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Yapay Zekâya Bağımlılık Ölçeğinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması

Yıl 2024, Cilt: 6 Sayı: 3, 306 - 315, 30.09.2024
https://doi.org/10.56639/jsar.1509301

Öz

Bu çalışmada, Morales-García ve ark. (2024) tarafından geliştirilmiş olan Yapay Zekâya Bağımlılık Ölçeğini (Scale for Dependence on Artificial Intelligence - DAI) Türkçe diline uyarlayarak güvenirlik ve geçerliliğinin incelenmesi amaçlanmıştır. Ölçek üniversite öğrencilerinin Yapay zekâya bağımlılık düzeylerini ölçmeyi amaçlamaktadır. Çalışma dört aşamada gerçekleştirilmiştir. Ölçeğin Türkçe’ye çevrilmesi, açımlayıcı ve doğrulayıcı faktör analizi, madde geçerliği, güvenirlik. Çalışma kapsamında 584 katılımcının oluşturduğu üniversite öğrencilerinden veri toplanmıştır. Ölçeğin geçerliliğini test etmek amacıyla Açımlayıcı Faktör Analizi ve Doğrulayıcı Faktör Analizi yapılmıştır. Açımlayıcı Faktör Analizinde ölçeğin tek boyutlu bir yapıda olduğu ve varyansın % 58,955’inin açıklandığı bulunmuştur. Güvenirlik için Cronbach Alfa iç tutarlılık katsayısı (.82) ve test- tekrar test değerleri (0,79) hesaplanmıştır. Tek boyut ve 5 maddeden oluşan Yapay Zekâya Bağımlılık Ölçeği için yapılan doğrulayıcı faktör analizinde; x2/df=2.609 [χ2=13.045 (Sd=, p<.05)], RMESA=.077, SRMR=.03, CFI=.98, GFI=.98, AGFI=.94, NFI=.97 ve TLI=.96 değerlerine ulaşılmıştır. Elde edilen uyum indeksleri değeri sonucunda modelin iyi bir uyuma sahip olduğu ortaya konulmuştur. Yapılan madde analizinde maddelerin ayırt edici özelliğe sahip olduğu görülmüştür. Buna göre, ölçeğin tamamı için elde edilen değerlerin ölçeğin güvenirliğine yönelik yeterli kanıtlar sunduğu söylenebilir. Türkçe diline uyarlanan Yapay Zekâya Bağımlılık Ölçeğinin, üniversite öğrencilerinin yapay zekâya bağımlılık düzeylerini ölçmek için geçerli ve güvenilir bir ölçme aracı olduğu sonucuna ulaşılmıştır.

