Likert items and scales produce what we call ordinal data, i.e., data that can be ranked. Many researchers tend to use Likert scales to do things that they were never designed to do They are very flexible and very useful, provided you use them carefully. Likert scales are very frequently used to measure constructs like satisfaction rates, attitudes towards different things, and more. I will not go into any of this in more detail here, but if you want to find out more, this post has some additional information. This helps researchers to probe different aspects of the same construct (or ‘ latent variable’), by putting together information from all the related items. Sometimes, sets of similar items are dispersed in the same questionnaire. Apples are rubbishĮach of these items measures a variable, i.e., a construct about which we want to learn more. (1) Strongly Agree (2) Agree (3) Neither agree nor disagree (4) Disagree (5) Strongly Disagree a. Indicate what you think about the following statements using the scale below: Likert scales and ordinal data What are Likert scales?Ī Likert-type question (or ‘item’) asks respondents to select one of several (usually four or five) responses that are ranked in order of strength. Those of you who are unlucky enough to have studied statistics may want to skip to the next section. Those who are not familiar with the fascinating minutiae of quantitative research can find a discussion of Likert scaling and ordinal data in the section that immediately follows. Specifically, what sparked my interest was one study in the collection, which used Likert scales to record participants’ attitudes towards a certain educational construct. Substantive comments on the book have been published elsewhere but what I want to do in this post, instead, is share some thoughts regarding a common statistical mistake and a common misconception about published works. This post has been prompted by an edited collection that I was recently asked to review. You should also take a look at the list of additional resources. If that is the case, you will probably be want to skip directly to the part of this post where I talk about a common mistake people make with ordinal data and mean values. Welcome! Chances are that you landed on this page looking for information on Likert scales and averages.
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