Likert Scale Data Analysis and Interpretation

Whenever you want to carry out a survey of an event among a group of individuals, it is not usually ideal to ask them questions where they have to provide a yes or no answer. Usually, people are not sure of where they stand about a subject matter, and they prefer to have different options to select from when they want to provide answers.

For instance, if your company just introduced a new policy, and you want to find out if the employees love the policy. You may need to ask them to show their degree of love for the policy instead of telling them to answer you in a polar manner.

A Likert Scale is a five-point or seven-point scale that allows individuals to show their level of agreement to a particular statement.

We want to find out in this blog how we can analyze the data we have generated from the Likert Scale and how we can further interpret them. Therefore, you should continue reading this blog in your best interest to find out everything you need to know.

What is a Likert Scale?

A Likert scale is a questionnaire method of carrying out survey research. It is the most used form of scaling responses in carrying out survey studies. You will find the Likert Scale useful when you want to determine where a set of individuals in a population stand concerning a statement.

Likert scales usually ask you to show your level of agreement to a particular statement, and it can span from strongly agree to strongly disagree.

Usually, we have the 5 point Likert scale format, which shows variations of agreement, including the following;

• Strongly agree
• Agree
• Neutral
• Disagree
• Strongly disagree

Another popular format is the 7-point Likert scale, which includes somewhat agree and somewhat disagree in addition to the 5-point Likert scale.

Likert Scale Data Analysis

We are most times faced with the challenge of analyzing data we generate from Likert scales.

The question usually comes to if we should use parametric or nonparametric tests to analyze data from Likert scales. It could be quite challenging to measure or analyze Likert data using mean, standard deviation, or ANOVA because Likert data are discrete, ordinal, and limited in range. Usually, when you carry out surveys, you will only consider a specific part of the population. When you use parametric tests, it could be challenging to get something meaningful out of them.

Nonparametric tests still work for ordinal data that do not follow a normal distribution. Still, the challenge could be that nonparametric tests may not have a high chance of detecting the existence of an effect, considering the data may not be enough.

Over the years, experts have agreed to use the median to measure the central tendency for data generated from the Likert Scale. Similarly, instead of using parametric tests like t-tests, regression, correlation, or ANOVA, contingency tables, frequencies, the Mann-Whitney U test, and the Spearman rho assessment should be used.

However, if the Likert data are normally distributed, perhaps, near normal, and the sample size contains a minimum of five to 10 observations in a group, you can proceed to use parametric tests. But, again, the essential thing is to ensure the normal distribution of the population.

Furthermore, parametric tests make more sense because they give more unbiased answers, which means you can use them to measure Likert scale responses. Once you can ensure that your population is more frequently distributed, you may also find the mean of the data. Ideally, to find the mean, you can add up the scores from each of the questions to get the aggregate score for each participant, helping you to get the standard deviation.

The first step is to get your analysis right by tabulating your results. Usually, strongly agree is represented as 1 on a 5-point Likert scale, while agree is defined as 2. Neutral carries the value 3, with disagree having 4, and strongly disagree carrying 5. Next, get the mode of the most frequent item and get the mean score of each participant.

The mode will tell you the most common response, while the mean will give you the average answer. You may then create a bar chart to represent the data, with one column representing each response. You may then analyze using any of the methods above.

How to Interpret the Likert Scale Data

Once you are done with the analysis, what you need to find out is whether or not your results give you any significant difference that supports or against your hypothesis. The term "significance" varies depending on the type of test you’ve used.

Conclusion

If you want to give people the privilege to better express themselves about an issue or statement, the Likert scale is your best bet. Instead of telling people to answer yes or no, you can find out their level of agreement to a particular statement.

Analyzing the Likert scale can be a bit tricky. Still, if you can get a more normal distribution in a population, you will be able to perform parametric tests, including mean, standard deviation, ANOVA, and others.

I have more than 8 years of experience in the field of Digital Marketing and Data Analysis. I am currently working as a Digital marketing specialist in Ppcexpo.