Assessing Inter-rater Reliability With Heterogeneous Variance Components Models: Flexible Approach Accounting for Contextual Variables

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00568726" target="_blank" >RIV/67985807:_____/23:00568726 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11410/23:10456212

Result on the web

<a href="https://dx.doi.org/10.3102/10769986221150517" target="_blank" >https://dx.doi.org/10.3102/10769986221150517</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3102/10769986221150517" target="_blank" >10.3102/10769986221150517</a>

Result language

angličtina

Original language name

Assessing Inter-rater Reliability With Heterogeneous Variance Components Models: Flexible Approach Accounting for Contextual Variables

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/GA21-03658S" target="_blank" >GA21-03658S: Theoretical foundations of computational psychometrics</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Educational and Behavioral Statistics

ISSN

1076-9986

e-ISSN

1935-1054

Volume of the periodical

48

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

35

Pages from-to

349-383

UT code for WoS article

000931779800001

EID of the result in the Scopus database

2-s2.0-85148070280