Understanding Likert Scale in Six Sigma

Likert Scale Six Sigma

Have you ever wondered how organizations can efficiently gather and analyze feedback to drive continuous improvement in their processes? The answer lies in the powerful combination of Likert scales and Six Sigma methodology. In this blog post, we will delve into the relationship between these two tools, explore best practices for analyzing Likert scale data, and provide tips for designing effective surveys. Get ready to uncover the secrets of leveraging the Likert Scale Six Sigma in your projects!

Key Takeaways

  • Likert scales are an useful tool for Six Sigma projects, enabling precise measurement of customer satisfaction 
  • Analysis of Likert scale data should focus on frequency distribution to accurately reflect sentiment while avoiding misinterpretations and overgeneralizations.
  • Techniques such as designing even/odd point scales, crafting clear questions, using descriptive statistics & visual representations can help ensure successful surveys.

What is a Likert Scale

A Likert scale is a type of psychometric scale commonly used in research and surveys to measure attitudes, perceptions, or opinions. It is named after its inventor, Rensis Likert, who developed the scale to assess the level of agreement or disagreement among respondents to a particular question or statement.

A typical Likert scale provides a range of response options ranging from one extreme attitude to another, with a balanced number of intermediate options in between. The scale is often designed to be symmetrical, meaning each level should be equidistant from the others. A common format might include five to seven choices, with labels such as:

  • Strongly Disagree
  • Disagree
  • Neither Agree nor Disagree
  • Agree
  • Strongly Agree

Some scales offer more options for a more detailed assessment, such as a 7-point or even 9-point scale, but 5-point scales are among the most commonly used.

Respondents select the option that best aligns with their viewpoint. The data collected can then be analyzed quantitatively, sometimes by assigning numerical values to each response option and calculating mean scores, or qualitatively by identifying patterns or trends in the responses.

It’s worth noting that while the Likert scale is a popular tool in research, it has its limitations. For example, it may not capture the full complexity of an individual’s feelings or attitudes, and the design of the scale (such as the wording of questions or the number of options provided) can influence the results. Nonetheless, Likert scales are a widely-used tool in social science research, organizational studies, and many other fields.

The role of Likert scales in Six Sigma projects

The likert scale has been proven to be a very effective model for helping customers identify and express their preferences. In quality management, quality is what the customer desires, so effective tools for eliciting requirements are essential. These scales are particularly useful in pinpointing areas for development and evaluating progress over time, offering a more precise and reliable way to measure variables such as customer satisfaction, employee engagement, and product evaluation.

Consider a company deploying a Likert scale survey to assess customer satisfaction with their product or service. Based on the data collected, the company can:

  • Identify the most pressing issues impacting customer satisfaction
  • Implement targeted improvements
  • Regularly administer surveys to monitor the impact of their improvements
  • Persistently fine-tune their processes

Benefits of using Likert scales in Six Sigma

One of the primary benefits of using Likert scales in Six Sigma projects is the increased response willingness from participants. The scales offer respondents the opportunity to provide more comprehensive feedback, as they can select from a variety of responses instead of just one. This makes it easier for respondents to articulate their opinions and sentiments, leading to heightened response rates.

Furthermore, Likert scales enable efficient data collection and enhanced data analysis. The use of these scales allows for:

  • Rapid acquisition of large amounts of data in a short period of time
  • Facilitating informed decision-making based on the collected data
  • Providing a more comprehensive understanding of participant preferences
  • Offering an improved perspective on the current state of the project

Analyzing Likert Scale Data: Techniques and Best Practices

Analyzing Likert scale data involves distinguishing between ordinal and continuous data, as well as understanding the role of attribute data in the process. Moreover, the judicious selection of fitting descriptive statistics and visual aids can aid in effective data interpretation and decision-making.

This section covers various techniques and best practices for analyzing Likert scale data.

Ordinal vs. continuous data

Likert scale data is ordinal, reflecting categories or values with a logical sequence, rather than continuous data, which reflects measurements and can assume any numerical value within a specified range. This distinction is important because ordinal data, like that found on an ordinal scale, does not allow for measuring exact values or differences between values, unlike an interval scale.

As a result, statistical tests such as t-tests and ANOVA are not applicable for analyzing Likert scale data. Instead, one must employ specific statistical methods for analyzing ordinal data. Grasping the nature of ordinal data and opting for suitable analysis methods guarantees accurate and meaningful conclusions from Likert scale data.

Descriptive statistics for Likert scale analysis

When analyzing Likert scale data, choosing the right descriptive statistics is essential. While measures of central tendency, such as mean, median, and mode, are often used, they may not accurately reflect the true sentiment of the data, given the ordinal nature of Likert scale responses.

Instead, frequency distribution is often more advantageous for describing Likert scale data. This approach provides the number of responses for each category, offering a better understanding of the sentiment of the data. Applying frequency distribution provides a clearer view of response distribution, guiding more informed decision-making based on your survey outcomes.

