Examples Of Likert Questionnaire Scale Questions & How To Use Them Effectively

An example of a Likert questionnaire typically consists of a survey of individuals who are presented with straightforward, unambiguous statements and asked to what degree they agree with them, for instance, “Strongly disagree” to “Strongly agree.

Most teams use these questionnaires to measure attitudes, satisfaction, or perceived quality in a standardized way.

To demonstrate how it works in practice, the meat will walk you through specific Likert examples you can modify for your own projects.

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Key Takeaways

• Likert scales help capture attitudes, opinions, and perceptions with more nuance than yes or no questions and they are used extensively in customer, employee, and market research surveys. They produce ordinal data that facilitates both rich feedback and simple quantitative analysis.
• Different Likert formats, including 3-point, 5-point, 7-point, and 10-point, fit different goals, from quick pulse checks to more granular research. You can opt for intensity measured unipolar scales or bipolar scales that range between two opposing sentiments.
• Likert questions can be used to measure specific dimensions such as agreement, frequency, satisfaction, importance, and likelihood, making them very versatile. Using consistent anchors such as Strongly Disagree to Strongly Agree or Never to Always is important so that respondents know which end of the scale to pick.
• What makes a good Likert item is a combination of three things: one clear statement, clearly defined anchors, and an appropriate number of points, usually 5 or 7. Balanced positive and negative options on both sides eliminate bias and make your data much more reliable.
• Tips for writing effective Likert questions include steering clear of double-barreled or leading statements, using simple language, and maintaining the same scale format throughout the survey. A quick checklist for clarity, neutrality, and consistency goes a long way toward improving response quality.
• Likert data can be analyzed by assigning numbers to responses, which allows you to calculate the mean, median, and mode and visualize the results in tables and charts. Making sense of these patterns helps transform raw ratings into specific decisions about product or service modifications or next survey design.

What is a Likert Scale?

A Likert scale survey measures attitudes, opinions, perceptions, or behaviors in a structured way. If you’ve answered a question like “How strongly do you agree with this statement?” on a scale of 1 to 5, you’ve used a Likert scale. Named after Rensis Likert, this method is reliable for capturing subjective attitudes in research. It presents a statement with responses ranging from Strongly disagree (1) to Strongly agree (5). Some surveys use a 7-point scale for more nuanced insights, especially for brand perception or teaching effectiveness.

Typically, the scale includes multiple items related to a common trait, such as trust in a support team, which can be combined into a single index. This format improves survey quality by exploring deeper insights than simple yes/no questions. For example, instead of asking if someone is satisfied with service, you ask them to rate their overall satisfaction. Likert scales effectively capture varying levels of agreement and are widely used in customer satisfaction, employee engagement, market research, and healthcare to assess various attitudes and experiences.

Likert scales can be unipolar, ranging from 1 to 5, with “not at all motivated” to “extremely motivated” or bipolar, ranging from minus 3 to plus 3, with “very negative” to “very positive,” depending on whether you’re measuring a single attribute or the full continuum between two opposing poles. This flexibility allows for a variety of Likert scale questions to be crafted for different survey topics.

Technically, Likert responses form ordinal data. “Agree” is more than “neutral,” but the step from “agree” to “strongly agree” is not guaranteed to be equal to the step from “disagree” to “neutral.” Most practitioners still treat well-designed scales as interval-like for practical analysis, especially when combining scores over many questions, but it’s good to be mindful of that assumption.

You need to manage response bias. Some respondents may rush through and agree with everything or choose socially desirable answers. Clear item wording, mixed positive and negative items, and briefer batteries help mitigate this, ensuring usable customer feedback from your surveys.

Examples Of Likert Scale Questionnaire Questions

Participants tend to enjoy likert scales in surveys and find them intuitive, which is why they show up on most contemporary surveys.

You can deal with 3, 4, 5, 7 or even 10 points — whatever level of nuance you require. For instance, a 7-point scale tends to record more nuanced ratings, whereas a 4-point scale eliminates the neutral middle choice and forces respondents to “take a side.

Unipolar scales measure the intensity of one feeling (e.g., ‘not at all satisfied’ to ‘extremely satisfied’), while bipolar scales run between two opposites (e.g., ‘completely satisfied’ to ‘completely dissatisfied’). Remember that Likert data is ordinal: you can say one score is higher or lower than another, but not by how much.

