Assessing Reliability of Expert Ratings Among Judges Responding to a Survey Instrument Developed to Study the Long Term Efficacy of the ABET Engineering Criteria, EC2000

In today’s assessment processes, especially those evaluations that rely on humans to make subjective judgements, it is necessary to analyze the quality of their ratings. The psychometric issues associated with assessment provide the lens through which researchers interpret results and important decisions are made. Therefore, inter-rater agreement (IRA) and inter-rater reliability (IRR) are pre-requisites for rater-dependent data analysis. A survey instrument cannot provide “good” information if it is not reliable; in other words, reliability is central to the validation of an instrument. When judges cannot be shown to reliably rate a performance, item, or target, the question becomes why the judges’ responses are different from one another. If the judges’ ratings covary unreliably because the construct is poorly defined or the rating framework is defective, then the resultant scores will have questionable meaning. On the other hand, if the judges’ ratings differ because they have a true difference in opinion, this is of importance to the researcher and may not necessarily diminish the validity of the scores. The intraclass correlation coefficient (ICC) is the most efficient method to assess these rater differences and identify the specific sources of inconsistency in measurement. This study examined how ICCs can be used to inform researchers of the extent in which legitimate differences of opinion may appear as a lack of reliability and/or agreement, demonstrating the need for analyzing survey data beyond standard descriptive statistics. Overall, both the IRA and IRR correlations, as calculated by ICC, ranged from .79 to .91 indicating high levels of agreement and consistency in the scoring among the judges\u27 ratings. When group membership was accounted for the IRA values increased suggesting the common judges agreed more than those judges who varied in their perspectives

A Prospective Investigation to Develop Data-Driven Interventions and Improve Process Efficiency at a Level II Trauma Center

INTRODUCTION: The purpose of this investigation was to better understand process inefficiencies in a Level II trauma center through the identification and classification of flow disruptions. Data-driven interventions were systematically developed and introduced in an effort to reduce disruptions threatening the optimal delivery of trauma care. METHOD: Medical human factors researchers observed disruptions during resuscitation and imaging in 117 trauma cases. Data was classified using the human factors taxonomy Realizing Improved Patient Care through Human-centered Operating Room Design for Threat Window Analysis (RIPCHORD-TWA). Interdisciplinary subject matter experts (SMEs) utilized a human factors intervention matrix (HFIX) to generate targeted interventions designed to address the most detrimental disruptions. A multiple-baseline interrupted time-series (ITS) design was used to gauge the effectiveness of the interventions introduced. RESULTS: Significant differences were found in the frequency of disruptions between the pre-intervention (n=65 cases, 1137 disruptions) and post-intervention phases (n=52 cases, 939 disruptions). Results revealed significant improvements related to ineffective communication (x2 (1, n=2076) = 24.412, p=0.00, x2 (1, n=1031) = 9.504, p=0.002, x2 (1, n=1045) = 12.197, p=0.000); however, similar levels of improvement were not observed in the other targeted areas. CONCLUSION: This study provided a foundation for a data-driven approach to investigating precursor events and process inefficiencies in trauma care. Further, this approach allowed individuals on the front lines to generate specific interventions aimed at mitigating systemic weaknesses and inefficiencies frequently encountered in their work environment