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Applicability of Machine Learning in the Measurement of Emotional Intelligence

  • Manish Sharma
  • Shikha N. Khera
  • Pritam B. Sharma
Article
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Abstract

The Trait Meta Mood Scale (TMMS) is one of the widely used instruments for measuring the emotional intelligence. This scale helps in ascertaining the overall emotional intelligence and can be used by organizations to handle the workforce and hence increase the efficiency and effectiveness by taking corrective measures, thereby transforming the organizations. If a large data set is available with some missing value, it becomes difficult to find the overall emotional intelligence of the given group and carry out the statistical analysis. This work proposes a model which applies neural network to find out the missing data and to perform regression. The model provides a flexible system to measure emotional intelligence. It paves a way for the application of machine learning in the TMMS scale of emotional intelligence but also in other scales of emotional intelligence.

Keywords

Trait Meta Mood Scale Neural networks Regression Machine learning Emotional intelligence 

References

  1. 1.
    Salovey P, Mayer JD, Goldman SL, Turvey C, Palfai TP (1995) Emotional attention, clarity, and repair: Exploring emotional intelligence using the Trait Meta-Mood Scale. American Psychological Association, WashingtonGoogle Scholar
  2. 2.
    Kithenham BA (2012) Systematic review in software engineering: where we are and where we should be going. In: Proceedings of the 2nd international workshop on evidential assessment of software technologies, pp 1–2Google Scholar
  3. 3.
    Khasawneh OY (2018) Technophobia without boarders: the influence of technophobia and emotional intelligence on technology acceptance and the moderating influence of organizational climate. Comput Hum Behav 88:210–218CrossRefGoogle Scholar
  4. 4.
    Palomba A (2018) Virtual perceived emotional intelligence: how high brand loyalty video game players evaluate their own video game play experiences to repair or regulate emotions. Comput Hum Behav 85:34–42CrossRefGoogle Scholar
  5. 5.
    Gutiérrez-Cobo MJ, Cabello R, Fernández-Berrocal P (2017) Performance-based ability emotional intelligence benefits working memory capacity during performance on hot tasks. Sci Rep 7(1):11700CrossRefGoogle Scholar
  6. 6.
    Brito-Costa S, Castro FV, Moisao A, Alberty A, de Almeida H, Fernández MIR (2016) Psychometric properties of Portuguese version of trait meta-mood scale (TMMS24). Int J Dev Educ Psychol (Revista INFAD de Psicología) 2(1):133–142CrossRefGoogle Scholar
  7. 7.
    Goleman D, McKee A, George B, Ibarra H (2018) HBR Emotional intelligence boxed set (6 Books) (HBR emotional intelligence series). Harvard Business Press, BrightonGoogle Scholar
  8. 8.
    Mayer JD, Salovey P, Caruso DR, Sitarenios G (2003) Measuring emotional intelligence with the MSCEIT V2.0. Emotion 3:97–105CrossRefGoogle Scholar
  9. 9.
    Wong CS, Wong PM, Law KS (2007) Evidence on the practical utility of wong’s emotional intelligence scale in Hong Kong and mainland China. Asia Pac J Manag 24:43–60CrossRefGoogle Scholar
  10. 10.
    Law KS, Wong CS, Song LJ (2004) The construct and criterion validity of emotional intelligence and its potential utility for management studies. J Appl Psychol 89(3):483–496CrossRefGoogle Scholar
  11. 11.
    Tapia M (2001) Measuring emotional intelligence. Psychol Rep 88(2):353-64sCrossRefGoogle Scholar
  12. 12.
    Schutte NS, Malouff JM, Hall LE, Haggerty DJ, Cooper JT, Golden CJ et al (1998) Development and validation of a measure of emotional intelligence. Personal Individ Differ 25:167–177CrossRefGoogle Scholar
  13. 13.
    Bar-On R (2004) The bar-on emotional quotient inventory (EQ-i): Rationale, description and psychometric properties. In: Geher G (ed) Measuring emotional intelligence: common ground and controversy. Nova Science, HauppaugeGoogle Scholar
  14. 14.
    Hamme C (2003) Group emotional intelligence: the research and development of an assessment instrument. Dissertation, Rutgers, New Brunswick, NJGoogle Scholar
  15. 15.
    Myers IB, Myers PB (1995)[1980] Gifts differing: understanding personality type Mountain view. Davies-Black Publishing, CaliforniaGoogle Scholar
  16. 16.
    Belbin R (1993) A reply to the belbin team-role self-perception inventory by furnham, Steele and Pendleton. J Occup Organ Psychol 66(3):259–260CrossRefGoogle Scholar
  17. 17.
    Posner BZ, Kouzes JM (1988) Development and validation of the leadership practices inventory. Educ Psychol Measur 48(2):483–496CrossRefGoogle Scholar
  18. 18.
    Bass BM, Aviolo BJ (1990) Multifactor leadership questionnaire. Consulting Psychologists Press, Palo AltoGoogle Scholar
  19. 19.
    Pekaar KA, Bakker AB, van der Linden D, Born MP (2018) Self-and other-focused emotional intelligence: development and validation of the rotterdam emotional intelligence scale (REIS). Person Individ Differ 120:222–233CrossRefGoogle Scholar
  20. 20.
    Duda OR (2015) Pattern classification. Wiley, HobokenGoogle Scholar
  21. 21.
    Alpaydin E (2017) Introduction to machine learning. MIT Press, CambridgeGoogle Scholar
  22. 22.
    Zureda JM (1996) Introduction to artificial neural system. West Publishing Company, EaganGoogle Scholar
  23. 23.
    Wikipedia. Regression analysis. https://en.wikipedia.org/wiki/Regressionanalysis. Accessed 15 Apr 2018

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Manish Sharma
    • 1
  • Shikha N. Khera
    • 1
  • Pritam B. Sharma
    • 2
  1. 1.Delhi Technological UniversityNew DelhiIndia
  2. 2.Amity UniversityManesarIndia

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