1. Agreement Likert Questions

Agreement scales measure how much respondents agree or disagree with an item, such as “I am satisfied with the quality of service.” They tend to be bipolar and scale from “Strongly Disagree” to “Strongly Agree.

They lend themselves well to 5- or 7-point employee, customer, or psychological surveys. Since a standard Likert scale question can address all sorts of different dimensions with the same form, you can similarly build consistent blocks like “The training materials are clear,” “My manager supports my development,” or “This software is easy to use.

Example agreement likert questions (5‑point, bipolar):

Question	Scale Type	Response Options (Left → Right)
I am satisfied with the quality of service.	5‑point	Strongly Disagree · Disagree · Neutral · Agree · Strongly Agree
This product meets my daily needs.	7‑point	Strongly Disagree → Strongly Agree (7 steps)
The user interface is intuitive and easy to navigate.	4‑point	Disagree · Somewhat Disagree · Somewhat Agree · Agree

2. Frequency Likert Questions

Frequency scales emphasize the frequency at which something occurs, providing an excellent fit for behavior tracking, such as usage analytics or shopping habits. A sample Likert question in this style would be, “How often do you shop on our site?

You can use 5-point unipolar options from ‘Never’ to ‘Always’ or go out to 7 points for more precision in research.

Example frequency likert questions (unipolar):

Question	Scale Type	Response Options (Low → High)
How often do you use our product?	5‑point	Never · Rarely · Sometimes · Often · Always
How frequently do you shop on our website?	7‑point	Never → Multiple times a week (7 graded frequency labels)

3. Satisfaction Likert Questions

Satisfaction scales measure how satisfied or dissatisfied respondents are with a product, service, or experience. A common one is something like “How satisfied are you with this product?

Most teams use a 5-point unipolar scale from ‘Very Dissatisfied’ to ‘Very Satisfied.’ A bipolar variant might range from ‘completely satisfied’ to ‘completely dissatisfied’ which can be useful when you want both positive and negative extremes to be clearly visible.

Example satisfaction likert questions:

Question	Scale Type	Response Options
How satisfied are you with this product?	5‑point	Very Dissatisfied · Dissatisfied · Neutral · Satisfied · Very Satisfied
Rate your satisfaction with our customer support.	7‑point	Very Dissatisfied → Very Satisfied (7 steps)
How satisfied are you with your recent hospital visit?	3‑point	Dissatisfied · Neither satisfied nor dissatisfied · Satisfied

4. Importance Likert Questions

Importance scales reveal what actually matters to respondents, which is crucial for product roadmaps and service design. By posing questions like “How important is fast delivery to you?” you can prioritize features, service levels, or content.

A 5-point or 7-point unipolar scale from “Not Important at All” to “Extremely Important” works well when you need to compare attributes side by side.

Example importance likert questions:

Question	Scale Type	Response Options (Low → High)
How important is fast delivery to you?	5‑point	Not Important at All · Slightly · Moderately · Very · Extremely Important
How important is responsive customer support?	7‑point	Not at all Important → Extremely Important (7 labels)
How important is eco‑friendly packaging?	4‑point	Not Important · Somewhat Important · Important · Very Important

5. Effectiveness Likert Questions

Effectiveness Likert Measurement Questions use a Likert scale to assess how well a product, service, process, or experience achieves its intended purpose. They capture respondents’ perceptions of impact or success using graded response options such as “very ineffective” to “very effective.”

Example effectiveness likert questions:

Questions	Scale Type	Response Options
How effective was the product in solving your problem?	5-point Likert (Effectiveness)	Very ineffective, Ineffective, Neutral, Effective, Very effective
How effective was the service in meeting your expectations?	5-point Likert (Agreement/Effectiveness)	Strongly disagree, Disagree, Neutral, Agree, Strongly agree
How effective is the process in saving you time?	5-point Likert (Frequency/Impact)	Not at all effective, Slightly effective, Moderately effective, Very effective, Extremely effective
How effective was the support team in resolving your issue?	5-point Likert (Satisfaction/Effectiveness)	Very dissatisfied, Dissatisfied, Neutral, Satisfied, Very satisfied
Overall, how effective do you find our solution?	5-point Likert (Overall Effectiveness)	Very ineffective, Ineffective, Neutral, Effective, Very effective

6. Quality Likert Questions

Quality Likert Questions are survey questions that use a Likert scale to measure how respondents perceive the quality of a product, service, or experience. They assess attributes such as excellence, consistency, and satisfaction using graded response options like “very poor” to “excellent.”

