Assessing Inter-Rater Reliability with Heterogeneous Variance Components Models: Flexible Approach Accounting for Contextual Variables

Autor/inn/en	Martinková, Patrícia; Bartoš, František; Brabec, Marek
Titel	Assessing Inter-Rater Reliability with Heterogeneous Variance Components Models: Flexible Approach Accounting for Contextual Variables
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 3, S.349-383 (35 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Martinková, Patrícia) ORCID (Bartoš, František)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221150517
Schlagwörter	Interrater Reliability; Bayesian Statistics; Statistical Inference; Hierarchical Linear Modeling; Error of Measurement; Alternative Assessment; Program Proposals; Grants; Peer Evaluation; Simulation + Suchen Sie Ihr Suchwort? Interrater-Reliabilität; Inferential statistics; Schließende Statistik; Messfehler; Grant; Finanzielle Beihilfe; Simulation program; Simulationsprogramm
Abstract	Inter-rater reliability (IRR), which is a prerequisite of high-quality ratings and assessments, may be affected by contextual variables, such as the rater's or ratee's gender, major, or experience. Identification of such heterogeneity sources in IRR is important for the implementation of policies with the potential to decrease measurement error and to increase IRR by focusing on the most relevant subgroups. In this study, we propose a flexible approach for assessing IRR in cases of heterogeneity due to covariates by directly modeling differences in variance components. We use Bayes factors (BFs) to select the best performing model, and we suggest using Bayesian model averaging as an alternative approach for obtaining IRR and variance component estimates, allowing us to account for model uncertainty. We use inclusion BFs considering the whole model space to provide evidence for or against differences in variance components due to covariates. The proposed method is compared with other Bayesian and frequentist approaches in a simulation study, and we demonstrate its superiority in some situations. Finally, we provide real data examples from grant proposal peer review, demonstrating the usefulness of this method and its flexibility in the generalization of more complex designs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01