Introduction to Likert Scale Questions: Understanding Their Importance and Advantages

In the world of surveys and questionnaires, one name stands out prominently — Rensis Likert. The Likert scale, named after this renowned psychologist, has become a staple in many fields of study, from social science to business, education, and healthcare. Its versatility and simplicity have contributed to its widespread use, offering a nuanced yet user-friendly way to measure attitudes, perceptions, and opinions.

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A number of statistical assumptions are made in the application of his (Thurstone’s) attitude scale e.g. that the scale values of the statements are independent of the attitude distribution of the readers who sort the statements assumptions which as Thurstone points out, cannot always be verified. The method is more over laborious. It seems legitimate to enquire whether it actually does its work better than the simple scales which may be employed and the same breath to ask also whether it is not possible to construct equally reliable scales without making unnecessary statistical assumptions.”‍– Rensis Likert, (1932) [1]

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Likert scale questions can provide rich, insightful data, going beyond a binary yes/no dichotomy to capture shades of agreement or disagreement. This method can reveal deeper insights about your respondents, allowing you to understand the intensity of their feelings or beliefs towards a particular statement or topic. The result? More robust, nuanced data that can lead to more effective decision-making and action planning.

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Breaking Down Ordinal and Likert Scale Questions: Definitions, Differences, and Similarities

Definition and explanation of ordinal scale questions.

Before we delve deeper into Likert scales, let's start with the basics. Likert scale questions fall under a broader category known as 'ordinal scale' questions.

An ordinal scale is a type of measurement scale that allows respondents to rank their answers on a relative scale. The classic example is asking someone to rate a movie on a scale from 1 to 5, where 1 is "Didn't Like at All" and 5 is "Loved It". The numbers here denote an order, not a precise measurement. The distance between 1 and 2 might not be the same as the distance between 3 and 4 in terms of how much someone enjoyed the movie.

Definition and explanation of Likert scale questions.

A Likert scale, on the other hand, is a specific type of ordinal scale specifically designed to measure the degree of agreement or disagreement with a statement.

Typical Likert scale questions present:

• A statement (e.g " I enjoy using product X") and
• Ask respondents to indicate their agreement or disagreement on a 5 or 7 point scale, from " Strongly Disagree" to "Strongly Agree"

Differences and similarities between Likert scale and ordinal questions.

The key difference between general ordinal scale questions and Likert scale questions lies in what they're designed to measure. While ordinal scale questions can be used to rank any variable, Likert scale questions are specifically designed to measure :

• Attitudes
• Beliefs
• Perceptions

Identifying Ideal Situations for Likert Scale Questions: Beneficial Research Scenarios and Case Studies

Situations and types of research where Likert scale questions are most beneficial.

Likert scale questions are beneficial when you're aiming to measure subjective attributes that can't be measured directly — such as attitudes, feelings, or opinions.

They are often used in:

• Customer Satisfaction Surveys
• Employee Engagement Surveys
• Product Evaluations
• Academic Research

For instance, in a customer satisfaction survey for an e-commerce business, Likert scale questions could be used to gauge customers' satisfaction with various aspects of their shopping experience, such as website navigability, product quality, and customer service responsiveness.

In another scenario, a university might use Likert scale questions in an end-of-semester evaluation, asking students to rate their agreement with statements like "The course material was engaging" or "The instructor was responsive to questions."

When used effectively, Likert scale questions can uncover deeper insights about your target audience or population, providing a nuanced view of their attitudes, beliefs, or experiences. They allow companies to understand where they stand in terms of public perception.

Advantages of Likert scale questions in a survey

• Easy to use: Likert scale survey is a universal method of collecting data or information, which means it is easy to understand and respond.
• Easy to report: When the survey creator tends to work around quantitative data, it is easier to communicate the respondents' results.
• Option to answer in the neutral: Since a Likert scale survey involves using a scale, respondents can answer in the neutral if they choose to do so.
• Time-saving survey method: Finally, it's effortless to run these kinds of surveys as they are not time-consuming.

Here's an example of how it looks when you use a Likert Scale in our WhatsApp Market Research Platform.

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Crafting Effective Likert Scale Questions: Construction Guidelines, Best Practices, and Examples

How to construct clear and effective Likert scale questions.