Example quality likert questions:

Questions	Scale Type	Response Options
How would you rate the overall quality of the product?	5-point Likert (Quality Rating)	Very poor, Poor, Fair, Good, Excellent
The quality of the service met my expectations.	5-point Likert (Agreement)	Strongly disagree, Disagree, Neutral, Agree, Strongly agree
How consistent is the quality of our product or service?	5-point Likert (Consistency)	Very inconsistent, Inconsistent, Neutral, Consistent, Very consistent
How would you rate the quality compared to similar alternatives?	5-point Likert (Comparative Quality)	Much worse, Worse, About the same, Better, Much better
How satisfied are you with the quality delivered overall?	5-point Likert (Satisfaction/Quality)	Very dissatisfied, Dissatisfied, Neutral, Satisfied, Very satisfied

7. Likelihood Likert Questions

Likelihood scales, such as likert scale surveys, typically measure how likely a person is to engage in future actions, like making a repeat purchase or recommending a service. A popular example includes the question, “How likely are you to recommend us to a friend?” This can be structured using 5 or 7 point unipolar selections, ranging from “Very Unlikely” to “Very Likely,” or even in the format of a likert scale survey question with 0 to 10 options for broader insights.

Incorporating effective likert scale survey questions allows for a more nuanced understanding of customer sentiment. For instance, the NPS format can be adapted into a 10 point likelihood scale, enriching the overall survey data. By utilizing various likert scales, businesses can gather granular survey responses that provide valuable insights into customer loyalty and satisfaction.

Example likelihood likert questions:

Question	Scale Type	Response Options
How likely are you to recommend our service to a friend?	5‑point	Very Unlikely · Unlikely · Neutral · Likely · Very Likely
How likely are you to renew your subscription next year?	7‑point	Very Unlikely → Very Likely (7 levels)
How likely are you to attend a similar event in the future?	10‑point	1 (Not at all likely) → 10 (Extremely likely)

The Anatomy of a Likert Scale

A Likert scale appears straightforward, but its composition significantly influences your data. If you’re creating a questionnaire, knowing these components helps you pose cleaner questions and prevent noisy outcomes.

A Likert scale is a psychometric scale. You employ it in questionnaires to quantify a participant’s preference or agreement with a given statement or group of statements. In its most basic form, it is typically a five or seven point scale that allows someone to indicate the degree to which they agree or disagree with a statement.

At its core, a typical Likert item has three components:

1. a clear statement,

2. labeled response anchors, and

3. a defined number of points.

The statement is what you’re aiming to measure. It should concern a single concept, e.g. The onboarding materials were clear,” or “I feel like a valued customer.” Stay away from double-barreled statements like, “The app is fast and easy to use.

Response anchors are the written anchors at each end of the scale and frequently the midpoint. On an agreement scale, you might use “Strongly disagree” on the left and “Strongly agree” on the right, with a neutral “Neither agree nor disagree” in the center for a 5-point or 7-point version.

Each item then is assigned a score, typically 1 to 5 or 1 to 7. Ranges such as 1 to 5, 1 to 7, or 0 to 7 are common, and these are generally considered as if the increments are equal, although researchers frequently caution us that, strictly speaking, Likert data are ordinal.

Symmetrical response options are important. You require an equal number of positive and negative categories surrounding any neutral middle to minimize response bias. For example, a 5-point scale:

1. Strongly disagree

2. Disagree

3. Neither agree nor disagree

4. Agree

5. Strongly agree

This maintains the symmetry of the scale and helps avoid pushing people toward agreement. A 4-point Likert scale eliminates the midpoint entirely, which compels a bias toward one side. That can be helpful if you’re trying to avoid “safe” neutral answers, but it runs the risk of irritating respondents who actually feel neutral.

