What is a Likert Scale? Definition, examples, and when to use

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Posted Jun 14, 2023

In the ever-evolving market research landscape, understanding consumer behavior and preferences is crucial for businesses to make informed decisions. Likert scales offer a powerful tool for capturing and analyzing respondents' attitudes, opinions, and perceptions. Named after the psychologist Rensis Likert, who introduced this measurement tool in the 1930s, Likert Scales provide a structured format for respondents to indicate their level of agreement or disagreement for a series of statements. From evaluating customer satisfaction to measuring brand perception, Likert Scales help uncover key drivers and patterns that inform strategic decision making.

What is a Likert Scale?

A Likert (pronounced “lick-ert”) Scale is a rating scale that consists of a series of statements or items expressing a specific opinion or belief. For each statement, respondents indicate their level of agreement or disagreement on a 5-point or 7-point bipolar scale . The scale can be presented in a horizontal or vertical layout, with response options ranging from "strongly disagree" to "strongly agree" or similar variations. The scale's design allows for capturing a nuanced understanding of the intensity of an individual's attitude, without making it so detailed that respondents find it difficult to answer. Market researchers rely on Likert scales to quantify subjective data, uncover trends, and identify key drivers influencing consumer behavior.

How can Likert Scales help me?

Likert scaling offers several benefits, including:

  • Standardization: Likert scaling provides a  standardized response format, ensuring that all respondents answer the survey questions in a consistent manner. This uniformity allows for easier data analysis and comparison.
  • Quantification of subjective data: By assigning numerical values to responses, a Likert scale enables researchers to quantify subjective opinions for some  statistical tests.
  • Granularity: Likert scales capture the nuances of agreement or disagreements  by offering a range of response options, providing more detailed insights than binary choices.
  • Ease of administration: Likert scales are easy to administer, making them a practical choice for small and large-scale studies.
  • Versatility: Likert scales are one of the most popular approaches used in survey research because they can be customized to suit various research objectives and contexts.

Designing Likert Scales for surveys

Appropriate response formats

Selecting the appropriate response format is crucial to gathering accurate and reliable data. Likert scales are most commonly presented as 5- or 7- point scales. The choice of format depends on factors such as the research objectives, the complexity of the construct(s) being measured, and the desired level of granularity. While a 5-point scale provides simplicity and ease of use, a higher number of response options, such as a 7-point, allows for more nuanced responses. It is important to strike a balance between obtaining detailed data and avoiding respondent fatigue.

Including a neutral/zero value

A common approach for Likert scaling  is to provide a neutral midpoint, which allows respondents to express indifference or neutrality. A neutral midpoint allows respondents to express ambivalence towards a statement, enabling them to most accurately convey their indifference. By including a neutral midpoint, the risk of inflated agreement or disagreement results is minimized.

On the other hand, many  researchers believe that respondents misuse neutral midpoints and are wary of using them.  These individuals may falsely utilize the neutral option for statements they have an opinion on, whether they agree or disagree. Consequently, it is important to note that including and excluding a neutral midpoint in scale design have their respective advantages and disadvantages.

How to write effective statements

When writing Likert scale statements, it is important to ensure clarity, relevance, and unbiased wording. Here are some guidelines for creating effective statements:

Use clear language to write statements that are easily understandable and free of jargon. Avoid using double negatives or complex sentence structures that can confuse respondents.

  • Unclear statement: "I don't disagree that the product is not unsatisfactory."
  • Clear statement: "I am satisfied with the product."

Avoid biased language to maintain objectivity and avoid leading respondents toward a specific response. Biased statements can skew the data and compromise the validity of the findings.

  • Biased: "The product is excellent, isn't it?"
  • Unbiased: "Please rate the quality of the product."

Keep statements answerable by asking respondents for information they can give you. Avoid asking respondents about time periods they can’t recall and don’t ask respondents about information they simply don’t have.

  • Not easy to answer: “Thinking about the past year, how many times have you set an alarm to wake you up?”
  • Easy to answer: “Thinking about the past week, how many times have you set an alarm to wake you up?”

Balance positive and negative statements to capture a range of opinions and avoid response bias. This balance ensures a more accurate representation of respondents’ true attitudes.

  • Positive statement: "The product exceeds my expectations."
  • Negative statement: "The product fails to meet my expectations."

By following these guidelines, you can ensure that your Likert scale statements are clear, relevant, unbiased, and effectively capture the attitudes or perceptions you aim to measure.

