About BEAR

The Berkeley Evaluation and Assessment Research (BEAR) Center designs and delivers educational assessment instruments, performs research in assessment and psychometrics, and trains graduate students in these areas.

We collaborate with researchers in universities across the United States and abroad to develop software and other resources for constructing, managing, administering, and analyzing assessments.

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Our Software

In addition to our research in psychometrics, the BEAR Center is dedicated to the development of software that can facilitate the delivery and analysis of assessments, providing teachers with innovative and useful ways to assess students.

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Recent Publications

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News & Events

  • An Overview of Foreign Language Assessment at the Defense Language Institute Foreign Language Center, Monterey, CA

    Seumas Rogan, Defense Language Institute

    The mission of the Defense Language Institute Foreign Language Center (DLIFLC) is to provide culturally-based, foreign language education, training, evaluation and sustainment to enhance the security of the United States of America. This presentation will provide an overview of selected activities within DLIFLC's Language Proficiency Assessment Directorate with an emphasis on current research needs and opportunities for academic collaboration.

  • Special QME Seminar: Sources of Error in IRT Trait Estimation: Effects on Trait Score Bias and Confidence Interval Coverage Rates

    Leah Feuerstahler, University of Minnesota

    Item response theory (IRT) models item response probabilities as a function of item characteristics and latent trait scores. Within an IRT framework, trait score misestimation results from (1) random error, (2) the trait score estimation method, (3) errors in item parameter estimation, and (4) model misspecification. Through a simulation study, I explore the relative effects of these error sources on the confidence interval coverage rates for trait scores.

  • Special QME Seminar: A Weakly-Informative Group-Specific Prior Distribution for Meta-analysis

    Christopher Thompson, Florida State University

    While Bayesian meta-analysis has flourished both in methodological and substantive work, group-specific Bayesian modeling remains scarce. Common practice for choosing prior distributions entails using typical non-informative priors. Currently, there is a push to use more informative prior distributions. In this paper I propose a weakly-informative group-specific prior distribution.

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