Confirmatory Factor Analysis of Complex Problem Solving Skills of Upper Secondary School Students
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Abstract
Complex problem-solving skills are crucial for career development and future living in everyday life. Instilling complex problem-solving thinking methods in students at the school level will prepare them to face the future world effectively. The objective of this research is to study the confirmatory components and indicators of complex problem-solving skills of upper secondary school students. This research analyses the second-order confirmatory components of complex problem-solving skills among upper secondary school students. The study found that future skills consist of five components: 1) the ability to assess the complexity of situations, 2) the ability to identify problems, 3) the ability to analyze anomalies, 4) the ability to see the interconnectedness of each causal factor of problems, and 5) the ability to devise creative solutions to manage the causal factors and their impacts. The weight of each component of the latent variables was positive, ranging from 0.96 to 0.99. The components of the ability to identify problems and the ability to see the interconnectedness of each causal factor had the highest weights (ß = 0.99), followed by the ability to analyze anomalies and the ability to devise creative solutions to manage the causal factors and their impacts, which were equal in weight (ß = 0.98), and the ability to assess the complexity of situations (ß = 0.96). The Confirmatory Factor Analysis (CFA) model was examined for alignment or consistency with empirical data. The results indicated that the model is congruent with the empirical data, evidenced by a Chi-square value of 363.75, a p-value of 0.00, and degrees of freedom (df) of 235. This demonstrates that the Chi-square value is not significantly different from zero at the .05 level, indicating a good fit of the model to the data. Additionally, the model shows excellent fit indices: comparative fit index (CFI) and Tucker–Lewis index (TLI) both at 0.99, standardized root mean squared residual (SRMR) at 0.02, and root mean square error of approximation (RMSEA) at 0.03. These values confirm the hypothesis that the model aligns well with the empirical data.
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