The Likert Scale: Examples + Survey Template

Definition: A Likert scale is a survey question used to measure attitudes and opinions towards a statement. Satisfaction and agreement are commonly used as measurements. Likert scales typically contain a 1-5 or 1-7 point scale. Each Likert statement may be analyzed separately or summed to create a group score. Because of this summed total, Likert scales are also called summative scales.

Getting started: The Likert scale template below includes the mobile-ready 5-point scale and a 10-point Likert scale using a standard matrix question. You can collect responses with a web link, QR code, or a survey panel. The Likert scale output and export will give you simple data points to benchmark, analyze, and use in statistical models.

A Likert scale has various terms associated with it. Below is an explanation of each to help avoid confusion and give you a general understanding of how this question type is structured.

This refers to the number of answer choices. In the example above, there are five (5) points, called a 5-point scale. A 5-point scale is most common. Sometimes, researchers use 7-point or 9-point scales. The odd numbers give respondents the option to choose a middle number. If a researcher decides to remove the "neutral" option, that will become a 4-point scale.

If a researcher removes the "neutral" option, that would become a 4-point scale.

This is the rating you want to receive for each Likert item. In the above example, the response anchor is "satisfaction," with a score of five being perfectly satisfied and one being perfectly dissatisfied.

A Likert item is a single question within a Likert scale. "Report layout" is the first Likert item in the example above.

Ordinal data is concerned with the order and ranking of a set of values. Because Likert scales ask for opinions or ratings, they are ordinal data by definition. This is commonly confused with interval data.

Interval data relates to the distances between two points. Common examples of interval data are temperature and weight. A Likert scale may have an even difference between each answer that researchers can score, summarize, and measure, but when numbers are assigned to the values, this is called a latent variable.

A Likert scale question evaluates items related to a single topic. Here are some of the most common uses of a Likert scale, with an example of what can be measured:

• Employee surveys: Rating the items related to work engagement. Columns measure agreement and rows relate to the company's mission and leadership.
• Customer surveys: Quantify the different aspects of customer support. Columns pertain to satisfaction, and rows relate to support accuracy and timeliness.
• Product research: Understanding what users like and dislike about a specific feature or product. Columns would pertain to satisfaction, and rows would relate to support accuracy, timeliness, value, and usefulness.
• Clinical research: Assess the behaviors and attitudes of a particular subject. For example, how would a person agree on different statements related to company culture.

There are two ways to create a Likert scale survey:

• A matrix question: Use this element type if you need more than five rating choices.
• A multiple-rating question: predefined with a 5-point scale. It is formatted perfectly for mobile devices with rows and labels using custom CSS.

You can upload unlimited rows for each Likert scale question within a survey. However, to help reduce respondent fatigue, we recommend including no more than 10 rows per Likert question.

Be sure to add other questions to your survey, such as asking for the employee department. You can use this data to help find trends and patterns when analyzing the Likert results.

The length of the scale is essential when conducting a study using a Likert Scale. When Rensis Likert developed the scale in 1932, a 7-point scale was initially used. This provided three points in either direction plus the option of neutral. Below are some of the common types of scales used.

A scale with more points is often thought to provide more precision between answers. While this may be true, a consideration often overlooked is the time it takes for a respondent to answer. Longer scales may discourage respondents from completing your survey due to fatigue. We recommend using a 5-point scale as this is easy for respondents to fill out on desktop and mobile devices and strike a balance between longer Likert scales.

When a researcher wants to force a respondent to make a choice and not choose neutral, a 4-point scale can be used. Anything lower than a 4-point scale would lead to less precise data.

This is one of the most popular lengths for a Likert scale. This scale provides two points on each side plus a single neutral option.

This scale provides three points on each side plus a single neutral option. This option gives slightly more precision than the 5-point scale while keeping the survey length manageable for respondents. Therefore, we recommend not over a 7-point scale when conducting a Likert scale study.

To increase accuracy A 10-point Likert scale is used. The 10-point Likert scale has no neutral option and forces respondents to pick an answer and not choose neutral. It has 5 points on each side.

