Zihinsel İş Yükünün Ölçümü: CarMen-Q Ölçeğinin Türkçe’ye Uyarlaması

Yıl 2020, Cilt: 15 Sayı: 60, 675 - 691, 31.10.2020

Meltem Akca , Meltem Yavuz , Mübeyyen Tepe Küçükoğlu

https://doi.org/10.19168/jyasar.708357

Cited By: 4

Öz

Bu araştırmanın amacı Rubio-Valdehita ve meslektaşları (2017) tarafından geliştirilen Zihinsel İş Yükü Ölçeğini (CarMen-Q) Türkçe ’ye uyarlamak ve ölçeğin psikometrik özelliklerini incelemektir. 268 katılımcıdan elde edilen verilere uygulanan doğrulayıcı faktör analizi sonuçlarına göre, ölçeğin orijinal formundaki gibi dört boyutlu bir yapıda olduğu saptanmıştır. Bu boyutlar; bilişsel iş yükü, geçici iş yükü, duygusal iş yükü ve performansa bağlı iş yükü talepleridir. Türkçe Zihinsel İş Yükü Ölçeğinin madde-toplam korelasyon katsayılarının 0.12 ile 0.74 arasında olduğu gözlemlenmiş, iç tutarlık katsayısı α = 0.90 olarak hesaplanmıştır. Ayrıca, Türkçe Zihinsel İş Yükü Ölçeğinde yer alan boyutlar arasındaki ve bu boyutların işten ayrılma niyeti değişkeni ile olan yordama (predictive validity) geçerliliği de değerlendirilmiştir. Analiz bulguları, Türkçe Zihinsel İş Yükü Ölçeği’nin Türkiye’deki örneklem üzerinde yeterli düzeyde geçerlilik ve güvenilirlik değerlerine sahip olduğunu ortaya koymuştur.

Anahtar Kelimeler

Zihinsel İş Yükü, Ergonomi, Ölçek Uyarlama

Measurement of Mental Workload: Turkish Adaptation of CarMen-Q Questionnaire

Öz

The purpose of this study is to adapt the original version of Rubio-Valdehita et al.’s (2017) Mental Workload Scale (CarMen-Q) to Turkish and examine its psychometric properties. According to the confirmatory factor analysis results (n = 268), factors were yielded four dimensions as in the original scale. These dimensions are cognitive demands, temporal demands, emotional demands and performance demands. While the item-total correlation coefficients of the Turkish Carmen-Q Questionnaire ranged from 0.12 to 0.74, the Cronbach’s alpha coefficient for Turkish Carmen-Q was 0.90. Besides, predictive validity was evaluated by the level of turnover intentions of the participants. The findings proved that Turkish adaptation of Carmen-Q scale had an adequate level of reliability and validity over Turkish sample.

