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Shashi Bhushan; Anoop Kumar – Measurement: Interdisciplinary Research and Perspectives, 2024
The data we encounter in real life often contain missing values. In sampling methods, missing value imputation is done with different methods. This article proposes novel logarithmic type imputation methods for estimating the population mean in the presence of missing data under ranked set sampling (RSS). According to the determined theoretical…
Descriptors: Research Problems, Sampling, Computation, Mathematical Formulas
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Olvera Astivia, Oscar L. – Measurement: Interdisciplinary Research and Perspectives, 2021
Partially specified correlation matrices (not to be confused with matrices with missing data or EM-correlation matrices) can appear in research settings such as integrative data analyses, quantitative systematic reviews or whenever the study design only allows for the collection of certain variables. Although approaches to fill in these missing…
Descriptors: Correlation, Matrices, Statistical Analysis, Research Problems
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Ames, Allison J. – Measurement: Interdisciplinary Research and Perspectives, 2018
Bayesian item response theory (IRT) modeling stages include (a) specifying the IRT likelihood model, (b) specifying the parameter prior distributions, (c) obtaining the posterior distribution, and (d) making appropriate inferences. The latter stage, and the focus of this research, includes model criticism. Choice of priors with the posterior…
Descriptors: Bayesian Statistics, Item Response Theory, Statistical Inference, Prediction
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Cadogan, John W.; Lee, Nick – Measurement: Interdisciplinary Research and Perspectives, 2016
In this commentary from Issue 14, n3, authors John Cadogan and Nick Lee applaud the paper by Aguirre-Urreta, Rönkkö, and Marakas "Measurement: Interdisciplinary Research and Perspectives", 14(3), 75-97 (2016), since their explanations and simulations work toward demystifying causal indicator models, which are often used by scholars…
Descriptors: Causal Models, Measurement, Validity, Statistical Analysis
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Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M. – Measurement: Interdisciplinary Research and Perspectives, 2016
One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…
Descriptors: Causal Models, Measurement, Research Problems, Structural Equation Models
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Scholten, Annemarie Zand; Maris, Gunter; Borsboom, Denny – Measurement: Interdisciplinary Research and Perspectives, 2011
In his opening statements, Humphry takes a critical attitude with respect to psychometric modeling. As he advances and discusses Michell's well-known criticisms, the reader is certain that this paper must have something on offer that deviates considerably from conventional psychometrics. After all "most psychometricians have either explicitly or…
Descriptors: Psychometrics, Research Methodology, Models, Measurement Techniques
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von Eye, Alexander – Measurement: Interdisciplinary Research and Perspectives, 2009
Universals were said to exist if superordinate variables can be established that are parallel in several individuals or groups of individuals. In addition, these variables can be shown to exhibit dimensional identity in the sense that their relationships with other latent variables are not conditioned on individuals or membership in groups of…
Descriptors: Research Methodology, Personality, Psychological Characteristics, Individual Psychology