily lives and advances in the economy, but the U.S. struggles with stark racial/ethnic inequities in STEM. In Study 1, through person-centered analyses, I identified four and five patterns of math and science motivational beliefs in 9th and 11th grade, respectively. For each racial/ethnic group, I also charted the stability and changes in those patterns for 9th to 11th grade, in addition to testing how the patterns at 11th grade were associated with adolescents’ STEM performance and career aspirations. Study 2 examined the relations between race/ethnicity, parents’ STEM support, parents’ STEM degree/occupation, and adolescents’ STEM motivational beliefs. Findings suggested that parents’ STEM support could be leveraged to promote adolescents’ STEM motivational beliefs, with the most pronounced benefit observed among parents without a STEM degree/occupation. Study 3 examined how adolescents’ STEM motivational beliefs and GPAs were associated with STEM indicators from two related contexts, neighborhoods and schools. Findings suggested that the direct and indirect effects of neighborhood STEM jobs and school STEM indicators on adolescents’ STEM motivational beliefs and performance were largely nonsignificant. Across Studies 2 and 3, findings suggested several racial/ethnic differences in the mean levels of family, school, and adolescent STEM indicators. However, results also showed substantial racial/ethnic similarities regarding the associations between contextual factors and adolescents’ STEM motivational beliefs, aspirations, and performances. All three studies in this dissertation were guided by the situated expectancy-value theory and utilized the High School Longitudinal Study dataset (from the National Center for Educational Statistics). Each study, however, incorporated additional theoretical frameworks and used specific subsamples from the dataset that best matched their research aims.