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Description: Undergraduate STEM students’ motivations have a strong influence on whether and how they will persist through challenging coursework and into STEM careers. Proper conceptualization and measurement of motivation constructs, such as students’ expectancies and perceptions of value and cost (i.e., expectancy value theory; EVT) and their goals (i.e., achievement goal theory; AGT), are necessary to understand and enhance STEM persistence and success. Research findings suggest the importance of exploring multiple measurement models for motivation constructs, including traditional confirmatory factor analysis (CFA), exploratory structural equation models (ESEM), and bifactor models, but more research is needed to determine whether the same model fits best across time and context. As such, we measured undergraduate biology students’ EVT and AGT motivations across three semesters and investigated which measurement model best fit the data. Then we examined the measurement invariance of this model across the three semesters. Having determined the best-fitting measurement model and type of invariance, we used scores from the best performing model to predict biology achievement. Measurement results suggested an ESEM-Bifactor model had the best data-model fit for EVT and an ESEM model had the best data-model fit for AGT. Motivation factors predicted small to medium-sized amounts of variance in biology course outcomes each semester. Our findings expand best practices for modeling motivation constructs and support prior findings that suggest productive motivations, like subjective task value, can predict postsecondary STEM outcomes.