Problem Statement Although some consider college student self-efficacy a unified construct with recommendations for measurement using questionnaires with total scores with “high reliability and validity”, separate analyses of subdomains may be warranted. Research Question Are there different findings at the subscale level in models of college student self-efficacy (course and social) that consider sex, language fluency and use, academic year, stress preventive resources, and depressive symptomology? Purpose of the Study This paper considers a contextual psycho-social model of college-student well-being among undergraduates at an English-language university in an ethno-culturally/linguistically diverse city in British Columbia, Canada. It differentiates between patterns of association of key variates from the model with two identified subdomains of college student self-efficacy, i.e., course and social subdomains. Research Method Participants were undergraduates at a mid-sized university. Participants completed a battery of questionnaires. Data on focal variables [college self-efficacy (course and social), background variables (sex, immigrant/citizenship generation, instructional language fluency and use, academic year), personality s, stress preventive resources, and depressive symptomology] were included in multivariate general linear model (GLM), hierarchical regression and correlational analyses. Findings Multivariate GLM analyses of the college self-efficacy subscales yielded omnibus effects (SSType I) for generation, language fluency and home use, academic year, select personality and stress preventive resources variates (ps<.05). Although there were key commonalities, the pattern of statistical significance differed by criterion variable in corresponding univariate GLM (SSType I) and hierarchical regression analyses. Conclusions Examination of the College Self-Efficacy Inventory and its subscales highlights that although overall scores may be of use, comparative analyses of findings at the subscale level should be considered. Importantly, it is not sufficient to compare/contrast ‘statistical significance’ profiles, statistical comparisons of corresponding correlations elucidates differential findings and assists developing nomothetic networks and targeted interventions to improve student outcomes.