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Çevrimiçi Oyun Oynama Hakkında Üstbilişler Ölçeği’nin Türkçe Versiyonunun Psikometrik Özellikleri

Yıl 2021, Cilt: 22 Sayı: 3, 314 - 326, 30.09.2021
https://doi.org/10.51982/bagimli.896088

Öz

Amaç: Bu çalışmanın amacı Çevrimiçi Oyun Oynama Hakkında Üstbilişler Ölçeği’nin Türkçe versiyonunun (MOGS-T) psikometrik özelliklerini araştırmaktır.
Yöntem: Video oyunu oynayanların katıldığı iki çalışma yapılmıştır (n1 = 196, n2 = 150) ve katılımcılar demografik bilgi formu, MOGS-T, Oyun Bağımlılığı Ölçeği, İnternet Bağımlılığı Ölçeği ve Depresyon Kaygı Stres Ölçeği’ni doldurmuştur.
Bulgular: Çalışmanın bulguları MOGS-T’nin iç tutarlılığının ve test-tekrar test güvenilirliğinin yüksek olduğunu göstermiştir. Birinci çalışmada, MOGS-T’nin faktör yapısı açımlayıcı faktör analizi ile sınanmıştır. Çalışmanın bulguları MOGS-T’nin çevrimiçi oyun oynama hakkında pozitif üstbilişler ve çevrimiçi oyun oynama hakkında negatif üstbilişler olmak üzere iki faktörlü yapıya sahip olduğunu göstermiştir. Ölçeğin faktör yapısını doğrulamak ve yordama geçerliliğini ölçmek üzere ikinci çalışma yapılmıştır. Hiyerarşik regresyon analizleri, çevrimiçi oyunlarla ilgili olumlu üstbilişlerin haftalık çevrimiçi oyun saatlerini, çevrimiçi oyunlarla ilgili olumsuz üstbilişlerin İnternet bağımlılığını ve çevrimiçi oyunla ilgili her iki üstbilişin de oyun bağımlılığını anlamlı olarak yordadığını göstermiştir.
Sonuç: MOGS-T güvenilir ve geçerli psikometrik özelliklere sahip bir ölçektir.

