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Field Assignment, Field Choice and Preference Matching of Ethiopian High School Students

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  • Derbachew Asfaw

    (Hawassa University)

  • Zeytu Gashaw

    (Hawassa University)

Abstract

We examined the determinants of the admittance of students into their top wished-fields of study by university students using data from Ethiopian National Educational Assessment and Examination Agency. It is based on a 2016 cohort of 41,371 applicants in Social Science and 92,135 applicants in Natural Science, who were admitted to public universities in Ethiopia. We use a binary logistic regression model applied to four broadly defined fields in Social Science streaming and found that students’ place of residence, gender, EHEECE admission grade and age of the student have a significant positive impact on the decision process towards admitting students into their top wished-fields. Results also showed that there were significant positive interaction effects of EHEECE admission grade, gender and wished-fields on the decision process. We noticed a fair selection between girls and boys into the field of Law and Theatrical Fine Art and Music. For girls the odds of being admitted into the field of Other Social Science and Humanities were relatively better than the odds of being admitted into Business and Economics. We use a polytomous logit regression model applied to seven broadly defined fields in Natural Science streaming and found no selection bias in admitting applicants into the field of first and second ordered preferences among girls and boys, whilst there were a variation among the fields ranked thereafter.

Suggested Citation

  • Derbachew Asfaw & Zeytu Gashaw, 2021. "Field Assignment, Field Choice and Preference Matching of Ethiopian High School Students," Annals of Data Science, Springer, vol. 8(2), pages 185-204, June.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:2:d:10.1007_s40745-018-0182-z
    DOI: 10.1007/s40745-018-0182-z
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    References listed on IDEAS

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    Cited by:

    1. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.

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