Creating effective Likert scale questions requires a thoughtful approach. Here are some guidelines to get you started:

• Keep it simple: Ensure your questions are clear, concise, and easy to understand. Avoid jargon and complex language.
• Be specific: Focus on one attribute per question. If you're asking about website navigation and product range, split it into two separate questions.
• Ensure neutrality: Your questions should be unbiased and not lead the respondent towards a particular answer.
• Make sure the scale is relevant to the topic of the question.
• Pre-test the questions with a small group of people to ensure that they are clear and easy to understand.
• Be careful about adjectives: Your response options need to include adjectives that are easily understandable. When using attributes in the response option, there should be no confusion about which grade is higher than the other. It is appropriate to start from extremes (Extremely unsatisfied or similar) come to a neutral opinion (neither satisfied nor dissatisfied) and then a positive adjective scale (extremely satisfied or same).

Choosing the right scale

When writing ordinal closed-ended questions, it is important to choose the right scale. There are two types of ordinal scales: unipolar and bipolar.

• Unipolar scales have a single dimension, such as importance or satisfaction. The scale typically ranges from low to high, with a neutral option in the middle.
• Bipolar scales have two dimensions, such as positive and negative. The scale typically ranges from very positive to very negative, with a neutral option in the middle.

To help you with choosing the right scale, we have created a Question bank specifically for Liker Scale Questions. We have included all of the different types of Likert Scales you can use. We will be using screenshots from our question bank in the examples below.

Here are some examples of ordinal closed-ended questions:

Unipolar scale: How important is it to you that the government addresses income inequality?

• Very important
• Somewhat important
• Slightly important
• Not at all important

Bipolar scale: How likely or unlikely is it that you will switch jobs in the next two years?

• Very likely
• Somewhat likely
• Neither likely nor unlikely
• Somewhat unlikely
• Very unlikely

Dos and Don'ts of Writing Likert Scale Questions

Do:

• Use consistent scales: If 1 means "Strongly Disagree" in one question, it should mean the same in all others.
• Balance the scale: Include an equal number of positive and negative response options.

Don't:

• Don't use double negatives: It can confuse respondents and lead to inaccurate responses.
• Don't make assumptions: Ensure your questions don't presume anything about the respondent.

Examples of Well-Constructed Likert Scale Questions

Here are a few examples of well-crafted Likert scale questions:

• "On a scale of 1-5, with 1 being 'Very Unsatisfied' and 5 being 'Very Satisfied', how would you rate your overall experience with our customer service team?"
• "Please indicate your level of agreement with the following statement: 'I feel comfortable voicing my ideas and concerns in team meetings.' (1- Strongly Disagree, 5- Strongly Agree)"
• "How likely are you to recommend our product to a friend or colleague? (1- Not at all likely, 5- Extremely likely)"

These examples show that, when constructed correctly, Likert scale questions can be an invaluable tool in your survey strategy, providing in-depth, nuanced insights that can drive meaningful action.

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Deciding on the Number of Options in Likert Scale Questions: Analysing Scale Points and Pros-Cons of Odd vs. Even Options

Understanding the impact of the number of scale points.

The number of scale points in a Likert scale question can significantly impact the data you collect. Too few options might oversimplify the responses, while too many can overwhelm respondents and create confusion.

So, how do you determine the ideal number of options for your question? It can largely depend on what you're measuring. A five or seven-point scale is most commonly used, as it offers a balance between respondent comfort and data detail. However, it’s worth noting that a larger scale might be beneficial when measuring something with a broader range, like customer satisfaction or employee engagement.

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Pros and Cons of Odd vs. Even Number of Options

When creating your Likert scale, you'll also need to decide whether to use an odd or even number of options. An odd-numbered scale includes a neutral middle point (for example, "neither agree nor disagree"), while an even-numbered scale forces a choice between positive and negative responses.

The advantage of an odd-numbered scale is that it allows for neutrality, which can be more accurate for those who genuinely don't have a strong opinion. On the other hand, an even-numbered scale can help avoid the middle option bias, where respondents avoid taking a position and default to the neutral response. However, it can also frustrate respondents who truly feel neutral about a topic.

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Choosing Appropriate Labels for Likert Scale Options: Importance, Guidelines, and Effective Examples

Importance of precise, clear labels.

