• Home
  • Search
  • Latent Factor Analysis of Regional Growth in Belarus Using Structural Equation Modeling
  • https://doi.org/10.37661/1816-0301-2025-22-4-24-35Copy DOI Icon

Latent Factor Analysis of Regional Growth in Belarus Using Structural Equation Modeling

Show More
  • Abstract
  • Literature Map
  • References
  • Similar Papers
Abstract

Objectives. This study provides a quantitative assessment of the impact of latent variables on regional economic activity and examines the interactions among various economic sectors and their contributions to regional growth. The analysis focuses on identifying latent factors of socio-economic development across Belarusian regions during the period 2016-2024, using factor analysis techniques and structural equation modeling (SEM). Methods. To reduce data dimensionality and identify latent factors, exploratory factor analysis (EFA) was applied using the Principal Component Analysis (PCA) extraction method. A structural equation model was constructed using the SEMOPY library in Python to estimate relationships among the identified factors. To assess the quality of the model, standard fit indices were used: CFI – comparative fit index; TLI – Tucker-Lewis index; RMSEA – root mean square error of approximation. The values of these indices allow evaluating the degree of consistency between the model and the empirical data. The model is based on a system of 23 socio-economic indicators across 128 administrative districts and cities of regional subordination. Results. The resulting SEM demonstrates high internal consistency and statistical reliability (CFI = 0,98; TLI = 0,97; RMSEA = 0,04), revealing significant causal linkages between latent factors. It was established that the financial sector is a key driver of investment activity, while growth in the housing stock directly stimulates consumer demand. Negative relationships were identified between agricultural potential and financial stability, as well as between industrial development and financial sustainability. Conclusion. The developed model is an effective analytical tool for formulating evidence-based regional policy, optimizing resource allocation, and strategic planning. Promising directions for future research include incorporating time lags, adding indicators of innovation and human potential, and applying spatial econometrics methods.

Similar Papers
  • Research Article
  • Citations32

Education differences in cancer fatalism: The role of information-seeking experiences.

  • Aug 23, 2016
  • Journal of Health Psychology
  • Amber S Emanuel +3
  • Research Article
  • Citations4

Psychometric evaluation of the United States-adapted perceived perioperative competence scale-revised: A national survey

  • Dec 18, 2023
  • International Journal of Nursing Studies Advances
  • Shannon Maio +2
  • Research Article
  • Citations14

Factor structure of the patient health questionnaire-9 and measurement invariance across gender and age among Chinese university students

  • Jan 06, 2023
  • Medicine
  • Yang Wang +7
  • PDF
  • Research Article

Community participation in community-based surveillance of infectious diseases: A structural equation modeling approach based on the theory of reasoned action

  • Jun 01, 2025
  • International Journal of One Health
  • Ahmed Azeez Hasan +6
  • PDF
  • Research Article
  • Citations10

Cultural adaptation to Bolivian Quechua and psychometric analysis of the Patient Health Questionnaire PHQ-9

  • Jan 09, 2024
  • BMC Public Health
  • Juan Carlos Bazo-Alvarez +6
  • PDF
  • Research Article
  • Citations3

Exploratory and Confirmatory Factor Analysis on the Values Aspects of Teacher Leadership Model

  • Dec 01, 2020
  • Universal Journal of Educational Research
  • Hamidah Yusof +3
  • Research Article

Short version of the Inventory of Parental Representations, a self-report for attachment assessment among adolescents

  • Apr 01, 2023
  • BMC Psychiatry
  • Marilou Lamourette +3
  • Research Article
  • Citations10

Evaluation of a Cognitive Behavioral Model of Tinnitus Distress: A Cross-Sectional Study Using Structural Equation Modeling

  • Jul 01, 2020
  • Ear & Hearing
  • Lucy Handscomb +3
  • Research Article

Evidence for a Somatic and Non-Somatic Factor Structure in the Patient Health Questionnaire-8 in a Military Sexual Assault Sample.

  • Jun 30, 2025
  • Military medicine
  • Nicole D Duby +1
  • Research Article

Factors associated with antiretroviral therapy adherence among adolescents living with HIV in Addis Ababa, Ethiopia: structural equation modeling.

  • Feb 28, 2026
  • AIDS research and therapy
  • Gebre G Gudisa +1
  • Research Article
  • Citations2

Menstrual Practice Needs Scale short form (MPNS-SF) and rapid (MPNS-R): development in Khulna, Bangladesh, and validation in cross-sectional surveys from Bangladesh and Uganda

  • Jul 01, 2024
  • BMJ Open
  • Julie Hennegan +15
  • PDF
  • Research Article
  • Citations7

Authentic Leadership Questionnaire applied to Brazilian nurses: evidence of validity

  • Jan 01, 2022
  • Revista Latino-Americana de Enfermagem
  • Vanessa Gomes Maziero +6
  • Research Article
  • Citations8

Evaluating the determinants of sugary beverage consumption among overweight and obese adults: An application of the integrative model of behavioural prediction

  • Nov 14, 2017
  • Health Education Journal
  • Maria Collado-Rivera +3
  • Research Article

Community-built environment and self-rated health in Western China: a latent serial mediation within SEM of sleep quality and family functioning.

  • Apr 24, 2026
  • Frontiers in public health
  • Lansicheng Yao +3
  • Research Article

Abstract LB145: The relationship of socio-behavioral factors to the symptom experience of Asian American breast cancer survivors: a structural equation model

  • Apr 14, 2023
  • Cancer Research
  • Seo Yun Kim +3
Cactus Communications logo

Copyright 2026 Cactus Communications. All rights reserved.