Structural Equation Modeling of Factors Influencing the Lifelong Learning City
Keywords:
Causal Factors, Chiang Rai Municipality, Structural Equation Modeling, The Lifelong Learning CityAbstract
This research aimed to analyze the Structural Equation Modeling of Factors Influencing the Lifelong Learning City. The sample consisted of 450 residents in the Chiang Rai Municipality, obtained through multi-stage sampling. The research instrument was a questionnaire with a content validity (IOC) ranging from 0.67 to 1.00 and Cronbach’s alpha coefficients between 0.814 and 0.912. The dataset demonstrated exceptional sampling adequacy with a Kaiser-Meyer-Olkin (KMO) index of 0.944. Data were analyzed using Structural Equation Modeling (SEM). The results indicated that the developed causal relationship model was in good agreement with the empirical data, meeting all statistical fit indices. All latent variables demonstrated strong construct validity, with Composite Reliability (CR) ranging from 0.858 to 0.945 and Average Variance Extracted (AVE) between 0.488 and 0.689. Path analysis revealed that Government Policy and Support (GPp) exerted the highest total effect on the Lifelong Learning City (SLC) at 0.836, operating entirely through indirect causal pathways. Regarding direct effects, Educational Quality and Infrastructure (EIQLL) had the highest direct impact (0.477), followed by Community Learning Culture (SCILL) (0.275) and Economy and Environment (EESLL) (0.166), respectively. Collectively, the hypothesized variables explained 75.9% of the variance in the Lifelong Learning City (R2 = 0.759).
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