If you’ve spent any amount of time designing questionnaires, you’ve almost certainly encountered the phrase Likert scale. In fact, the term has become so common that many researchers use it to describe almost any question that asks participants to rate something on a numbered scale.
Technically speaking, that’s not correct.
This misconception is understandable. Likert scales are among the most common tools used in UX research, human factors engineering, market research, psychology, and the social sciences. But they are only one member of a much larger family of measurement scales. Another powerful approach, though often overlooked, is the semantic differential (SD) scale.
At first glance, the two appear remarkably similar. Both ask participants to place themselves somewhere along a scale, both generate quantitative data, and both are commonly used to understand people’s experiences with products, services, or brands. Yet they were designed to answer different kinds of questions and each has unique strengths and weaknesses.
Understanding those differences can help researchers design better questionnaires, avoid common pitfalls, and ultimately collect higher-quality data.
What the Two Methods Have in Common
Before discussing their differences, it’s worth recognizing that Likert and semantic differential scales share several characteristics.
Both ask participants to evaluate some aspect of their experience by selecting one response from an ordered set of options. They both produce quantitative data that allows us to analyze statistically and follow up with qualitative questions when appropriate. Both can help researchers understand participants’ perceptions of a product, service, concept, or brand.
Because they often appear visually similar, it’s easy to mistake one for the other. However, the similarity is largely superficial.
Likert Scales
A true Likert item asks participants how strongly they agree or disagree with a statement.
For example:
The device was easy to learn.
Strongly Disagree ◄────────► Strongly Agree
The important characteristic isn’t that there are five or seven response options. It’s that respondents are expressing their level of agreement with a written statement.
This distinction is often overlooked. Researchers frequently refer to any numbered rating question as a “Likert scale,” even when participants are rating satisfaction, likelihood, confidence, or other constructs that have nothing to do with agreement. While the terminology has become commonplace in casual conversation, understanding what a Likert item actually measures encourages researchers to think more carefully about what they’re asking participants to evaluate.
Likert scales are popular for good reason. They are intuitive, easy for participants to understand, straightforward to analyze, and well-suited for measuring attitudes toward specific statements. Because the responses are numeric, researchers can easily summarize results, compare groups, benchmark products over time, and evaluate changes following design modifications.
They are also particularly useful when the researcher has a clearly defined hypothesis to test. If the objective is to determine whether users agree that an interface is easy to navigate or whether instructions are understandable, a Likert item provides a direct way of measuring that agreement.
Limitations of Likert Scales
Despite their popularity, Likert scales are not without limitations.
One well-known challenge is the acquiescence bias: the tendency for some participants to agree with statements regardless of their true opinion. Additionally, many respondents avoid selecting the most extreme response options even when those responses best reflect their views. This tendency toward the middle can reduce the sensitivity of the measurement.
Another limitation is more subtle but, in our experience, far more common: poorly written statements.
Researchers sometimes try to capture too many ideas within a single statement. Consider the following example:
“The medical device was well designed, making me feel confident and secure in my choices while using it.”
At first glance, this seems like a perfectly reasonable statement. In reality, it combines several different psychological constructs into a single question. Participants may interpret it in terms of usability, confidence, safety, decision-making, or even aesthetics. Two respondents could provide the same answer while thinking about entirely different aspects of the experience.
For that reason, Likert statements should be as clear and unidimensional as possible. If a statement attempts to measure multiple ideas simultaneously, researchers may struggle to interpret what participants are actually agreeing or disagreeing with.
Another important consideration is that Likert items measure agreement with statements. They do not directly measure emotions, perceptions, or impressions. Researchers are asking participants to respond to their hypothesis rather than freely describe how they experienced the product.
The Five-Point vs. Seven-Point Debate
Few methodological topics generate as much discussion among researchers as the “correct” number of response options.
Before long, one side of the room is passionately defending five-point scales while another insists that seven-point scales provide greater sensitivity. Someone inevitably suggests meeting in the middle with six response options, only to discover they’ve accidentally started a second debate about whether surveys should even include a neutral response.