Kaynakça

  • Akıllı, H., Kemahlı, F., Okudan, K. & Polat, F. (2008). Ekolojik Ayak İzinin Kavramsal İçeriği ve Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi’nde Bireysel Ekolojik Ayak İzi Hesaplaması, Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(15), 21-35.
  • Akten, S., Gül, A. & Akten, M. (2012). Korunan Doğal Alanlarda Kullanılabilecek Ziyaretçi Yönetim Modelleri ve Karşılaştırılması. Turkish Journal of Forestry, 13(1), 57-65.
  • Andereck, K. L., Valentine, K. M., Knopf, R. C. & Vogt, C. A. (2005). Residents Perceptions of Community Tourism Impacts. Annals of Tourism Research. 32(4), 1056-1076.
  • Agarwal, R. (2022). Impact of Human Dependency on Artificial Intelligence, International Advanced Research Journal in Science, Engineering and Technology, 9 (9), 101-104.
  • Aharonovich, E., Liu, X., Nunes, E., and Hasin, D. S. (2002). Suicide attempts in substance abusers: effects of major depression in relation to substance use disorders. Am. J. Psychiatry 159, 1600–1602. https://doi.org/10.1176/appi.ajp.159.9.1600
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Alpar, R. (2014). Spor Sağlık ve Eğitim Bilimlerinden Örneklerle Uygulamalı İstatistik ve Geçerlik Güvenirlik. 2. Baskı. Detay Yayıncılık. Ankara.
  • Aytaç, M. ve Öngen, B. (2012). Doğrulayıcı faktör analizi ile yeni çevresel paradigma ölçeğinin yapı geçerliliğinin incelenmesi, İstatistikçiler Dergisi, 5, 14-22.
  • Baumgartner, H. & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139-161.
  • Bayram, N. (2004). Sosyal bilimlerde SPSS ile veri analizi. Ezgi Kitabevi, Bursa.
  • Beard, K. W., & Wolf, E. M. (2001). Modification İn The Proposed Diagnostic Criteria For Internet Addiction. Cyberpsychology & Behavior, 4(3), 377-383.
  • Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191.
  • Bu, E. T., & Skutle, A. (2013). After the ban of slot machines in Norway: A new group of treatment-seeking pathological gamblers? Journal of Gambling Studies, 29(1), 37-50. https://doi.org/10.1007/s10899-011-9287-4
  • Büyüköztürk, Ş. (2023). Sosyal Bilimler İçin Veri Analizi El Kitabı, İstatistik Araştırma Deseni SPSS Uygulamaları ve Yorum. 30. Baskı Pegem Akademi, Ankara.
  • Brown, S. A., Inaba, R. K., Gillin, J. C., Schuckit, M. A., Stewart, M. A., & Irwin, M. R. (1995). Alcoholism and affective disorder: Clinical course of depressive symptoms. Am. J. Psychiatry 152, 45-52. https://doi.org/10.1176/ajp.152.1.45
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press, New York.
  • Chianella, R. (2021). Addictive digital experiences: the influence of artificial intelligence and more-than-human design. In 14th International Conference of the European Academy of Design, Safe Harbours for Design Research, 9 (5), 1-13.
  • Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 1-10.
  • Dosovitsky, G. & Bunge, E. L. (2021). Bonding with bot: User feedback on a chatbot for social isolation. Frontiers in Digital Health, 3, 735053. https://doi.org/10.3389/fdgth.2021.735053
  • DSM. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Association, D. S. M. T. F., & American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5, No. 5). Washington, DC: American psychiatric association.
  • Evans, D. C. (2017). Bottlenecks: aligning UX design with user psychology. Apress.
  • Farghaly Abdelaliem, S. M., Dator, W. L. T., & Sankarapandian, C. (2023). The Relationship between Nursing Students’ Smart Devices Addiction and Their Perception of Artificial Intelligence. Healthcare, 11(1), 110.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Bus. Inf. Syst. Eng. 66, 111-126. https://doi.org/10.1007/s12599-023-00834-7
  • Gilder, D. A., Wall, T. L., & Ehlers, C. L. (2004). Comorbidity of select anxiety and affective disorders with alcohol dependence in Southwest California Indians. Alcohol. Clin. Exp. Res. 28, 1805-1813. https://doi.org/10.1097/01.ALC.0000148116.27875.B0
  • Gillath, O., Aİ, T., Branicky, M., Keshmiri, S., Davison, R. & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Comput Human Behav. 115:106607. https://doi.org/10.1016/j.chb.2020.106607
  • Hair, J. F. Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Prentice Hall.
  • Hambleton, R. K. & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-30. Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press. Oxford
  • Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling. A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hu, B., Mao, Y. & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Comput Human Behav. 2023;143747. https://doi.org/10.1016/j.chb.2023.107760
  • Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X. & Wang, Y. (2024). AI Technology panic-is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents. Psychology Research and Behavior Management, 1087-1102.
  • Huang, M. H. & Rust, R. T. (2018). Artificial intelligence in service, Journal of Service Research, 212, 155-172.
  • Karasar, N. (2018). Bilimsel araştırma yöntemi. (24. Basım). Nobel Yayın Dağıtım Ankara
  • Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059
  • Kaimara, P., Oikonomou, A., and Deliyannis, I. (2022). Could virtual reality applications pose real risks to children and adolescents? A systematic review of ethical issues and concerns. Virtual Real. 26, 697–735. https://doi.org/10.1007/s10055-021-00563-w
  • Kaiser, H. F. (2016). The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151. https://doi.org/10.1177/001316446002000116
  • Kline, P. (2005). An Essay Guide to Factor Analysis. Routledge, London.
  • Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications, New York, NY.
  • Koo, T. K. & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163. https://doi.org/10.1016/j.jcm.2016.02.012
  • Krstić, L., Aleksić, V., & Krstić, M. (2022). Artificial intelligence in education: A review. Conference: 9th International Scientific Conference Technics and Informatics in Education, IT Education and Practice Review paper. 223-228. https://doi.org/10.46793/TIE22.223K
  • Leech, N., Barrett, K. C., & Borgan, G. A. (2005). SPSS for intermediate statistics use and intarpretation (Second Edition ed.). New Jersey, London.
  • Meyers, L. S., Gamst, G. & Guarino, A. J. (2006). Applied Multivariate Research: Design and Interpretation. Sage.
  • Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B. & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Front. Educ. 9:1323898. https://doi.org/10.3389/feduc.2024.1323898
  • Nunes, E. V., & Rounsaville, B. J. (2006). Comorbidity of substance use with depression and other mental disorders: from diagnostic and statistical manual of mental disorders, fourth edition (DSM-IV) to DSM-V. Addiction 101, 89–96. https://doi.org/10.1111/j.1360-0443.2006.01585.x
  • Sairitupa-Sanchez, L. Z., Collantes-Vargas, A., Rivera-Lozada, O., & Morales-García, W. C. (2023). Development and validation of a scale for streaming dependence (SDS) of online games in a Peruvian population. Frontiers in Psychology, 14, 1184647.
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Adaptation of the Dependence to Artificial Intelligence Scale into Turkish: Validity and Reliability Study