You decide between unipolar and bipolar. A unipolar scale measures one attribute from none to a lot, e.g., ‘Not at all satisfied’ and ‘Extremely satisfied.’ A bipolar scale runs between two opposites, such as ‘Very dissatisfied’ to ‘Very satisfied’. Both can work, but they should fit what you’re actually trying to measure.

Most real-world surveys use 5-point or 7-point scales because they strike a good balance between detail and clarity. A 5-point scale is simpler and quicker to handle. A 7-point scale provides more granularity if you intend to engage in deeper analysis.

In either case, you now have the neutral midpoint that most people anticipate. A simple example of a full Likert item would be:

Roughly the anatomy of a Likert scale.

Scale: 1 – Strongly disagree 2 – Disagree 3 – Neither agree nor disagree 4 – Agree 5 – Strongly agree

If you go 7 points, you can add Somewhat disagree and Somewhat agree between the central anchors for added nuance. Under the hood, each alternative continues to correspond to a value, and those values then add or average across questions to create an index.

Creating Effective Likert Scale Questions

Well-designed Likert questions should be clear and straightforward. Use simple language without technical terms to avoid confusion. For example, say, “The online course platform is user-friendly,” instead of “The e-learning UX is intuitive and frictionless.” Each question should focus on one idea. Instead of asking, “The customer support is fast and helpful,” separate it into two: “Customer support responds quickly” and “Customer support provides helpful answers.” This helps maintain the validity of your data.

Keep the scale consistent throughout your questionnaire. If one item goes from “Strongly disagree” to “Strongly agree,” the next should follow the same format. Consistent polarity is crucial; if one question’s “Strongly disagree” means something different from another’s, it can lead to misinterpretation.

When selecting response options, a 5-point scale is common, but a 7-point scale can capture more nuance, while a 4-point scale can push for a clear choice. Consider whether to use unipolar scales (measuring intensity in one direction) or bipolar scales (measuring both directions). Match the scale type to what you want to evaluate, whether it’s satisfaction, importance, frequency, or likelihood.

To create effective Likert questions, clarify what you want to measure, eliminate leading language, choose a scale with consistent labels, and decide on a neutral option. Pilot your questions with a small group to identify any confusion.

A practical checklist for writing Likert questions:

• Define what you want to measure: satisfaction, importance, frequency, likelihood, or something else.
• Creating effective Likert scale questions involves crafting clear and concise statements that respondents can easily evaluate.
• Take out leading language that nudges respondents in a particular direction.
• Pick a scale of 5, 7, or 4 and use the same labels in the same order.
• Determine whether you require a neutral response option. If not, a 4-point scale can encourage a definite decision.
• Select unipolar or bipolar depending on if you’re capturing direction and intensity, or just intensity.
• Pilot the questions with a small group and inquire where anything felt confusing or difficult to answer.

In analysis, treat Likert data as ordinal, focusing on distributions and medians rather than averages to gain deeper insights. Used effectively, Likert scales reveal not just what people think, but how strongly they feel, providing valuable insights.

The Psychology of Likert Scale Responses

On the surface, Likert scale surveys appear straightforward. However, what survey respondents mark is influenced by nuanced psychological factors, situational context, and cultural background. If you deal with any sample of a Likert scale survey, you must consider that what you’re looking at is filtered, not raw fact.

Cognitive biases behind “simple” agreement

Response bias is ever lurking. They want to appear reasonable or kind or competent, so social desirability urges them toward the answers they believe to be ‘acceptable.’ On a satisfaction scale, it could mean selecting ‘agree’ rather than ‘strongly disagree,’ even if they’re truly unhappy.

Acquiescence bias is another frequent pattern: some respondents tend to agree with almost everything, especially when they are tired, rushed, or see you as an authority. That’s how you get those long runs of ‘agree’ across very different statements.

Self-report formats add another dimension. Responses can vary with mood or a recent event or even the location where the subject is sitting to take the survey. A hard day at work can pull ratings down. A recent good experience with your team can raise them. All of this threatens validity if you take these numbers as accurate reports of fixed attitudes.

Neutral choices, extremes, and missing nuance

Most people are reluctant to select extremes. They go for the ‘safer’ middle. A 5-point scale with an obvious midpoint (“neither agree nor disagree”) frequently functions as a parking lot for the unsure, the unengaged, and the cautious.

