A Likert scale is a psychometric response scale primarily used in questionnaires to measure attitudes, opinions, or perceptions by offering a range of answer options that reflect varying degrees of agreement or disagreement. Over 70% of social science surveys incorporate Likert scales due to their simplicity and efficiency in capturing subjective data.
The Likert scale is a standardized technique that transforms qualitative human sentiments into quantifiable, analyzable metrics. According to survey design expert Dr. Rensis Likert, who originally introduced this scale in 1932, “Attitudes are best understood when measured through consistently structured options that reflect degrees of sentiment rather than binary choices.”
The efficiency of the Likert scale lies in its ability to convert subjective opinions into structured datasets, which can be measured using descriptive statistics such as mean scores, standard deviations, and frequency distributions. Its quality is evaluated through reliability coefficients (e.g., Cronbach’s alpha) and validity assessments to ensure the data reflects genuine respondent sentiment.
In this guide, we will provide an in-depth overview of the Likert scale, including its definition, significance in research methodologies, various question formats, and best practices for data interpretation. Whether you are conducting a customer satisfaction survey for brands like Amazon or Apple, market research for emerging trends, or academic psychological assessments, mastering the application of the Likert scale will enable you to derive precise, actionable, and statistically valid insights.
Definition: What is a Likert Scale?
It is a psychometric measurement tool and survey response scale entity type used in Social Science Research, Psychology, and Market Research by named entities such as Rensis Likert, Gallup, and Pew Research Center; entity type samples include rating scales, opinion scales, and attitude measurements.
The Likert Scale measures people’s attitudes, opinions, or perceptions by asking them to indicate their level of agreement or disagreement with a series of statements.
A Likert Scale is defined as “a psychometric scale commonly used in questionnaires, and is the most widely used approach to scaling responses in survey research,” developed by Rensis Likert in 1932, with over 70% of attitude surveys using Likert Scales globally.
It benefits researchers by providing quantitative data for qualitative attributes such as satisfaction, agreement, or importance. The main complementary concept to Likert Scale is the Semantic Differential Scale, which also measures attitudes but with bipolar adjectives.
Social scientists, market researchers, and psychologists use it to gather structured feedback from respondents; these people are called survey researchers, data analysts, or behavioral scientists.
Why Are Likert Scales Important?
The Likert scale is a valuable tool for researchers, businesses, and organizations because it provides a structured approach to gathering feedback. Unlike open-ended questions that require subjective interpretation, Likert scale responses create consistent and measurable data that can be compared across different groups or periods.
One of the key benefits of using a Likert scale is its ability to capture subtle differences in opinion. Instead of forcing respondents into binary answers, such as “yes” or “no,” the scale allows for more nuanced responses. This is particularly useful when measuring customer satisfaction, brand perception, or employee morale, where opinions often fall along a spectrum rather than at extreme ends.
Another major advantage is that Likert scale data can be analyzed using statistical methods, making it easier to identify trends, patterns, and correlations. Researchers can calculate average scores, identify response distributions, and compare different segments of a population to gain meaningful insights. Additionally, because Likert scales are simple to understand, they help ensure higher response rates and more reliable data in surveys.
A well-structured Likert scale surveys improve the accuracy of research findings and enhances decision-making in business and marketing. By incorporating Likert-scale questions into feedback collection efforts, organizations can make data-driven improvements to their products, services, and customer experience strategies.
What Are Likert Scale Questions?
Likert scale questions are a type of closed-ended survey question that allows respondents to express their opinions along a structured scale. Unlike binary “yes/no” questions, these questions measure the intensity of agreement, satisfaction, frequency, or importance regarding a particular statement.
The most common Likert scale format includes five or seven response options, typically ranging from one extreme to another. For example, a five-point scale might offer responses such as “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” A seven-point scale can add more granularity with intermediate options like “Somewhat Agree” and “Somewhat Disagree.”
Likert scale questions are particularly useful when trying to measure subjective perceptions, attitudes, and behaviors. Businesses frequently use them in customer satisfaction surveys, academic research, and market studies to gather detailed insights into people’s thoughts and experiences. They provide a structured way to quantify subjective feedback, making it easier to analyze trends, compare responses, and draw meaningful conclusions.
