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• What Is a Likert Scale? | Guide & Examples

What Is a Likert Scale? | Guide & Examples

Published on July 3, 2020 by Pritha Bhandari and Kassiani Nikolopoulou. Revised on June 22, 2023.

A Likert scale is a rating scale used to measure opinions, attitudes, or behaviors.

It consists of a statement or a question, followed by a series of five or seven answer statements. Respondents choose the option that best corresponds with how they feel about the statement or question.

Because respondents are presented with a range of possible answers, Likert scales are great for capturing the level of agreement or their feelings regarding the topic in a more nuanced way. However, Likert scales are prone to response bias , where respondents either agree or disagree with all the statements due to fatigue or social desirability or have a tendency toward extreme responding or other demand characteristics .

Likert scales are common in survey research , as well as in fields like marketing, psychology, or other social sciences.

Download Likert scale response options

Table of contents

What are likert scale questions, when to use likert scale questions, how to write strong likert scale questions, how to write likert scale responses, how to analyze data from a likert scale, advantages and disadvantages of likert scales, other interesting articles, frequently asked questions about likert scales.

Likert scales commonly comprise either five or seven options. The options on each end are called response anchors . The midpoint is often a neutral item, with positive options on one side and negative options on the other. Each item is given a score from 1 to 5 or 1 to 7.

The format of a typical five-level Likert question, for example, could be:

• Strongly disagree
• Neither agree nor disagree
• Strongly agree

In addition to measuring the level of agreement or disagreement, Likert scales can also measure other spectrums, such as frequency, satisfaction, or importance.

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Researchers use Likert scale questions when they are seeking a greater degree of nuance than possible from a simple “yes or no” question.

For example, let’s say you are conducting a survey about customer views on a pair of running shoes. You ask survey respondents “Are you satisfied with the shoes you purchased?”

A dichotomous question like the above gives you very limited information. There is no way you can tell how satisfied or dissatisfied customers really are. You get more specific and interesting information by asking a Likert scale question instead:

“How satisfied are you with the shoes you purchased?”

• 1 – Very dissatisfied
• 2 – Dissatisfied
• 4 – Satisfied
• 5 – Very satisfied

Likert scales are most useful when you are measuring unobservable individual characteristics , or characteristics that have no concrete, objective measurement. These can be elements like attitudes, feelings, or opinions that cause variations in behavior.

Each Likert scale–style question should assess a single attitude or trait. In order to get accurate results, it is important to word your questions precisely. As a rule of thumb, make sure each question only measures one aspect of your topic.

For example, if you want to assess attitudes towards environmentally friendly behaviors, you can design a Likert scale with a variety of questions that measure different aspects of this topic.

Here are a few pointers:

Include both questions and statements

Use both positive and negative framing, avoid double negatives, ask about only one thing at a time, be crystal clear.

A good rule of thumb is to use a mix of both to keep your participants engaged during the survey. When deciding how to phrase questions and statements, it’s important that they are easily understood and do not bias your respondents in one way or another.

If all of your questions only ask about things in socially desirable ways, your participants may be biased towards agreeing with all of them to show themselves in a positive light.

• Positive framing
• Negative framing

Respondents who agree with the first statement should also disagree with the second. By including both of these statements in a long survey, you can also check whether the participants’ responses are reliable and consistent.

Double negatives can lead to confusion and misinterpretations, as respondents may be unsure of what they are agreeing or disagreeing with.

• Bad example
• Good example

Avoid double-barreled questions (asking about two different topics within the same question). When faced with such questions, your respondents may selectively answer about one topic and ignore the other. Questions like this may also confuse respondents, leading them to choose a neutral but inaccurate answer in an attempt to answer both questions simultaneously.

The accuracy of your data also relies heavily on word choice:

• Pose your questions clearly, leaving no room for misunderstanding.
• Make language and stylistic choices that resonate with your target demographic.
• Stay away from jargon that could discourage or confuse your respondents.

When using Likert scales, how you phrase your response options is just as crucial as how you phrase your questions.

Here are a few tips to keep in mind.

Decide on a number of response options

Choose the type of response option, choose between unipolar and bipolar options, make sure that you use mutually exclusive options.

More options give you deeper insights but can make it harder for participants to decide on one answer. Fewer options mean you capture less detail, but the scale is more user-friendly.

Usually, researchers include five or seven response options. It’s a good idea to include an odd number so that there is a midpoint. However, if you want to force your respondents to choose, an even number of responses removes the neutral option.

You can measure a wide range of perceptions, motivations, and intentions using Likert scales. Response options should strive to cover the full range of opinions you anticipate a participant can have.