Yıl 2024, Cilt: 6 Sayı: 3, 306 - 315, 30.09.2024
https://doi.org/10.56639/jsar.1509301

Öz

In this study, Morales-García et al. It was aimed to examine the reliability and validity of the Scale for Dependence on Artificial Intelligence (DAI) developed by (2024) by adapting it to the Turkish language. The scale aims to measure the addiction levels of university students to artificial intelligence. The study was carried out in four stages. Translation of the scale into Turkish, exploratory and confirmatory factor analysis, item validity, reliability. Within the scope of the study, data was collected from 584 participants, university students. Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to test the validity of the scale. In the Exploratory Factor Analysis, it was found that the scale had a unidimensional structure and 58.955% of the variance was explained. For reliability, Cronbach Alpha internal consistency coefficient (.82) and test-retest values (0.79) were calculated. In the confirmatory factor analysis conducted for the Artificial Intelligence Addiction Scale, which consists of a single dimension and 5 items; x2/df=2.609 [χ2=13.045 (Sd=, p<.05)], RMESA=.077, SRMR= .03, CFI=.98, GFI=.98, AGFI=.94, NFI=.97 and TLI=.96. has been reached. As a result of the fit index values obtained, it was revealed that the model had a good fit. In the item analysis, it was seen that the items had distinctive features. Accordingly, it can be said that the values obtained for the entire scale provide sufficient evidence for the reliability of the scale. It has been concluded that the Artificial Intelligence Dependency Scale, adapted to the Turkish language, is a valid and reliable measurement tool to measure the addiction levels of university students to artificial intelligence.

