Rating and ranking preparedness characteristics important for veterinary workplace clinical training: a novel application of pairwise comparisons and the Elo algorithm

Abstract

Quantitatively eliciting perspectives about a large number of similar entities (such as a list of competences) is a challenge for researchers in health professions education (HPE). Traditional survey methods may include using Likert items. However, a Likert item approach that generates absolute ratings of the entities may suffer from the "ceiling effect," as ratings cluster at one end of the scale. This impacts on researchers' ability to detect differences in ratings between the entities themselves and between respondent groups. This paper describes the use of pairwise comparison (this or that?) questions and a novel application of the Elo algorithm to generate relative ratings and rankings of a large number of entities, on a unidimensional scale. A study assessing the relative importance of 91 student "preparedness characteristics" for veterinary workplace clinical training (WCT) is presented as an example of this method in action. The Elo algorithm uses pairwise comparison responses to generate an importance rating for each preparedness characteristic on a scale from zero to one. This is continuous data with measurement variability which, by definition, spans an entire spectrum and is not susceptible to the ceiling effect. The output should allow for the detection of differences in perspectives between groups of survey respondents (such as students and workplace supervisors) which Likert ratings may be insensitive to. Additional advantages of the pairwise comparisons are their low susceptibility to systematic bias and measurement error, they can be quicker and arguably more engaging to complete than Likert items, and they should carry a low cognitive load for respondents. Methods for evaluating the validity and reliability of this survey design are also described. This paper presents a method that holds great potential for a diverse range of applications in HPE research. In the pursuit quantifying perspectives on survey items which are measured on a relative basis and a unidimensional scale (e.g., importance, priority, probability), this method is likely to be a valuable option.