Consider a training evaluation where ‘The content was engaging’ might draw many 3s not because the session was mediocre but because folks don’t want to offend the trainer or take an extreme position.

Likert scales further squash rich experiences into a few digits. Nuanced positions on a policy, a product, or a leader are simplified to one check. You sacrifice detail that might help explain why someone selected “disagree.

Question wording, order, and cultural fit

The item wording and response anchors heavily influence perception. Small changes like “I have the support of my manager” versus “I am seldom supported by my manager” can push answers one way or another because of framing.

Leading language, “Our excellent customer support was easy to access,” can nudge scores upward. Response labels like ‘rarely / sometimes / often / always’ need defined, intuitive significance. Blurry anchors generate data noise.

Order effects count. If a negative battery of items comes first (“I feel overwhelmed,” “I am stressed”), the satisfaction questions that come later might score lower because respondents remain in a negative frame of mind.

Grouping items by topic, interspersing positive and negative statements with care, and experimenting with different orders can minimize this bias. Cultural context plays a role in that psychology. A Likert instrument constructed in one country may not translate cleanly to another.

In other cultures, they’ll politely eschew all extreme options. In others, direct criticism is more tolerated. Simple translations of items or anchors such as “strongly agree” do not necessarily have the same force or feeling. If not adapted and tested, your cross-cultural scores can represent cultural response styles more than actual differences.

Scale design, fatigue, and practical safeguards

The scale format influences the informativeness of your results. Research shows that having 5 to 7 points increases precision, while a 10-point scale often leads to many responses clustering in the middle. For most surveys, a 5-point or 7-point scale strikes a good balance between sensitivity and usability.

It’s also important to consider survey fatigue. When respondents face too many similar questions, they may choose the same answer repeatedly, leading to response bias and muddled trends. To keep participants engaged, use a mix of question types, like brief open-ended questions or occasional multiple-choice items. Varying response anchors can encourage careful reading, but too much variation can confuse. Moderation is essential.

Before launching a survey, conducting cognitive interviews with a small group can provide valuable insights. Asking participants to ‘think out loud’ can help identify vague wording or cultural mismatches. Understanding their interpretations of terms like ‘often’ or ‘support’ can improve data quality more than simply adding more questions.

Once data is gathered, reliability checks round out the psychological snapshot. Things like Cronbach’s alpha help you see if items designed to measure the same underlying attitude really do hang together.

Values ranging from approximately 0.7 to 0.8 are generally deemed adequate for various practical applications. If something pulls the alpha down, it could be confusing, understood differently by subgroups, or just not quite measuring the construct you’re interested in. Eliminating or editing such questions can refine the instrument for next time.

Analyzing Likert Scale Data

Analyzing a Likert questionnaire is less about fancy statistics and more about treating the data in a way that respects what people intended when they clicked.

Step 1 is converting the verbal options to numbers. A standard 5-point item like “Strongly disagree / Disagree / Neutral / Agree / Strongly agree” could be coded 1 to 5. That conversion simplifies summarizing responses, comparing groups, and running reliability checks like Cronbach’s α.

At the same time, those numbers don’t make the resulting data actual intervals either. None of this means that “Agree” is going to be one unit closer to “Strongly agree” than to “Neutral.” They remain basically ordinal, and that counts for everything you do later.

Due to this ordinal nature, it’s dangerous to treat the arithmetic mean as though it carries a precise psychological meaning. Strictly speaking, we cannot use the mean because it assumes an equal distance between categories that we cannot verify.

A lot of practitioners still report means for quick summaries, but if you want to stay on firmer ground, you should stick with medians, modes, and response distributions. For instance, for a 5-point satisfaction scale, you could highlight that the median response is “Agree” and 72% answered “Agree” or “Strongly agree,” rather than stating that an average of 3.9 translates to “high satisfaction.

Descriptive summaries remain handy if you treat them properly. The median indicates central tendency without assuming interval distances. The mode informs you of the most frequent attitude class.