How to mitigate response bias

It's important to be aware of potential response biases that can impact the accuracy and reliability of the data. Respondents may exhibit social desirability bias, in which they modify their responses to present themselves in a more socially acceptable or desirable light; acquiescence bias, a tendency to agree or respond affirmatively regardless of their true beliefs or opinions; or extreme response bias, consistently leaning towards extreme ends of a scale.

Researchers can utilize several strategies to mitigate these biases:

  • Provide clear and concise instructions at the beginning of the survey, emphasizing the importance of honest and thoughtful responses to minimize careless responding.
  • Assure respondents of their anonymity to encourage honest and unbiased feedback, reducing the likelihood of social desirability bias.
  • Randomize the order in which statements are presented to minimize order bias, where respondents may tend to agree or disagree consistently throughout the survey.
  • Include a neutral midpoint, such as "Neither agree nor disagree," to account for respondents who may genuinely hold neutral views on certain statements.
  • Include validation checks within the survey to identify respondents who are not engaging thoughtfully or consistently. 
  • Account for cultural differences in the understanding and interpretation of response options, and ensure that the scale is culturally appropriate and relevant for the survey’s  target audience.

Tips for better Likert Scale data analysis

Consider the data type

Likert scale data is generally considered ordinal in nature. The response options are ordered to show the relative position or intensity of respondents' attitudes or opinions. Each response option corresponds to a level of agreement or disagreement, however, the intervals between response options may not be equal.

Many market researchers  argue that Likert scale data can be treated as interval data under certain conditions. This way the intervals between response options are assumed to be equal. For example, the difference between, "strongly agree" and "agree" is assumed to be the same as the difference between "agree" and "neutral." Treating Likert scale data as interval allows for more sophisticated statistical analyses.

Accurately score the data

Once the Likert scale data has been collected, it is essential to analyze and interpret the results accurately. Common work to analyze Likert scale data includes reverse coding, summing, averaging and box scores.

Reverse Coding

In some cases, researchers may need to reverse the scoring of certain items to ensure consistency across scales. For example, if a Likert scale item is worded positively and higher scores mean agreement, reverse coding would assign a higher score to indicate disagreement. This step is crucial when combining items or calculating overall scores.


Summing involves adding up the scores for each respondent across multiple scale items. This approach provides an aggregated score that represents the respondent's overall attitude or perception towards the measured construct. Summing is commonly used when Likert scale items are designed to measure a single underlying construct.


Averaging entails calculating the mean score for each Likert scale item or construct. It measures central tendency and allows for comparison and interpretation across different items or groups. Averaging is useful when researchers want to understand the average level of agreement or disagreement with specific statements.

Box Scores

The Top 2 Box score (sometimes called T2B or T2Box) is a common method for summarizing agreement in  Likert scale questions. To calculate this number, sum up the values for all the responses falling into the top two categories of the scale (ex: Strongly agree + Agree). Bottom 2 Box score  (B2B or B2Box) is a common method for summarizing disagreement in  Likert scale questions. This would be summing up the values for all responses falling into the bottom two categories of the scale  (ex: Strongly disagree + Disagree). Other common box scores used to summarize data outside of Top 2 Box and Bottom 2 Box are Top Box, Bottom Box, Top 3 Box and Bottom 3 Box.

Notes on presentation

Consider using the following presentation techniques to effectively present Likert scale results.


Presenting Likert scale results in a table allows for easy comparison and interpretation. Use rows for each statement and columns for response options, displaying the frequency or percentage of respondents in each category.

Stacked Bar Charts

Stacked bar charts are visually appealing and provide a clear representation of the distribution of responses. Use vertical or horizontal bars to display the frequency or percentage of respondents selecting each response option. Stacked bar charts are particularly useful when comparing multiple Likert scale items or groups. 

Spider Charts

Spider charts, also known as radar charts, display Likert scale results on a circular graph. Each response option is represented by a spoke extending from the center, and the length of the spoke indicates the frequency or percentage of responses in that category. Spider charts are effective for comparing the overall profiles of multiple statements or groups.

Closing thoughts

Likert scales are a valuable tool in market research, providing a structured and reliable method for measuring attitudes, perceptions, and preferences. They offer numerous benefits, including ease of administration, flexibility in design, and the ability to capture nuanced responses. Researchers can gather valuable insights from Likert scale data by carefully designing Likert scale statements, considering potential biases, and employing effective administration and analysis techniques. Consider incorporating Likert scales into your next study to gain deeper insights and uncover actionable findings to drive business success.

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