Likert Scales

How can you measure subjective topics like user satisfaction, agreement, or ease of use? A common and effective tool for this is the Likert Scale. Named after psychologist Rensis Likert, this scale is mainly used in surveys. It helps researchers quantify attitudes and opinions. Respondents indicate their agreement, frequency, likelihood, importance, or satisfaction with a statement. This method is familiar to participants and provides structured feedback on user experience.

What is the Likert Scale?

A Likert scale helps turn feelings and opinions into numbers. This makes it easier to spot trends and patterns in the data. It moves beyond simple binary choices (like Yes/No) to capture the intensity of a respondent’s feelings or beliefs.

The typical structure involves two parts:

1. The Item: A declarative statement or a direct question about the attitude object (e.g., “The website was easy to navigate,” or “How satisfied were you with the checkout process?”).
2. The Scale: A range of ordered response options, usually presented horizontally or vertically, from which the respondent selects the one that best reflects their position.

Common examples of response anchors used on Likert scales include:

• Agreement: Strongly Disagree – Disagree – Neither Agree nor Disagree – Agree – Strongly Agree
• Frequency: Never – Rarely – Sometimes – Often – Always
• Satisfaction: Very Dissatisfied – Dissatisfied – Neutral – Satisfied – Very Satisfied
• Likelihood: Very Unlikely – Unlikely – Neither Unlikely nor Likely – Likely – Very Likely
• Importance: Not at all Important – Slightly Important – Moderately Important – Important – Very Important

The number of response options usually ranges from 4 to 7. The most common scales are 5-point and 7-point. Choosing between an odd or even number of points depends on whether to include a true neutral midpoint.

Key Components and Considerations of a Likert Scale

Crafting effective Likert scale questions requires attention to several details:

1. The Item/Statement: This must be clear, concise, and unambiguous, focusing on a single concept or attitude. Avoid jargon and “double-barreled” questions that ask about two different things in one statement (e.g., “The website was fast and easy to use”).
2. The Response Scale & Anchors: The design of the scale itself is critical:
   • Number of Points: 5-point scales are very common, offering a midpoint. 7-point scales offer more granularity. 4-point or 6-point scales (even numbers) force respondents to lean towards one side, eliminating a neutral option, which can be useful but may frustrate genuinely neutral participants.
   • Labels (Anchors): Provide clear, understandable labels for the response options. At minimum, label the endpoints (e.g., “Strongly Disagree,” “Strongly Agree”). Labeling all points can increase clarity but ensure the labels suggest roughly equal psychological distance between points. Consistency in anchor phrasing across multiple items is important.
   • Balance and Symmetry: The scale should be balanced, offering an equal number of positive and negative options around the neutral midpoint (if included).
   • Visual Layout: Present the scale clearly, often as horizontal radio buttons, ensuring the order is logical (e.g., negative to positive).
3. Handling “N/A” or “Don’t Know”: Consider if an “escape hatch” option like “Not Applicable” or “Don’t Know” is needed for certain questions. If included, keep it visually separate from the main scale points to avoid disrupting the scale’s flow.
4. Scoring for Analysis: Assign numerical values to each response option (e.g., 1 = Strongly Disagree, 5 = Strongly Agree). Ensure the scoring direction is consistent (higher numbers consistently mean more positive/frequent/important, etc.). 1. Be careful when analyzing data. Calculating means is common (treating the data as interval), but Likert scale data is technically ordinal. The gap between “Agree” and “Strongly Agree” may not feel the same as the gap between “Neutral” and “Agree.” Analyzing frequencies, medians, and modes is statistically safer. However, means are often used for practical reasons, especially when combining multiple items into a composite score.

Why Likert Scales are Valuable in UX Research: 

Likert scales are staples in UX research for several compelling reasons:

• Quantifying Subjectivity: They provide a structured way to measure intangible concepts like user satisfaction, perceived ease of use, trust, or attitude towards a design.
• Benchmarking and Tracking: Enable teams to establish baseline measurements and track changes in user attitudes over time (e.g., before and after a redesign), compare scores between different user segments, or even benchmark against industry standards (using established questionnaires).
• Identifying Strengths and Weaknesses: Analyzing response patterns across different items can highlight specific aspects of a product or experience that users perceive particularly positively or negatively.
• Respondent Familiarity and Ease of Use: The format is widely recognized and generally easy for participants to understand and complete quickly.
• Standardization and Comparability: Offers a standardized format for collecting attitudinal data, facilitating comparisons across different questions, studies, or time points.
• Foundation for Standardized Questionnaires: Many validated UX questionnaires rely heavily on Likert scales, including:
  • SUS (System Usability Scale): Measures perceived usability.
  • SEQ (Single Ease Question): Measures perceived ease of completing a task.
  • SUPR-Q (Standardized User Experience Percentile Rank Questionnaire): Measures overall user experience.
  • NPS (Net Promoter Score): Uses a Likert-like scale (0-10) to measure likelihood to recommend.

Using Likert Scales: Advantages and Important Considerations

While versatile and useful, it’s important to be aware of the strengths and potential drawbacks of Likert scales:

Advantages:

• Relatively easy to design, administer, and score.
• Familiar and intuitive format for most respondents.
• Provides quantifiable data from subjective user opinions.
• Captures the intensity or degree of feeling, not just direction.
• Highly versatile for measuring various constructs (agreement, satisfaction, frequency, etc.).
• Facilitates statistical analysis, comparisons, and tracking over time.

Potential Considerations & Biases:

• Subjective Interpretation: Respondents might interpret the meaning of scale points (e.g., “Sometimes,” “Agree”) differently.
• Central Tendency Bias: Some respondents tend to avoid the extreme endpoints and choose options closer to the middle.
• Acquiescence Response Bias: A tendency for some respondents to agree with statements regardless of their content (mitigate by including negatively worded items carefully).
• Social Desirability Bias: Participants might provide answers they feel are more socially acceptable or favorable to the researcher.
• Ordinal vs. Interval Data: The debate about whether the psychological distance between scale points is truly equal affects which statistical analyses are technically most appropriate (mean vs. median/mode).
• Midpoint Issues: Neutral options can be interpreted differently (indifference, ambivalence, lack of opinion). Omitting the midpoint forces a choice but can frustrate some users.
• Item Wording is Crucial: Ambiguous, leading, or double-barreled questions will yield unreliable and invalid data.

Leveraging Likert Scales for Meaningful UX Insights

Likert scales are key tools for UX researchers. They help measure user attitudes, opinions, and perceptions in a clear way. Their structured format is easy for respondents. This makes them great for tracking satisfaction, gauging agreement, and assessing frequency. They form the backbone of standard UX questionnaires like SUS and SEQ.

Researchers should be aware of response biases and data analysis challenges, like ordinal versus interval data. With careful scale design and thoughtful interpretation, Likert scales can offer valuable insights. They capture what users think and how strongly they feel. However, they usually don’t explain the deep why behind the ratings. To uncover that, qualitative methods like interviews are often needed. These can be guided by survey results and conducted on platforms like Userlytics. Still, Likert scales provide a reliable and efficient way to measure user sentiment and track experience metrics over time. They are vital for any thorough UX research strategy.