Abstract
The purpose of this study is to explore the relationship between various protective factors with academic outcomes of Latina/o high school students. We use two groups of protective influences, individual and family, and their relationship to 12th grade mathematics achievement, dropout rates, and enrollment in post-secondary education. Latent class analysis was used to identify academic protective profiles, or latent groups/classes, among high school Latina/o students (N = 1610) and assess group differences with respect to gender, SES, immigrant status, student’s native language, preschool attendance, and 10th grade mathematics. Results indicated the presence of four academic protective groups, which differed with respect to academic discussions with parents, and attitudes about mathematics. The four classes are compared with respect to academic outcomes and differences are discussed as well as implications for practice.
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Notes on contributors
Alma Boutin-Martinez
Dr. Alma Boutin-Martinez is a Senior Institutional Research Analyst at Fielding Graduate University. Her research interests include educational program evaluation, learning outcomes assessment, and developmental psychology.
Rebeca Mireles-Rios
Dr. Rebeca Mireles-Rios is an assistant professor in the Department of Education at the University of California, Santa Barbara. Her research interests focus on Latinx adolescents' perceptions of teacher support; the role of familial expectations on education communication; and trajectories into higher education.
Karen Nylund-Gibson
Dr. Karen Nylund-Gibson is a professor of Quantitative Research Methodology in the Department of Education at the University of California, Santa Barbara. Her research interests are in the development and application of latent variable models, with a focus on mixture models, including latent class and latent transition analysis models.
Odelia Simon
Odelia Simon is a doctoral candidate in the Department of Education at the University of California, Santa Barbara. Her research interests include measurement invariance and application of latent variable models and student math identity.