ارزیابی هوش هیجانی با استفاده از تحلیل پوششی داده‌ها(نمونه موردی: دانشجویان دانشگاه آزاد اسلامی واحد یادگار امام (ره) )

محورهای موضوعی : روانشناسی تربیتی

فاطمه مرادی ¹ ( گروه ریاضی، واحد یادگار امام(ره) شهر ری، دانشگاه آزاد اسلامی، تهران، ایران )
مهناز مقبولی ² ( گروه ریاضی، واحد هادیشهر، دانشگاه آزاد اسلامی، هادیشهر، ایران )

تاریخ دریافت : 1396/08/13 تاریخ پذیرش : 1397/02/19 تاریخ انتشار : 1397/09/01

کلید واژه: هوش هیجانی, خودآگاهی, خودمدیریتی, آگاهی‌اجتماعی, مدیریت‌رابطه, مدل‌های تحلیل پوششی داده‌ها,

چکیده مقاله :

برای سال‌های متمادی تصور بر این بود که ضریب هوشی نماینده میزان موفقیت افراد است. در دهه اخیر تحقیقات در یافتند که علاوه بر ضریب هوشی، عاملی به نام هوش هیجانی نیز جز شاخص‌های ارزیابی موفقیت یک فرد می‌تواند قلمداد شود. در ابتدای پیدایش شاخص هوش هیجانی، روانشناسان بیشتر برروی جنبه‌های شناختی همانند حافظه و حل مسئله تأکید می‌کردند. اما خیلی زود دریافتند که جنبه‌های غیرشناختی مانند عوامل عاطفی و اجتماعی نیز دارای اهمیت زیادی هستند. برای بررسی اهمیت این شاخص در ارزیابی‌ها یک نمونه تصادفی 81 تایی از دانشجویان رشته‌های الکترونیک و مکانیک دانشگاه آزاد اسلامی واحد یادگار امام (ره) انتخاب شد. ابزار گردآوری، پرسشنامه هوش هیجانی برگرفته شده از کتاب تراویس برادبری می‌باشد. بعد از جمع‌آوری داده‌ها، با استفاده از مدل‌های تحلیل پوششی داده‌ها، عملکرد هوش هیجانی دانشجویان مورد ارزیابی قرار گرفت. سپس برای تمیز دادن دانشجویانی که عملکرد آنها خوب تشخیص داده شده بود از مدل‌های ابرکارایی استفاده شد. نتایج نشان دهنده آن است که دانشجویان الکترونیک عموماً رتبه بالاتری را به خود اختصاص می‌دهند. همچنین بررسی تأثیر ضریب چهار فاکتور انتخابی(خودمدیریتی، خودآگاهی، آگاهی‌اجتماعی، مدیریت‌رابطه) در این مطالعه، نشان می‌دهد که مدیریت رابطه بیشترین تأثیر و خود مدیریتی کمترین تأثیر را در ارزیابی هوش هیجانی این تعداد از دانشجویان به خود تخصیص داده است.

چکیده انگلیسی:

For many years, it was believed that Intelligence Quota (IQ) was the benchmark showing how successful one is. However, recent researches have revealed that IQ is not the only factor in one's success but also, other criteria called as Emotional Quota (EQ) has taken its place in many researches. Since the inception of this structure, psychologists mainly paid attention on its cognitive aspects, such as memory and problem solving. Soon, they realized that non-cognitive aspects like emotional and social factors played a key role as well. To study the importance of this issue, a random sample of 81 Electronic and Mechanic engineering graduates from Islamic Azad University, Yadegar Emam Branch, were selected. They were handed out a questioner on EQ derived from Travis-Bradberry book. The collected data evaluated applying Data Enveloping Analysis (DEA) models. In order to rank and distinguish the students’ performance, super efficiency models were employed.  The results show that the Electronic graduates scored some higher rank. Top of all, the study conflicts the selected four factors in evaluating EQ and reveals that the relationship- management factor has the most effect and self-management factor can take the least influence in this evaluation.

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Charnes A., Cooper, W. W., & Rhodes, E. L. (1978), Measuring the efficiency of decision making units, European Journal of Operational research, 2(6), 429-444.

Chiang C. I., Hwang, M. J., & Liu, Y. H.  (2011), Determining a common set of weights in a DEA problem using a separation vector, Mathematical and Computer Modelling, 54(9–10), 2464–2470.

Cook W. D. & Bala, K. (2007), Performance measurement with classification information in DEA: Input-oriented model, OMEGA, 37, 39-52.

Cooper, W. W., Seiford, L. M., & Tone, K. (2006), Introduction to Data Envelopment Analysis and Its uses: with DEA-Solver Software and References, Springer Publication, Boston, 2^nd Edition.

Doyle, J., & Green, R. (1994), Efficiency and Cross-Efficiency in DEA: Derivations, Meanings and Uses, Journal of the Operational Research Society, 45,567-578.

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Jahanshahloo, G. R., Hoseinzadeh Lotfi, F., & Nikoo Maram, H. (2016), Data Envelopment Analysis and Its Application, Islamic Azad University, Tehran Science and Research Branch Publication, Tehran, First Edition.

Lopez-Cabrales, A., Pérez-Luño, A., & Cabrera, R. V. (2003), Knowledge as a mediator between HRM practices and innovative activity, Human Resource Management, 48 (4), 485–503.

Mayer J., Caruso, D., & Salovey, P. (2000), Emotional intelligence meets traditional standards for intelligence, Intelligence, 27,267-98.

