STRUCTURAL EQUATION MODEL ANALYSIS OF FACTORS AFFECTING THE ADJUSTMENT OF HIGH SCHOOL STUDENTS IN SCIENCE-MATHEMATICS PROGRAMS UNDER THE SECONDARY EDUCATION SERVICE AREA OFFICE

Abstract

This research aims to 1) develop a structural equation model that affects the adjustment of high school students in science-mathematics programs under the secondary education service area office, and 2) examine the consistency of the structural equation model that affects the adjustment of high school students in science-mathematics programs under the secondary education service area office with empirical data. The sample group consisted of 200 high school students in the Science-Mathematics curriculum for the academic year 2566 from schools under the secondary education service area office, Phitsanulok. The sampling method used was multi-stage sampling. Data collection was conducted through a questionnaire using Rating Scale 5-level, with a confidence level of 0.981. The data analysis was performed using statistical software.

          The research findings that: 1) The development of structural equation model that affects the adjustment of high school students in science-mathematics programs under the secondary education service area office, The model comprised 3 latent variables and 13 observed variables. There was a positive relationship among the observed variables, with correlation coefficients ranging from .460 to .800, indicating a moderate to high level of correlation, statistically significant at the .01 level. When analyzing to confirm the structural validity of the model measuring the 3 latent variables, all models exhibited structural validity, with factor loadings ranging from 0.73 to 0.93, and statistically significant at the .05 level. And 2) The results of the examination of the model's fit with the empirical data reveal that all 3 components of the structural equation model influencing students' adaptation exhibited structural validity. The factor loadings ranged from 0.74 to 0.92, and the percentage of variance explained between the indicators and components ranged from 54.1% to 85.3%. Moreover, the model demonstrated good fit with the empirical data, supported by a p-value greater than .05 (p-value = 0.1725). Additionally, the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) were close to 1 (CFI = 0.996, TLI = 0.994), while the Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR) were close to 0 (RMSEA = 0.030, SRMR = 0.020), and when analyzing the causal relationship path that affects student adjustment, it was found that adjustment was directly influenced by social support and emotional quotient. They are most directly influenced by social support. It has an influence size of 0.539, followed by emotional quotient. The influence size was 0.421. In addition, adjustment was indirectly influenced by social support. through emotional quotient which has an influence size of 0.356