Experience in statistical modeling of population psychological state dynamics during critical situations

The article presents findings in a longitudinal study on the psychological adaptation of the population during the shock period triggered by the special military operation (2022). Using a sample of 313 respondents across six consecutive survey waves and employing probabilistic-statistical modeling methods, the dynamics of psychological well-being and coping strategies were analyzed. Two stable structural clusters of psychological indicators emerged: overall psychological well-being (comprising emotional, social, and personal components) and constructive coping (including positive reappraisal, acceptance, and humor). Composite indices were constructed for each cluster, revealing distinct dynamics: relative stability in “well-being” versus a significant decline in “coping” following the announcement of partial mobilization. Four distinct trajectory types were identified for each index, forming “mirror-image” pairs. Membership in a specific adaptation type was not determined by socio-demographic factors (gender, age, education, income) but was associated with initial socio-psychological attitudes. Using econometric models of nested dichotomies, statistical predictors of adaptation were identified: “trust in institutions,” “social optimism,” “identity-related attitudes,” and “cognitive strategies.” The study demonstrates that, under crisis conditions, individual adaptation trajectories are statistically predictable based on psychological attitudes measured during the initial survey period, highlighting the role of these initial attitudes as predictors of future adaptation.

1. Aivazyan S.A., Fantazzini D. (2014). Econometrics‑2: Advanced Course with Applications in Finance. Moscow: Magistr, INFRA-M. (In Russ.)

2. Balatsky E.V., Ekimova N.A. (2007). The inertia effect in the formation of social moods. Monitoring Public Opinion: Economic and Social Changes, no. 3 (83), pp. 85–94. (In Russ.)

3. Gavrilets Yu.N., Kudrov A.V., Tarakanova I.V. (2022). Statistical analysis and modeling of the interrelationship between regional economy and science. Economics and Mathematical Methods, no. 58(4), pp. 56–70. (In Russ.)

4. Gulin K.A., Dementyeva I.N. (2009). Economic conditions and social well-being of the population in the Northwest regions of Russia during the crisis. Economic and Social Changes in the Region: Facts, Trends, Forecast, no. 4. (In Russ.)

5. Latova N.V. (2024). Russians’ satisfaction with various aspects of life: A decade-long trend against the backdrop of socio-economic crises. Sociological Studies (Socis), no. 9, pp. 17–29. (In Russ.)

6. Nestik T.A. (2023). Social optimism of Russians in crisis conditions: Results of a longitudinal study. Psychological Journal, no. 44(3), pp. 5–17. (In Russ.)

7. Nestik T.A., Selezneva A.V. et al. (2021). The problem of the psychological state of society and political processes in contemporary Russia. Voprosy Psikhologii, no. 67 (5), pp. 3–14. (In Russ.)

8. Okolskaya L.A. (2024). Emotions of Russians in 2014–2024. Sotsiologicheskie Issledovaniya, no. 9, pp. 30–42. (In Russ.)

9. Savin S.D. (2024). Crisis-stabilization dynamics of mass consciousness in Russian society. Sociological Studies (Socis), no. 6, pp. 76–87. (In Russ.)

10. Smoleva E.O. (2023). Socio-psychological state of the population of Vologda Oblast during crisis periods: Socio-structural characteristics. Sociological Studies (Socis), no. 7, pp. 40–52. (In Russ.)

11. Toshchenko Zh.T., Kharchenko S.V. (1996). Social Mood. Moscow: Academia. 196 pp. (In Russ.)

12. Fabrikant M.S. (2023). The relationship between social trust and anxiety about the future: A comparative cross-cultural perspective. Social Psychology and Society, vol. 14, no. 4, pp. 120–134. (In Russ.)

13. Shestopal E.B. (2022). The impact of the psychological state of Russian society on public policy. Political Science, no. 3, pp. 181–202. (In Russ.)

14. Yurevich A.V. (2019). Empirical assessment of the psychological state of contemporary Russian society (analysis of statistical data). Psychological Journal, no. 40 (5), pp. 84–96. (In Russ.)

15. Akaike H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, no. 19, pp. 716–723.

16. Arslan R.C., Walther M.P., Tata C.S. (2020). Formr: A study framework allowing for automated feedback generation and complex longitudinal experience-sampling studies using R. Behavior Research Methods, no. 52 (1),

17. pp. 376–387.

18. Hair J.F., Black W.C. et al. (2010). Multivariate data analysis. Hoboken: Prentice Hall.

19. Hosmer T., Hosmer D.W., Fisher L.L. (1983). A comparison of the maximum likelihood and discriminant function estimators of the coefficients of the logistic regression model for mixed continuous and discrete variables. Communications in Statistics, no. B12, pp. 577–593.

20. Hosmer D.W., Hosmer T. et al. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, no. 16, pp. 965–980.

21. Menard S. (1995). Applied logistic regression analysis. Sage university series on quantitative applications in the social sciences. Thousand Oaks (CA): Sage.

22. Scharbert J., Reiter T. et al. (2023). A global experience-sampling methodstudy of well-being during times of crisis: The CoCo project. Social and Personality Psychology Compass, no. 17 (10), e12813.

23. Schweizer K., Schneider R. (1997). Social optimism as generalized expectancy of a positive outcome. Personality and Individual Differences, no. 22(3), pp. 317–325.