Salovey, P., & Mayer, J. D.  (1990), Emotional Intelligence, Imagination Cognition and Personality, 9(3), 185-211.

Schutt, N. C. (2001), Emotional intelligence and Inter Personal Relations, Social Psychology and Psychiatry, 141, (4), 523-535.

Trinidad, D. R. (2004), The Proactive association of emotional intelligence with psychosocial smoking risk factor for adolescents, Personality and Individual differences, 36, 4, 945-954.

Taylor S. E., & Kemeny, M. E. (2000), Psychological Resources, Positive Illusions, and Health, American Psychology, 55(1), 99-109.

Zhon, J., & George, J. (2003), Awaking employee creativity: the role of leader emotional intelligence, The leadership Quarterly, 14, 4-5-5450568.

Vanrooy, D. L. (2005), Group Differences in Emotional Intelligence Scores, Personality and Individual Differences, 38, 3, 689-700

Wu, J., Sun, J., & Liang, L. (2012), Cross efficiency evaluation method based on weight-balanced data envelopment analysis model, Computers & Industrial Engineering, 63(2), 513–519.

Yang, G. L., Yang, J. B., Liu, W. B.  & Li, X. X.  (2013), Cross-efficiency aggregation in DEA models using the evidential-reasoning approach, European Journal of Operational research, 231, 393-404.

Andersen, P & Petersen, N.C. (1993), A Procedure for Ranking Efficient Units in Data Envelopment Analysis, Management Science, 39(10), 1261-1264.

Austin, E. J., Saflofske, D. H., & Egan, V. (2005), Personality, well-being and health correlates of trait emotional intelligence, Personality and individual differences, 38(3), 546-558.

Bala, K & Cook, W. D. (2003), Performance measurement with classification information: an enhanced additive DEA model, OMEGA, 31, 439-450.

Banker, R. D., Charnes, A., & Cooper, W. W. (1984), Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30(9), 1078-1092.

Bar-On, R. (2000), Emotional and social intelligence: Insights from the emotional quotient Inventory, In R. Bar-On & J.D.A Parker. (Eds), the Handbook of Emotional Intelligence (1 (, Jossey-Bass, San Francisco, 368-388.

Bloom, B. S. (1976), Human Characteristics and School learning, Mc Graw-Hill, New York, First Edition.

Bradberry, T., & Greaves, J. (2005), The Emotional Intelligence Quick Book: Everything you need to know to put your EQ to Work, Fireside Publication, First Edition.

Charbonneau, I., & Adelheid, A. M. (2002), Emotional intelligence and leadership in adolescents, Personality Journal Differences, 33(7), 1101-1113.

Charnes A., Cooper, W. W., & Rhodes, E. L. (1978), Measuring the efficiency of decision making units, European Journal of Operational research, 2(6), 429-444.

Chiang C. I., Hwang, M. J., & Liu, Y. H.  (2011), Determining a common set of weights in a DEA problem using a separation vector, Mathematical and Computer Modelling, 54(9–10), 2464–2470.

Cook W. D. & Bala, K. (2007), Performance measurement with classification information in DEA: Input-oriented model, OMEGA, 37, 39-52.

Cooper, W. W., Seiford, L. M., & Tone, K. (2006), Introduction to Data Envelopment Analysis and Its uses: with DEA-Solver Software and References, Springer Publication, Boston, 2^nd Edition.

Doyle, J., & Green, R. (1994), Efficiency and Cross-Efficiency in DEA: Derivations, Meanings and Uses, Journal of the Operational Research Society, 45,567-578.

Goleman, D. (1995), Emotional intelligence, New York Bantam Books.

Jahanshahloo, G. R., Hoseinzadeh Lotfi, F., & Nikoo Maram, H. (2016), Data Envelopment Analysis and Its Application, Islamic Azad University, Tehran Science and Research Branch Publication, Tehran, First Edition.

Lopez-Cabrales, A., Pérez-Luño, A., & Cabrera, R. V. (2003), Knowledge as a mediator between HRM practices and innovative activity, Human Resource Management, 48 (4), 485–503.

Mayer J., Caruso, D., & Salovey, P. (2000), Emotional intelligence meets traditional standards for intelligence, Intelligence, 27,267-98.

Salovey, P., & Mayer, J. D.  (1990), Emotional Intelligence, Imagination Cognition and Personality, 9(3), 185-211.

Schutt, N. C. (2001), Emotional intelligence and Inter Personal Relations, Social Psychology and Psychiatry, 141, (4), 523-535.

Trinidad, D. R. (2004), The Proactive association of emotional intelligence with psychosocial smoking risk factor for adolescents, Personality and Individual differences, 36, 4, 945-954.

Taylor S. E., & Kemeny, M. E. (2000), Psychological Resources, Positive Illusions, and Health, American Psychology, 55(1), 99-109.

Zhon, J., & George, J. (2003), Awaking employee creativity: the role of leader emotional intelligence, The leadership Quarterly, 14, 4-5-5450568.

Vanrooy, D. L. (2005), Group Differences in Emotional Intelligence Scores, Personality and Individual Differences, 38, 3, 689-700

Wu, J., Sun, J., & Liang, L. (2012), Cross efficiency evaluation method based on weight-balanced data envelopment analysis model, Computers & Industrial Engineering, 63(2), 513–519.

Yang, G. L., Yang, J. B., Liu, W. B.  & Li, X. X.  (2013), Cross-efficiency aggregation in DEA models using the evidential-reasoning approach, European Journal of Operational research, 231, 393-404.