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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
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Mingya Huang; David Kaplan – Journal of Educational and Behavioral Statistics, 2025
The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Statistical Inference, Predictor Variables
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Martinková, Patrícia; Bartoš, František; Brabec, Marek – Journal of Educational and Behavioral Statistics, 2023
Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables, such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for the implementation of policies with the potential to decrease measurement error…
Descriptors: Interrater Reliability, Bayesian Statistics, Statistical Inference, Hierarchical Linear Modeling
Pashley, Nicole E.; Miratrix, Luke W. – Journal of Educational and Behavioral Statistics, 2021
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Sales, Adam C.; Hansen, Ben B. – Journal of Educational and Behavioral Statistics, 2020
Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, "R," approaches a cut point, "c," from either side. Alternative methods target the average treatment effect in a small region around "c," at the cost of an assumption that treatment assignment,…
Descriptors: Regression (Statistics), Computation, Statistical Inference, Robustness (Statistics)
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2021
Large-scale assessments (LSAs) use Mislevy's "plausible value" (PV) approach to relate student proficiency to noncognitive variables administered in a background questionnaire. This method requires background variables to be completely observed, a requirement that is seldom fulfilled. In this article, we evaluate and compare the…
Descriptors: Data Analysis, Error of Measurement, Research Problems, Statistical Inference
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Wang, Tianyou – Journal of Educational and Behavioral Statistics, 2009
Holland and colleagues derived a formula for analytical standard error of equating using the delta-method for the kernel equating method. Extending their derivation, this article derives an analytical standard error of equating procedure for the conventional percentile rank-based equipercentile equating with log-linear smoothing. This procedure is…
Descriptors: Error of Measurement, Equated Scores, Statistical Analysis, Statistical Inference
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Azen, Razia; Traxel, Nicole – Journal of Educational and Behavioral Statistics, 2009
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Descriptors: Regression (Statistics), Predictor Variables, Measurement, Simulation
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Fox, J.-P.; Wyrick, Cheryl – Journal of Educational and Behavioral Statistics, 2008
The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…
Descriptors: Item Response Theory, Models, Markov Processes, Monte Carlo Methods
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Klockars, Alan J.; Hancock, Gregory – Journal of Educational and Behavioral Statistics, 1997
The use of finite intersection tests (FIT) to unify methods for simultaneous inference and to test orthogonal contrasts is discussed. Multiple comparison procedures that combine FIT with sequential hypothesis testing are illustrated, and a simulation strategy is presented to generate values needed for FIT methods. (SLD)
Descriptors: Comparative Analysis, Hypothesis Testing, Simulation, Statistical Inference
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Bonett, Douglas G.; Seier, Edith – Journal of Educational and Behavioral Statistics, 2003
Derived a confidence interval for a ratio of correlated mean absolute deviations. Simulation results show that it performs well in small sample sizes across realistically nonnormal distributions and that it is almost as powerful as the most powerful test examined by R. Wilcox (1990). (SLD)
Descriptors: Correlation, Equations (Mathematics), Hypothesis Testing, Sample Size