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Polynomial regression is a proven method to calculate person-environment (PE) interest fit between the RIASEC (realistic, investigative, artistic, social, enterprising and conventional) interests of a student and the RIASEC profile of a study program. The method has shown much larger effects of PE interest fit on academic achievement than earlier approaches in literature. However, the polynomial regression method in its current form only focuses on establishing the regressed interest fit (RIF) of a population of students with their study environments, in order to observe how large the general impact of PE interest fit can become on academic achievement. The present study (N = 4407 across n = 22 study programs) further validates this method towards new applications by theoretically deriving two measures of RIF that only affect a single environment like a study program. Analyses show that the use of RIF for a single study environment results in an even stronger positive relation between PE interest fit and academic achievement of r = 0.36, compared to r = 0.25 for the original polynomial regression method. Analyses also show that RIF for one environment can be used to generate interpretable and reliable RIASEC environment profiles. In sum, RIF for a single (study) environment is a promising operationalization of PE interest fit which facilitates both empirical research as well as the practical application of interest fit in counseling settings. • New methods of regressed interest fit (RIF) calculate the interest fit between a student and a specific study program. • RIF12 shows a stronger positive relation between interest fit and academic achievement than previously found. • RIF6 uses regression to extract specific RIASEC program profiles from the variance in student RIASEC data. • The presented RIF can facilitate both empirical research and study counseling. [ABSTRACT FROM AUTHOR]

STEM (Science, Technology, Engineering and Mathematics) enrolments in higher education are declining while the STEM gender gap of female underrepresentation seems to widen. The present study addresses both issues by exploring how the fit between a student's vocational interests and the STEM field contributes to a (non-) STEM study choice. Data was collected in the unique setting of an open access and low cost higher education system, which allowed for study of vocational interests without unwanted influence of admission conditions. Specifically, we assessed the interest fit of N = 9162 first-year Belgian university students with (1) the STEM field (i.e., STEM fit) and (2) their specific program of choice (i.e., program fit). Results indicated STEM fit indeed predicted STEM study choice, with a stronger effect in female students. Results also indicated that female students showed a better specific program fit. In order to promote student STEM enrolment and address the gender gap, the present study therefore advocates a gender-specific approach to attract more students with appropriate interest profiles. • The STEM field is characterized by comparing STEM students to non-STEM students. • A STEM choice can be largely understood through vocational interests. • STEM fit predicts STEM study choice. • This predictive effect is even stronger for female students. • Female students show a better interest fit with their (STEM) program. [ABSTRACT FROM AUTHOR]