Beck Depresyon Ölçeği İçin Sıralı Lojistik Regresyon Analizinin Kullanılabilirliği

Year 2024, , 68 - 77, 31.08.2024

Canan Demir , Sıddık Keskin , Hamit Mirtagioğlu , Yıldırım Demir

https://doi.org/10.56941/odutip.1492859

Abstract

Amaç: Çalışmada; Beck Depresyon Ölçeği için, bu ölçeğin ordinal olduğu durum dikkate alınarak Ordinal lojistik regresyon modelinin kullanılabilirliği değerlendirilmiştir.

Materyal ve Metod: Çalışma, …. Üniversitesi Sağlık Hizmetleri Meslek Yüksekokulu birinci ve ikinci sınıf öğrencileri ile gerçekleştirilmiştir. Çalışmaya katılmayı kabul eden toplam 664 gönüllü öğrenci çalışmaya dahil edilmiş, evrenden örneklem alınmamıştır. Öğrencilere sosyo-demografik özellikleri içeren 8 sorunun yanı sıra 21 maddelik Beck Depresyon Ölçeği soruları sorulmuştur. Çalışmada bağımlı değişken olarak 4 sıra kategorili (Normal-Hafif-Orta-Şiddetli) depresyon durumu alınmış ve diğer sosyo-demografik değişkenlerin depresyon durumu değişkeni ile ilişkisi incelenmiştir.

Bulgular: Çalışmada uyum iyiliği ölçütlerinden Nagelkerke yalancı R2 değeri 0.062 olarak bulunmuştur. Ayrıca model uyum kriteri -2LL (Log-likelihood) test istatistiği (p<0.05) anlamlı bulunmuştur.

Sonuç: Sonuç olarak, değişkenler ile depresyon arasındaki ilişkilerin belirlenmesinde sıralı lojistik regresyon analizinin kullanılabilirliği vurgulanmıştır.

Keywords

Beck Depresyon Ölçeği, İstatistiksel Analiz, Sıralı Lojistik Regresyon Analizi

Usability of Ordinal Logistic Regression Analysis for Beck Depression Inventory

Abstract

Objective: In the study; For the Beck Depression Scale, the usability of the Ordinal logistic regression model was evaluated, taking into account the situation in which this scale was ordinal.

Materials and Methods: The study was conducted in the first and second-year students of the ……. University, Health Services Vocational School. A total of 664 volunteer students who accepted to participate in the study were included in the study, and no sample was taken from the population. The students were asked 8 questions including socio-demographic characteristics, as well as questions of the 21-item Beck Depression Scale. Depression status with 4 order categories (Normal-Mild-Moderate-Severe) was taken as the dependent variable in the study, and the relationship of other socio-demographic variables with the depression status variable was examined.

Results: In the study, Nagelkerke pseudo R2 value, one of the goodness of fit criteria, was found to be 0.062. In addition, the model fit criterion -2LL (Log-likelihood) test statistic (p<0.05) was found significant.

Conclusion: As a result, the usability of ordered logistic regression analysis in determining the relationships between the variables and depression was emphasized.

Keywords

Beck Depression Inventory, Statistical Analysis, Ordinal Logistic Regression Analysis

References

• Anonymous 1. World Health Organization. Depression fact sheets. https://www.who.int/ health-topics/depression#tab=tab_1. Accessed 05 April 2020.
• Bedaso A, Kediro G, Yeneabat T. Factors associated with depression among prisoners in southern Ethiopia: a cross-sectional study. BMC Research Notes 2018; 11: 637-6.
• Palmer GA, Happe MC, Paxson JM, Jurek BK, Graca JJ, Olson SA. Psychometric properties of the Beck Depression Inventory-II for OEF/OIF veterans in a polytrauma sample. Military Medicine 2014; 179: 879-884.
• Anonymous 2. National Institute of Mental Health, Depression, Transforming the understanding and treatment of mental illness. https://www.nimh.nih.gov/health/topics/depression/index.shtml#part_145397. Accessed 02 April 2020.
• Taycan O, Kutlu L, Çimen S, Aydın N. Relation between sociodemographic characteristics depression and burnout levels of nurse working in university hospital. Anatolian Journal of Psychiatry 2006; 7: 100-108.
• Wang YP, Gorenstein C. Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. Brazilian Journal of Psychiatry 2013; 35: 416-431.
• Gok AC, Özdemir A. Forecasting the sector portions of banks by logistic regression analysis. Dokuz Eylül University Journal of Faculty of Business 2011; 1: 43-51.
• Alpar R. Applied multivariate statistical methods. Ankara, Delta Yayıncılık; 2013.
• Liu X. Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling. London, Sage Publications; 2016.
• Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. New York, John Wiley & Sons; 2013.
• Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Archives of General Psychiatry 1961; 4: 561-571.
• Hisli N. The reliability and validity of Beck depression inventory for university students. Turkish Journal of Psychology. 1989; 7:3-13.
• Akın HB, Şentürk E. Analysing levels of happiness of individuals with ordinal logistic analysis. Oneri 2012; 10: 183-193.
• Keskin S. Overview of pseudo r-square measures. Güler A (Editor) Current Academic Studies in Health Sciences, Cetinje, IVPE, 2018. p.1-9.
• Goktolga ZG, Karakıs E. Determination of the Relationship between Students' Economic Status and Their Happiness by Ordinal Logistic Regression Method: The Case of Cumhuriyet University. EconWorld2017@Paris Proceedings, Paris, France; 2017, July 25-27.
• Ugurlu CT, Usta HG, Şimşek AS. Absence trend of undergraduates: logistic prediction. Kastamonu Education Journal 2018; 26: 345-349.
• Orcanlı K, Oktay E. Pirim M. Investigation of Factors Affecting Satisfaction with Time-Use: Example of Atatürk University Students. Journal of Graduate School of Social Sciences 2019; 23: 797-825.
• Eygu H, Gulluce A. Determination of customer satisfaction in conservative concept hotels by ordinal logistic regression analysis. Journal of Financial Risk Management 2017; 6: 269-284.
• Eygu H, Kılınç A. The research of factors that affects on life satisfaction: the sample of Kayseri province. OPUS International Journal of Society Researches 2020; 16: 3591-3618.
• Turgut V. Investigation of sequential logistic regression model. Van Yuzuncu Yil University, Institute of Health Sciences 2020.