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Adrian Quintero; Emmanuel Lesaffre; Geert Verbeke – Journal of Educational and Behavioral Statistics, 2024
Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach…
Descriptors: Bayesian Statistics, Factor Analysis, Factor Structure, Sampling
Franco-Martínez, Alicia; Alvarado, Jesús M.; Sorrel, Miguel A. – Educational and Psychological Measurement, 2023
A sample suffers range restriction (RR) when its variance is reduced comparing with its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how…
Descriptors: Factor Analysis, Factor Structure, Scores, Sampling
Salim Nabhan; Anita Habók – SAGE Open, 2025
As the integration of digital technologies continues to shape academic landscapes, assessing digital literacy in the context of academic writing becomes paramount. Several instruments and frameworks are available for measuring digital literacy and examining it from different perspectives; however, none are suitable for measuring the digital…
Descriptors: Digital Literacy, Academic Language, Writing (Composition), Measures (Individuals)
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Multilevel structural equation (MSEM) models allow researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This paper…
Descriptors: Sampling, Structural Equation Models, Factor Structure, Monte Carlo Methods
Kim, Seohyun; Lu, Zhenqiu; Cohen, Allan S. – Measurement: Interdisciplinary Research and Perspectives, 2018
Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables.…
Descriptors: Bayesian Statistics, Structural Equation Models, Computation, Social Science Research
Soto, Christian; Gutierrez de Blume, Antonio P.; Asún, Rodrigo; Jacovina, Matthew; Vásquez, Claudio – Frontline Learning Research, 2018
The purpose of this research endeavor was to develop and validate a new measurement tool predicated on previous research to assess learners' metacomprehension during reading. In two separate studies with Chilean undergraduate students (N = 923), we demonstrate the versatility and utility of our proposed Metacomprehension Inventory (MI). In Study…
Descriptors: Undergraduate Students, Foreign Countries, Factor Structure, Correlation
Ersanli, Ercümend; Mameghani, Shiva Saeighi – Journal of Education and Practice, 2016
In the present study, the Tolerance Scale developed by Ersanli (2014) was adapted to the Iranian culture, and its validity and reliability were investigated in the case of Iranian college students. The participants consisted of 552 Iranian college students (62% male, M = 20.84, S.D.: 1.53) selected using the convenience sampling method. The sample…
Descriptors: Foreign Countries, College Students, Construct Validity, Reliability