This quantitative study examined the predictive impact of a series of factors on female community college students' intention to transfer in science, technology, engineering, and mathematics (STEM). The STEM Student Success Literacy survey (SSSL) was utilized to collect data from a large, diverse community college located in Florida. After the data cleaning and preparation, the authors first constructed a model that measures potentially predictive factors such as social capital, student engagement, and chilly climate. Then, a regression model including these factors and demographic characteristics was tested using a multinomial regression analysis. Findings generated implications for future research, policy, and practice to better serve and assist female students in their pursuit of a STEM degree.