MCMC in Educational Research

Authored by: Levy Roy , J. Mislevy Robert , T. Behrens John

Handbook of Markov Chain Monte Carlo

Print publication date:  May  2011
Online publication date:  May  2011

Print ISBN: 9781420079418
eBook ISBN: 9781420079425
Adobe ISBN:


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Quantitative educational research has traditionally relied on a broad range of statistical models that have evolved in relative isolation to address different facets of its subject matter. Experiments on instructional interventions employ Fisherian designs and analyses of variance; observational studies use regression techniques; and longitudinal studies use growth models in the manner of economists. The social organization of schooling—of students within classrooms, sometimes nested within teachers, of classrooms within schools, schools within districts, districts within states, and states within nations—necessitates hierarchical analyses. Large-scale assessments employ the complex sampling methodologies of survey research. Missing data abound across levels. And most characteristically, measurement error and latent variable models from psychometrics address the fundamental fact that what is ultimately of most interest, namely what students know and can do, cannot be directly observed: a student’s performance on an assessment may be an indicator of proficiency but, no matter how well the assessment is constructed, it is not the same thing as proficiency. This measurement complexity exacerbates computational complexity when researchers attempt to combine models for measurement error with models addressing the aforementioned structures. Further difficulties arise from an extreme reliance on frequentist interpretations of statistical methods that limit the computational and interpretive machinery available (Behrens and Smith, 1996). In sum, most applied educational research has been marked by interpretive limitations inherent in the frequentist approach to testing, estimation, and model building, a plethora of independently created and applied conceptual models, and computational limitations in estimating models that would capture the complexity of this applied domain.

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