Likert scales are among the most frequently used instruments in questionnaire surveys. A Likert scale consists of statements and pre-defined responses that measure the intensity of the respondents’ feelings towards the preceding statement. Here’s an example of such as a statement, or ‘item’:
Likert scales are easy for respondents to understand, and easy for researchers to interpret, which is why they are used very frequently in student projects and more formal research. However, despite their popularity (or maybe because of it), they are often used in loose ways that are not always optimal.
Here are four tips to help you avoid common pitfalls. In this post you will learn:
- How to correctly pronounce ‘Likert’;
- If it is better to use odd or even numbers of responses;
- What the best number of responses is;
- Why you should not use weighted averages when analysing Likert data.
1. Lick, not Like
Likert scales were created by Rensis Likert, a sociologist at the University of Michigan. The proper pronunciation of his name is “Lick – uhrt”. The pronunciation “like – uhrt”, though common, is incorrect.
2. Getting even helps
A Likert item consists of a prompt and a set of responses. Most frequently, there are five responses for each item, often ranging from Strongly agree to Strongly disagree. Seven-item scales are also quite common. When using an odd number of responses, the mid-range is a ‘neutral’ option, such as “no opinion”, “neither agree nor disagree”, “not sure” or some phrase to that effect.
Such a practice can be problematic for at least two reasons: Firstly, many respondents tend to avoid voicing extreme opinions or taking a stand on controversial topics. This means that respondents are likely to select a ‘safe’ choice at the centre of the scale if one is available, rather than reveal their ‘true’ opinion – a phenomenon called the central tendency bias. This is especially the case when respondents are conscious of power imbalances (e.g., students responding to a questionnaire designed by their professors or teachers engaging with university-based research).
A second potential problem with middle options is that they can be hard to interpret. While we might assume that it means something along the lines of ‘I have no strong views either way’, this may not be true of all respondents. For some respondents, for example, the ‘neutral option’ could mean that ‘I don’t care either way’; for others it may mean that ‘I have no knowledge of this’.
We can avoid some of these problems by using items that have an even number of responses. In the following example, respondents are presented with four ‘true’ options, which encourage them to voice a positive or negative opinion. This response format is called a ‘forced choice’ or ‘ipsative’ item.
Figure 2 shows an ipsative item. This contains four ‘proper’ responses under the statement, in order to force respondents to register some agreement or disagreement. There is also an additional ‘opt-out’ option for those respondents who truly cannot respond, but the wording of the item and the layout discourage its unnecessary use.
Disclaimer 1: Whether you use a ‘neutral’ option or not will depend a lot on your research aims, and the power dynamics in your research context. You might want to read more about the pros and cons of adding a neutral option in this article by TalentMap.Embed from Getty Images
3. Less is more
Some Likert items contain large numbers of possible response options (7, 9 or 10) to capture a variety of positions. While such scales seem quite sensitive and accurate, they are not always very helpful. For one thing, any benefit from large numbers of options is subject to the law of diminishing returns. From the 7-option format and upwards, the scales just become too cumbersome to use, any additional benefits are cancelled out by respondent fatigue, and reliability plummets. Secondly, the analytical sensitivity of the scales is compromised because respondents tend to interpret the scales in different ways: what I describe as “often” may mean the same, in absolute terms, as what you might call “sometimes”. This phenomenon is amplified when the number of potential responses is large.
When interpreting the data, Likert items with many potential responses can sometimes be helpfully condensed into fewer, more meaningful categories. If you have an item with seven or nine responses, but a small sample size, this could mean that most responses have been selected by very few participants. This is problematic because small numbers of respondents often limit the effectiveness of certain statistical procedures. In such cases, it might make sense to group all the ‘positive’ and ‘negative’ answers together. Doing so would involve the loss of some analytical detail, but this is an imperfect universe…Embed from Getty Images
4. The mean is meaningless
The most common mistake in interpreting Likert scale data is reporting the mean values for responses. I have ranted about this practice elsewhere, but here’s the gist: To facilitate coding or save space on a questionnaire, we sometimes use numbers to represent response options in Likert items (e.g., Figure 3, top). These numerals are just descriptive codes, not ‘true’ numbers. From a mathematical perspective, a ‘Strongly Agree’ response indicates more agreement than ‘Agree’, but it does not show agreement that is five times stronger than ‘Strongly Disagree’. We could just as easily have used colours to anchor the responses, or any other symbol to show the same effect (e.g., Figure 3, bottom). In other words, we can use the data from Likert items (ordinal data, to be technical) if we want to rank responses, but that’s about the limit of what we can do with them .