Research has consistently shown that reliability generally improves as additional response options are added, but those improvements diminish quickly. The largest gains occur when moving from very small scales to approximately five to seven response options. Beyond that point, additional categories typically provide little practical benefit.
In our view, researchers spend far more time debating the number of response options than they do improving the quality of the questions themselves. Whether a questionnaire uses five or seven response options is unlikely to determine the success of a study. Writing clear, focused, and interpretable questions almost certainly will.
Semantic Differential Scales
Unlike Likert scales, semantic differential scales do not ask participants whether they agree with a statement.
Instead, respondents position their experience between two opposing adjectives.
For example:
Reliable ◄────────► Unreliable
Simple ◄────────► Complex
Professional ◄────────► Unprofessional
Rather than agreeing with the researcher’s interpretation, participants describe where they believe the product falls between two opposing characteristics.
Originally developed by Charles Osgood and colleagues during the 1950s, semantic differential scales were intended to measure attitudes toward objects, ideas, people, and experiences using multiple bipolar adjective pairs. Historically, researchers organized these adjective pairs around three recurring dimensions: evaluation, potency, and activity.
Today, semantic differential scales continue to be widely used in UX research, branding, marketing, and product development because they provide an efficient way of capturing subjective impressions.
Strengths of Semantic Differential Scales
One of the biggest strengths of semantic differential scales is that they encourage participants to describe an experience rather than react to the researcher’s wording.
Because researchers are not asking participants whether they agree with a particular statement, semantic differential scales often introduce less acquiescence bias. Participants simply indicate where their experience falls between two opposing concepts. This makes semantic differential scales particularly useful during exploratory research.
Early concept evaluations, brand perception studies, emotional design research, and formative usability studies often seek to understand how users naturally perceive a product rather than confirm a predefined hypothesis. In these situations, semantic differential scales can provide richer insights into participants’ overall impressions.
Another practical advantage is that semantic differential scales often feel less leading. Instead of asking whether a participant agrees that a product is “easy to use,” researchers simply ask participants where they would place the product between “difficult” and “easy.” While the difference may seem subtle, it changes the nature of the evaluation.
Challenges of Semantic Differential Scales
Despite their advantages, semantic differential scales require careful construction. The most obvious challenge is selecting appropriate adjective pairs.
Not every pair of opposing words truly represents opposite concepts. Some adjectives are interpreted differently across cultures, generations, or educational backgrounds. Others appear to be opposites but actually measure different constructs.
Vocabulary also matters. Researchers should ensure that participants understand the terminology being used. A sophisticated adjective pair may be perfectly appropriate for expert clinicians but confusing for a general patient population.
Researchers should also resist the temptation to create adjective pairs based solely on intuition. Well-designed semantic differential scales often emerge through iterative development, beginning with qualitative research to understand how participants naturally describe a product before refining those descriptions into reliable bipolar pairs.
As with any questionnaire, context matters. The best adjective pairings for evaluating a luxury automobile may be entirely inappropriate for evaluating an insulin pump.
Which Approach Should You Use?
Researchers sometimes ask whether Likert or semantic differential scales are “better.”
In reality, they answer different questions.
If the objective is to measure agreement with a specific statement or evaluate a predefined hypothesis, Likert scales are often an excellent choice. They are straightforward, familiar to participants, and easy to compare across studies.
If the goal is to understand broader perceptions, attitudes, or emotional responses (particularly during exploratory research), semantic differential scales may provide richer information while introducing less response bias.
Many studies benefit from using both approaches. A questionnaire might use semantic differential scales to understand overall perceptions of a product while including carefully written Likert items to evaluate specific design objectives or hypotheses.
Final Thoughts
Likert scales and semantic differential scales are both valuable tools for understanding users’ experiences, but they are not interchangeable. While they may look similar on the page, they were designed to measure different constructs and support different research objectives.
Perhaps the most important takeaway is that selecting the right response scale is only one part of designing a high-quality questionnaire. Clear wording, thoughtful construct definition, and questions that match the objectives of the study will almost always have a greater impact on data quality than whether the response scale contains five points or seven.
Understanding the distinction between these two approaches won’t just improve your terminology; it will help you ask better questions, collect better data, and make better research decisions.
