A Predictive Study of Family Adaptability Based on Cultural and Economic Indicators
Keywords:
Family Adaptability, Cultural Orientation, Acculturation, Economic Indicators, Financial Stability, Machine Learning, Predictive ModelingAbstract
Objective: The present study aimed to predict family adaptability based on cultural orientation and economic indicators using both statistical and machine learning approaches in a Canadian population.
Methods and Materials: This study employed a quantitative, cross-sectional, descriptive–correlational design. The statistical population consisted of married adults residing in Canada in 2025, from which 486 participants were selected using a stratified random sampling method based on the Krejcie and Morgan table. Data were collected using standardized instruments, including the Family Adaptability subscale of the Family Adaptability and Cohesion Evaluation Scales IV (FACES IV), a Cultural Orientation Scale assessing individualism, collectivism, traditionalism, and acculturation, and a socioeconomic questionnaire measuring income index, financial stability, and economic pressure. The reliability and validity of all instruments were confirmed in prior research and re-evaluated in the present study. Data analysis was conducted using IBM SPSS Statistics (version 27) for descriptive statistics, Pearson correlations, and multiple regression analysis. Additionally, machine learning models including Random Forest, Support Vector Machine, and Gradient Boosting were implemented in Python using Scikit-learn to enhance predictive accuracy.
Findings: The results revealed that collectivism (β = 0.31, p < 0.001), financial stability (β = 0.36, p < 0.001), acculturation (β = 0.18, p < 0.001), and individualism (β = 0.12, p = 0.007) were significant positive predictors of family adaptability, whereas economic pressure (β = -0.29, p < 0.001) was a significant negative predictor. The regression model explained 46% of the variance in family adaptability (R² = 0.46, p < 0.001). Machine learning results indicated that the Gradient Boosting model achieved the highest predictive performance (R² = 0.74), followed by Random Forest (R² = 0.71) and Support Vector Machine (R² = 0.66). Feature importance analysis identified financial stability and economic pressure as the most influential predictors.
Conclusion: The findings demonstrate that both cultural and economic factors play a critical and complementary role in shaping family adaptability, with economic conditions exerting particularly strong effects. The integration of machine learning approaches provided enhanced predictive accuracy and revealed complex relationships among variables, highlighting the importance of multidimensional and data-driven approaches in family research. These results underscore the need for interventions and policies that simultaneously address financial stability and cultural integration to promote adaptive family functioning.
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References
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