Addressing The Issues of Low Degrees of Freedom and Associated Poor Validation Estimates of Science Education Models: An Advocacy for The Application of Confirmatory Network Analysis
Year: 2025
Type: article
Study design: Observational Study
Abstract: Generally, science education models have a low degree of freedom df, and this psychometric aspect negatively impacts the overall goodness of fit estimates obtained on conducting CB-SEM-based Confirmatory Factor Analysis (CFA) on the hypothesized latent models using the Maximum Likelihood (ML) estimator. To address this issue, the state-of-the-art Confirmatory Network Analysis (CNA) approach is advocated, which uses the adjacency matrix in estimation and treats the data type of the data collected using survey questionnaires as ordinal using the appropriate Diagonally Weighted Least Squares (DWLS) estimator to provide the proper scaled goodness of fit estimates of the network. In the present study, the goodness of fit estimates of a science education model involving the perception of 918 secondary school students of India on their science constructivist learning environment, epistemological beliefs in science, science self-efficacy, science academic flow, and science academic achievement were found from the traditional latent variable modeling and the latest network perspectives. Data analysis was conducted using SPSS AMOS Ver 23.0, several packages of R and through generated using ChatGPT Ver. 5. The scaled goodness of fit estimates of confirmatory network analysis produced a better picture of the reality over confirmatory factor analysis, and provided deeper insights on the interactions among the studied variables as were graphically established by the differences in the Bland-Altman plots of the CFI, TLI, RMSEA and SRMR estimates of the two approaches. Educational and psychometric implications of the study are discussed.
DOI: 10.14419/k92yd937
Authors: Syamala Pilla, Rajib Chakraborty
Institution: Lovely Professional University
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4414984200
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