Evaluating Modes of Observations Using Hierarchical Signal Detection Rater Models

Abstract

Scoring of performance can be based on different modes of observation, which can include live and videotaped observations. Although live observations have been the traditional format of measuring examinee performance in education and in healthcare, videotaped observations provide educators and testing agencies the promise of unbiased and standardized evaluations, offering practical solutions to limitations of real-time scoring. This study proposes a measurement model taking into account different modes of observation, using an extension of the hierarchical rater model based on signal detection theory (HRM-SDT). A hierarchical rater model is motivated by the nested structure of scores assigned by raters – scores assigned by raters become indicators of performance quality, which in turn become indicators of examinee ability. This study extends the hierarchical structure of the scoring process to include modes of observation, which serves as an intermediary level between the latent categorical indicator of performance quality and examinee ability, forming a three-level HRM-SDT. Analyses based on real-world data showed differences in the quality of scores from live observations and videotaped recordings. Compared to the traditional HRM-SDT, the overall model fit improved when including modes of observation. Simulations using different sample sizes and conditions provide implications for uses of this model.

Keywords:

• Hierarchical rater model
• direct observation
• rater effects
• signal detection theory

Article Information

Conflict of Interest Disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical Principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was not supported.

Role of the Funders/Sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgements: The ideas and opinions expressed herein are those of the authors alone, and endorsement by the author’s institutions is not intended and should not be inferred.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.