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Chengcheng Li – ProQuest LLC, 2022
Categorical data become increasingly ubiquitous in the modern big data era. In this dissertation, we propose novel statistical learning and inference methods for large-scale categorical data, focusing on latent variable models and their applications to psychometrics. In psychometric assessments, the subjects' underlying aptitude often cannot be…
Descriptors: Statistical Inference, Data Analysis, Psychometrics, Raw Scores
Joshua B. Gilbert; James S. Kim; Luke W. Miratrix – Annenberg Institute for School Reform at Brown University, 2022
Analyses that reveal how treatment effects vary allow researchers, practitioners, and policymakers to better understand the efficacy of educational interventions. In practice, however, standard statistical methods for addressing Heterogeneous Treatment Effects (HTE) fail to address the HTE that may exist within outcome measures. In this study, we…
Descriptors: Item Response Theory, Models, Formative Evaluation, Statistical Inference
Ames, Allison; Myers, Aaron – Educational Measurement: Issues and Practice, 2019
Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…
Descriptors: Bayesian Statistics, Psychometrics, Models, Predictive Measurement
Chen, Jinsong; de la Torre, Jimmy; Zhang, Zao – Journal of Educational Measurement, 2013
As with any psychometric models, the validity of inferences from cognitive diagnosis models (CDMs) determines the extent to which these models can be useful. For inferences from CDMs to be valid, it is crucial that the fit of the model to the data is ascertained. Based on a simulation study, this study investigated the sensitivity of various fit…
Descriptors: Models, Psychometrics, Goodness of Fit, Statistical Analysis
Revuelta, Javier – Psychometrika, 2004
Two psychometric models are presented for evaluating the difficulty of the distractors in multiple-choice items. They are based on the criterion of rising distractor selection ratios, which facilitates interpretation of the subject and item parameters. Statistical inferential tools are developed in a Bayesian framework: modal a posteriori…
Descriptors: Multiple Choice Tests, Psychometrics, Models, Difficulty Level
Yan, Duanli; Almond, Russell; Mislevy, Robert – ETS Research Report Series, 2004
Diagnostic score reports linking assessment outcomes to instructional interventions are one of the most requested features of assessment products. There is a body of interesting work done in the last 20 years including Tatsuoka's rule space method (Tatsuoka, 1983), Haertal and Wiley's binary skills model (Haertal, 1984; Haertal & Wiley, 1993),…
Descriptors: Comparative Analysis, Models, Bayesian Statistics, Statistical Inference
Sinharay, Sandip – ETS Research Report Series, 2004
Assessing fit of psychometric models has always been an issue of enormous interest, but there exists no unanimously agreed upon item fit diagnostic for the models. Bayesian networks, frequently used in educational assessments (see, for example, Mislevy, Almond, Yan, & Steinberg, 2001) primarily for learning about students' knowledge and…
Descriptors: Bayesian Statistics, Networks, Models, Goodness of Fit