Analyzing and Interpreting Data From Likert-Type Scales

TLDR: Reading this article is to provide readers who do not have extensive statistics background with the basics needed to understand the basics of Likert scale concepts.

Abstract: Likert-type scales are frequently used in medical education and medical education research. Common uses include end-of-rotation trainee feedback, faculty evaluations of trainees, and assessment of performance after an educational intervention. A sizable percentage of the educational research manuscripts submitted to the Journal of Graduate Medical Education employ a Likert scale for part or all of the outcome assessments. Thus, understanding the interpretation and analysis of data derived from Likert scales is imperative for those working in medical education and education research. The goal of this article is to provide readers who do not have extensive statistics background with the basics needed to understand these concepts. Developed in 1932 by Rensis Likert1 to measure attitudes, the typical Likert scale is a 5- or 7-point ordinal scale used by respondents to rate the degree to which they agree or disagree with a statement (table). In an ordinal scale, responses can be rated or ranked, but the distance between responses is not measurable. Thus, the differences between “always,” “often,” and “sometimes” on a frequency response Likert scale are not necessarily equal. In other words, one cannot assume that the difference between responses is equidistant even though the numbers assigned to those responses are. This is in contrast to interval data, in which the difference between responses can be calculated and the numbers do refer to a measureable “something.” An example of interval data would be numbers of procedures done per resident: a score of 3 means the resident has conducted 3 procedures. Interestingly, with computer technology, survey designers can create continuous measure scales that do provide interval responses as an alternative to a Likert scale. The various continuous measures for pain are well-known examples of this (figure 1). FIGURE 1 Continuous Measure Example TABLE Typical Likert Scales