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Оценка Персонала С Использованием Бинарной Регрессии // Staff Appraisal Using A Binary Regression

Author

Listed:
  • A. Zinchenko A.

    (Financial University)

  • А. Зинченко А.

    (Финансовый университет)

Abstract

The paper shows the possibility of the efficient evaluation of candidates for positions with the help of the binary-regression. The absence of expertise in using math methods by personnel departments makes recruitment process modeling inefficient, so the results obtained via binary-regression is of great importance.The purpose of the research is to show the relationship between the data in CVs and the fact of passing the probation period by employees. The author had at his disposal data of candidates’ CVs provided by several HR- agencies to their clients. Some of employees had passed the probation, some of them had not passed. To carry out the research the author chose three types of models - logit, probit, gompit.To estimate the parameters and the quality of the constructed models the author wrote the code in Maple computer algebra system. According to the results of the research the best predicting model was chosen. В статье показана возможность эффективной оценки кандидатов на должности при помощи моделей бинарного выбора. Применение математических методов в данной области способно повысить объективность принятия кадровых решений, а также упростить работу менеджеров по персоналу в случае осуществления массового подбора, что является обычной практикой для кадровых агентств.Цель исследования - показать наличие статистической зависимости между информацией, указанной в резюме работника, и фактом прохождения испытательного срока. В качестве статистики автор располагал резюме работников, рекомендованных несколькими кадровыми агентствами своим клиентам. Среди этих данных присутствовали резюме людей, оставшихся работать в фирме и не прошедших испытательный срок. Для исследования были выбраны три типа моделей: пробит, логит и гомпит.Для оценки параметров и качества построенных моделей разработана программа в среде Maple. Исходя из полученных результатов, была выбрана модель, наиболее удачно предсказывающая прохождение работником испытательного срока.

Suggested Citation

  • A. Zinchenko A. & А. Зинченко А., 2015. "Оценка Персонала С Использованием Бинарной Регрессии // Staff Appraisal Using A Binary Regression," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, issue 2, pages 135-141.
    • Handle: RePEc:scn:financ:y:2015:i:2:p:135-141
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    References listed on IDEAS

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    1. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    2. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
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