Anahtar Kelimeler

Mental Workload, Ergonomics, Scale Adaptation

Kaynakça

• Akca, M., & Yavuz, M.(2018). Kanıta Dayalı Uygulamalar Modeli Çerçevesinde Geliştirilen İcraatçı Liderlik Ölçeği’nin Türkçe Uyarlaması. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6(ICEESS’18), 255-262.
• Ayre, C., & Scally, A. J. (2014). Critical values for Lawshe’s content validity ratio: revisiting the original methods of calculation. Measurement and Evaluation in Counseling and Development, 47(1), 79-86.
• Balfe, N., Crowley, K., Smith, B., & Longo, L. (2017). Estimation of train driver workload: extracting taskload measures from on-train-data-recorders. In International Symposium on Human Mental Workload: Models and Applications (pp. 106-119). Springer, Cham.
• Barlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information technology, learning, and performance journal, 19(1), 43.
• Bayık, M. E., & Gürbüz, S. (2016). Ölçek uyarlamada metodoloji sorunu: Yönetim ve örgüt alanında uyarlanan ölçekler üzerinden bir araştırma. İş ve İnsan Dergisi, 3(1), 1-20.
• Boet, S., Sharma, B., Pigford, A. A., Hladkowicz, E., Rittenhouse, N., & Grantcharov, T. (2017). Debriefing decreases mental workload in surgical crisis: a randomized controlled trial. Surgery, 161(5), 1215-1220.
• Brislin, R. W., Lonnen, W. J., & Thorndike, E. M. (1973). Cross-cultural research methods. New York, NY: Wiley.
• Cain, B. (2007). A review of the mental workload literature. Defence Research And Development Toronto (Canada).
• Carswell, C. M., Lio, C. H., Grant, R., Klein, M. I., Clarke, D., Seales, W. B., & Strup, S. (2010). Hands-free administration of subjective workload scales: acceptability in a surgical training environment. Applied ergonomics, 42(1), 138-145.
• Charles, R. L., & Nixon, J. (2019). Measuring mental workload using physiological measures: a systematic review. Applied ergonomics, 74, 221-232.
• Chiorri, C., Garbarino, S., Bracco, F., & Magnavita, N. (2015). Personality traits moderate the effect of workload sources on perceived workload in flying column police officers. Frontiers in psychology, 6, 1835.
• Çapık, C. (2014). Geçerlik ve güvenirlik çalışmalarında doğrulayıcı faktör analizinin kullanımı. Anadolu Hemşirelik ve Sağlık Bilimleri Dergisi, 17(3), 196-205.
• De Waard, D., & te Groningen, R. (1996). The measurement of drivers' mental workload. Netherlands: Groningen University, Traffic Research Center.
• Delice, E. K. (2016). Acil Servis Hekimlerinin Nasa-Rtlx Yöntemi ile Zihinsel İş Yüklerinin Değerlendirilmesi: Bir Uygulama Çalışması. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 30(3), 645-662.
• DeVellis, R. F. (2016). Scale development: Theory and applications. California, ABD: Sage Publications.
• Gonzalez, S. (2003). The relationship of academic workload typologies and other selected demographic variables to burnout levels among full-time faculty in Seventh-day Adventist colleges and universities in North America.Doctoral Thesis,Andrews University.
• Gülkaç, H. (2013). Pilotların zihinsel iş yüklerinin NASA-TLX yöntemiyle ölçülmesi, Yüksek Lisans Tezi,Kocaeli Üniversitesi, Kocaeli.
• Gürbüz, S., & Şahin, F. (2016). Sosyal bilimlerde araştırma yöntemleri: Felsefe-yöntem- analiz. 3. Baskı. Ankara: Seçkin Yayıncılık.
• Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. Multivariate Data Analysis: Global Edition (7th Edition), Pearson Higher Education, London, 2010.
• Harrington, D. (2009). Confirmatory factor analysis. Oxford university press.
• Hart, S. G. (2006). NASA-Task Load Index (NASA-TLX); 20 years later. In Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting (pp. 904-908). Santa Monica, CA: Human Factors & Ergonomics Society.
• Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology, 52, 139-183.
• Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic journal of business research methods, 6(1), 53-60.
• Jin, X., Zheng, B., Pei, Y., & Li, H. (2017, July). A method to estimate operator’s mental workload in multiple information presentation environment of agricultural vehicles. In International Conference on Engineering Psychology and Cognitive Ergonomics (pp. 3-20). Springer, Cham.
• Karadağ, M., & Cankul, İ. (2015a). Hekimlerde Zihinsel İş Yükü Değerlendirmesi. The Journal of Academic Social Science Studies, (35), 361-370.
• Karadağ, M., & Cankul, İ. (2015b). Hemşirelerde Zihinsel İş Yükü Değerlendirmesi. Anadolu Hemşirelik ve Sağlık Bilimleri Dergisi, 18(1),26-34.
• Kline, P. (2014). An easy guide to factor analysis. Routledge.
• Lawshe, C. H. (1975). A quantitative approach to content validity 1. Personnel psychology, 28(4), 563-575.
