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Activity Number:
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237
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
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Sponsor:
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Social Statistics Section
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| Abstract - #302208 |
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Title:
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Measurement Covariates in Repeated Measure Latent Variable Models
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Author(s):
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Louis T. Mariano*+ and Brian W. Junker
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Companies:
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RAND Corporation and Carnegie Mellon University
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Address:
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1200 S. Hayes St., Arlington, VA, 22202-5050,
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Keywords:
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repeated measures ; item response theory ; rater bias ; Bayesian hierarchical models
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Abstract:
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When modeling a latent quantity, repeated ratings--measures of a related variable from a subjective source--are often available. For example, a single student's essay on a standardized test may be scored by more than one grader. The availability of repeated ratings allows for the consideration of individual rater bias and variability in the estimation of the latent quantity, and hierarchical models based in item response theory have been introduced to model rater effects. We demonstrate how these models may be extended to include covariates of the rating process. For example, how do features of an essay grader's training affect their performance and thus the estimate of a student's writing proficiency? We first formulate a design matrix that indicates the available raters and covariates and also reflects necessary identifiability constraints. Using this, we cast the overall rating effects as a linear combination of individual rater and covariate effects and discuss competing options for including these overall effects in a model hierarchy. We use data from a rating study of a 5th grade student assessment to illustrate the method.
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