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Likert scale

What is a Likert scale?

A Likert scale is a popular rating scale used in surveys and questionnaires to measure attitudes, opinions, and perceptions of respondents. The scale presents a series of statements, and respondents indicate their level of agreement or disagreement on a range of options, typically presented as a 5- or 7-point scale.

Synonyms: summative scale

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When to use a Likert scale?

  • Likert scale questions can gauge the level of satisfaction by asking users to rate their experience on dimensions such as usability, performance, UX design, or overall satisfaction.
  • Determining the importance or priority of different features within a product. Users can rate the perceived value or significance of each feature, helping UX designers understand which areas to focus on.
  • Evaluating the usability of a product. Likert scale questions can gather user feedback on aspects like ease of use, intuitiveness, clarity of instructions, or navigation, providing insights into areas where improvements are needed.

Benefits of a Likert scale

  • Ease of Analysis: Likert scale responses can be easily summarized and analyzed using statistical techniques such as mean, median, mode, or frequency distributions. 
  • Efficient Data Collection: The scale presents a structured response format, reducing cognitive load and streamlining the data collection process, making it efficient for large-scale surveys or studies with multiple participants.
  • Flexibility: Likert scales can be adapted to various research contexts and tailored to specific questions or topics of interest. The scale can range from 5 to 7 or more points, depending on the desired level of granularity or differentiation required.
  • Comparability: Likert scale data can be compared over time, between different user segments, or against industry benchmarks. This allows for tracking changes, evaluating the effectiveness of design iterations, and benchmarking against competitors or industry standards.

How to create a Likert scale?

  1. Define the Purpose: Identify the construct or concept you want to measure, such as user satisfaction, agreement, importance, or frequency of behavior.
  2. Generate Statements: Develop a set of statements that capture different aspects of the construct you’re measuring. These statements should be clear, concise, and cover the range of possible responses. 
  3. Response Options: Decide on the number of response options for your Likert scale. Common options include a 5-point scale (e.g., Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree) or a 7-point scale. Ensure the response options cover the full range of the construct being measured.
  4. Arrange the Scale: Organize the response options in a logical order. Typically, this means starting with the most negative response option and ending with the most positive. Ensure the scale flows smoothly from one option to the next.
  5. Formatting: Format the Likert scale using a consistent and visually appealing design. Clearly indicate the labels for the response options and ensure they are easily distinguishable. Use proper spacing and alignment to enhance readability.
  6. Clear Instructions: Clearly explain the purpose of the Likert scale and provide instructions on how to respond. Ensure participants understand how to interpret the statements and select the appropriate response option.
  7. Open-Ended Questions: Consider supplementing the Likert scale with open-ended questions to gather qualitative insights and allow participants to provide additional comments or explanations.

 

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FAQ

How to analyze likert scale data?

Analyzing Likert scale data should be approached with both quantitative and qualitative perspectives to gain a comprehensive understanding of the results. It’s important to interpret the data within the context of your research goals and be cautious of potential limitations or biases in the data collection process.

What type of data is a likert scale?

A Likert scale generates ordinal data, which falls within the broader category of categorical data. Ordinal data represents variables with a specific order or rank but does not have equal intervals between the categories.

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