Kaynakça

  • 1. Billieux J et al. Problematic involvement in online games: A cluster analytic approach. Comput Hum Behav 2015; 43: 242-250.
  • 2. Rumpf HJ et al. Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective: Commentary on: A weak scientific basis for gaming disorder: Let us err on the side of caution (van Rooij et al., 2018). J Behav Addict 2018; 7(3): 556-561.
  • 3. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5). Washington, DC: American Psychiatric Publishing, 2013.
  • 4. World Health Organization. (2018). International Classification of Diseases (ICD) information sheet. ICD purpose and uses. Available from: http://www.who.int/classifications/icd/factsheet/en/
  • 5. Garcia-Oliva C, Piqueras JA. Experiential avoidance and technological addictions in adolescents. J Behav Addict 2016; 5(2): 293-303.
  • 6. Hyun GJ et al. Risk factors associated with online game addiction: a hierarchical model. Comput Hum Behav 2015; 48: 706-713.
  • 7. Sung Y, Nam TH, Hwang MH. Attachment style, stressful events, and Internet gaming addiction in Korean university students. Pers Individ Differ 2020; 154: 109724.
  • 8. King DL, Delfabbro PH. Is preoccupation an oversimplification? A call to examine cognitive factors underlying internet gaming disorder. Addiction 2014; 109(9): 1566-1567.
  • 9. King DL, et al. Toward a consensus definition of pathological video-gaming: A systematic review of psychometric assessment tools. Clin Psychol Rev 2013; 33: 331-342.
  • 10. Charlton JP, Danforth ID. Distinguishing addiction and high engagement in the context of online game playing. Comput Hum Behav 2007; 23(3): 1531-1548.
  • 11. Ko CH et al. Evaluation of the diagnostic criteria of Internet gaming disorder in the DSM-5 among young adults in Taiwan. J Psychiatr Res 2014; 53: 103-110.
  • 12. Wichstrøm L et al. Symptoms of internet gaming disorder in youth: predictors and comorbidity. J Abnorm Child Psychol 2019; 47(1): 71-83.
  • 13. King DL, Delfabbro PH. The cognitive psychopathology of Internet gaming disorder in adolescence. J Abnorm Child Psychol 2016; 44(8): 1635-1645.
  • 14. Laier C, Wegmann E, Brand M. Personality and cognition in gamers: Avoidance expectancies mediate the relationship between maladaptive personality traits and symptoms of Internet-gaming disorder. Front. Psychiatry 2018; 9: 1-8.
  • 15. Caselli G, Marino C, Spada MM. Modelling online gaming metacognitions: The role of time spent gaming in predicting problematic Internet use. J Ration Emot Cogn Behav Ther 2020; 1-11.
  • 16. Wells A, Matthews G. Modelling cognition in emotional disorder: The S-REF model. Behav Res Ther 1996; 34: 881-888.
  • 17. Sun X, Zhu C, So SHW. Dysfunctional metacognition across psychopathologies: A meta-analytic review. Eur Psychiatry 2017; 45: 139-153.
  • 18. Hamonniere T, Varescon I. Metacognitive beliefs in addictive behaviours: A systematic review. Addict Behav 2018; 85: 51-63.
  • 19. Spada MM, Wells A. A metacognitive model of problem drinking. Clin Psychol Psychother 2009; 16: 383-393.
  • 20. Spada MM, Caselli G, Wells A. A triphasic metacognitive formulation of problem drinking. Clin Psychol Psychother 2013; 20: 494-500.
  • 21. Spada MM, Caselli G, Nikčević AV, Wells A. Metacognition in addictive behaviors. Addict Behav 2015; 44: 9-15.
  • 22. Jauregui P, Urbiola I, Estevez A. Metacognition in pathological gambling and its relationship with anxious and depressive symptomatology. J Gambl Stud 2016: 32(2): 675-688.
  • 23. Spada MM, Giustina L, Rolandi S, Fernie BA, Caselli G. Profiling metacognition in gambling disorder. Behav Cogn Psychother 2015; 43(5): 614-622.
  • 24. Alma L et al. Metacognitions in smoking: Evidence from a cross-cultural validation of the metacognitions about smoking questionnaire in a Turkish sample. Psychiatry Res 2018; 259: 160-168.
  • 25. Casale S, Caponi L, Fioravanti G. Metacognitions about problematic Smartphone use: Development of a self-report measure. Addict 2020; 109: 106484.
  • 26. Casale S, Caplan SE, Fioravanti G. Positive metacognitions about Internet use: The mediating role in the relationship between emotional dysregulation and problematic use. Addict Behav 2016; 59: 84-88.
  • 27. Spada MM, Langston B, Nikčević AV, Moneta GB. The role of metacognitions in problematic internet use. Comput Hum Behav 2008; 24(5): 2325-2335.
  • 28. Spada MM, Caselli G. The metacognitions about online gaming scale: Development and psychometric properties. Addict Behav 2017; 64: 281-286.
  • 29. Marino C, Spada MM. Dysfunctional cognitions in online gaming and internet gaming disorder: A narrative review and new classification. Curr Addict Rep 2017; 4(3): 308-316.
  • 30. Aydın O, Güçlü M, Ünal-Aydın P, Spada MM. Metacognitions and emotion recognition in Internet Gaming Disorder among adolescents. Addict Behav Rep 2020; 12: 100296.
  • 31. Yılmaz AE, Gençöz T, Wells A. Psychometric characteristics of the Penn State Worry Questionnaire and Metacognitions Questionnaire‐30 and metacognitive predictors of worry and obsessive–compulsive symptoms in a Turkish sample. Clin Psychol Psychother 2008; 15(6): 424-439.
  • 32. Lemmens JS, Valkenburg PM, Peter J. Development and validation of a game addiction scale for adolescents. Media Psychol 2009; 12(1): 77-95.
  • 33. Baysak E, Kaya FD, Dalgar I, Candansayar S. Online game addiction in a sample from Turkey: Development and validation of the Turkish version of game addiction scale. Klinik Psikofarmakoloji Bülteni 2016; 26(1): 21-31.
  • 34. Lovibond PF, Lovibond SH. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther 1995; 33(3): 335-343.
  • 35. Sarıçam H. (2018). The psychometric properties of Turkish version of Depression Anxiety Stress Scale-21 (DASS-21) in health control and clinical samples. J Cogn Behav Psychoter Res 2018; 7(1): 19-30.
  • 36. Pawlikowski M, Altstötter-Gleich C, Brand M. Validation and psychometric properties of a short version of Young’s Internet Addiction Test. Comput Hum Behav 2013; 29(3): 1212-1223.
  • 37. Kutlu M, Savcı M, Demir Y, Aysan F. Young İnternet Bağımlılığı Testi Kısa Formunun Türkçe uyarlaması: Üniversite öğrencileri ve ergenlerde geçerlilik ve güvenilirlik çalışması. Anadolu Psikiyatri Dergisi 2016; 17(1): 69-76.
  • 38. Arbuckle JL. Amos 18.0 [Computer software].Chicago, IL: Small Waters, 2009.
  • 39. Wheaton B, Muthen B, Alwin DF, Summers G. Assessing reliability and stability in panel models. Sociol Methodol 1977; 8(1): 84-136.
  • 40. Browne MW, Cudeck R. Alternative ways of assessing model fit. Bollen KA, Long JS (Editors.), Testing Structural Equation Models. Newbury Park, CA: Sage, 1993: 136-162.
  • 41. Kline RB. Principles and practice of structural equation modeling (2nd Edition ed.). New York: The Guilford Press, 2005.
  • 42. Griffiths MD et al. Working towards an international consensus on criteria for assessing Internet gaming disorder: A critical commentary on Petry et al. (2014). Addiction 2016; 111(1): 167-175.
  • 43. Starcevic V. Behavioural addictions: A challenge for psychopathology and psychiatric nosology. ANZJP 2016; 50(8): 721-725.
  • 44. Normann N, van Emmerik AA, Morina N. The efficacy of metacognitive therapy for anxiety and depression: A meta‐analytic review. Depress Anxiety 2014; 31(5): 402-411.
  • 45. Castro‐Calvo J et al. Expert appraisal of criteria for assessing gaming disorder: An international Delphi study. Addict 2021. Available from: https://doi.org/10.1111/add.15411