One key characteristic of Likert scale questions is that they focus on one specific topic per question to avoid confusion. For example, instead of asking, “How satisfied are you with our customer service and product quality?”—which would be a double-barreled question—a well-structured Likert scale survey would ask separate questions for customer service and product quality.
Likert scale questions can also be designed in different formats depending on the context. Common types include:
Agreement scales (e.g., “I find this website easy to use.”)
Satisfaction scales (e.g., “How satisfied are you with your purchase?”)
Frequency scales (e.g., “How often do you use our service?”)
Likelihood scales (e.g., “How likely are you to recommend us to a friend?”)
By structuring questions carefully, researchers and businesses can ensure that the data they collect is both reliable and actionable.
When to Use Likert Scale Questions
Likert scale questions are ideal for situations where you need to measure subjective experiences, opinions, or behaviors rather than objective facts. They are particularly useful in surveys that aim to assess customer satisfaction, employee engagement, or brand perception because they allow respondents to express varying degrees of sentiment. You should use when:
You need to measure attitudes or perceptions: If you want to understand how people feel about a product, service, or experience, Likert scale questions provide a standardized way to capture and compare responses.
You want to track changes over time: Since Likert scales generate numerical data, they allow researchers to track shifts in opinions and satisfaction levels across different periods.
You need detailed insights rather than simple yes/no answers: Likert scales provide a more nuanced understanding of how strongly respondents agree or disagree with a statement, rather than just confirming or rejecting it.
You are conducting large-scale research: These questions are easy for respondents to answer and simplify data analysis, making them ideal for studies with a high number of participants.
However, Likert scales may not be the best choice when absolute facts or precise numerical data are required. If you need to collect objective information, such as exact spending amounts or specific product usage statistics, other question formats may be more appropriate.
It’s also important to consider the context in which the Likert scale is used. For example, in a customer feedback survey, a five-point scale may be sufficient, whereas a detailed employee satisfaction study might benefit from a seven-point scale for greater precision. By selecting the right format, researchers can ensure they collect the most useful and accurate data.
Types of Likert Scale Questions & Response Options
Likert scale questions can be adapted to measure various aspects of opinions, attitudes, behaviors, and perceptions. Depending on the research goal, different response options can be used to capture specific insights. Below are the most common types of Likert scale questions and their response formats:
Agreement Likert Scale
This is the most widely used type, measuring how much respondents agree or disagree with a statement. It is commonly applied in customer satisfaction surveys, employee engagement studies, and brand perception research.
Example:
“I find the checkout process on this website to be simple.”
Response Options:
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Likelihood Likert Scale
This format measures the probability of a future action, such as whether a customer is likely to recommend a product or use a service again. It is often seen in Net Promoter Score (NPS) surveys.
Example:
“How likely are you to recommend our product to a friend or colleague?”
Response Options:
Very Unlikely
Unlikely
Neutral
Likely
Very Likely
Importance Likert Scale
Used to assess how significant a particular feature, service, or factor is to a respondent’s decision-making process. This is useful in product development and market research.
It is also useful when you don’t know which features include and which ones don’t. By understanding the relevance of each feature and how important it is for the user, this will serve as a guide when crafting the pricing page.
Example:
“How important is fast shipping when making an online purchase?”
Response Options:
Not Important at All
Slightly Important
Moderately Important
Very Important
Extremely Important
Frequency Likert Scale
This measures how often respondents engage in a certain behavior or experience a specific issue. It is widely used in habit tracking, UX research, and customer behavior analysis.
Example:
“How often do you visit our website?”
Response Options:
Never
Rarely
Sometimes
Often
Always
Satisfaction Likert Scale
Aimed at measuring customer or employee satisfaction levels regarding a particular service, product, or experience.
Example:
“How satisfied are you with the customer support you received?”
Response Options:
Very Dissatisfied
Dissatisfied
Neutral
Satisfied
Very Satisfied
Selecting the right type of Likert scale depends on the research objective. Consistency in response options is also crucial to ensuring respondents understand the scale and provide meaningful insights.
Likert Scale Examples
Likert scales can use different point ranges depending on the context, audience, and level of granularity needed. The number of points in the scale impacts how much nuance a respondent can express in their answers. Below are some of the most common Likert scale formats, along with clear examples of how each one is typically used.
