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Prediction of College Major Persistence Based on Vocational Interests, Academic Preparation, and First-Year Academic Performance

Abstract

We hypothesized that college major persistence would be predicted by first-year academic performance and an interest-major composite score that is derived from a student’s entering major and two work task scores. Using a large data set representing 25 four-year institutions and nearly 50,000 students, we randomly split the sample into an estimation sample and a validation sample. Using the estimation sample, we found major-specific coefficients corresponding to the two work task scores that optimized the prediction of major persistence. Then, we applied the estimated coefficients to the validation sample to form an interest-major composite score representing the likelihood of persisting in entering major. Using the validation sample, we then tested a theoretical model for major persistence that incorporated academic preparation, the interest-major composite score, and first-year academic performance. The results suggest that (1) interest-major fit and first-year academic performance work to independently predict whether a student will stay in their entering major and (2) the relative importance of two work task scores in predicting major persistence depends on the entering major. The results support Holland’s theory of person-environment fit and suggest that academic performance and interest-major fit are key constructs for understanding major persistence behavior.

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Acknowledgment

We thank Nancy Petersen, James Sconing, and Kyle Swaney of ACT, Inc. for their helpful reviews and technical guidance of this study.

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Correspondence to Jeff Allen.

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Allen, J., Robbins, S.B. Prediction of College Major Persistence Based on Vocational Interests, Academic Preparation, and First-Year Academic Performance. Res High Educ 49, 62–79 (2008). https://doi.org/10.1007/s11162-007-9064-5

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Keywords

  • College major persistence
  • Holland’s theory
  • Person-environment fit
  • College GPA
  • Hierarchical logistic regression