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Mauricio Garnier-Villarreal; Terrence D. Jorgensen – Grantee Submission, 2024
Model evaluation is a crucial step in SEM, consisting of two broad areas: global and local fit, where local fit indices are use to modify the original model. In the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are used to select the parameters that can be added to improve the fit. The…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Indexes
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Chunhua Cao; Benjamin Lugu; Jujia Li – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality, sample size, model size, the magnitude of misspecified path effect, and the choice or prior on the performance of the fit indices was also…
Descriptors: Structural Equation Models, Bayesian Statistics, Measurement, Error of Measurement
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Emma Somer; Carl Falk; Milica Miocevic – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon's correction and…
Descriptors: Scores, Structural Equation Models, Comparative Analysis, Sample Size
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van Laar, Saskia; Braeken, Johan – Practical Assessment, Research & Evaluation, 2021
Despite the sensitivity of fit indices to various model and data characteristics in structural equation modeling, these fit indices are used in a rigid binary fashion as a mere rule of thumb threshold value in a search for model adequacy. Here, we address the behavior and interpretation of the popular Comparative Fit Index (CFI) by stressing that…
Descriptors: Goodness of Fit, Structural Equation Models, Sampling, Sample Size
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Kanadli, Sedat; Arslantas, Haci Ismail; Inandi, Yusuf – Asia Pacific Education Review, 2023
In the literature, there are a great number of primary studies that examine the relationship between professional burnout, job satisfaction, and life satisfaction of education workers and that do not have consistent results. The aim of this study is to establish a model that will explain the life satisfaction of education workers by determining…
Descriptors: Predictor Variables, Life Satisfaction, Teacher Attitudes, Foreign Countries
Deng, Lifang; Yuan, Ke-Hai – Grantee Submission, 2022
Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed…
Descriptors: Structural Equation Models, Path Analysis, Weighted Scores, Error of Measurement
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Fauzi, Muhammad Ashraf – Knowledge Management & E-Learning, 2022
Partial least square structural equation modelling (PLS-SEM) has been used as a popular research method in various disciplines, including knowledge management (KM). This paper reviews how PLS-SEM has been used in KM studies, which focus on knowledge sharing in the context of virtual community (VC). The review includes 30 articles published from…
Descriptors: Least Squares Statistics, Structural Equation Models, Research Methodology, Knowledge Management
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Wang, Ning; Tan, Aik-Ling; Xiao, Wu-Rong; Zeng, Feng; Xiang, Jiong; Duan, Wei – Journal of Baltic Science Education, 2021
Learning experiences can affect students' interest in STEM (science, technology, engineering, and mathematics) careers. Applying the social cognitive career theory, this study tested and compared the effect size and effect mechanism of formal learning experiences (FLE) and informal learning experiences (ILE) on 1133 tenth-grade students' interest…
Descriptors: Learning Experience, STEM Education, Vocational Interests, Structural Equation Models