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Model-Based Reliability to Check for Disparities in Ratings of Internal and External Applicants

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    0493164 - ÚI 2019 RIV DE eng A - Abstract
    Martinková, Patrícia - Goldhaber, D. - Erosheva, E.
    Model-Based Reliability to Check for Disparities in Ratings of Internal and External Applicants.
    Abstractband. VIII. European Congress on Methodology. Abstract Book. Jena: Institut für Psychologie, Friedrich-Schiller-Universität, 2018. s. 102-102.
    [2018 European Congress on Methodology /8./. 25.07.2018-27.07.2018, Jena]
    R&D Projects: GA ČR GJ15-15856Y
    Institutional support: RVO:67985807
    Keywords : Generalizability * Linear Mixed Models * Reliability * Variance Decomposition
    OECD category: Statistics and probability

    ZÁKLADNÍ ÚDAJE: Abstractband. VIII. European Congress on Methodology. Abstract Book. Jena: Institut für Psychologie, Friedrich-Schiller-Universität, 2018. s. 102-102. 2018 European Congress on Methodology /8./. 25.07.2018-27.07.2018, Jena. Grant CEP: GA ČR GJ15-15856Y. ABSTRAKT: In this work we address disparities in ratings of internal and external applicants. We develop model-based inter-rater reliability (IRR) estimate to account for various sources of measurement error, their hierarchical structure and the presence of covariates, such as assessed status, that have the potential to moderate IRR. Using dataset of ratings of applicants to teaching positions in Spokane district in Washington, USA, we first test for bias in ratings of applicants external to the district, which is shown to be significant even after including various measures of teacher quality in the model. Moreover, withmodel-based IRR, we show that consistency between raters is significantly lower when rating external applicants. We further address how IRR affects the predictive power of measurement in different scenarios and conclude the work by discussing policy implications and applications of our model-based IRR estimate for teacher hiring practices.
    Permanent Link: http://hdl.handle.net/11104/0286582

     
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