COMPARATIVE ANALYSIS OF PSYCHOMETRIC TESTING ON TWO PROFESSIONALS TO SELECT BEST CANDIDATE DURING SELECTION PROCESS

Authors

  • Dr. Shruti D Naik Associate Professor, CAssociate Professor, CMS Business School, Jain (Deemed to be University) No.17, Sheshadri Raod, MS Business School, Jain (Deemed to be University) No.17, Sheshadri Raod, Author

DOI:

https://doi.org/10.61841/k3mcfq16

Keywords:

Psychometric, Aptitude, Cognitive abilities,, Career Drivers

Abstract

The objective of this paper was to measure individuals' mental capabilities and preferred behavioral style fitting the suitability for a role with training team. The position was of a training manager which required aptitude and cognitive abilities. The tests were used to identify the extent to which candidates' match skills, knowledge, motives and abilities required to perform the role. Various tests were administered on both the candidates. An ability test was also designed and administered on the top performers of the organization. All the information were collected from the psychometric (PSI), Career Drivers (RSI), Motives (TAT) and ability tests to identify the hidden aspects of candidates followed by a presentation and face-to-face interview.

 

Downloads

Download data is not yet available.

References

1. H. He and E. A. Garcia, “Learning fromimbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.

2. A. Estabrooks, T. Jo, and N. Japkowicz, “Amultiple resampling method for learning fromimbalanced data sets,” Computational Intelligence, vol. 20, no. 1, pp. 18–36, 2004.

3. H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,” in Advances in Intelligent Computing, pp. 878–887, Springer, 2005.

4. N. V. Chawla, N. Japkowicz, and A. Kotcz, “Editorial: special issue on learning from imbalanced data sets,”

ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 1–6, 2004.

5. U. Bhowan, M. Johnston, and M. Zhang, “Developing new fitness functions in genetic programming for classification with unbalanced data,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42,no. 2, pp.406–421, 2012.

6. J.-H. Xue and P. Hall, “Why does rebalancing class-unbalanced data improve AUC for linear discriminant analysis?” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, pp. 1109–1112, 2015.

7. R. Batuwita and V. Palade, “Class imbalance learning methods for support vector machines,” in Imbalanced Learning: Foundations, Algorithms, and Applications, pp. 83–99, John Wiley & Sons, Berlin, Germany, 2013.

8. V. L´opez, A. Fern´andez, S. Garc´ıa, V. Palade, and F. Herrera, “An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics,” Information Sciences, vol. 250, pp. 113–141, 2013.

9. F. Provost, “Machine learning from imbalanced data sets 101,” in Proceedings of the AAAI’2000Workshop on Imbalanced Data Sets, pp. 1–3, 2000.

10. L. Pelayo and S. Dick, “Applying novel resampling strategies to software defect prediction,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS ’07), pp. 69– 72, June 2007.

11. J. Long, J.-P. Yin, E. Zhu, and W.-T. Zhao, “A novel active cost sensitive learning method for intrusion detection,” in Proceedings of the 7th International Conference on Machine Learning and Cybernetics (ICMLC ’08), pp. 1099–1104, IEEE, Kunming, China, July 2008.

12. K. Zahirnia, M. Teimouri, R. Rahmani, and A. Salaq, “Diagnosis of type 2 diabetes using cost-sensitive learning,” in Proceedings of the 5th International Conference on Computer and Knowledge Engineering (ICCKE ’15), pp. 158–163, October 2015.

13. M. Kubat, R. C.Holte, and S.Matwin, “Machine learning for the detection of oil spills in satellite radar images,” Machine Learning, vol. 30, no. 2-3, pp. 195–215, 1998.

14. T. Fawcett and F. Provost, “Adaptive fraud detection,” Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 291–316, 1997.

15. I. Triguero, S. del R´ıo, V. L´opez, J. Bacardit, J. M. Ben´ıtez, and F. Herrera, “ROSEFW-RF: the winner algorithm for the ECBDL’14 big data competition: an extremely imbalanced big data bioinformatics problem,” Knowledge-Based Systems, vol. 87, pp. 69–79, 2015.

16. K. V. Uma,” Improving the Classification accuracy of Noisy Dataset by Effective Data Preprocessing”,

International Journal of Computer Applications (0975 – 8887) Volume 180 – No.36, April 2018

17. Nittaya Kerdprasop and Kittisak Kerdprasop,” A Heuristic-Based Decision Tree Induction Methodfor Noisy Data”, T.-h. Kim et al. (Eds.): DTA/BSBT 2011, CCIS 258, pp. 1–10, 2011.

18. Dragan Gamberger, Nada Lavrac,” FILTERING NOISY INSTANCES AND OUTLIERS”, H. Liu et al. (eds.), Instance Selection and Construction for Data Mining, © Springer Science+Business Media Dordrecht 2001

19. THOMAS G. DIETTERICH,” An Experimental Comparison of Three Methodsfor Constructing Ensembles of Decision Trees:Bagging, Boosting, and Randomization”, Machine Learning, 40, 139–157, 2000, Kluwer Academic Publishers. Manufactured in The Netherlands.

20. Cèsar Ferri, José Hernández-Orallo, Peter Flach,” Setting decision thresholds when operating conditions are uncertain”, Data Mining and Knowledge Discovery (2019) 33:805–847, https://doi.org/10.1007/s10618- 019-00613-7

21. Ludmila I. Kuncheva · Juan J. Rodríguez,” A weighted voting framework for classifiers ensembles”, Knowl Inf Syst (2014) 38:259–275, DOI 10.1007/s10115-012-0586-6

22. Shaghayegh Gharghabi · Chin-Chia Michael Yeh · Yifei Ding, Wei Ding · Paul Hibbing Samuel LaMunion

· Andrew Kaplan, Scott E. Crouter · Eamonn Keogh,” Domain agnostic online semantic segmentation formulti-dimensional time series”,Data Mining and Knowledge Discovery (2019) 33:96–130, https://doi.org/10.1007/s10618-018-0589-3

23. Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

24. A Witten, I.H. and Frank, E. (2005) Data Mining: Practical machine learning tools and techniques. 2nd edition Morgan Kaufmann, San Francisco.

Downloads

Published

30.06.2020

How to Cite

Naik, D. S. D. (2020). COMPARATIVE ANALYSIS OF PSYCHOMETRIC TESTING ON TWO PROFESSIONALS TO SELECT BEST CANDIDATE DURING SELECTION PROCESS. International Journal of Psychosocial Rehabilitation, 24(6), 2683-2708. https://doi.org/10.61841/k3mcfq16