Likert Scale - Scoring and Analysis

Scoring and Analysis

After the questionnaire is completed, each item may be analyzed separately or in some cases item responses may be summed to create a score for a group of items. Hence, Likert scales are often called summative scales.

Whether individual Likert items can be considered as interval-level data, or whether they should be treated as ordered-categorical data is the subject of considerable disagreement in the literature, with strong convictions on what are the most applicable methods. This disagreement can be traced back, in many respects, to the extent to which Likert items are interpreted as being ordinal data.

There are two primary considerations in this discussion. First, Likert scales are arbitrary. The value assigned to a Likert item has no objective numerical basis, either in terms of measure theory or scale (from which a distance metric can be determined). The value assigned to each Likert item is simply determined by the researcher designing the survey, who makes the decision based on a desired level of detail. However, by convention Likert items tend to be assigned progressive positive integer values. Likert scales typically range from 2 to 10 – with 5 or 7 being the most common. Further, this progressive structure of the scale is such that each successive Likert item is treated as indicating a ‘better’ response than the preceding value. (This may differ in cases where reverse ordering of the Likert Scale is needed).

The second, and possibly more important point, is whether the ‘distance’ between each successive Likert item is equivalent, which is inferred traditionally. For example, in the above five-point Likert Scale, the inference is that the ‘distance’ between items 1 and 2 is the same as between items 3 and 4. In terms of good research practice, an equidistant presentation by the researcher is important; otherwise it a bias in the analysis may result. For example, a four-point Likert Scale–Poor, Average, Good, Very Good–is unlikely to have all equidistant items since there is only one item that can receive a below average rating. This would arguably bias any result in favor of a positive outcome. On the other hand, even if a researcher presents what he or she believes is an equidistant scale, it may not be interpreted as such by the respondent.

A good Likert scale, as above, will present a symmetry of Likert items about a middle category that have clearly defined linguistic qualifiers for each item. In such symmetric scaling, equidistant attributes will typically be more clearly observed or, at least, inferred. It is when a Likert scale is symmetric and equidistant that it will behave more like an interval-level measurement. So while a Likert scale is indeed ordinal, if well presented it may nevertheless approximate an interval-level measurement. This can be beneficial since, if it was treated just as an ordinal scale, then some valuable information could be lost if the ‘distance’ between Likert items were not available for consideration. The important idea here is that the appropriate type of analysis is dependent on how the Likert scale has been presented.

Given the Likert Scale's ordinal basis, summarizing the central tendency of responses from a Likert scale by using either the median or the mode is best, with ‘spread’ measured by quartiles or percentiles. Non-parametric tests should be preferred for statistical inferences, such as chi-squared test, Mann–Whitney test, Wilcoxon signed-rank test, or Kruskal–Wallis test. While some commentators consider that parametric analysis is justified for a Likert scale using the Central Limit Theorem, this should be reserved for when the Likert scale has suitable symmetry and equidistance so an interval-level measurement can be approximated and reasonably inferred.

Responses to several Likert questions may be summed, providing that all questions use the same Likert scale and that the scale is a defensible approximation to an interval scale, in which case they may be treated as interval data measuring a latent variable. If the summed responses fulfill these assumptions, parametric statistical tests such as the analysis of variance can be applied. These can be applied only when 4 to 8 Likert questions (preferably closer to 8) are summed.

Data from Likert scales are sometimes converted to binomial data by combining all agree and disagree responses into two categories of "accept" and "reject". The chi-squared, Cochran Q, or McNemar test are common statistical procedures used after this transformation.

Consensus based assessment (CBA) can be used to create an objective standard for Likert scales in domains where no generally accepted or objective standard exists. Consensus based assessment (CBA) can be used to refine or even validate generally accepted standards.