Some of the most common types of items include:

• Agreement: Strongly Agree, Agree, Neither Agree nor Disagree, Disagree, Strongly Disagree
• Quality: Very Poor, Poor, Fair, Good, Excellent
• Likelihood: Extremely Unlikely, Somewhat Unlikely, Likely, Somewhat Likely, Extremely Likely
• Experience: Very Negative, Somewhat Negative, Neutral, Somewhat Positive, Very Positive

Some researchers also include a “don’t know” option. This allows them to distinguish between respondents who do not feel sufficiently informed to give an opinion and those who are “neutral” on the topic. However, including a “don’t know” option may trigger unmotivated respondents to select that for every question.

On a unipolar scale, you measure only one attribute (e.g., satisfaction). On a bipolar scale, you can measure two attributes (e.g., satisfaction or dissatisfaction) along a continuum.

Your choice depends on your research questions and aims. If you want finer-grained details about one attribute, select unipolar items. If you want to allow a broader range of responses, select bipolar items.

Unipolar scales are most accurate when five-point scales are used. Conversely, bipolar scales are most accurate when a seven-point scale is used (with three scale points on each side of a truly neutral midpoint.)

Avoid overlaps in the response items. If two items have similar meanings, it risks making your respondent’s choice random.

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Before analyzing your data, it’s important to consider what type of data you are dealing with. Likert-derived data can be treated either as ordinal-level or interval-level data . However, most researchers treat Likert-derived data as ordinal: assuming there is not an equal distance between responses.

Furthermore, you need to decide which descriptive statistics and/or inferential statistics may be used to describe and analyze the data obtained from your Likert scale.

You can use descriptive statistics to summarize the data you collected in simple numerical or visual form.

• Ordinal data: To get an overall impression of your sample, you find the mode, or most common score, for each question. You also create a bar chart for each question to visualize the frequency of each item choice.
• Interval data: You add up the scores from each question to get the total score for each participant. You find the mean , or average, score and the standard deviation , or spread, of the scores for your sample.

You can use inferential statistics to test hypotheses , such as correlations between different responses or patterns in the whole dataset.

• Ordinal data: You hypothesize that knowledge of climate change is related to belief that environmental damage is a serious problem. You use a chi-square test of independence to see if these two attributes are correlated.
• Interval data: You investigate whether age is related to attitudes towards environmentally friendly behavior. Using a Pearson correlation test, you assess whether the overall score for your Likert scale is related to age.

Lastly, be sure to clearly state in your analysis whether you treat the data at interval level or at ordinal level.

Analyzing data at the ordinal level

Researchers usually treat Likert-derived data as ordinal . Here, response categories are presented in a ranking order, but the distances between the categories cannot be presumed to be equal.

For example, consider a scale where 1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, and 5 = strongly disagree.

In this scale, 4 is more negative than 3, 2, or 1. However, it cannot be inferred that a response of 4 is twice as negative as a response of 2.

Treating Likert-derived data as ordinal, you can use descriptive statistics to summarize the data you collected in simple numerical or visual form. The median or mode generally is used as the measure of central tendency . In addition, you can create a bar chart for each question to visualize the frequency of each item choice.

Appropriate inferential statistics for ordinal data are, for example, Spearman’s correlation or a chi-square test for independence .

Analyzing data at the interval level

However, you can also choose to treat Likert-derived data at the interval level . Here, response categories are presented in a ranking order, and the distance between categories is presumed to be equal.

Appropriate inferential statistics used here are an analysis of variance (ANOVA) or Pearson’s correlation . Such analysis is legitimate, provided that you state the assumption that the data are at interval level.

In terms of descriptive statistics, you add up the scores from each question to get the total score for each participant. You find the mean , or average, score and the standard deviation , or spread, of the scores for your sample.

Likert scales are a practical and accessible method of collecting data.

• Quantitative: Likert scales easily operationalize complex topics by breaking down abstract phenomena into recordable observations. This enables statistical testing of your hypotheses.
• Fine-grained: Because Likert-type questions aren’t binary ( yes/no , true/false , etc.) you can get detailed insights into perceptions, opinions, and behaviors.
• User-friendly: Unlike open-ended questions, Likert scales are closed-ended and don’t ask respondents to generate ideas or justify their opinions. This makes them quick for respondents to fill out and ensures they can easily yield data from large samples.

Problems with Likert scales often come from inappropriate design choices.

