Likert Scale | Definition & Examples

A Likert scale (pronounced “Lick-urt”) is a research instrument used to collect data on people’s beliefs, experiences, or opinions.

A Likert scale consists of a series of statements or questions (called items) about a topic. The respondent rates their level of agreement with each item using a 5- or 7-point scale, with response options ranging from “Strongly disagree” to “Strongly agree.”

Likert scales are commonly used in areas ranging from clinical psychology to market research.

5-point Likert scale example
Please rate your agreement with the following statements.
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
I can’t function without my morning coffee
I like my coffee without any added sugar or cream
I prefer coffee over tea

What is a Likert scale?

A Likert scale is a research tool used to measure someone’s feelings or behavior. The technical definition of a Likert scale is quite specific: a Likert scale includes a series of statements (called Likert items) alongside a 5- or 7-point scale with response options that range from “Strongly disagree” to “Strongly agree,” with a neutral midpoint (“Neither agree nor disagree”). For each statement, the respondent selects the option that best matches how they feel.

Many other rating scales closely resemble Likert scales. These Likert-type scales follow the same format as a Likert scale (a series of questions or statements with response options), but the response options may measure constructs like frequency or satisfaction.

Note
A common mistake is to refer to a single Likert-type item (one statement or question with response options) as a Likert scale. However, by definition, a Likert scale includes multiple Likert-type items. Responses to all items are combined to quantify someone’s attitude or beliefs concerning a topic.

Likert scale design

A Likert scale includes several Likert-type items that all relate to a central topic. Each item includes a question or statement and a series of response options.

Designing a brand new Likert scale is a lengthy process, as several iterations of writing, testing, and validation must be completed to ensure the scale is measuring what it’s supposed to (i.e., that it meets the requirements for different types of validity).

Researchers will therefore often use existing Likert scales whose reliability and validity have already been demonstrated. However, if you are creating your own Likert scale, you must carefully consider the wording of your questions and response options.

Writing strong Likert scale questions

Likert scales are susceptible to response bias—for instance, people may respond in a certain way if they are confused, tired, or worried about whether their answers are socially acceptable. Carefully considering the wording of questions will help you collect high-quality data. Keep the following in mind when writing Likert scale questions or statements.

  • Ask about one thing at a time. Avoid double-barrelled questions or statements, such as “I like the fabric and color of my new coat.” If someone likes the fabric of their coat but not the color, how would they respond?
  • Use both positive and negative framing. People tend to respond more favorably to positive statements than negative ones. You can control for this response bias by including questions with both positive and negative framing.
  • Be as clear as possible. Keep in mind the reading level of your target sample. If someone cannot understand a question, they cannot respond to it accurately.
  • Carefully consider word choice. It’s best to use simple, plain language when writing statements for a Likert scale. Consider the statement “This website has absolutely gorgeous design” versus “This website has pleasing design.” People will likely rate their agreement with the first statement much lower than the second, regardless of how they feel about the website’s design.

Likert scale response options

A key element of a Likert scale is the response options provided to the participant. A classic Likert scale includes a series of statements for which participants rate their level of agreement using a 5- or 7-point scale ranging from “Strongly disagree” to “Strongly agree.” However, some Likert-type scales measure properties like frequency, satisfaction, or importance. Several decisions must be made when designing your response options.

Unipolar vs bipolar scales

A scale that ranges from zero to positive is unipolar. Frequency is an example of a unipolar scale: something can happen often or not at all, but it cannot happen a negative number of times.

Unipolar scale example
The following response options range from zero to positive:

How satisfied are you with your recent customer service experience?

  • Not at all satisfied
  • Slightly satisfied
  • Moderately satisfied
  • Satisfied
  • Very satisfied

A scale that ranges from negative to positive is bipolar (or dichotomous). Level of agreement is bipolar—it has both negative and positive extremes (disagreement and agreement, respectively).

Bipolar scale example
Consider how level of satisfaction can instead be expressed as a bipolar scale:

How satisfied are you with your recent customer service experience?

  • Very dissatisfied
  • Somewhat dissatisfied
  • Neither satisfied nor dissatisfied
  • Somewhat satisfied
  • Very satisfied

The distinction between unipolar and bipolar scales is often subtle. It’s often best to choose the option that will make your Likert scale easiest for the respondent to complete. Most importantly, when designing a Likert-type scale, be sure to include response options that span the full range of possible responses.

Including a neutral response option

If a Likert-type scale has an odd number of response options, the middle option is generally a “neutral” response. Academics have varying opinions on whether it is a good idea to include a neutral response option.

Studies have indicated that scales with a neutral option have better reliability than those that do not (Kankaraš & Capecchi, 2024), but some researchers worry that the respondent may default to choosing a neutral option if they are unsure or do not wish to provide their true opinion. Always carefully consider how any design choices might impact the results and interpretation of your work.

5- vs 7-point scales

The majority of Likert-type scales include 5 response options, but some instead include 7. A 5-point scale is generally better for unipolar constructs, whereas a 7-point scale is better for bipolar constructs. Some researchers may choose to forgo numbered response options altogether and instead ask participants to provide their response using a sliding scale with extreme ends (and potentially the midpoint) labeled.

Regardless of the scale you choose, it is important to include response options that are clear and distinct. In some cases, numbers can be added to response options for clarity; for example, “Occasionally (about 30% of time)” may be clearer than just “Occasionally.”

Some common response options are outlined in the table below, or you can download the PDF for a more comprehensive list of Likert response options.

