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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
Coffman, Donna L. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Mediation is usually assessed by a regression-based or structural equation modeling (SEM) approach that we refer to as the classical approach. This approach relies on the assumption that there are no confounders that influence both the mediator, "M", and the outcome, "Y". This assumption holds if individuals are randomly…
Descriptors: Structural Equation Models, Simulation, Regression (Statistics), Probability
Levy, Roy; Mislevy, Robert J. – US Department of Education, 2004
The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…
Descriptors: Probability, Markov Processes, Monte Carlo Methods, Bayesian Statistics

Burrell, Quentin L. – Journal of Documentation, 1987
Describes a circulation model for academic research libraries which uses the mixed Poisson model, incorporating ageing of library materials, to predict future use of monographs and to suggest weeding procedures based on frequency of circulation. Longitudinal studies are examined and statistical details are appended. (Author/LRW)
Descriptors: Academic Libraries, Graphs, Higher Education, Library Circulation
Caulkins, Jonathan P. – Journal of Policy Analysis and Management, 2002
In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…
Descriptors: Statistical Inference, Models, Policy Analysis, Sampling