• Response bias: Due to social desirability bias , people often avoid selecting the extreme items or disagreeing with statements to seem more “normal” or show themselves in a favorable light.
• Fatigue/inattention: In Likert scales with many questions, respondents can get bored and lose interest. They may absent-mindedly select responses regardless of their true feelings. This results in invalid responses.
• Subjective interpretation: Some items can be vague and interpreted very differently by respondents. Words like “somewhat” or “fair” don’t have precise or narrow definitions.
• Restricted choice: Since Likert-type questions are closed-ended, respondents sometimes have to choose the most relevant answer even if it may not accurately reflect reality.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

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What is a likert scale.

14 min read In this guide, we’ll cover everything you need to know about likert scales, from what a likert scale is to how it works, and how you can use likert scale questions.

Understanding customer sentiment towards your brand, product or service is complex. You have to account for attitudes, opinions and perceptions, all of which influence how much a customer likes (or dislikes) what you offer.

You need a more comprehensive way to measure customer sentiment — one of the best ways to do so is with likert scale questions.

Get started with our free survey maker tool today

Likert scale definition

A likert scale, or rating system, is a measurement method used in research to evaluate attitudes, opinions and perceptions.

Likert scale questions are highly adaptable and can be used across a range of topics, from a customer satisfaction survey, to employment engagement surveys, to market research.

For each question or statement, subjects choose from a range of answer options. For example:

• Strongly agree
• Strongly disagree

In studies where answer options are coded numerically, ‘Strongly agree’ would be rated 1 or 5, respectively increasing or decreasing for each response, e.g. in the above example, 5, 4, 3, 2 and 1.

Some likert scales use a seven-point likert scale with 1 being  ‘Strongly Agree’ and 7 being ‘Strongly disagree’ (or reversed). In the middle, a neutral statement like ‘neither agree nor disagree’.

As well as judging positive and negative statements, a likert scale survey question can judge frequency, quality, or feelings of importance. You could use a likert scale to understand how customers view product features, or what product upgrades they’d most like to see next.

The granularity it provides over simple yes or no responses means you can uncover degrees of opinion, giving an accurate and representative understanding of feedback.

Here are a few likert scale examples:

Benefits of using likert scale questions

Likert scale options have several benefits, especially if you want to align data to a specific scale. Here’s a few benefits:

1.   They’re easy to understand

A likert scale is easy to understand as responders simply rank their preference based on the point likert scale you choose.

For example, depending on whether they strongly agree or strongly disagree, they just select their response.  This is sometimes referred to as a symmetric agree disagree scale.

A likert scale is also easy to analyze based on the responses given using a rating scale, as they can be collated numerically and filtered based on responses.

2.   Ideal for single topic surveys

A likert scale question is ideal for single topic surveys , as the data can be easily analyzed to judge sentiment or feelings towards particular things.

NPS surveys often use a likert scale to judge sentiment towards customer service.

Rather than ranging from strongly agree to strongly disagree, you’d use ‘highly satisfied’ to ‘highly dissatisfied’.

You can use likert scales to judge customers’ feelings about specific parts of your service, product or brand, then follow-up with a more detailed study.

3.   Likert scale questionnaires are versatile

Likert scale questionnaires help you evaluate preferences, sentiment, perspectives, behaviors or opinions.

You can implement them in a standard questionnaire, or use site intercepts on specific pages. You could have a likert scale questionnaire pop up after a webinar to get feedback on content and ideas.

4.   They don’t force specific responses

Rather than extreme response categories, e.g. giving respondents only two options when discussing polarizing topics, likert scales provide flexibility.

However, for difficult topics, respondents may feel they have to answer a certain way to avoid being seen as ‘extreme’. Just remind them survey responses are anonymous.

5.   Likert scale questions are great for sentiment analysis

A likert scale is effective when trying to assess sentiments towards your business, brand, product or service.

Likert scale responses can judge sentiment, along with reasons for the sentiment. For example, you could collate data in a statistical analysis platform, filtering responses to see what percentage of customers are satisfied, versus those that aren’t.

You could go a step further and break the percentages down, e.g. those who are highly satisfied versus those who’re just satisfied. How can you convert those customers into true evangelists?

It’s important you only use a likert scale questionnaire when asking about a singular topic, otherwise you risk confusing respondents and damaging the legitimacy of your study.

6.   They keep respondents happy

One of the pitfalls with conventional survey design is that researchers can use overly broad questions, limited to yes or no answers. These sorts of questions can frustrate respondents (as they give them no real way to provide context or accurate answers), leading to them rushing through surveys, affecting the quality of your data.

What are the limitations of likert scales?

While likert scaling is highly effective at measuring opinions and sentiments, it does have some limitations:

1.   Response choices limit real understanding

While likert scales help determine sentiment, they aren’t as effective helping understand why people feel a certain way. There’s also no interpretation of the sentiment between each choice, whether positive or negative.

