Likert scaling consists of questions that are answerable with a statement that is scaled with 5 or 7 options that the respondent can choose from.
Have you ever answered a survey question that asks to what extent you agree with a statement? The answers were probably: strongly disagree, disagree, neither disagree nor agree, agree, or strongly agree. Well, that’s a Likert question.
Regardless of the name—a satisfaction scale, an agree-disagree scale, or a strongly agree scale—the format is pretty powerful and a widely used means of survey measurement, primarily used in customer experience and employee satisfaction surveys.
In this article, we’ll answer some common questions about Likert scales and how they are used, though most importantly Likert scale scoring and interpretation. Learn our advice about how to benefit from conclusions drawn from satisfaction surveys and how to use them to implement changes that will improve your business!
What are Likert scale and Likert scale questionnaires?
A Likert scale usually contains 5 or 7 response options—ranging from strongly agree to strongly disagree—with differing nuances between these and a mandatory mid-point of neither agree nor disagree (for those who hold no opinion). The Likert-type scale got its name from psychologist Rensis Likert, who developed it in 1932.
Likert scales are a type of closed-ended question, like common yes-or-no questions, they allow participants to choose from a predefined set of answers, as opposed to being able to phrase their opinions in their own words. But unlike yes-or-no questions, satisfaction-scale questions allow for the measurement of people’s views on a specific topic with a greater degree of nuance.
Since these questions are predefined, it’s essential to include questions that are as specific and understandable as possible.
Answer presets can be numerical, descriptive, or a combination of both numbers and words. Responses range from one extreme attitude to the other, while always including a neutral opinion in the middle.
A Likert scale question is one of the most commonly used in surveys to measure how satisfied a customer or employee is. The most common example of their use is in customer satisfaction surveys, which are an integral part of market research.
Are satisfaction-scale questions the best survey questions?
Maybe you’ve answered one too many customer satisfaction surveys with Likert scales in your lifetime and now consider them way too generic and bland. But, the fact is they are one of the most popular types of survey questions.
Why?
First of all, they are pretty appealing to respondents because they are easy to understand and do not require too much thinking to answer.
And, while binary (yes-or-no) questions offer only two response options (i.e., if a customer is satisfied with your products and services or not), satisfaction-scale questions provide a clearer understanding of customers’ thoughts and opinions.
By using well-prepared additional questions, questions about particular products or service segments can be asked. That way, getting to the bottom of customer dissatisfaction is possible, making it easier to find a way to address their complaints and improve their experience.
Such surveys enable figuring out why customers are satisfied with one product but not another. This empowers the recognition of products and service areas that customers are confident in while helping to find ways to improve others.
When it comes to analyzing and interpreting survey scale results, Likert questions are helpful because they provide quantitative data that is easy to code and interpret. Results can also be analyzed through cross-tabulation analysis (we’ll get back to that later).
Likert scale examples: The types and uses of satisfaction scale questions
Likert questions can be used for many kinds of research. For example, determine the level of customer satisfaction with the latest product, assess employee satisfaction, or get post-event feedback from attendees after a specific event.
Questions can take different forms, but the most common is the 5-point or 7-point Likert scale question. There are 4-point and even 10-point Likert scale questions as well.
How to choose from these options?
The most common is the 5-point question. Most researchers advise the use of at least five response options (if not more). This ensures that respondents have enough choices to express their opinion as accurately as possible.
Some researchers suggest always using an even number of responses so respondents are not presented with a neutral answer, therefore having to “choose a side.” This is to avoid a tepid response even when respondents have an opinion, which is one of the most common types of errors in surveying.
Likert scale interpretation: Analyzing Likert scale/type data
Likert scale interpretation involves analyzing the responses to understand the participants’ attitudes toward the statements.
It’s important to note that Likert scales provide a quantitative representation of attitudes but do not necessarily capture underlying reasoning or motivations. Qualitative methods, such as interviews or open-ended questions, are often used in conjunction with Likert scales to gain a deeper understanding of participants’ perspectives.
Overall, Likert scale interpretation of data involves analyzing the numerical ratings, considering the directionality of the scale, examining central tendency and variability, identifying response patterns, and conducting comparative analyses to draw meaningful conclusions about people’s attitudes or opinions.
How to analyze satisfaction survey scale questions
For a survey to be its best, how gathered information is analyzed is as important as the gathering itself. That’s why we’ll now turn to the most effective ways of analyzing responses from satisfaction survey scales.
When using Likert scale questions, the analysis tools used are mean, median, and mode. These help better understand the information collected.
The mean (or average) is the average value of data, calculated by adding all the numbers and dividing this sum by the total number of values offered to respondents. The median is the middle value of a data set, while the mode is the number that occurs most often.
Some other useful ways of analyzing information are filtering and cross tabulation.
How to use filtering and cross tabulation for your Likert scale analysis?
Using a filter, the responses of one particular group of respondents are focused upon and the rest filtered out. For example, how female customers rate a product can be determined by filtering out male respondents, while concentrating on customers aged 20 to 30 can be gleaned by filtering out older respondents.
Cross tabulation, on the other hand, is a method to compare two sets of information in one chart and analyze the relationship between multiple variables. In other words, it can show the responses of a particular subgroup while it can also be combined with other subgroups.
Say you want to look at the responses of unemployed female respondents aged 20 to 30. By using cross tabulation, all three parameters—gender, age, and employment status—can be combined and correlation calculated.
If this all sounds confusing, SurveyPlanet luckily doesn’t just offer great examples of surveys and the ability to create custom themes, but also the power to export survey results into several different formats, such as Microsoft Excel and Word, CSV, PDF, and JSON files.