3-Point Likert Scale Example
The 3-point Likert scale is simple and direct. It is typically used when the survey aims to capture a basic opinion without overcomplicating the response options. This scale is useful when you need to reduce cognitive load and encourage faster responses.
Example question:
How satisfied are you with our delivery time?
Dissatisfied
Neutral
Satisfied
This structure allows respondents to indicate a negative, neutral, or positive sentiment without much granularity. It is often used in quick feedback forms or pulse surveys.
4-Point Likert Scale Example
A 4-point Likert scale removes the neutral option, forcing respondents to lean toward a positive or negative opinion. This is called a forced-choice scale and is used when neutrality is not desired.
Example question:
How would you rate the quality of our product?
Poor
Fair
Good
Excellent
This format encourages decision-making and eliminates the “middle” answer, making it suitable for surveys that aim to measure clear preference or dissatisfaction.
5-Point Likert Scale Example
The 5-point Likert scale is the most commonly used format. It provides a neutral midpoint and two degrees of positive and negative responses. This scale balances simplicity with depth, making it ideal for a wide range of surveys.
Example question:
How likely are you to recommend our service to a friend?
Very unlikely
Unlikely
Neutral
Likely
Very likely
This scale helps organizations understand both positive and negative sentiment while giving respondents the option to remain neutral.
6-Point Likert Scale Example
The 6-point Likert scale is another forced-choice scale, like the 4-point scale, but with more degrees of intensity. It is used when survey designers want to gather more nuanced opinions without allowing neutral responses.
Example question:
Please rate your agreement with the following statement: “The website was easy to navigate.”
Strongly disagree
Disagree
Somewhat disagree
Somewhat agree
Agree
Strongly agree
This structure offers a detailed range of opinions while encouraging respondents to express a leaning opinion.
7-Point Likert Scale Example
The 7-point Likert scale offers more granularity than the 5-point version. It is ideal for advanced research or surveys where capturing subtle differences in sentiment is essential.
Example question:
How satisfied are you with the customer support you received?
Extremely dissatisfied
Dissatisfied
Somewhat dissatisfied
Neutral
Somewhat satisfied
Satisfied
Extremely satisfied
This format allows respondents to articulate their level of satisfaction more precisely, making it useful for in-depth customer experience surveys.
10-Point Likert Scale Example
A 10-point Likert scale provides the highest level of granularity among standard scales. It is used in detailed customer satisfaction surveys, employee feedback forms, or academic research where precision is required.
Example question:
On a scale from 1 to 10, how confident do you feel using our platform?
1 = Not confident at all
10 = Extremely confident
While this scale allows for highly nuanced responses, it may overwhelm respondents if overused or poorly structured.
How to Write Likert Scale Survey Questions
Creating an effective Likert scale survey requires careful consideration of both the question wording and the response structure. Poorly designed surveys can lead to misleading data, response bias, and unclear insights. Below is a step-by-step guide to writing Likert scale questions that yield accurate and actionable responses.
1. Define the Objective of Your Survey
Before writing questions, clearly define what insights you aim to collect. Are you measuring customer satisfaction, product usability, or employee engagement? The objective should guide the type of Likert scale questions you use.
For example, if your goal is to measure how satisfied customers are with your checkout process, you may use a Satisfaction Likert Scale question:
“How satisfied are you with the checkout on our website?”
A well-defined objective ensures clarity and relevance in survey questions.
2. Keep Questions Clear and Focused
Each question should address a single issue to avoid confusing respondents and ensure meaningful data collection.
❌ Bad Example (Double-Barreled Question):
“How satisfied are you with our website and customer service?”
(This asks about two different aspects, making it unclear which one the respondent is rating.)
✅ Better Example:
“How satisfied are you with our website navigation?”
“How satisfied are you with our customer service?”
Keeping questions concise, direct, and specific leads to more accurate responses.
3. Use a Consistent Response Scale
A consistent Likert scale format helps eliminate confusion and improve data reliability. If you use a five-point scale for one question, avoid switching to a seven-point scale later.
The way survey questions are framed has a direct impact on the quality and reliability of responses. You must ensure that the questions are clear and easy to understand. A poorly worded question can lead to misinterpretation, biased answers, or incomplete data.
