When designing survey instruments, researchers frequently encounter the question of how to classify different types of response formats. A fundamental consideration is whether a specific measurement scale qualifies as nominal or ordinal data, and the Likert scale sits precisely at this intersection of statistical categorization and practical application. Understanding the true nature of this scale is essential for selecting the correct analytical methods and ensuring the validity of research findings, as misclassification can lead to inappropriate statistical tests and misleading interpretations.
Defining the Core Concepts
To address the classification of the Likert scale, it is necessary to first define the foundational concepts of nominal and ordinal data. Nominal data represent categories that are distinct and mutually exclusive, possessing no inherent order or ranking; examples include gender, race, or types of product preferences. In contrast, ordinal data maintain a clear rank or sequence, indicating that one value is higher or greater than another, but they do not specify the exact magnitude of difference between the ranks. The ambiguity surrounding the Likert scale arises because it incorporates elements of both definitions, challenging researchers to examine its structure more closely.
The Structure of a Likert Scale
A standard Likert scale presents respondents with a statement and a range of options reflecting the degree of agreement or disagreement. These options typically progress from "Strongly Disagree" to "Disagree," then "Neutral," followed by "Agree," and finally "Strongly Agree." This progression creates a logical hierarchy, implying that a respondent who selects "Strongly Agree" holds a more favorable view than one who selects "Disagree." This inherent progression is the primary reason why the Likert scale is widely classified as ordinal rather than nominal.
Why It Is Ordinal
The ordinal nature of the Likert scale stems from the fact that the categories have a defined order, but the intervals between these categories are not necessarily equal. The psychological distance between "Strongly Disagree" and "Disagree" may not be identical to the distance between "Agree" and "Strongly Agree." Because the scale ranks responses without guaranteeing uniform metric distances, it fits the definition of ordinal data perfectly. Treating the scale as nominal would discard this valuable ordering information, which is often the most critical aspect of the collected data.
Common Misclassification and Confusion
Despite the consensus regarding its ordinal nature, the Likert scale is frequently misclassified as nominal, particularly in introductory research methods courses or by practitioners new to statistics. This confusion often arises from a misunderstanding of the scale’s components. While the individual points on the scale might seem like distinct categories, the intentional construction of the scale is to measure the intensity of an attitude. Because this intensity is ranked, the data cannot be considered purely nominal, which applies only to categories like zip codes or brand names where order is irrelevant.
Implications for Statistical Analysis
The classification of the Likert scale as ordinal has direct consequences for the statistical tests that can be appropriately applied. Parametric tests, which assume interval-level data and normal distribution, are generally considered inappropriate for strictly ordinal data. Consequently, researchers are typically advised to use non-parametric tests such as the Mann-Whitney U test, the Kruskal-Wallis test, or the Wilcoxon signed-rank test to analyze Likert scale data. Treating the data as interval by calculating means is a common practice, but it remains a subject of statistical debate due to the scale's ordinal foundation.
Practical Considerations and Modern Usage
While the theoretical classification of the Likert scale as ordinal is clear, modern research practice often blurs the lines between ordinal and interval analysis. Many experienced researchers treat the scale as approximate interval data, arguing that the aggregate response of a large group tends to approximate normality. This pragmatic approach allows for the use of parametric tests like t-tests and ANOVA, which are more powerful and familiar to a broader audience. However, this practice requires justification and an understanding of the potential limitations regarding the sensitivity of the results.