Visual representation of Likert scale data

Visual representations, such as bar graphs, line graphs, and pie charts, can effectively display Likert scale data and highlight trends. These visualizations can facilitate the recognition of patterns and trends in the data, as well as the comparison of different responses to the Likert scale.

For example, a diverging stacked bar chart is a type of bar chart that enables comparison of different responses to a Likert scale. Utilizing diverging color schemes for different Likert scale categories (for instance, red for strong disagreement, yellow for neutrality, and green for strong agreement) simplifies data representation and facilitates easier communication of survey results.

Tips for Designing Effective Likert Scale Surveys

The design of effective Likert scale surveys is fundamental for acquiring precise and valuable data. This section offers guidance on selecting the optimal number of response categories, achieving balance between even and odd-point scales, and formulating clear, unbiased questions.

Adhering to these guidelines can help you design surveys that provide insightful data for your Six Sigma projects.

Clearly Define Your Research Objectives

Before designing your Likert scale, it’s important to clearly define your research objective. What do you aim to measure? Having a focused research goal is crucial because it informs the kind of questions you will ask and how you’ll interpret the responses.

Choosing the right number of response categories

The number of response categories in a Likert scale survey should be carefully considered. Most commonly, Likert scales have 5 or 7 response options. However, employing too many or too few response categories can result in confusion or limited response choices for participants.

When selecting the number of response categories, it is crucial to strike a balance between providing a meaningful order of options and avoiding overwhelming respondents. A scale with 5 response categories typically offers an appropriate balance, allowing respondents to express their opinions without becoming overwhelmed by excessive choices.

Balancing even and odd-point scales

When constructing a Likert scale survey, it is essential to consider the balance between even and odd-point scales. An even point scale necessitates that respondents select a preference, while odd-point scales permit neutral responses. To achieve balance, even-point scales should include a neutral option, such as “neither agree nor disagree”, while odd-point scales should incorporate an additional option, such as “strongly agree” or “strongly disagree”.

The choice between even and odd-point scales depends on the survey’s goals. Even-point scales are suitable for measuring a respondent’s preference, while odd-point scales are suitable for gauging a respondent’s opinion. By considering the purpose of your survey and the information you aim to gather, you can select the appropriate balance between even and odd-point scales.

Crafting clear and unbiased questions

Drafting fair questions for a Likert scale is a crucial aspect of your research design that directly impacts the validity and reliability of your findings. Firstly, your questions should be framed in clear, concise language that can be easily understood by all respondents, thereby reducing the risk of misinterpretation. Secondly, the questions must be directly related to your research objective to ensure that the data collected is relevant and purposeful.

 Neutral phrasing is essential to avoid leading respondents toward a specific answer, thus mitigating bias. Moreover, incorporating both positively and negatively phrased questions can further help in counteracting any response biases, such as the tendency for people to agree with statements as a default. By adhering to these guidelines, you’ll be more likely to craft fair and unbiased questions that yield reliable and valuable data.

For example, instead of asking, “Do you believe the customer service you received was excellent?”, a more impartial question would be, “On a scale of 1 to 5, how satisfied are you with the customer service you received?” By crafting clear and unbiased questions, you can ensure that survey participants provide accurate and meaningful responses.

Common Challenges and Misconceptions in Analyzing Likert Scale Data

Despite the valuable insights provided by Likert scales, data analysis can present challenges and misconceptions. This section delves into common hurdles and misconceptions in Likert scale data analysis, like misinterpretation of central tendency measures and overgeneralization of findings.

Comprehending and tackling these challenges can guarantee precise and significant interpretations when you gather data from your survey.

Misinterpreting central tendency measures

Central tendency measures, such as mean and standard deviation, are often used to describe data. However, when applied to Likert scale responses, these measures may not accurately reflect the true sentiment of the data due to the ordinal nature of the responses.

Instead, frequency distribution is often more informative when describing Likert scale data. By providing the number of responses for each category, frequency distribution offers a better understanding of the data’s sentiment. This approach helps avoid the pitfalls of misinterpreting central tendency measures and enables you to make more informed decisions based on your survey results.

Overgeneralizing findings

Overgeneralizing findings from Likert scale data can lead to inaccurate conclusions, as the data may not accurately represent the genuine attitudes and opinions of the population being studied. To avoid overgeneralization, it is essential to consider the survey’s context, the population being studied, and the limitations of the data, such as the number of respondents and the response rate.

Additionally, using descriptive statistics and visual representations of the data can help gain a more comprehensive understanding of the results. Thoughtful interpretation of Likert scale data and avoidance of overgeneralization ensures that your conclusions accurately capture respondents’ genuine sentiments, fostering meaningful enhancements in your Six Sigma projects.


In conclusion, Likert scales and Six Sigma methodology form a powerful combination for obtaining valuable insights and driving continuous improvement. By understanding the relationship between these tools, applying best practices for analyzing Likert scale data, and designing effective surveys, you can successfully leverage Likert scales in your Six Sigma projects. May your journey into the world of Likert scales and Six Sigma lead to meaningful improvements and a deeper understanding of the perspectives of your customers and employees.

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