Psychometric Characteristics of Turkish Version of Metacognitions about Online Gaming Scale

Yıl 2021, Cilt: 22 Sayı: 3, 314 - 326, 30.09.2021
https://doi.org/10.51982/bagimli.896088

Öz

Objective: The purpose of the present study was to assess the psychometric properties of the Turkish version of the Metacognitions about Online Gaming Scale (MOGS-T).
Method: Two studies were carried out with samples of video gamers (n1 = 196, n2 = 150) who filled a set of questionnaires including the demographic information form, MOGS-T, Gaming Addiction Scale, Internet Addiction Test, and Depression Anxiety Stress Scales (DASS).
Results: MOGS-T had good internal consistency and test-retest reliability. The factor structure of the MOGS-T was examined through exploratory factor analysis in the first study. A two-factor solution with positive metacognitions about online gaming and negative metacognitions about online gaming subscales showed the best fit to the data. A second study was performed to verify the factor structure of the scale and examine the predictive ability of MOGS-T factors. Hierarchical regression analyses demonstrated that positive metacognitions about online gaming significantly predicted weekly online gaming hours, negative metacognitions about online gaming significantly predicted Internet addiction, and both metacognitions about online gaming significantly predicted gaming addiction.
Conclusion: MOGS-T has reliable and valid psychometric properties for this population.