How to interpret Likert scale data?
When information has been gathered and analyzed, it’s time to present it to stakeholders. This is the final stage of research. Analyzing the results of Likert scale questionnaires is a vital way to improve services and grow a business. Presenting the results correctly is a key step.
Here’s how to develop a clear goal and present it understandably and engagingly.
1. Compare new and old information to ensure a better understanding of progress
Compare the newly obtained information with data gathered from previous surveys. Sure, information gathered from the latest research is valuable on its own, but not helpful enough. For example, it tells you if customers are currently satisfied with products or services, but not whether things are better or worse than last year.
The key to improving customer service—and thus developing a business—is comparing current responses with previous ones. This is called longitudinal analysis. It can provide valuable insights about how a business is developing, if things are improving or declining, and what issues need to be solved.
If there is no previous data, then start collecting feedback immediately in order to compare results with future surveys. This is called benchmarking. It helps keep track of progress and how products, services, and overall customer satisfaction changes over time.
2. Compare information with other types of data and objective indicators
The most crucial information to compare new findings with is previous surveys. But it is highly recommended to constantly compare findings with other types of information, such as Google Analytics, sales data, and other objective indicators.
Another good practice is comparing qualitative with quantitative data. The more information, the more accurate the research results, which will help better convey findings to stakeholders. This will also improve business decision-making, strengthening the experiences of customers and employees.
3. Make a visual representation: Help the audience understand the data better
Numbers are easier to understand when suitable visual representation is provided. However, it is essential to use a medium that adequately highlights key findings.
Line graphs, pie charts, bar charts, histograms, scatterplots, infographics, and many more techniques can be used.
But don’t forget good old tables. Even if they’re not so visually dynamic and a little harder on the eyes, some information is simply best presented in tables, especially numerical data.
Working with all of these options, more satisfactory presentations can be created.
4. Focus on insights instead of just the numbers
When presenting findings to stakeholders, don’t just focus on the numbers. Instead, highlight the conclusions about customer or employee satisfaction drawn from the research. That way, everyone present at the meeting will gain a deeper understanding of what you’re trying to convey.
A valuable and exciting piece of advice is to focus on the story the numbers tell. Don’t simply list the numbers collected. Instead, use relevant examples and connect all the information, building on each dataset to make a meaningful whole.
Define and describe problems that need to be solved in engaging and easy-to-understand terms so that listeners don’t have a hard time understanding what is being shared. Include suggestions that could improve, for example, customer experience outcomes. It is also important to share findings with the relevant teams, listen to their perspectives, and find solutions together.
An example of Likert scale data analysis and interpretation
Let’s consider an example scenario and go through the steps of analyzing and interpreting Likert scale data.
Scenario: A company conducts an employee satisfaction survey using a Likert scale to measure employees’ attitudes toward various aspects of their work environment. The scale ranges from 1 (Strongly Disagree) to 5 (Strongly Agree).
Item 1: “I feel valued and appreciated at work.”
Item 2: “My workload is manageable.”
Item 3: “I receive adequate training and support.”
Item 4: “I have opportunities for growth and advancement.”
Item 5: “My supervisor provides constructive feedback.”
How to analyze Likert scale data
Step 1: Calculate mean scores by summing up the responses and dividing by the number of respondents.
Item 1: Mean score = (4+5+5+4+3)/5 = 4.2
Item 2: Mean score = (3+4+3+3+4)/5 = 3.4
Item 3: Mean score = (4+4+5+4+3)/5 = 4.0
Item 4: Mean score = (3+4+3+2+4)/5 = 3.2
Item 5: Mean score = (4+3+4+3+5)/5 = 3.8
Step 2: Assess central tendency by looking at the distribution of responses to identify the most frequent response or central point.
Item 1: 4 (Agree) is the most frequent response.
Item 2: 3 (Neutral) is the most frequent response.
Item 3: 4 (Agree) is the most frequent response.
Item 4: 3 (Neutral) is the most frequent response.
Item 5: 4 (Agree) is the most frequent response.
Step 3: Consider Variability by assessing the range or spread of responses to understand the diversity of opinions.
Item 1: Range = 5-3 = 2 (relatively low variability)
Item 2: Range = 4-3 = 1 (low variability)
Item 3: Range = 5-3 = 2 (relatively low variability)
Item 4: Range = 4-2 = 2 (relatively low variability)
Item 5: Range = 5-3 = 2 (relatively low variability)
Step 4: Identify response patterns By looking for consistent agreement or disagreement across items or patterns of response clusters.
Step 5: Comparative analysis of responses among different groups, such as other departments or job positions, to identify attitude variations.
Likert scale interpretation example overview
In this example, there is a pattern of agreement on items related to feeling valued at work (Item 1), receiving training and support (Item 3), and receiving constructive feedback (Item 5). However, there is a relatively neutral response pattern for workload manageability (Item 2) and growth opportunities (Item 4).
For example, you could compare responses between different departments to see if there are significant differences in employee satisfaction levels.
Interpreting Likert scale results
Based on the analysis, employees feel valued and appreciated at work (Item 1) and perceive adequate training and support (Item 3). However, there may be room for improvement regarding workload manageability (Item 2), opportunities for growth (Item 4), and the provision of constructive feedback (Item 5).
The relatively low variability across items suggests moderate agreement within the group. However, the neutral response pattern for workload manageability and opportunities for growth may indicate areas that require attention to enhance employee satisfaction.
Explore useful SurveyPlanet features for data analyzing
Likert scales are a highly effective way of collecting qualitative data. They help you gain a deeper understanding of customers’ or employees’ opinions and needs.
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