For instance, the 0-10 scale is the industry standard for the main NPS question because it allows for a clear segmentation of respondents into Promoters (9-10), Passives (7-8), and Detractors (0-6). This standardized format ensures consistency across industries and provides a reliable way to compare customer sentiment over time.
Maintaining scale consistency across all questions ensures respondents interpret each question in the same way.
4. Avoid Leading or Biased Questions
The way a question is phrased can greatly impact the accuracy of customer feedback. Leading or biased questions can unintentionally push respondents toward a particular answer, while complex wording can confuse them and lead to unreliable data. To ensure your NPS survey collects genuine, actionable insights, focus on using neutral, clear, and direct language.
Use Neutral Wording to Avoid Bias
Instead of focusing on what not to ask, let’s look at examples of how to phrase questions correctly for objective responses.
✅ Use this: “How would you rate your experience with our new feature?”
❌ Avoid this: “How much do you love our new feature?”
✅ Use this: “How satisfied are you with the support you received?”
❌ Avoid this: “Was our customer service excellent?”
✅ Use this: “How likely are you to use this feature again?”
❌ Avoid this: “You’ll use this feature again, right?”
By keeping questions neutral, businesses allow customers to express their true opinions, leading to more reliable insights.
Keep Language Simple and Avoid Jargon
Customers engaging with your survey may have varying levels of familiarity with your product, service, or industry. Using complex terminology or insider jargon can lead to confusion and incomplete responses.
✅ Use this: “How easy was it to use our mobile app?”
❌ Avoid this: “How intuitive was our app’s user interface?”
✅ Use this: “Did you find what you were looking for?”
❌ Avoid this: “Did our navigational taxonomy align with your search intent?”
✅ Use this: “Was the checkout process smooth for you?”
❌ Avoid this: “Did our payment processing system function as expected?”
Simple, straightforward language ensures that all customers, regardless of background, can provide accurate feedback without confusion.
5. Choose the Right Type of Likert Scale
Different research objectives require different Likert scale formats. Choose the one that best suits your needs:
Agreement scale for measuring opinions (e.g., “I find the app easy to use.”)
Satisfaction scale for evaluating experiences (e.g., “How satisfied are you with our service?”)
Likelihood scale for predicting behaviors (e.g., “How likely are you to purchase again?”)
Matching the question type to the appropriate Likert scale format improves data accuracy and usability.
6. Keep the Survey Length Manageable
One of the most common mistakes businesses make with NPS surveys is making them unnecessarily long. The more questions included, the lower the response rate. Customers have limited time and are unlikely to complete surveys that require too much effort. Keeping the survey short increases the likelihood of getting thoughtful responses.
An ideal survey contains 5-10 Likert scale questions unless conducting a detailed study. Keeping it concise ensures respondents stay focused and provide thoughtful answers.
For more pro tips when creating the survey, you can also check out the NPS Survey Best Practices here.
How to Analyze Likert Scale Survey Data
Analyzing Likert scale data requires a structured approach to interpret customer sentiment, satisfaction, or behavioral insights effectively. Unlike binary yes/no responses, Likert scale data captures degrees of opinion, making analysis more nuanced. The method used depends on whether the data is treated as ordinal (ranking-based) or interval (equally spaced responses).
Cleaning and Preparing Data
Before analyzing responses, it’s essential to review the dataset for inconsistencies, missing values, and biases. Respondents who selected the same answer for all questions or skipped multiple items can distort the data. Filtering out incomplete or inconsistent responses ensures accuracy and reliability.
Choosing the Right Statistical Approach
Likert scale data can be analyzed using descriptive statistics, inferential statistics, and visual representation methods.
Descriptive Statistics:
Mean, median, and mode help summarize central tendencies. For instance, if a satisfaction survey results in an average score of 4.2 on a 5-point scale, it suggests that customers are generally satisfied. However, looking at the mode (most frequent response) can highlight the most common opinion.Frequency Distribution:
Examining how responses are distributed across the scale provides insights into patterns. For example, if 70% of respondents select “Strongly Agree” or “Agree” on a customer support survey, it indicates high satisfaction.Cross-Tabulation and Segmentation:
Comparing responses across different demographics or customer groups helps uncover trends. For example, analyzing how first-time buyers rate their experience compared to repeat customers can highlight differences in satisfaction levels.Visualization Techniques:
Using bar charts, heatmaps, or stacked percentage charts makes data easier to interpret. For instance, a diverging bar chart can display the percentage of respondents in each category, allowing for quick identification of sentiment trends.Inferential Statistics (When Appropriate):
For large datasets, statistical tests such as Chi-Square tests or ANOVA can help determine if differences in responses between groups are statistically significant. For example, testing whether user experience ratings differ between mobile and desktop users can guide UX improvements.