The response anchors are the top labels in a Likert scale. There are only two response anchors in the example above: "Dissatisfied" and "Satisfied".

In the example above, The Likert scale is formatted efficiently for digital devices by labeling only the two endpoints. If you want to define what answer or feeling each number is related to, include a key above the scale.

Numerous scale categories can be used depending on the project. The above example is a "Satisfaction Scale". 1 would represent perfect dissatisfaction, while 5 would represent perfect satisfaction. Here are some of the most common Likert scale categories.

• Very dissatisfied
• Somewhat dissatisfied
• Neither dissatisfied or satisfied
• Somewhat satisfied
• Very satisfied

• Strongly Disagree
• Disagree
• Neither agree nor disagree
• Agree
• Strongly Agree

• Very unlikely
• Somewhat unlikely
• Neither likely nor unlikely
• Somewhat likely
• Very likely

• Never
• Rarely
• Sometimes
• Often
• Always

When doing customer or product research, a Likert scale won't show you the relative importance of items compared to one another. For example, if a hotel asks how important various amenities are, guests will likely rate all features as important; this drawback could be referred to as "List Order Bias."

For research projects like this, a MaxDiff question should be used to determine what amenities are important to one another. This question type will give you the probability of an item being selected as most important while showing divisive attributes or attributes with an equal number of likes and dislikes.

Respondents may avoid answering at the extremes or end-points of each item (e.g "Very Dissatisfied") to avoid being perceived as having extreme views. This is called central tendency bias.

Respondents may agree with statements as presented or mark every answer as satisfied as not to appear negative. Agreeing with all statements is called acquiescence bias.

The order in which Likert scale items appear can directly affect how people answer each Likert item. Depending on the study you are running, this may need to be accounted for. You can randomize the order in which each Likert item appears if you want to reduce question order bias.

Respondents may avoid negative answer choices for the fear that their answers will be used against them. This fear of repercussions is a general form of response bias. This is common in employment surveys. You can make your survey anonymous to combat this pitfall. Anonymous surveys can be used to collect more honest feedback and reduce this bias.

Likert items can also be summed to create a total score for each respondent. Because of this summed total, Likert scales are sometimes referred to as summative scales.

The most common way of displaying the results of a Likert Question is to show a data table showing the number of times each answer was submitted.

Technically speaking, Likert data is ordinal, so calculating the mean is not statistically accurate, but a weighted average can still give you quick insights. A weighted average can get an average score for each Likert item or for the group of Likert items. The example below shows that the report layout has a weighted average of 2.3, which is clearly the lowest-scored item. The weighted average gives quick, actionable data without a deep statistical dive.

Since Likert data is ordinal, using the median is considered the best practice to measure the central tendency for each Likert item.

If you want to use Likert data to build predictive models, you need to use a particular type of regression called ordinal logistic regression.

For example, you could have a customer survey that starts with a Net Promoter Score followed by a Likert scale asking for satisfaction ratings for items like price, customer support, etc. Then, the output of a regression model might give insights that every 1-point increase in price satisfaction is associated with an increased 80% odds that the Net Promoter Score would improve by 1 point.

Psychologist Rensis Likert developed the Likert scale in 1932 for an article in the "Archive of Psychology" titled "A Technique for the Measurement of Attitudes." He attempted to build a scale that made it easy to judge attitudes and opinions.

Before this, researchers used the Thurstone scale. This method required a lot of work and the use of judges. It starts by asking a test group of respondents to write down their feelings toward a subject. The responses are collected, and then a set of judges would rate each response from 1 to 11, with 1 being very unfavorable and 11 being very favorable. The responses are then filtered down to 10 or less. Each response now has a value that is the average from the judges. The final questionnaire is now given to collect responses, asking them to mark what answer choices they agree with. This time, scores are calculated using the judges' values.

Likert proved that while an attitude can take the form of many words, there can only be a specific range that feeling can be in, generally from one extreme to the other. Furthermore, Likert proved that his method gave similar results to the Thurstone scale but with less work. The results were confirmed in another paper by Likert in 1934.