Kaynakça

  • Akıllı, H., Kemahlı, F., Okudan, K. & Polat, F. (2008). Ekolojik Ayak İzinin Kavramsal İçeriği ve Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi’nde Bireysel Ekolojik Ayak İzi Hesaplaması, Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(15), 21-35.
  • Akten, S., Gül, A. & Akten, M. (2012). Korunan Doğal Alanlarda Kullanılabilecek Ziyaretçi Yönetim Modelleri ve Karşılaştırılması. Turkish Journal of Forestry, 13(1), 57-65.
  • Andereck, K. L., Valentine, K. M., Knopf, R. C. & Vogt, C. A. (2005). Residents Perceptions of Community Tourism Impacts. Annals of Tourism Research. 32(4), 1056-1076.
  • Agarwal, R. (2022). Impact of Human Dependency on Artificial Intelligence, International Advanced Research Journal in Science, Engineering and Technology, 9 (9), 101-104.
  • Aharonovich, E., Liu, X., Nunes, E., and Hasin, D. S. (2002). Suicide attempts in substance abusers: effects of major depression in relation to substance use disorders. Am. J. Psychiatry 159, 1600–1602. https://doi.org/10.1176/appi.ajp.159.9.1600
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Alpar, R. (2014). Spor Sağlık ve Eğitim Bilimlerinden Örneklerle Uygulamalı İstatistik ve Geçerlik Güvenirlik. 2. Baskı. Detay Yayıncılık. Ankara.
  • Aytaç, M. ve Öngen, B. (2012). Doğrulayıcı faktör analizi ile yeni çevresel paradigma ölçeğinin yapı geçerliliğinin incelenmesi, İstatistikçiler Dergisi, 5, 14-22.
  • Baumgartner, H. & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139-161.
  • Bayram, N. (2004). Sosyal bilimlerde SPSS ile veri analizi. Ezgi Kitabevi, Bursa.
  • Beard, K. W., & Wolf, E. M. (2001). Modification İn The Proposed Diagnostic Criteria For Internet Addiction. Cyberpsychology & Behavior, 4(3), 377-383.
  • Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191.
  • Bu, E. T., & Skutle, A. (2013). After the ban of slot machines in Norway: A new group of treatment-seeking pathological gamblers? Journal of Gambling Studies, 29(1), 37-50. https://doi.org/10.1007/s10899-011-9287-4
  • Büyüköztürk, Ş. (2023). Sosyal Bilimler İçin Veri Analizi El Kitabı, İstatistik Araştırma Deseni SPSS Uygulamaları ve Yorum. 30. Baskı Pegem Akademi, Ankara.
  • Brown, S. A., Inaba, R. K., Gillin, J. C., Schuckit, M. A., Stewart, M. A., & Irwin, M. R. (1995). Alcoholism and affective disorder: Clinical course of depressive symptoms. Am. J. Psychiatry 152, 45-52. https://doi.org/10.1176/ajp.152.1.45
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press, New York.
  • Chianella, R. (2021). Addictive digital experiences: the influence of artificial intelligence and more-than-human design. In 14th International Conference of the European Academy of Design, Safe Harbours for Design Research, 9 (5), 1-13.
  • Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 1-10.
  • Dosovitsky, G. & Bunge, E. L. (2021). Bonding with bot: User feedback on a chatbot for social isolation. Frontiers in Digital Health, 3, 735053. https://doi.org/10.3389/fdgth.2021.735053
  • DSM. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Association, D. S. M. T. F., & American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5, No. 5). Washington, DC: American psychiatric association.
  • Evans, D. C. (2017). Bottlenecks: aligning UX design with user psychology. Apress.
  • Farghaly Abdelaliem, S. M., Dator, W. L. T., & Sankarapandian, C. (2023). The Relationship between Nursing Students’ Smart Devices Addiction and Their Perception of Artificial Intelligence. Healthcare, 11(1), 110.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Bus. Inf. Syst. Eng. 66, 111-126. https://doi.org/10.1007/s12599-023-00834-7
  • Gilder, D. A., Wall, T. L., & Ehlers, C. L. (2004). Comorbidity of select anxiety and affective disorders with alcohol dependence in Southwest California Indians. Alcohol. Clin. Exp. Res. 28, 1805-1813. https://doi.org/10.1097/01.ALC.0000148116.27875.B0
  • Gillath, O., Aİ, T., Branicky, M., Keshmiri, S., Davison, R. & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Comput Human Behav. 115:106607. https://doi.org/10.1016/j.chb.2020.106607
  • Hair, J. F. Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Prentice Hall.
  • Hambleton, R. K. & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-30. Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press. Oxford
  • Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling. A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hu, B., Mao, Y. & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Comput Human Behav. 2023;143747. https://doi.org/10.1016/j.chb.2023.107760
  • Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X. & Wang, Y. (2024). AI Technology panic-is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents. Psychology Research and Behavior Management, 1087-1102.
  • Huang, M. H. & Rust, R. T. (2018). Artificial intelligence in service, Journal of Service Research, 212, 155-172.
  • Karasar, N. (2018). Bilimsel araştırma yöntemi. (24. Basım). Nobel Yayın Dağıtım Ankara
  • Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059
  • Kaimara, P., Oikonomou, A., and Deliyannis, I. (2022). Could virtual reality applications pose real risks to children and adolescents? A systematic review of ethical issues and concerns. Virtual Real. 26, 697–735. https://doi.org/10.1007/s10055-021-00563-w