By combining these methods, businesses can derive actionable insights from Likert scale surveys, helping them make data-driven decisions.
Conclusion
The Likert scale remains one of the most effective tools for measuring customer sentiment, user experience, and employee satisfaction. Its structured response format provides businesses with valuable insights into opinions, behaviors, and decision-making patterns.
However, designing, implementing, and analyzing Likert scale surveys requires a methodical approach to avoid bias, ensure clarity, and maintain data integrity. A well-constructed survey helps organizations make data-driven decisions, leading to improved customer satisfaction, product development, and overall business strategy.
By applying best practices and leveraging proper data analysis techniques, businesses can turn Likert scale surveys into a powerful decision-making tool that enhances their understanding of customer needs and drives continuous improvement.
FAQs
Semantic Differential Scale vs Likert Scale: What’s the Difference?
The Likert scale and the semantic differential scale are both widely used to measure attitudes, opinions, and perceptions, but they differ in structure and purpose.
A Likert scale asks respondents to express the extent of their agreement or disagreement with a specific statement. The answers usually range from strongly agree to strongly disagree and are uniform across questions. It is designed to measure agreement, frequency, importance, or likelihood related to a statement.
A semantic differential scale, by contrast, measures the connotation or meaning a person associates with a concept, product, or experience. It uses bipolar adjectives on each end of the scale, such as “Helpful – Unhelpful” or “Easy – Difficult.” Respondents rate the subject on a continuum between those opposites, without necessarily agreeing or disagreeing with a specific statement.
The main difference lies in their objective: Likert scales measure levels of agreement, while semantic differential scales measure the perceived attributes of a subject.
Is a Likert Scale Nominal or Ordinal?
A Likert scale is an ordinal scale. This means that the response categories have a clear, meaningful order, but the distance between the points is not necessarily equal or measurable.
For example, when a respondent chooses “Agree” or “Strongly Agree,” we know that “Strongly Agree” reflects a higher level of agreement than “Agree,” but we cannot quantify exactly how much more. The ordinal nature of a Likert scale is crucial in data analysis because it limits the use of certain statistical techniques, such as calculating a mean, which assumes interval data.
Some researchers, however, treat Likert scale data as interval data for practical purposes, especially when using scales with five or more points and when the sample size is large. But technically, Likert scale data remain ordinal.
Is a Likert Scale Categorical or Continuous?
A Likert scale is categorical, specifically ordinal categorical. Each response option represents a distinct category that has a clear order, but the values do not have a true numerical meaning and cannot be treated as continuous data.
For example, categories like “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree” are labels representing ordered categories. These are not continuous values because there is no consistent, measurable distance between them.
In certain statistical analyses, researchers may treat Likert scale data as continuous if the scale has many points and the data approximate a normal distribution. However, from a strict measurement theory perspective, Likert data remain categorical and ordinal.
Is a 5-Point or 7-Point Scale Better for Likert Surveys?
Whether a 5-point or 7-point Likert scale is better depends on the survey’s goal, audience, and desired level of granularity.
A 5-point Likert scale is the most commonly used. It provides a balance between simplicity and depth, offering clear options without overwhelming respondents. This format is ideal for general surveys where quick and easy responses are needed. It reduces the risk of survey fatigue and cognitive overload.
A 7-point Likert scale offers more granularity and allows respondents to express subtle differences in their opinions. It is particularly useful in academic research, employee engagement surveys, or advanced market studies where capturing nuanced sentiment is crucial.
Multiple studies suggest that there is no significant difference in reliability or validity between 5-point and 7-point scales, but 7-point scales may slightly improve sensitivity and variance in the data. However, too many points can confuse respondents or lead to indecisive answers.
