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Roy Levy; Daniel McNeish – Journal of Educational and Behavioral Statistics, 2025
Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…
Descriptors: Models, Statistical Analysis, Measurement, Data Interpretation
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Julia-Kim Walther; Martin Hecht; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized…
Descriptors: Sample Size, Hierarchical Linear Modeling, Structural Equation Models, Matrices
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Kuan-Yu Jin; Yi-Jhen Wu; Ming Ming Chiu – Measurement: Interdisciplinary Research and Perspectives, 2025
Many education tests and psychological surveys elicit respondent views of similar constructs across scenarios (e.g., story followed by multiple choice questions) by repeating common statements across scales (one-statement-multiple-scale, OSMS). However, a respondent's earlier responses to the common statement can affect later responses to it…
Descriptors: Administrator Surveys, Teacher Surveys, Responses, Test Items
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A. R. Georgeson – Structural Equation Modeling: A Multidisciplinary Journal, 2025
There is increasing interest in using factor scores in structural equation models and there have been numerous methodological papers on the topic. Nevertheless, sum scores, which are computed from adding up item responses, continue to be ubiquitous in practice. It is therefore important to compare simulation results involving factor scores to…
Descriptors: Structural Equation Models, Scores, Factor Analysis, Statistical Bias
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Ming-Chi Tseng – Structural Equation Modeling: A Multidisciplinary Journal, 2025
This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within x within and between x within latent interaction under the CLPM model framework is compared between the one-step Bayesian LMS method and the two-step multiple imputation analysis through a simulation…
Descriptors: Guidelines, Bayesian Statistics, Self Esteem, Depression (Psychology)
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Xinjian Fu; Yingxiang Li – European Journal of Education, 2025
University student academic competitions can test students' learning outcomes, improve their academic performance and stimulate their interest in learning. Exploring the behavioural mechanisms influencing students' academic competition is quite important, but there is currently little research on this topic. This study aims to fill this gap in the…
Descriptors: College Students, Student Participation, Competition, Structural Equation Models
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Hongfeng Zhang; Fanbo Li; Xiaolong Chen – Journal of Educational Computing Research, 2025
This study addresses the gap in understanding graduate students' sustained engagement behavior (SEB) with generative artificial intelligence (GAI) by integrating the Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT), and Theory of Reasoned Action (TRA) into a comprehensive embedding model. It introduces the Technology…
Descriptors: Graduate Students, Artificial Intelligence, Learner Engagement, Foreign Countries
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Njål Foldnes; Jonas Moss; Steffen Grønneberg – Structural Equation Modeling: A Multidisciplinary Journal, 2025
We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable and biased in known directions. To take this into account, we design model-based trend predictions to approximate the…
Descriptors: Goodness of Fit, Structural Equation Models, Robustness (Statistics), Prediction
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Haixiang Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Mediation analysis is an important statistical tool in many research fields, where the joint significance test is widely utilized for examining mediation effects. Nevertheless, the limitation of this mediation testing method stems from its conservative Type I error, which reduces its statistical power and imposes certain constraints on its…
Descriptors: Structural Equation Models, Statistical Significance, Robustness (Statistics), Comparative Testing
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Suppanut Sriutaisuk; Yu Liu; Seungwon Chung; Hanjoe Kim; Fei Gu – Educational and Psychological Measurement, 2025
The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model fit of structural equation models for ordinal variables with multiply imputed data. However, previous studies only examined the performance of MI2S-based residual-based test statistics. This study extends previous research by examining the performance of two…
Descriptors: Structural Equation Models, Error of Measurement, Programming Languages, Goodness of Fit
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Sergio Dominguez-Lara; Mario A. Trógolo; Rodrigo Moreta-Herrera; Diego Vaca-Quintana; Manuel Fernández-Arata; Ana Paredes-Proaño – Journal of Psychoeducational Assessment, 2025
Academic engagement plays a crucial role in students' learning and performance. One of the most popular measures for assessing this construct is the Utrecht Work Engagement Scale for Students (UWES-S), which is based on a tridimensional conceptualization consisting of dedication, vigor, and absorption. However, prior research on its factor…
Descriptors: Learner Engagement, College Students, Foreign Countries, Factor Analysis
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Philipp Sterner; Kim De Roover; David Goretzko – Structural Equation Modeling: A Multidisciplinary Journal, 2025
When comparing relations and means of latent variables, it is important to establish measurement invariance (MI). Most methods to assess MI are based on confirmatory factor analysis (CFA). Recently, new methods have been developed based on exploratory factor analysis (EFA); most notably, as extensions of multi-group EFA, researchers introduced…
Descriptors: Error of Measurement, Measurement Techniques, Factor Analysis, Structural Equation Models
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Afef Saihi; Mohamed Ben-Daya; Moncer Hariga – Education and Information Technologies, 2025
The integration of AI-chatbots into higher education offers the potential to enhance learning practices. This research aims to explore the factors influencing AI-chatbots adoption within higher education, with a focus on the moderating roles of technological proficiency and academic discipline. Utilizing a survey-based approach and advanced…
Descriptors: Technology Uses in Education, Artificial Intelligence, Higher Education, Technology Integration
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Hao Zhang; Shihan Chen; Sen Zheng – Education and Information Technologies, 2025
Based on the instructional interaction principles outlined by Chen and Wang (2016) in third-generation distance learning, this study employs a recursive logical perspective on the evolution of the theory of interaction in distance education. It constructs a structural equation model to measure the mediating utility path of the learner's proactive…
Descriptors: Personality Traits, Assertiveness, Interaction, Distance Education
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Faruk Polatcan; Nursat Biçer; Onur Er – SAGE Open, 2025
The objective of this study is to ascertain the relative influences and predictive relationships between metacognitive listening strategies, critical listening attitudes and academic listening skills of Turkish teacher candidates. In consideration of the ease of accessibility and economic factors, the participants were selected through the…
Descriptors: Foreign Countries, Preservice Teachers, Metacognition, Listening
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