Likert scale questions: examples and design pitfalls for customer surveys

A Likert scale question asks people how much they agree or disagree with a statement, usually on five labeled points from "Strongly disagree" to "Strongly agree." It is quick to answer and easy to count, which is why it appears in almost every customer survey. It also has a known weakness: people tend to agree with statements. Use it for attitudes you can phrase neutrally, keep the labels standard, and report the full spread of answers rather than only an average.

What a Likert scale is

The Nielsen Norman Group's guide to rating scales explains that the method is named after the psychologist Rensis Likert, who created it in the 1930s (his paper was published in 1932). Strictly, a Likert scale is a set of related agreement statements whose answers are combined into one overall score. A single statement with agree-disagree answers is a Likert item, or a Likert-type question. In everyday use, people call either one a Likert question.

NN/g notes that Likert items typically use five response points, sometimes seven or nine, and each point is usually labeled in words. The standard five-point labels are:

Strongly disagree / Disagree / Neither agree nor disagree / Agree / Strongly agree

NN/g recommends sticking to these standard labels rather than inventing new ones.

Likert question examples for customer and market surveys

Each of these is a single statement rated on the five-point agreement scale. They are written for a business audience; adapt the nouns to yours.

• "I can find the information I need in our current reporting tool without help."
• "Our team spends more time on manual data entry than it should."
• "I trust the numbers in our monthly inventory report."
• "The price we pay for our current software is reasonable for what we get."
• "Our current supplier responds quickly when an order goes wrong."
• "I would need approval from someone else to change this tool."

Notice what they have in common: one idea each, concrete nouns, and no adjectives that praise your product. A statement like "Our innovative platform makes reporting easy and fast" fails on all three counts: it is about two things (easy, fast), and it asks people to agree with your marketing.

Pitfall 1: Acquiescence bias

Pew Research Center's guide to writing survey questions describes acquiescence bias: some respondents tend to agree with whatever statement they are shown, more so when an interviewer is present. NN/g lists it, along with social desirability bias, as the two main weaknesses of the Likert format.

Two ways to reduce it:

• Use item-specific wording where you can. Pew recommends offering a choice between alternatives instead of an agree-disagree statement. Instead of "The reporting tool is easy to use (agree/disagree)," ask "How easy or difficult is the reporting tool to use?" with Very easy / Somewhat easy / Somewhat difficult / Very difficult. Pew's 2019 study of question formats also notes that construct-specific answer labels ("Yes, have done this" rather than "Yes") are a proven way to deal with acquiescence.
• Be careful with reversed statements. Some questionnaires alternate positive and negative statements to catch people who agree with everything. NN/g cites Sauro and Lewis (2011), who found that alternation can confuse both respondents, who may not notice the switch, and researchers, who may forget to reverse-code the answers. If you use reversed items, check the coding twice.

Pitfall 2: Social desirability

People want to give answers that look good. NN/g notes that asking for names or other identifying details increases this bias. Statements like "Our team follows the documented process" will draw more agreement than reality supports. If identity is not needed, do not collect it, and consider asking about recent specific behavior instead: "In the past two weeks, how many times did someone on your team skip the documented process?"

Pitfall 3: Unbalanced or invented labels

NN/g's survey best practices warns that a scale with more positive than negative options pushes answers upward. "Strongly agree / Agree / Somewhat agree / Disagree" has three positive options and one negative one. Keep an equal number on each side of the midpoint.

Pitfall 4: No way out

A neutral midpoint and "Not applicable" mean different things. NN/g recommends adding a separate "Not applicable" option so you can tell people who have no opinion from people for whom the statement does not apply. A respondent who has never used your supplier's support line should not be forced to pick "Neither agree nor disagree" about how quickly it responds.

Pitfall 5: Stopping at the number

A rating tells you where someone landed, not why. NN/g suggests an optional follow-up text box, such as "Why did you choose this rating?" Use it on the one or two statements that matter most, not on every item. Open-ended survey questions explains the cost of too many text boxes.

Read the distribution, not only the average

It is tempting to score the five points 1 to 5 and report the mean. The mean can hide the result that matters.

A hypothetical example: 200 veterinary clinic managers rate "The price we pay for our practice software is reasonable." In version A, all 200 choose the midpoint (3), so the mean is 3.0. In version B, 100 choose Strongly disagree (1) and 100 choose Strongly agree (5). The mean is (100 × 1 + 100 × 5) ÷ 200 = 600 ÷ 200 = 3.0. Same average, opposite markets: one indifferent, one split between clinics that feel overcharged and clinics that are content.

Report the share in each category, and a simple "top two" figure (Agree plus Strongly agree) alongside the "bottom two." If a group matters to your decision, such as clinics with more than five vets, show its distribution separately.

Likert or something else?

NN/g compares Likert items with semantic differential scales, where respondents place themselves between two opposite adjectives such as "easy" and "difficult." Semantic differentials take more thought to answer because the middle points are usually unlabeled, but they avoid asking people to agree with a statement. Likert items are more flexible: some statements, like "I would need approval from someone else to change this tool," have no natural pair of opposite adjectives.

If you need people to choose between priorities, a rating scale is the wrong tool, because everyone can rate everything "Agree." Use a ranking or forced-choice question instead. Types of survey questions covers when to use each.

Your next step

Take your draft agreement statements and run each through three checks: one idea only, no praise words, and a separate "Not applicable" where some respondents will not have the experience. Rewrite the most important two as item-specific questions and compare.

When a Likert result surprises you, such as a split on price, talk to a few people on each side before acting on it. Instant Expert can find people who match a description you write, such as practice managers at independent veterinary clinics. You review who it finds, it sends your invitations, and you pay only for calls that get booked. The directory page for operations professionals in veterinary services is one place to start.