To make this even clearer: We would be very unlikely to say that ‘the average response is agree and three quarters‘, and using numbers to express the same idea makes no more sense. Similarly, we can describe the fruit on a grocery stand, noting that strawberries are smaller than apples, which are smaller than watermelons, and we can count how many fruit of each type are on sale, but we would never say that ‘the fruit on display are, on average, apples’. Reporting that the average of ‘two agrees and a strongly disagree’ is ‘disagree’ is just as bizarre.
Once more: when it comes to analysing the data that Likert items produce, reporting the mean makes very little mathematical sense (I am being charitable: others have called it an ‘indefensible‘ practice, and one of the seven ‘deadly sins‘ of statistics). Other metrics, such as the median or the mode are more appropriate. (You can find out more about these metrics here).
For similar reasons, it is best to use Range and InterQuartile Range (IQR), but notThe Standard Deviation, when we want to estimate the spread of responses in a Likert scale. It is also safer to avoid statistical procedures that rely on the mean (e.g. t-tests) in most cases. Non-parametric tests, such as the Mann‐Whitney U-test, the Wilcoxon signed‐rank test and the Kruskal‐Wallis test are better alternatives. For presenting data, it’s best to use bar charts, rather than histograms.
Disclaimer 2: Under certain circumstances, a Likert scale (i.e., a collection of Likert items) can produce data that are suitable for calculating means, or running statistical tests that rely on the mean. These can be called ‘ordinal approximations of continuous data’. Experienced statisticians can probably get away with this, and they might be able to argue convincingly why their approach was appropriate. But if you’re doing a student project, the conservative approach suggested here is safer.
Additional reading about Likert scales
The advice and opinions in the previous sections were written to help you use Likert scales more effectively in your research projects. It has not been my intention to create an authoritative or comprehensive research methods guide, and I strongly encourage you to follow up on some of the things that you’ve just read. Some more resources that you may find helpful include the following:
- Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140)
- Cohen, L., Manion, L., & Morrison, K. (2000). Research methods in education (5th edn). New York: Routledge. (pp. 253-255)
- Gilbert, G. N. (2008). Researching social life (3rd edn). London: SAGE. (pp. 212ff.).
Limitations of Likert scales
- Jamieson, S. (2004). Likert scales: how to (ab) use them. Medical Education, 38(12), 1217-1218.
- Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for Likert scale items? Educational and Psychological Measurement, 31(3), 657-674.
- Jacoby, J, & Matell, M.S. (1971). Three-point Likert scales are good enough. Journal of marketing research, 8(4), 495-500.
Some different views about Likert scales
The articles listed below describe perspectives on Likert scaling that are not in line with the recommendations I have made above.
- Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625-632. [This is a ‘rogue’ article, where the argument is made that, despite what purists claim, parametric procedures are robust enough to yield usable findings even when fed with ordinal (i.e., Likert-type) data.]
- Sullivan, G. M., & Artino, A. R. (2013). Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. [This article extends the argument put forward by Norman (above). The authors concede that parametric tests tend to yield ‘correct’ results even if their assumptions are violated, but point out that “means are often of limited value unless the data follow a classic normal distribution and a frequency distribution of responses will likely be more helpful”. ]
Before you go: I hope that this information was helpful, but if there’s anything that was not clear, feel free to drop a line in the comments below. You may also want to check out some more posts I have written on quantitative research, including:
- On Likert scales, levels of measurement and nuanced understandings
- Designing better questionnaires: Using scales
If’ve come to this page while preparing for one of your student projects, I wish you all the best with your work. There’s a range of social sharing buttons below in case you feel like sharing this information among fellow students who might find it useful. Also feel free to ask any other questions you may have, using the contact form.
Featured Image by Michael Kwan [CC BY-NC-ND]