A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.
This handbook brings together contributions from leading psychometricians in a diverse array of fields around the globe.
R-project.org/package=QuantPsyc Fox, J., & Weisberg, S. (2010). An R companion to applied regression. Thousand Oaks: Sage. ... Judd, C. M., Kenny, D. A., & McClelland, G. H. (2001). Estimating and testing mediation and moderation in ...
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM).
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(2003) chose two statistics based on the Pearson residual (Eq. 10.7) to compare the observed value to its prediction (in this case the probability of success under the model).6 For each MCMC cycle, let pij (ω(t)) = E [ xij | ω(t) ] ...
Lee and Wagenmakers propose Bayes Factor as a solution. This approach is enthralling as it gives more intelligible answers than information criteria do. Bayes Factor of two models M1 and M2 is the ratio of their marginal likelihood BF1 ...
The goal of this book is to emphasize the formal statistical features of the practice of equating, linking, and scaling.
This comprehensive volume begins with a broad explanation of the sociocognitive perspective and the foundations of assessment, then provides a series of focused applications to major topics such as assessment arguments, validity, fairness, ...
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Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. Murray, J. S., Dunson, D. B., Carin, L., and Lucas, J. E. (2013). ... Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo ...