Abstract In this study we extend and assess the trifactor model for multiple-ratings data in which two different raters give independent scores for the same responses (e.g., in the GRE essay or to subset of PISA constructed-responses). The trifactor model was extended to incorporate a cross-classified data structure (e.g., items and raters) instead of a strictly hierarchical structure. we present a set of simulations to reflect the incompleteness and imbalance in real-world assessments. The effects of the rate of missingness in the data and of ignoring differences among raters are investigated using two sets of simulations. The use of the trifactor model is also illustrated with empirical data analysis using a well-known international large-scale assessment.
Trifactor Models for Multiple-Ratings Data
H. Shin,S. Rabe-Hesketh,Mark Wilson
Published 2019 in Multivariate Behavioral Research
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- Publication year
2019
- Venue
Multivariate Behavioral Research
- Publication date
2019-03-28
- Fields of study
Mathematics, Medicine, Psychology
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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