Structural equation modeling and implication to educational management of dataset on attitudes, opinions, and behaviors related to COVID-19 - Salazar, Catherine Aragon (Association for Scientific Computing Electrical and Engineering(ASCEE), 2023)
This quantitative study utilized R, specifically RStudio, as statistical analysis program in analyzing the dataset on attitudes, opinions, and behaviors with respect to COVID-19. The variables in the dataset are focused on survey items that ask respondents to describe their feelings, physical reaction, perception, attitudes and behaviors towards various issues and aspects that concerns the current pandemic. The study intends to determine dimensionality of the dataset through Principal Component Analysis (PCA) and factor analysis, find out the factor loadings about observed variables and latent variables in the dataset, generate a model through Structural Equation Modeling (SEM), and interpret how the findings relate to educational leadership and management in the new normal. Principal Component Analysis (PCA) reveals that there are four components with most significant representation in the dataset which was confirmed by the scree plots and factor analysis that four factors are sufficient to analyze the dataset. Loadings revealed that these four factors are media, spiritual, political, and cultural aspects. A model that can guide interpretation and future related studies about attitudes, opinions, and behaviors with respect to COVID-19 has been generated through Structural Equation Modeling. It was implied by the findings that the four factors and their observable variables may be used as bases by educational leaders in gathering and analyzing data about attitudes, opinions, and behaviors of school stakeholders towards COVID-19 which can guide them in crafting and implementing responses, innovations and policies that are parallel to pandemic context).