Choosing precise, clear labels for your scale options is crucial. Respondents should easily understand the degree of intensity each choice represents. Inconsistent or ambiguous labels can lead to inaccurate responses and affect the validity of your data.

When selecting your labels, strive for neutrality and avoid loaded terms that could introduce bias. Stick to simple, universally understood terms such as "Strongly Disagree," "Disagree," "Neither Agree nor Disagree," "Agree," and "Strongly Agree."

Direct vs Indirect scales

Select precise and customised scales. Choose scales that directly relate to your question. Avoid burdening respondents with the task of mapping a question to indirect or non-specific answer option scales. Here are examples of questions with indirect and direct constructs.

Question with indirect scales:

How much do you agree or disagree with the statement:

"The distribution of wealth in South Africa is just"?

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

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Construct-specific questions:

How fair or unfair do you think the distribution of wealth is in South Africa?

• Very Unfair
• Somewhat Unfair
• Neither Fair nor Unfair
• Somewhat Fair
• Very Fair

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In the indirect construct, the respondent must mentally map "fairness" to agreement/disagreement. The direct construct requires less cognitive effort, as the respondent only needs to contemplate the level of fairness/unfairness on a direct scale.

Examples of Commonly Used and Effective Labels

Here are some examples of commonly used and effective labels for Likert scale questions:

• For a five-point agreement scale: "Strongly Disagree," "Disagree," "Neither Agree nor Disagree," "Agree," "Strongly Agree"
• For a five-point frequency scale: "Never," "Rarely," "Sometimes," "Often," "Always"
• For a five-point quality scale: "Poor," "Below Average," "Average," "Above Average," "Excellent"

Remember, the key to successful Likert scale questions lies in their clarity, neutrality, and consistency. By thoughtfully considering the number of options and carefully selecting your labels, you can create questions that provide accurate, actionable insights.

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Mitigating Bias in Likert Scale Questions: Understanding Types of Bias and Strategies for Minimization

Understanding types of bias (e.g., central tendency bias, acquiescence bias, extreme response bias)

Despite their many strengths, Likert scale questions, like any survey method, are susceptible to various types of bias. Three commonly seen in Likert scales are central tendency bias, acquiescence bias, and extreme response bias.

Central tendency bias occurs when respondents avoid using extreme response categories and instead choose neutral or middle points, potentially diluting the data's accuracy.

Acquiescence bias, or "yea-saying", happens when respondents have a tendency to agree with all the questions, regardless of their content.

Extreme response bias is the opposite of central tendency bias. Some respondents may have a tendency to select the most extreme options, either on the positive or negative end of the scale.

Tips for Minimising Bias in Your Questions

1. Keep language simple and clear: Avoid complex language, double negatives, or jargon that could confuse respondents.
2. Balance your scale: Ensure an equal number of positive and negative response options.
3. Use reverse-coded items: This involves phrasing some questions so that agreement indicates a negative response or attitude.

The Role of Neutral Options and Reverse-Coded Items

Including a neutral option in your scale can help reduce acquiescence bias by giving respondents an option to select if they truly don't have a strong opinion.

Reverse-coded items, on the other hand, can help minimise both acquiescence bias and central tendency bias. By flipping the direction of the scale for some questions (i.e., positive items are rephrased to reflect a negative meaning), you can discourage automatic agreement and force respondents to pay more attention to each question.

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Analysing Data from Likert Scale Questions: Basics, Appropriate Statistical Methods, and Common Mistakes

Basics of analysing ordinal data.

Once you've collected your responses, the next step is analysing the data. Likert scale data is ordinal, meaning that while there is a clear order to responses, the intervals between points are not necessarily equal.

When analysing Likert scale data, descriptive statistics, such as frequencies, medians, and modes, are often appropriate. However, some researchers also use parametric tests (like t-tests or ANOVA) for Likert data, treating it as interval data under the assumption that the scale points are equally spaced.

Common Mistakes in the Analysis of Likert Scale Data

One common mistake is treating Likert scale data as if it were interval data when it's ordinal. While many statistical techniques used on Likert scale data treat it as interval data, it's important to remember the distinction and understand that doing so is technically a violation of the assumptions of these statistical tests.

Another common mistake is failing to examine the distribution of responses. Outliers and skewness can significantly impact your data analysis and should be taken into account when interpreting results.