Kaynakça

  • 1. Billieux J et al. Problematic involvement in online games: A cluster analytic approach. Comput Hum Behav 2015; 43: 242-250.
  • 2. Rumpf HJ et al. Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective: Commentary on: A weak scientific basis for gaming disorder: Let us err on the side of caution (van Rooij et al., 2018). J Behav Addict 2018; 7(3): 556-561.
  • 3. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5). Washington, DC: American Psychiatric Publishing, 2013.
  • 4. World Health Organization. (2018). International Classification of Diseases (ICD) information sheet. ICD purpose and uses. Available from: http://www.who.int/classifications/icd/factsheet/en/
  • 5. Garcia-Oliva C, Piqueras JA. Experiential avoidance and technological addictions in adolescents. J Behav Addict 2016; 5(2): 293-303.
  • 6. Hyun GJ et al. Risk factors associated with online game addiction: a hierarchical model. Comput Hum Behav 2015; 48: 706-713.
  • 7. Sung Y, Nam TH, Hwang MH. Attachment style, stressful events, and Internet gaming addiction in Korean university students. Pers Individ Differ 2020; 154: 109724.
  • 8. King DL, Delfabbro PH. Is preoccupation an oversimplification? A call to examine cognitive factors underlying internet gaming disorder. Addiction 2014; 109(9): 1566-1567.
  • 9. King DL, et al. Toward a consensus definition of pathological video-gaming: A systematic review of psychometric assessment tools. Clin Psychol Rev 2013; 33: 331-342.
  • 10. Charlton JP, Danforth ID. Distinguishing addiction and high engagement in the context of online game playing. Comput Hum Behav 2007; 23(3): 1531-1548.
  • 11. Ko CH et al. Evaluation of the diagnostic criteria of Internet gaming disorder in the DSM-5 among young adults in Taiwan. J Psychiatr Res 2014; 53: 103-110.
  • 12. Wichstrøm L et al. Symptoms of internet gaming disorder in youth: predictors and comorbidity. J Abnorm Child Psychol 2019; 47(1): 71-83.
  • 13. King DL, Delfabbro PH. The cognitive psychopathology of Internet gaming disorder in adolescence. J Abnorm Child Psychol 2016; 44(8): 1635-1645.
  • 14. Laier C, Wegmann E, Brand M. Personality and cognition in gamers: Avoidance expectancies mediate the relationship between maladaptive personality traits and symptoms of Internet-gaming disorder. Front. Psychiatry 2018; 9: 1-8.
  • 15. Caselli G, Marino C, Spada MM. Modelling online gaming metacognitions: The role of time spent gaming in predicting problematic Internet use. J Ration Emot Cogn Behav Ther 2020; 1-11.
  • 16. Wells A, Matthews G. Modelling cognition in emotional disorder: The S-REF model. Behav Res Ther 1996; 34: 881-888.
  • 17. Sun X, Zhu C, So SHW. Dysfunctional metacognition across psychopathologies: A meta-analytic review. Eur Psychiatry 2017; 45: 139-153.
  • 18. Hamonniere T, Varescon I. Metacognitive beliefs in addictive behaviours: A systematic review. Addict Behav 2018; 85: 51-63.
  • 19. Spada MM, Wells A. A metacognitive model of problem drinking. Clin Psychol Psychother 2009; 16: 383-393.
  • 20. Spada MM, Caselli G, Wells A. A triphasic metacognitive formulation of problem drinking. Clin Psychol Psychother 2013; 20: 494-500.
  • 21. Spada MM, Caselli G, Nikčević AV, Wells A. Metacognition in addictive behaviors. Addict Behav 2015; 44: 9-15.
  • 22. Jauregui P, Urbiola I, Estevez A. Metacognition in pathological gambling and its relationship with anxious and depressive symptomatology. J Gambl Stud 2016: 32(2): 675-688.
  • 23. Spada MM, Giustina L, Rolandi S, Fernie BA, Caselli G. Profiling metacognition in gambling disorder. Behav Cogn Psychother 2015; 43(5): 614-622.
  • 24. Alma L et al. Metacognitions in smoking: Evidence from a cross-cultural validation of the metacognitions about smoking questionnaire in a Turkish sample. Psychiatry Res 2018; 259: 160-168.
  • 25. Casale S, Caponi L, Fioravanti G. Metacognitions about problematic Smartphone use: Development of a self-report measure. Addict 2020; 109: 106484.
