Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes

Abstract

Abstract:A recent article by Jamieson in Medical Education outlined some of the (alleged) abuses of "Likert scales" with suggestions about how researchers can overcome some of the (alleged) methodological pitfalls and limitations [1] . However, many of the ideas advanced in the Jamison article, as well as a great many of articles it cited, and similar recent articles in medical, health, psychology, and educational journals and books, are themselves common misunderstandings, misconceptions, conceptual errors, persistent myths and "urban legends" about "Likert scales" and their characteristics and qualities that have been propagated and perpetuated across six decades, for a variety of different reasons. This article identifies, analyses and traces many of these aforementioned problems and presents the arguments, counter arguments and empirical evidence that show these many persistent claims and myths about "Likert scales" to be factually incorrect and untrue. Many studies have shown that Likert Scales (as opposed to single Likert response format items) produce interval data and that the F-test is very robust to violations of the interval data assumption and moderate skewing and may be used to analyze "Likert data" (even if it is ordinal), but not on an item-by-item "shotgun" basis, which is simply a current research and analysis practice that must stop. After sixty years, it is more than time to dispel these particular research myths and urban legends as well as the various damage and problems they cause, and put them to bed and out of their misery once and for all.

Emodiversity: Robust predictor of outcomes or statistical artifact?

“…The values of emodiversity are produced by the use of invalid arithmetic 188 interval data when summed and averaged (Carifio & Perla, 2007), the act of dividing an 197…”

Analysis of variables that are not directly observable: influence on decision-making during the research process

“…Because of the absence of a more precise measure (e.g., a ruler) it is possible to operationalize a continuous measurement (a 100-mm line) using an ordinal scale with seven anchored points, for attitude affirmations or as derived phrases of semantic differential (17) .…”

“…The response categories in Likert scales have a rank order, but the intervals between values cannot be presumed equal (23) . However, other authors contest this claim (17,21) . They report that this article, and the references cited in the article, suffers from misunderstandings and gross errors based on myths, not true and conceptual errors in Likert scales.…”

Analysis of the reliability of the fuzzy scale for assessing the students’ learning styles in Mathematics

“…Nevertheless, its usefulness presents some drawbacks as it has been pointed out, for instance, in [1,4,11,14,23]. In the literature some authors have suggested to establish a correspondence between each Likert response category and a fuzzy value chosen from a class of flexible fuzzy sets previously stated by experts (see, for instance, [16,17]).…”