Most measures of interrater agreement are defined for ratings regarding a group of targets, each rated by the same group of raters (e.g., the agreement of raters who assess on a rating scale the language proficiency of a corpus of argumentative written texts). However, there are situations in which agreement between ratings regards a group of targets where each target is evaluated by a different group of raters, like for instance when teachers in a school are evaluated by a questionnaire administered to all the pupils (students) in the classroom. In these situations, a first approach is to evaluate the level of agreement for the whole group of targets by the ANOVA one-way random model. A second approach is to apply subject-specific indices of interrater agreement like rWG, which represents the observed variance in ratings compared to the variance of a theoretical distribution representing no agreement (i.e., the null distribution). Both these approaches are not appropriate for ordinal or nominal scales. In this paper, an index is proposed to evaluate the agreement between raters for each single target (subject or object) on an ordinal scale, and to obtain also a global measure of the interrater agreement for the whole group of cases evaluated. The index is not affected by the possible concentration of ratings on a very small number of levels of the scale, like it happens for the measures based on the ANOVA approach, and it does not depend on the definition of a null distributions like rWG. The main features of the proposal will be illustrated in a study for the assessment of learning teacher behavior in classroom collected in a research conducted in 2018 at Roma Tre University.
Roma Tre University, Italy - ORCID: 0000-0002-2736-5697
Chapter Title
Measures of interrater agreement when each target is evaluated by a different group of raters
Authors
Giuseppe Bove
Language
English
DOI
10.36253/979-12-215-0106-3.28
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Book Title
ASA 2022 Data-Driven Decision Making
Book Subtitle
Book of short papers
Editors
Enrico di Bella, Luigi Fabbris, Corrado Lagazio
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press, Genova University Press
DOI
10.36253/979-12-215-0106-3
eISBN (pdf)
979-12-215-0106-3
eISBN (xml)
979-12-215-0107-0
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846