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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
Gabrielle T. Lee; Yu Sun; Sheng Xu; Kefan Kang – Journal of Applied Behavior Analysis, 2025
We implemented tact matrix training to teach tacts of spatial locations to four children (male, 4-7 years of age) on the autism spectrum in China. The experimental design involved a multiple-probe design across participants with pre- and postinstruction probes on untaught tacts and listener responses. Learning outcomes included taught tacts of…
Descriptors: Foreign Countries, Training, Matrices, Spatial Ability
SenthilKumar Anantharaman – Online Submission, 2025
Excellence in research is essential for advancing knowledge, fostering innovation, and addressing societal challenges. This article proposes the INDEX Strategy Framework as a structured, evidence-based guide to optimize the research process. The framework encompasses five critical phases: Identify Focus Areas, Network and Collaborate, Design…
Descriptors: Educational Research, Evidence Based Practice, Research Methodology, Scholarship
Binbin Zhao; Rim Razzouk – International Journal of Web-Based Learning and Teaching Technologies, 2024
In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of…
Descriptors: Music Education, Higher Education, Teaching Methods, Matrices
Haiyan Liu; Sarah Depaoli; Lydia Marvin – Structural Equation Modeling: A Multidisciplinary Journal, 2022
The deviance information criterion (DIC) is widely used to select the parsimonious, well-fitting model. We examined how priors impact model complexity (pD) and the DIC for Bayesian CFA. Study 1 compared the empirical distributions of pD and DIC under multivariate (i.e., inverse Wishart) and separation strategy (SS) priors. The former treats the…
Descriptors: Structural Equation Models, Bayesian Statistics, Goodness of Fit, Factor Analysis

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