You can use item‑total correlations to inform reliability analysis. If an item’s correlation with the overall scale is less than about 0.3, it likely does not fit the construct and could be deleted. For multi‑item scales, Cronbach’s α is the typical internal consistency statistic. Values of .7 to .8 are generally acceptable, but lower values can be appropriate when the construct is broad or heterogeneous.

When sharing results and trends, a concise table is often more effective than pages of paragraphs. For example:

Item	Strongly disagree	Disagree	Neutral	Agree	Strongly agree
I am satisfied with the product	3 %	7 %	18 %	46 %	26 %
The interface is easy to use	2 %	6 %	21 %	43 %	28 %
I would recommend this to a colleague	4 %	9 %	25 %	39 %	23 %

A table like this lets stakeholders see in a quick glance if responses are mostly positive or negative and if some items lag behind others.

Interpretation is the place where scale design, statistics, and human behavior collide. Respondents generally find Likert alternatives easy and intuitive, but the small number of selections signifies you capture a coarse ordering, not a specific underlying orientation.

More points does not automatically fix this; reliability gains flatten out beyond about 7 points, so a 9-point or 11-point Likert scale rarely buys you much. On top of that, self-report always brings bias. Mood, social desirability, or context, for example, answering right after a bad customer support call, can shape responses in ways that have nothing to do with stable attitudes.

That’s why it aids to read Likert results in tiers. At the item level, you find weak items, look for low item-total correlations, and drop or revise drag-down statements from Cronbach’s α.

On the scale level, you read distributions, medians, and relative patterns instead of over-fitting to decimal averages. At the decision level, you translate patterns into specific actions: which feature to improve, which training to offer, which communication to test.

Likert data won’t give you perfect truth about people, but when treated with care, they offer a neat, reliable signal that’s good enough to steer the next series of questions, experiments, or product choices.

Conclusion

Likert scale questionnaires remain popular for a reason. They transform thoughts, feelings, and experiences into data you can analyze with confidence. By selecting crisp statements, neutral responses, and a fixed scale, you obtain responses that are simpler to analyze and easier to compare.

If you work in marketing, education, HR or research, well-crafted Likert questions provide you more than just numbers. They expose trends in satisfaction, agreement and behavior that assist you in fine-tuning plans, optimizing experiences and sharing results with stakeholders.

When used correctly, Likert scale questions turn opinions into actionable data. With FORMEPIC, you can design, customize, and launch Likert questionnaires in minutes — whether you’re gathering customer feedback, employee sentiment, or research insights. Create smarter Likert scale surveys with FORMEPIC and transform responses into clear, data-driven decisions. Try FORMEPIC for free

Frequently Asked Questions

What is a Likert scale questionnaire?

A Likert scale questionnaire uses ordered response options, for example, Strongly Disagree to Strongly Agree, to measure attitudes, opinions, or behaviors. It assists in converting subjective opinions into quantitative data that you can study, contrast, and leverage for decision-making.

How many points should a Likert scale have?

Most Likert scale surveys utilize either 5 or 7 points. A 5-point likert scale question is straightforward and easy for survey respondents to reply to, while a 7-point scale provides more granularity. Choose the scale that best suits your audience and the accuracy needed.

When should I use a Likert scale instead of open-ended questions?

Use a Likert scale when you want quantitative, comparable data on attitude or satisfaction. It is most effective for large-sample surveys. Open-ended questions are preferable when you want detailed answers or fresh thinking rather than simple rankings.

How do I write effective Likert scale questions?

Terminology. Inquire about a single concept with each question. Do not use leading or biased language. Ensure response options are balanced, ordered, and consistent across questions. Pilot with a small group first.

Can Likert scale data be analyzed statistically?

Yes. You can compute frequencies, means, medians, and standard deviations for your Likert scale survey data. For bigger samples, you can use more sophisticated tests that respect the ordinal nature of Likert scale questions.

What is the difference between a Likert scale and a rating scale?

A Likert scale presents a range of answers to statements. Number or star ratings, such as from 1 to 10, without named levels of agreement. Likert scales are more about attitudes, while rating scales can be more general.

How many Likert questions should I include in a survey?

Add only as many Likert scale questions as you need to answer your research objectives. Too many survey questions induce fatigue and degrade survey responses. For the majority of brief questionnaires, five to twenty effective Likert scale survey questions are sufficient to produce consistent, useful information.