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Calibrating Item Families and Summarizing the Results Using Family Expected Response Functions

Author

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  • Sandip Sinharay
  • Matthew S. Johnson
  • David M. Williamson

Abstract

Item families, which are groups of related items, are becoming increasingly popular in complex educational assessments. For example, in automatic item generation (AIG) systems, a test may consist of multiple items generated from each of a number of item models. Item calibration or scoring for such an assessment requires fitting models that can take into account the dependence structure inherent among the items that belong to the same item family. Glas and van der Linden (2001) suggest a Bayesian hierarchical model to analyze data involving item families with multiple-choice items. We fit the model using the Markov Chain Monte Carlo (MCMC) algorithm, introduce the family expected response function (FERF) as a way to summarize the probability of a correct response to an item randomly generated from an item family, and suggest a way to estimate the FERFs. This work is thus a step towards creating a tool that can save significant amount of resources in educational testing, by allowing proper analysis and summarization of data from tests involving item families.

Suggested Citation

  • Sandip Sinharay & Matthew S. Johnson & David M. Williamson, 2003. "Calibrating Item Families and Summarizing the Results Using Family Expected Response Functions," Journal of Educational and Behavioral Statistics, , vol. 28(4), pages 295-313, December.
    • Handle: RePEc:sae:jedbes:v:28:y:2003:i:4:p:295-313
    DOI: 10.3102/10769986028004295
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    Cited by:

    1. Sun-Joo Cho & Amanda P. Goodwin, 2017. "Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 846-870, September.
    2. Quinn N. Lathrop & Ying Cheng, 2017. "Item Cloning Variation and the Impact on the Parameters of Response Models," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 245-263, March.

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