  • Kaiser, H. F. (2016). The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151. https://doi.org/10.1177/001316446002000116
  • Kline, P. (2005). An Essay Guide to Factor Analysis. Routledge, London.
  • Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications, New York, NY.
  • Koo, T. K. & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163. https://doi.org/10.1016/j.jcm.2016.02.012
  • Krstić, L., Aleksić, V., & Krstić, M. (2022). Artificial intelligence in education: A review. Conference: 9th International Scientific Conference Technics and Informatics in Education, IT Education and Practice Review paper. 223-228. https://doi.org/10.46793/TIE22.223K
  • Leech, N., Barrett, K. C., & Borgan, G. A. (2005). SPSS for intermediate statistics use and intarpretation (Second Edition ed.). New Jersey, London.
  • Meyers, L. S., Gamst, G. & Guarino, A. J. (2006). Applied Multivariate Research: Design and Interpretation. Sage.
  • Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B. & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Front. Educ. 9:1323898. https://doi.org/10.3389/feduc.2024.1323898
  • Nunes, E. V., & Rounsaville, B. J. (2006). Comorbidity of substance use with depression and other mental disorders: from diagnostic and statistical manual of mental disorders, fourth edition (DSM-IV) to DSM-V. Addiction 101, 89–96. https://doi.org/10.1111/j.1360-0443.2006.01585.x
  • Sairitupa-Sanchez, L. Z., Collantes-Vargas, A., Rivera-Lozada, O., & Morales-García, W. C. (2023). Development and validation of a scale for streaming dependence (SDS) of online games in a Peruvian population. Frontiers in Psychology, 14, 1184647.
  • Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness of fit measurement. Methods of Psychological Research Online, 8(2), 23-74.
  • Schuckit, M. A., Smith, T. L., Danko, G. P., Pierson, J., Trim, R., Nurnberger, J. I., ... & Hesselbrock, V. (2007). A comparison of factors associated with substance-induced versus independent depressions. Journal of Studies on Alcohol and Drugs, 68(6), 805-812.
  • Schumacker, R. E., & Lomax, R. G. (2016). A Beginner’s guide to structural equation modeling. 4th Edn, Taylor & Francis, New York, NY.
  • Seçer, İ. (2015). Psikolojik test geliştirme ve uyarlama süreci SPSS ve Lisrel uygulamaları, Ankara: Anı Yayıncılık.
  • Shahzad, U. (2022). A comparative analysis of artificial neural network and support vector machine for online transient stability prediction considering uncertainties. Australian Journal of Electrical and Electronics Engineering, 19(2), 101-116.
  • Sun, S., Yang, J., Chen, Y. H., Miao, J., & Sawan, M. (2022). EEG signals based internet addiction diagnosis using convolutional neural networks. Applied Sciences, 12(13), 6297.
  • Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
  • UNICEF. (2021). Adolescent Perspectives on Artificial Intelligence; United Nations Children's Fund (UNICEF), 1-30.
  • Van Rooij, A. J., Schoenmakers, T. M., Vermulst, A. A., Van Den Eijnden, R. J., & Van De Mheen, D. (2011). Online video game addiction: Identification of addicted adolescent gamers. Addiction, 106(1), 205-212.
  • Wiederhold, B. K. (2018). “Alexa, Are You My Mom?” the Role of Artificial Intelligence in Child Development. Cyberpsychol Behav Soc Net. 21 (8), 471-472. https://doi.org/10.1089/cyber. 2018.29120.bkw
  • Worthington, R. L., & Whittaker, T. A. (2016). Scale development research: A content analysis and recommendations for best practices. Couns. Psychol. 34, 806-838. https://doi.org/10.1177/0011000006288127
  • Xie, T. & Pentina, I. (2022). “Attachment theory as a framework to understand relationships with social chatbots: a case study of Replika”, Proceedings of the 55th Hawaii International Conference on System Sciences. 2046-2055.
  • Xie, T., Pentina, I, & Hancock, T. (2023). Friend, mentor, lover: does chatbot engagement lead to psychological dependence? J Serv Manag. 34 (4), 806-828. https://doi.org/10.1108/JOSM-02-2022-0072
  • Zuckerman, M. (1983). The distinction between trait and state scales is not arbitrary: Comment on Allen and Potkay's "On the arbitrary distinction between traits and states. Journal of Personality and Social Psychology, 44(5), 1083-1086. https://doi.org/10.1037/0022-3514.44.5.1083
Toplam 58 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Spor ve Egzersiz Psikolojisi
Bölüm Araştırma Makaleleri
Yazarlar

Buğra Çağatay Savaş 0000-0002-8698-6311

Erken Görünüm Tarihi 23 Eylül 2024
Yayımlanma Tarihi 30 Eylül 2024
Gönderilme Tarihi 2 Temmuz 2024
Kabul Tarihi 28 Ağustos 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 3

Kaynak Göster

APA Savaş, B. Ç. (2024). Yapay Zekâya Bağımlılık Ölçeğinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması. Herkes için Spor Ve Rekreasyon Dergisi, 6(3), 306-315. https://doi.org/10.56639/jsar.1509301

Creative Commons Lisansı

Herkes için Spor ve Rekreasyon dergisi tarafından yayınlanan eserler Creative Commons Atıf-GayriTicari-Türetilemez 4.0 Uluslararası (CC BY-NC-ND 4.0) ile lisanslanmaktadır.

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