• LoBiondo-Wood, G., & Haber, J. (2014). Reliability and validity. Nursing research. Methods and critical appraisal for evidence based practice, Mosby Elsevier.
• Longo, L. (2015). A defeasible reasoning framework for human mental workload representation and assessment. Behaviour & Information Technology, 34(8), 758-786.
• Longo, L., Rusconi, F., Noce, L., & Barrett, S. (2012). The Importance of Human Mental Workload in Web Design. In WEBIST (pp. 403-409).
• Mijović, P., Milovanović, M., Ković, V., Gligorijević, I., Mijović, B., & Mačužić, I. (2017). Neuroergonomics method for measuring the influence of mental workload modulation on cognitive state of manual assembly worker. In International Symposium on Human Mental Workload: Models and Applications (pp. 213-224). Springer, Cham.
• Mohammadian, Y., Malekpour, F., Malekpour, A., Zoghipour, S., & Malekpour, K. (2015). Study on mental workload of teachers and its correlation with their quality of life. Age, 30, 21-29.
• Moustafa, K., Luz, S., & Longo, L. (2017). Assessment of mental workload: a comparison of machine learning methods and subjective assessment techniques. In International symposium on human mental workload: Models and applications (pp. 30-50). Springer, Cham.
• Nunnally, J. C., & Bernstein, I. H. (1994). Validity. Psychometric theory, 3, 99-132.
• Reid, G.B. & Nygren, T.E. (1988). The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload, Advances in Psychology, 52, 185-218.
• Rosin, H., & Korabik, K. (1995). Organizational experiences and propensity to leave: A multivariate investigation of men and women managers. Journal of Vocational Behavior, 46(1), 1-16. Rubio, S., Díaz, E., Martín, J., & Puente, J. M. (2004). Evaluation of subjective mental workload: A comparison of SWAT, NASA‐TLX, and workload profile methods. Applied Psychology, 53(1), 61-86.
• Rubio-Valdehita, S., López-Núñez, M. I., López-Higes, R., & Díaz-Ramiro, E. M. (2017). Development of the CarMen-Q questionnaire for mental workload assessment. Psicothema, 29(4), 570-576.
• Sartori, R. (2020). Face Validity in Personality Tests: Psychometric Instruments and Projective Techniques in Comparison, Quality&Quantity, 44, 749-759.
• Smith, A. P., & Smith, H. N. (2017). Workload, fatigue and performance in the rail industry. In International Symposium on Human Mental Workload: Models and Applications (pp. 251-263). Springer, Cham.
• Szalma, J.L. (2008). Individual Differences in stress reaction. In Performance Under Stress, P.A. Hancock and JL. Szalma (ed.). Hampshire, UK:Ashgate.
• Şimşek, Ö. F. (2007). Yapısal Eşitlik Modellemesine Giriş:(Temel İlkeler ve Lisrel Uygulamaları). Ekinoks.
• Tsang, P. S., & Velazquez, V. L. (1996). Diagnosticity and multidimensional subjective workload ratings. Ergonomics, 39(3), 358-381.
• Tubbs-Cooley, H. L., Mara, C. A., Carle, A. C., & Gurses, A. P. (2018). The NASA Task Load Index as a measure of overall workload among neonatal, paediatric and adult intensive care nurses. Intensive and Critical Care Nursing, 46, 64-69.
• Ünnü, N. A. A., & Şentürk, B. (2020). All-in-One Academics: Mental Workload in Turkish Academic Employment. In Evaluating Mental Workload for Improved Workplace Performance (69-87). IGI Global.
• Van Acker, B. B., Parmentier, D. D., Vlerick, P., & Saldien, J. (2018). Understanding mental workload: from a clarifying concept analysis toward an implementable framework. Cognition, Technology & Work, 20(3), 351-365.
• Realyvásquez-Vargas, A., Z-Flores, E., Morales, L.C. & Garcia-Alcaraz, J.L. (2020). Mental Workload Assessment and Its Effects on Middle and Senior Managers in Manufacturing Companies, in A. Realyvásquez-Vargas, K. Arredondo-Soto, G. Hernández-Escobedo, & J. González-Reséndiz (Eds.), Evaluating Mental Workload for Improved Workplace Performance (109-137). Hershey, PA: IGI Global. doi:10.4018/978-1-7998-1052-0.ch006.
• Verwey, W. B. (2000). On-line driver workload estimation. Effects of road situation and age on secondary task measures. Ergonomics, 43(2), 187-209.
• Wickens, C. D. (2008). Multiple resources and mental workload. Human factors, 50(3), 449-455.
• Xiao, X., Wanyan, X., & Zhuang, D. (2015). Mental workload prediction based on attentional resource allocation and information processing. Bio-medical materials and engineering, 26(1), 871-879.
• Young, M. S., Brookhuis, K. A., Wickens, C. D., & Hancock, P. A. (2015). State of science: mental workload in ergonomics. Ergonomics, 58(1), 1-17.
• Zeilstra, M., van Wincoop, A., & Rypkema, J. (2017). The WASCAL-Tool: Prediction of Staffing for Train Dispatching as Part of the Design Process of Track Yards. In International Symposium on Human Mental Workload: Models and Applications (pp. 143-160). Springer, Cham.