What Is a Likert Scale?
A Likert scale is a psychometric rating scale used to measure attitudes, opinions, and perceptions. Respondents are presented with a statement and asked to indicate their level of agreement on a symmetric scale — typically ranging from "Strongly Disagree" to "Strongly Agree" with a neutral midpoint.
Developed by psychologist Rensis Likert in 1932, the scale was designed to measure attitudes more precisely than simple yes/no questions. By capturing the intensity of agreement or disagreement, Likert scales produce ordinal data that can be analysed statistically — making them a cornerstone of academic research, market research, and organisational surveys.
Classic 5-point Likert scale example
Statement: "The onboarding process was easy to follow."
Strongly Disagree
1
Disagree
2
Neutral
3
Agree
4
Strongly Agree
5
5-Point vs 7-Point Likert Scale: Which to Use?
5-Point Scale
Strongly Disagree / Disagree / Neutral / Agree / Strongly Agree. Simpler, faster to complete, and easier to analyse. Best for general audiences and shorter surveys.
7-Point Scale
Adds "Slightly Disagree" and "Slightly Agree" for more nuance. Better for academic research where fine-grained distinctions matter.
Rule of thumb: Use 5-point for business surveys, customer feedback, and employee surveys. Use 7-point for academic research where statistical precision matters.
Which Likert scale do you prefer in surveys you take?
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30+ Likert Scale Question Examples by Industry
Customer Experience
The product met my expectations.
The checkout process was easy to complete.
I received my order in a timely manner.
The customer support team resolved my issue effectively.
I would recommend this company to a friend or colleague.
The product quality was worth the price I paid.
Employee Engagement
I feel valued and recognised for my contributions.
My manager gives me clear and constructive feedback.
I have the resources I need to do my job effectively.
I understand how my work contributes to the company's goals.
I feel comfortable raising concerns with my manager.
I see a clear path for career growth at this organisation.
Education & Training
The course content was relevant to my learning goals.
The instructor explained concepts clearly.
The pace of the course was appropriate.
The course materials were well-organised and easy to follow.
I feel more confident in this subject after completing the course.
I would recommend this course to others.
Product & UX Research
The interface was intuitive and easy to navigate.
I was able to complete my task without difficulty.
The product helped me achieve my goal.
I would use this product again.
The product performed as I expected.
I found the onboarding process helpful.
Healthcare & Wellbeing
I felt listened to during my appointment.
The healthcare provider explained my treatment options clearly.
I felt comfortable asking questions during my visit.
The waiting time was acceptable.
I would recommend this healthcare provider to others.
I feel confident in the care plan I received.
How to Analyse Likert Scale Data
Common analysis approaches by research type
Mean score
Calculate the average response across all respondents. A mean of 4.2 on a 5-point scale indicates strong agreement. Track means over time to measure change.
Top-2 box score
Combine the percentage of respondents who selected "Agree" or "Strongly Agree". This is the most commonly reported metric in business surveys — easy to communicate to stakeholders.
Frequency distribution
Show the percentage of respondents at each scale point. This reveals the shape of opinion — whether responses cluster around the middle or polarise at the extremes.
Segmentation
Break down results by demographic or behavioural segments. A mean of 3.8 overall might hide a 4.5 among power users and a 2.9 among new users — two very different stories.
Common Likert Scale Mistakes to Avoid
Using an even number of points (4 or 6)
Fix: Even scales force respondents to choose a side, removing the neutral option. This introduces bias. Use 5 or 7 points unless you specifically want to force a directional response.
Mixing positive and negative statements
Fix: Keep all statements in the same direction (all positive or all negative) within a scale. Mixing directions causes confusion and increases response errors.
Using vague labels
Fix: Labels like "Sometimes" or "Often" mean different things to different people. Use specific, symmetric labels: Strongly Disagree / Disagree / Neutral / Agree / Strongly Agree.
Treating Likert data as interval data
Fix: The distance between "Disagree" and "Neutral" may not equal the distance between "Neutral" and "Agree". Be cautious about statistical operations that assume equal intervals.
Using too many Likert questions in a row
Fix: Long blocks of Likert questions cause "straight-lining" — respondents select the same option for every question. Break up blocks with different question types.
What do you use Likert scale questions for most?
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How many scale points do you prefer in a Likert question?
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