Remember, proper analysis of Likert scale data is as crucial as the construction of the scale itself. By understanding the nuances of your data, you can ensure that your conclusions are valid and accurate.

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Best Practices for Using Likert Scale Questions: Recap, Checklist, and Tips

Employ natural metrics. Use a natural metric when available, rather than vague quantifiers. Instead of using indistinct phrases like "regularly" or "often", ask the respondent about the number of times they performed an activity in a suitable time frame, such as "last week" or "last month". Use this method when the respondent is expected to know the answer with enough confidence and precision; otherwise, you may introduce a lot of noise.

Adopt balanced scales for questions with bipolar scales. Balanced scales comprise an equal number of positive and negative options, and the categories are approximately evenly spaced.

Question with vague quantifiers:

How often do you volunteer in local community projects: regularly, occasionally, rarely, or never?

• Regularly
• Occasionally
• Rarely
• Never

Question with natural metric:

How often do you volunteer in local community projects?

• More than once a week
• About once a week
• Two to three times a month
• About once a month
• A few times per year
• Never

Clearly label all options in an answer scale, not just the extremes. This makes it much easier for respondents, as all options in an answer scale are labelled rather than just the end ones.

Eliminate numerical labels unless they bear a true meaning. Adding numeric labels to answer options (like "1 = Strongly disagree, 2 = Disagree, ..., 5 = Strongly agree") can distract and potentially mislead respondents. Ensure you only use numerical labels when necessary.

Ensure scales approximate the actual distribution in the population or use open-ended questions to avoid biasing responses. This is important when the question requires the respondents to recall or estimate a number.

Maintain a logical order for answer options. Even in the absence of a clear direction for answer options, there's often a more intuitive ordering. Maintain a consistent format and order within the survey.

Lastly, even if you follow all this advice, ensure your questions are consistent. Even with well-designed question stems and answer options, they must also fit together. Otherwise, the mismatch can confuse respondents.

Recap of key points

Creating, deploying, and analysing Likert scale questions involve several considerations. Let's recap some key points:

• Likert scale questions are most beneficial when measuring subjective attributes like attitudes, feelings, or opinions.
• Always strive for clarity, simplicity, and neutrality in your questions and responses.
• Carefully decide on the number of options in your scale, understanding the impact of using an odd or even number of choices.
• Pay attention to potential bias and use strategies such as reverse-coding to mitigate it.

Here is a quick checklist for crafting, deploying, and analysing Likert scale questions:

• Is each question clear, concise, and free from jargon?
• Is the question specific, focusing on one attribute only?
• Are the scale options and their labels consistent across all questions?
• Have you used an appropriate number of scale points for your question?
• Have you considered potential bias and implemented strategies to mitigate it?
• Are you prepared to analyse the data with appropriate statistical methods?

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Conclusion: Applying Knowledge of Likert Scale Questions and Invitation for Discussion

Mastering the art of Likert scale questions can significantly enhance the quality and usefulness of your survey data. We hope this practical guide has empowered you with the knowledge and tools to construct, deploy, and analyse Likert scale questions with confidence.

We encourage you to apply these strategies in your next survey or research project. And remember, practice makes perfect. With time and experience, you'll become adept at creating insightful Likert scale questions that yield meaningful, actionable results.

We invite you to share your thoughts, experiences, and questions in the comments below. Your feedback will enrich our learning community and help others on their journey to mastering Likert scale questions. Thank you for reading, and we wish you the best in your research endeavours.

FAQ

What is the 5 point likert scale?

A type of psychometric response scale in which responders specify their level of agreement to a statement typically in five points: (1) Strongly disagree; (2) Disagree; (3) Neither agree nor disagree; (4) Agree; (5) Strongly agree.

Is a 5 or 7 Likert scale better?

The short answer is that 7-point scales are a little better than 5-points—but not by much. The psychometric literature suggests that having more scale points is better but there is a diminishing return after around 11 points (Nunnally 1978)

Are Likert scale questions quantitative or qualitative?

Quantitative. Likert scales give quantitative value to qualitative data. For example, it may be designed to measure how much a person agrees with a statement regarding a product's value and assigns a data point to it. This is one reason why the scale is almost universally loved.

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