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.

多项式回归是一种经过验证的方法，用于计算学生的 RIASEC（现实、调查、艺术、社会、进取和传统）兴趣与学习计划的 RIASEC 概况之间的人与环境 (PE) 兴趣匹配。与文献中的早期方法相比，该方法显示出体育兴趣匹配对学术成就的更大影响。然而，目前形式的多项式回归方法仅侧重于建立学生群体与其学习环境的回归兴趣拟合（RIF），以观察体育兴趣拟合对学业成绩的总体影响有多大。本研究 ( N  = 4407 跨n = 22 个学习项目）通过从理论上推导出两个只影响单一环境（如学习项目）的 RIF 测量值，进一步验证了这种方法在新应用中的应用。 分析表明，与原始多项式回归方法的r = 0.25 相比，在单一学习环境中使用 RIF 会导致 PE 兴趣匹配与学业成绩之间存在更强的正相关关系r = 0.36。分析还表明，一种环境的 RIF 可用于生成可解释且可靠的 RIASEC 环境配置文件。总而言之，针对单一（研究）环境的 RIF 是 PE 兴趣匹配的有希望的操作化，它促进了实证研究以及兴趣匹配在咨询环境中的实际应用。