Preview

Health care of the Russian Federation

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Decoding cognitive distortions in different age groups: cluster analysis

https://doi.org/10.47470/0044-197X-2025-69-5-475-480

EDN: olqesz

Abstract

Introduction. Cognitive biases are persistent thinking errors that influence perception, behavior, and decision-making. Studying them over time is important for understanding the psychological characteristics of perception and the formation of biases.

The purpose of the study. To determine age-specific profiles of cognitive biases in a 16–30 year student sample aged using cluster analysis. The primary objective was to apply cluster analysis to questionnaire results to identify persistent thinking patterns and biases in respondents of different ages.

Materials and methods. A total of three hundred twenty volunteers (71% women, 29% men) participated in the online survey. The average age of the group was 19.8 ± 4.7 years. The author’s CBB-8 questionnaire included 24 statements reflecting eight key biases; the internal consistency of the entire scale was α = 0.83. After standardization of the indices, the profiles were grouped using the k-means++ method (elbow criterion; optimal k = 3; silhouette = 0.41). Intercluster differences were tested by χ² and one-way ANOVA; two-tailed significance level of 0.05.

Results. Three profiles were identified. Cluster 0, “younger”: n = 105; 32.8% (95% CI 27.7–38.3); median age 18 years; low anxiety, high tolerance for uncertainty. Cluster 1, “senior student”: n = 134; 41.9% (95% CI 36.3–47.8); median 23.7 years (IQR 22–25); pronounced pessimism. Cluster 2, “intermediate”: n = 81; 25.3% (95% CI 20.8–30.5); median 21 years.

Research limitations. The study did not account for respondents’ socioeconomic status and was cross-sectional, not accounting for age-related changes.

Conclusion. Cluster analysis demonstrates the existence of three stable age-related profiles of cognitive biases in the youth sample. The resulting typology can be considered when developing educational programs on critical thinking and prejudice prevention.

Compliance with ethical standards. The study was approved by the Ethics Committee of the Research Institute for Healthcare Organization and Medical Management (approval No. 02-01_EC_2025 dated February 11, 2025). All participants provided informed voluntary consent to participate in the study.

Funding. The study had no sponsorship.

Conflict of interest. The authors declare the absence of obvious and potential conflicts of interest in connection with the publication of this article.

Received: March 26, 2025 / Accepted: June 24, 2025 / Published: October 31, 2025

About the Author

Yuriy Yu. Shvets
Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department
Russian Federation

PhD (Economics), Associate Professor, researcher, Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department, Moscow, 115088, Russian Federation

e-mail: jurijswets@yahoo.com



References

1. Tversky A., Kahneman D. Judgment under uncertainty: heuristics and biases. Science. 1974; 185(4157): 1124–31. https://doi.org/10.1126/science.185.4157.1124

2. Haselton M.G., Nettle D. The paranoid optimist: an integrative evolutionary model of cognitive biases. Pers. Soc. Psychol. Rev. 2006; 10(1): 47–66. https://doi.org/10.1207/s15327957pspr1001_3

3. Lieder F., Griffiths T.L. Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behav. Brain Sci. 2019; 43: e1. https://doi.org/10.1017/S0140525X1900061X

4. De Baets S., Vanderheyden K. Individual differences in the susceptibility to forecasting biases. Appl. Cogn. Psychol. 2021; 35(4): 1106–14. https://doi.org/10.1002/acp.3831

5. Vinichenko T.N., Kovaleva M.A., Gorelov V.V. Development of an approach to clustering students according to the level of their creative potential. Mezhdunarodnyi zhurnal gumanitarnykh i estestvennykh nauk. 2022; (12–2): 102–8. https://doi.org/10.24412/2500-1000-2022-12-2-102-108 https://elibrary.ru/uljygl (in Russian)

6. Moreno-Jiménez B., Bustos R., Matallana A., Miralles T. La evaluación del burnout. Problemas y alternativas. El CBB como evaluación de los elementos del proceso. Rev. Psicol. Trabajo Organ. 1997; 13(2): 185–207. (in Spanish)

7. Popov G.I., Konyukhov V.G., Markaryan V.S. Yashkina E.N. Statistical Data Processing [Statisticheskaya obrabotka dannykh]. Moscow; 2015: 107–12. https://elibrary.ru/vmeyax (in Russian)

8. Repina S.I. Verification the quality of clusters using silhouette analysis. Ekonomika i sotsium. 2024; (9): 958–75. https://doi.org/10.5281/zenodo.13918451 https://elibrary.ru/gznugm (in Russian)

9. Abdullaeva N.N., Kasimov A.A., Tsoi K.L. Phenomenology of functional cognitive disorders. Oriental Renaissance: Innovative, educational, natural and social sciences. 2023; (3): 871–80. (in Russian)

10. Bruine de Bruin W., Parker A.M., Fischhoff B. Decision-making competence: More than intelligence? Cur. Dir. Psychol. Sci. 2020; 29(2): 186–92. https://doi.org/10.1177/0963721420901592

11. Yakovleva Yu.A., Vakhnin N.A., Novikova E.S., Mysova V.V. Indicators for assessing the physical and socio-psychological health of students. Teoriya i praktika fizicheskoi kul’tury. 2023; (3): 55–7. https://elibrary.ru/gwaupe (in Russian)

12. Chizhkova M.B. Healthy behavior violation features among medical university students of different study years. Mir nauki. Pedagogika i psikhologiya. 2020; 8(1): 58. https://elibrary.ru/lwwxwb (in Russian)

13. Kvon G.M., Vaks V.B., Pozdeyeva O.G. Using the Likert scale in the study of motivational factors of students. Nauchno-metodicheskii elektronnyi zhurnal “Kontsept”. 2018; (11): 84–96. https://doi.org/10.24411/2304-120X-2018-11086 https://elibrary.ru/yombrr (in Russian)


Review

For citations:


Shvets Yu.Yu. Decoding cognitive distortions in different age groups: cluster analysis. Health care of the Russian Federation. 2025;69(5):475-480. (In Russ.) https://doi.org/10.47470/0044-197X-2025-69-5-475-480. EDN: olqesz

Views: 11


ISSN 0044-197X (Print)
ISSN 2412-0723 (Online)