  • 26. Casale S, Caplan SE, Fioravanti G. Positive metacognitions about Internet use: The mediating role in the relationship between emotional dysregulation and problematic use. Addict Behav 2016; 59: 84-88.
  • 27. Spada MM, Langston B, Nikčević AV, Moneta GB. The role of metacognitions in problematic internet use. Comput Hum Behav 2008; 24(5): 2325-2335.
  • 28. Spada MM, Caselli G. The metacognitions about online gaming scale: Development and psychometric properties. Addict Behav 2017; 64: 281-286.
  • 29. Marino C, Spada MM. Dysfunctional cognitions in online gaming and internet gaming disorder: A narrative review and new classification. Curr Addict Rep 2017; 4(3): 308-316.
  • 30. Aydın O, Güçlü M, Ünal-Aydın P, Spada MM. Metacognitions and emotion recognition in Internet Gaming Disorder among adolescents. Addict Behav Rep 2020; 12: 100296.
  • 31. Yılmaz AE, Gençöz T, Wells A. Psychometric characteristics of the Penn State Worry Questionnaire and Metacognitions Questionnaire‐30 and metacognitive predictors of worry and obsessive–compulsive symptoms in a Turkish sample. Clin Psychol Psychother 2008; 15(6): 424-439.
  • 32. Lemmens JS, Valkenburg PM, Peter J. Development and validation of a game addiction scale for adolescents. Media Psychol 2009; 12(1): 77-95.
  • 33. Baysak E, Kaya FD, Dalgar I, Candansayar S. Online game addiction in a sample from Turkey: Development and validation of the Turkish version of game addiction scale. Klinik Psikofarmakoloji Bülteni 2016; 26(1): 21-31.
  • 34. Lovibond PF, Lovibond SH. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther 1995; 33(3): 335-343.
  • 35. Sarıçam H. (2018). The psychometric properties of Turkish version of Depression Anxiety Stress Scale-21 (DASS-21) in health control and clinical samples. J Cogn Behav Psychoter Res 2018; 7(1): 19-30.
  • 36. Pawlikowski M, Altstötter-Gleich C, Brand M. Validation and psychometric properties of a short version of Young’s Internet Addiction Test. Comput Hum Behav 2013; 29(3): 1212-1223.
  • 37. Kutlu M, Savcı M, Demir Y, Aysan F. Young İnternet Bağımlılığı Testi Kısa Formunun Türkçe uyarlaması: Üniversite öğrencileri ve ergenlerde geçerlilik ve güvenilirlik çalışması. Anadolu Psikiyatri Dergisi 2016; 17(1): 69-76.
  • 38. Arbuckle JL. Amos 18.0 [Computer software].Chicago, IL: Small Waters, 2009.
  • 39. Wheaton B, Muthen B, Alwin DF, Summers G. Assessing reliability and stability in panel models. Sociol Methodol 1977; 8(1): 84-136.
  • 40. Browne MW, Cudeck R. Alternative ways of assessing model fit. Bollen KA, Long JS (Editors.), Testing Structural Equation Models. Newbury Park, CA: Sage, 1993: 136-162.
  • 41. Kline RB. Principles and practice of structural equation modeling (2nd Edition ed.). New York: The Guilford Press, 2005.
  • 42. Griffiths MD et al. Working towards an international consensus on criteria for assessing Internet gaming disorder: A critical commentary on Petry et al. (2014). Addiction 2016; 111(1): 167-175.
  • 43. Starcevic V. Behavioural addictions: A challenge for psychopathology and psychiatric nosology. ANZJP 2016; 50(8): 721-725.
  • 44. Normann N, van Emmerik AA, Morina N. The efficacy of metacognitive therapy for anxiety and depression: A meta‐analytic review. Depress Anxiety 2014; 31(5): 402-411.
  • 45. Castro‐Calvo J et al. Expert appraisal of criteria for assessing gaming disorder: An international Delphi study. Addict 2021. Available from: https://doi.org/10.1111/add.15411
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Psikolojide Davranış-Kişilik Değerlendirmesi
Bölüm Araştırma
Yazarlar

Merve Denizci Nazlıgül 0000-0002-6516-7341

Yankı Süsen 0000-0002-4942-2736

Yayımlanma Tarihi 30 Eylül 2021
Kabul Tarihi 1 Mayıs 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 22 Sayı: 3

Kaynak Göster

AMA Denizci Nazlıgül M, Süsen Y. Psychometric Characteristics of Turkish Version of Metacognitions about Online Gaming Scale. Bağımlılık Dergisi. Eylül 2021;22(3):314-326. doi:10.51982/bagimli.896088

Bağımlılık Dergisi - Journal of Dependence