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Mislevy, Robert J. – Measurement: Interdisciplinary Research and Perspectives, 2012
Paul E. Newton's "Clarifying the Consensus Definition of Validity" addresses the single most important, yet stubbornly protean, value in educational and psychological assessment. "Standards for Educational and Psychological Testing" (American Educational Research Association, American Psychological Association, & National Council on Measurement in…
Descriptors: Evidence, Validity, Educational Testing, Psychological Evaluation
Ligtvoet, Rudy – Psychometrika, 2012
In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable. However, the PCM is very restrictive with respect…
Descriptors: Simulation, Item Response Theory, Comparative Analysis, Scores
Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G. – Applied Psychological Measurement, 2012
When tests consist of multiple-choice and constructed-response items, researchers are confronted with the question of which item response theory (IRT) model combination will appropriately represent the data collected from these mixed-format tests. This simulation study examined the performance of six model selection criteria, including the…
Descriptors: Item Response Theory, Models, Selection, Criteria
Page, Lindsay C. – Journal of Research on Educational Effectiveness, 2012
Experimental evaluations are increasingly common in the U.S. educational policy-research context. Often, in investigations of multifaceted interventions, researchers and policymakers alike are interested in not only "whether" a given intervention impacted an outcome but also "why". What "features" of the intervention…
Descriptors: Educational Experiments, Educational Research, Research Methodology, Income
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Journal of Experimental Psychology: General, 2011
Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should…
Descriptors: Bayesian Statistics, Statistical Inference, Models, Prior Learning
Merkle, Edgar C. – Journal of Educational and Behavioral Statistics, 2011
Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…
Descriptors: Statistical Analysis, Factor Analysis, Bayesian Statistics, Comparative Analysis
Blake Buffini, Katrina; Gordon, Michael – British Journal of Guidance & Counselling, 2015
Young people are increasingly turning to online support, especially when traditional mental health services are not immediately available. Using a cross-sectional design, sociodemographic information and self-reported perceptions of the strength of the working alliance and client satisfaction were collected from a sample of participants (n = 78)…
Descriptors: Crisis Intervention, Online Systems, Synchronous Communication, Computer Mediated Communication
Wells, Ryan S.; Kolek, Ethan A.; Williams, Elizabeth A.; Saunders, Daniel B. – Journal of Higher Education, 2015
This study replicates and extends a 2004 content analysis of three major higher education journals. The original study examined the methodological characteristics of all published research in these journals from 1996 to 2000, recommending that higher education programs adjust their graduate training to better match the heavily quantitative and…
Descriptors: Research Methodology, Higher Education, Journal Articles, Educational Research
D'Mello, S. K., Ed.; Calvo, R. A., Ed.; Olney, A., Ed. – International Educational Data Mining Society, 2013
Since its inception in 2008, the Educational Data Mining (EDM) conference series has featured some of the most innovative and fascinating basic and applied research centered on data mining, education, and learning technologies. This tradition of exemplary interdisciplinary research has been kept alive in 2013 as evident through an imaginative,…
Descriptors: Data Analysis, Educational Research, Educational Technology, Interdisciplinary Approach
Kazemzadeh, Abe – ProQuest LLC, 2013
This dissertation studies how people describe emotions with language and how computers can simulate this descriptive behavior. Although many non-human animals can express their current emotions as social signals, only humans can communicate about emotions symbolically. This symbolic communication of emotion allows us to talk about emotions that we…
Descriptors: Natural Language Processing, Psychological Patterns, Computer Simulation, Discourse Analysis
Wandler, Damian V. – ProQuest LLC, 2010
Generalized fiducial inference is a powerful tool for many difficult problems. Based on an extension of R. A. Fisher's work, we used generalized fiducial inference for two extreme value problems and a multiple comparison procedure. The first extreme value problem is dealing with the generalized Pareto distribution. The generalized Pareto…
Descriptors: Comparative Analysis, Probability, Inferences, Simulation
Goldin, Ilya M.; Koedinger, Kenneth R.; Aleven, Vincent – International Educational Data Mining Society, 2012
Although ITSs are supposed to adapt to differences among learners, so far, little attention has been paid to how they might adapt to differences in how students learn from help. When students study with an Intelligent Tutoring System, they may receive multiple types of help, but may not comprehend and make use of this help in the same way. To…
Descriptors: Performance Factors, Intelligent Tutoring Systems, Individual Differences, Prediction
Ghosh, Indranil – ProQuest LLC, 2011
Consider a discrete bivariate random variable (X, Y) with possible values x[subscript 1], x[subscript 2],..., x[subscript I] for X and y[subscript 1], y[subscript 2],..., y[subscript J] for Y. Further suppose that the corresponding families of conditional distributions, for X given values of Y and of Y for given values of X are available. We…
Descriptors: Information Theory, Models, Programming, Mathematical Applications
Drummond, Gordon B.; Tom, Brian D. M. – Advances in Physiology Education, 2011
Statisticians use words deliberately and specifically, but not necessarily in the way they are used colloquially. For example, in general parlance "statistics" can mean numerical information, usually data. In contrast, one large statistics textbook defines the term "statistic" to denote "a characteristic of a…
Descriptors: Intervals, Research Methodology, Testing, Statistics
Choi, Jaehwa; Kim, Sunhee; Chen, Jinsong; Dannels, Sharon – Journal of Educational and Behavioral Statistics, 2011
The purpose of this study is to compare the maximum likelihood (ML) and Bayesian estimation methods for polychoric correlation (PCC) under diverse conditions using a Monte Carlo simulation. Two new Bayesian estimates, maximum a posteriori (MAP) and expected a posteriori (EAP), are compared to ML, the classic solution, to estimate PCC. Different…
Descriptors: Computation, Maximum Likelihood Statistics, Bayesian Statistics, Correlation

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