Likert response options

Likert response options
Property 5-point scale 7-point scale
Agreement
  1. Strongly disagree
  2. Disagree
  3. Neither agree nor disagree
  4. Agree
  5. Strongly agree
  1. Strongly disagree
  2. Disagree
  3. Somewhat disagree
  4. Neither agree nor disagree
  5. Somewhat agree
  6. Agree
  7. Strongly agree
Familiarity
  1. Very unfamiliar
  2. Unfamiliar
  3. Neutral
  4. Familiar
  5. Very familiar
  1. Very unfamiliar
  2. Unfamiliar
  3. Slightly unfamiliar
  4. Neutral
  5. Slightly familiar
  6. Familiar
  7. Very familiar
Frequency
  1. Never
  2. Rarely
  3. Sometimes
  4. Often
  5. Always
  1. Never
  2. Rarely (less than 10% of the time)
  3. Occasionally (about 30% of the time)
  4. Sometimes (about 50% of the time)
  5. Frequently (about 70% of the time)
  6. Usually (about 90% of the time)
  7. Always

Analyzing Likert scale data

The responses to individual Likert items are ordinal: they have a clear order, but the distance between response options cannot be quantified. For example, “Strongly agree” is clearly higher than “Agree,” but there’s no way to determine if the distance between “Strongly agree” and “Agree” is the same as the distance between “Agree” and “Neither agree nor disagree.”

Although Likert scale items comprise ordinal data, they are often treated as interval data in analysis, allowing researchers to quantify and compare difficult-to-express concepts like opinions or beliefs. Responses that range from “Strongly disagree” to “Strongly agree” may be converted to interval numbers ranging from 1 to 5 (or -2 to +2), allowing researchers to compute descriptive statistics like mean or standard deviation and use inferential statistics to compare groups and make predictions.

Note
Though the practice of converting ordinal data to interval data is common, there is some controversy surrounding whether this is a valid approach. It’s always important to provide justification for the methods you choose when writing up your work and to note any limitations.

The following techniques can be used to analyze ordinal Likert scale data.

Ordinal data analysis examples
If you choose to treat your Likert scale data as ordinal, you can compute descriptive statistics like median and mode for individual items or groups of related items. You can also calculate the frequency or percentage of different responses and describe the range of responses. Chi-square tests can be used to test hypotheses and compare differences between groups.

If you instead convert your Likert scale data to interval values, you might use the following analysis techniques.

Interval data analysis examples
Interval data can be described using central tendency measures like mean, median, and mode. Parametric tests like t tests can be used to test hypotheses and compare differences between groups.

Frequently asked questions about Likert scales

Is a Likert scale ordinal?

Yes—the Likert scale, which is often included in questionnaires, is an example of an ordinal measurement.

Ordinal data have a clear order (items can be logically sorted), but the distance between items is not uniform nor quantifiable.

A Likert scale asks someone to rate how much they agree with a series of statements. Response options typically include “Strongly disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly agree.”

Because these options have a clear rank (we can easily and objectively order them) but unclear spacing (i.e., the distance between “Disagree” and “Neutral” isn’t necessarily the same as the distance between “Neutral” and “Agree”), a Likert scale is considered an ordinal measurement.

Note: For analysis, Likert scale data are sometimes converted to numbers and treated as integer data. This process allows the research to use certain analysis techniques. It’s always important to carefully consider and provide justification for any analyses you decide to conduct.

How do you pronounce Likert scale?

The term Likert scale is commonly mispronounced. The correct pronunciation is “Lick-urt,” not “Lie-kert.” The scale is named after its creator, Rensis Likert, and this is how you correctly say his last name.

Why is validity so important in psychology research?

Psychology and other social sciences often involve the study of constructs—phenomena that cannot be directly measured—such as happiness or stress.

Because we cannot directly measure a construct, we must instead operationalize it, or define how we will approximate it using observable variables. These variables could include behaviors, survey responses, or physiological measures.

Validity is the extent to which a test or instrument actually captures the construct it’s been designed to measure. Researchers must demonstrate that their operationalization properly captures a construct by providing evidence of multiple types of validity, such as face validity, content validity, criterion validity, convergent validity, and discriminant validity.

When you find evidence of different types of validity for an instrument, you’re proving its construct validity—you can be fairly confident it’s measuring the thing it’s supposed to.

In short, validity helps researchers ensure that they’re measuring what they intended to, which is especially important when studying constructs that cannot be directly measured and instead must be operationally defined.

Should I use a 5- or 7-point Likert scale?

Though traditional Likert scales include a 5-point response scale, some research has indicated that 7-point scales provide more reliable results.

As a rule of thumb, 5-point scales are better for unipolar constructs, which range from zero to positive, such as frequency. You may want to use 7-point scales for bipolar (or dichotomous) constructs that range from negative to positive, such as quality—some evidence suggests that doing so can increase reliability.

What is a Likert type scale?

A Likert-type scale resembles a Likert scale—the respondent is presented with a series of statements, and they select their response from a set of ranked options.

However, for a true Likert scale, the respondent rates their level of agreement with these statements using a 5- or 7-point scale. Likert-type scales may instead ask people to rate constructs like frequency, satisfaction, or likelihood.

An example of a true Likert scale item is as follows:

Please rate your agreement with the following statement: I am most productive in the morning. 

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

A Likert-type scale may include items such as the following:

How frequently do you wake up before 8 a.m.?

  • Never
  • Rarely
  • Sometimes
  • Often
  • Always
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Emily Heffernan, PhD

Emily has a bachelor's degree in electrical engineering, a master's degree in psychology, and a PhD in computational neuroscience. Her areas of expertise include data analysis and research methods.