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Tenko Raykov; George Marcoulides; Randall Schumacker – Measurement: Interdisciplinary Research and Perspectives, 2024
An application of Bayesian factor analysis for evaluation of scale reliability is discussed, which is developed within the framework of latent variable modeling. The method permits direct point and interval estimation of the reliability coefficient of multiple-component measuring instruments using Bayesian inference. The approach allows also point…
Descriptors: Reliability, Bayesian Statistics, Measurement Techniques, Computer Software
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Tenko Raykov; George Marcoulides; James Anthony; Natalja Menold – Measurement: Interdisciplinary Research and Perspectives, 2024
A Bayesian statistics-based approach is discussed that can be used for direct evaluation of the popular Cronbach's coefficient alpha as an internal consistency index for multiple-component measuring instruments, as well as for testing its identity to scale reliability. The method represents an application of confirmatory factor analysis within the…
Descriptors: Reliability, Factor Analysis, Bayesian Statistics, Measurement Techniques
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A. M. Sadek; Fahad Al-Muhlaki – Measurement: Interdisciplinary Research and Perspectives, 2024
In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain…
Descriptors: Monte Carlo Methods, Accuracy, Artificial Intelligence, Guidelines
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Levy, Roy – Measurement: Interdisciplinary Research and Perspectives, 2022
Obtaining values for latent variables in factor analysis models, also referred to as factor scores, has long been of interest to researchers. However, many treatments of factor analysis do not focus on inference about the latent variables, and even fewer do so from a Bayesian perspective. Researchers may therefore be ill-acquainted with Bayesian…
Descriptors: Factor Analysis, Bayesian Statistics, Inferences, Decision Making
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Raykov, Tenko; Doebler, Philipp; Marcoulides, George A. – Measurement: Interdisciplinary Research and Perspectives, 2022
This article is concerned with the large-sample parameter estimator behavior in applications of Bayesian confirmatory factor analysis in behavioral measurement. The property of strong convergence of the popular Bayesian posterior median estimator is discussed, which states numerical convergence with probability 1 of the resulting estimates to the…
Descriptors: Bayesian Statistics, Measurement Techniques, Correlation, Factor Analysis
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Najera, Hector – Measurement: Interdisciplinary Research and Perspectives, 2023
Measurement error affects the quality of population orderings of an index and, hence, increases the misclassification of the poor and the non-poor groups and affects statistical inferences from binary regression models. Hence, the conclusions about the extent, profile, and distribution of poverty are likely to be misleading. However, the size and…
Descriptors: Poverty, Error of Measurement, Classification, Statistical Inference
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Wyse, Adam E.; McBride, James R. – Measurement: Interdisciplinary Research and Perspectives, 2022
A common practical challenge is how to assign ability estimates to all incorrect and all correct response patterns when using item response theory (IRT) models and maximum likelihood estimation (MLE) since ability estimates for these types of responses equal -8 or +8. This article uses a simulation study and data from an operational K-12…
Descriptors: Scores, Adaptive Testing, Computer Assisted Testing, Test Length