For example, a respondent might ‘slightly agree’ with a statement, but why? What made them feel that way, what influences their responses? This kind of granularity can only be achieved with qualitative methods.

With this in mind, to increase the accuracy of your survey data, it’s worth running any likert-based questionnaires in conjunction with qualitative research methods.

2.   Respondents might focus on one side of the sentiment

Depending how questions are written, respondents might focus on one side of the scale. If they feel their answers might somehow affect their reputation, lifestyle or portray them negatively, they’ll pick positive responses.

Also, depending on the topic, respondents may be less likely to take extreme sides of the likert scale, instead agreeing, disagreeing or remaining neutral.

3.   Previous questions can influence responses

With any quantitative survey, respondents can get into a ‘rhythm’ of answering questions. The result is that they start to respond a certain way (this can be exacerbated by poor questioning, long surveys and/or flicking between themes).

When to use a likert scale question

A likert scale question works best when assessing responses based on variables, e.g. sentiment, satisfaction, quality, importance, likelihood.

For example, you might ask a respondent: “How would you rate the quality of our products?”, and provide a response scale of:

Respondents get a range to which they agree or disagree, rather than a simple yes or no answer which is often insufficient. Ultimately, you’ll use likert scales to measure sentiment about something in more detail.

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How to write likert scale survey questions

When writing likert scale questions, to ensure you get accurate responses, there are several things to consider:

1.   Keep them simple

The best way to get accurate results is asking simple, specific questions. Be crystal clear what you’re asking respondents to judge, whether it’s their preference, opinion or otherwise.

For example: “How satisfied are you with our service?” and providing a standard scale, from very satisfied to very dissatisfied, provides no room for confusion.

2.   Make sure they’re consistent

Respondents should fully understand the likert scale they’re recording answers against, this means answers on either side of the scale should be consistent.

For example, if you say “completely agree” at one extreme, the other extreme should be “completely disagree”.

3.   Use appropriate scaling – unipolar scales and bipolar scales

Any likert scale will use either a unipolar scale or bipolar scale.

A bipolar scale should be used when you want respondents to answer with either an extremely positive or negative opinion. Sometimes, an even-point scale is used, where the middle option of “neither agree nor disagree” or “neutral” is unavailable. This is sometimes referred to as a “forced choice” method.

A unipolar scale works in the same way, but it starts from zero at one end, while an extreme is at the other. For example, if you ask how appealing your product is, your unipolar responses would go from “not appealing at all” to “extremely appealing”.

You should also aim to keep your scales odd because scales with an odd number of values ensure there’s a midpoint. Keep your scales limited to 5 or 7 points.

4.   Don’t make statements, ask questions

Creating an effective likert scale means asking questions, not statements. This way, you avoid bias.

This is because people tend to automatically agree with positive or established statements, or unconsciously respond in a positive way (acquiescence bias). This can damage your study.

Asking questions rather than making statements encourages less biased responses because respondents have to think about their answers.

For example, asking “How satisfied are you with the quality of this service?” provides respondents with a chance to answer truthfully.

5.   Switch your scale points

Switching your rating scale prevents respondents from falling into a rhythm and giving biased responses.

For example, if your point scale starts at 1, ‘completely agree’ and ends with 5, ‘completely disagree’, then you switch these around for a few questions so 1 is completely disagree and 5 becomes completely agree. This keeps respondents on their toes and engaged with the survey.

Here are some likert scale examples:

How to analyze likert scale survey data

Unlike many survey types , you can’t use the ‘mean’ as a measure of tendency because the mean response to likert survey questions has no meaning. In other words, understanding the average of those who strongly agree or disagree tells you nothing.

Instead, when analyzing likert scale data, measure the most frequent response to understand the overall sentiment of respondents.

For example, 87% ‘strongly agree’ that you offer a good service.

You can also compare the percentages for each response to see where respondents ultimately fall.

This is incredibly useful when you want to nurture customers — perhaps there’s something you can do for those who answered ‘agree’ rather than ‘strongly agree’.

The easiest way to present likert scale survey results is using a simple bar or pie chart showing the distribution of response types or answer options.

You could also visualize your responses using a diverging stacked bar chart:

Image Source: mbounthavong

Likert scale questions

One of the biggest benefits of using likert scale survey questions is they can be used for a variety of topics to gather quantitative data

Below are some likert scale examples to give you an idea when you can use them in market research, and what kind of insights you can generate using likert scale surveys.

Customer satisfaction surveys

How do you rate the quality of service you received?

• Exceptional

This kind of likert scale question can benefit from further qualitative questions to gather valuable feedback on why survey respondents feel the way they do.

Employee engagement survey

How satisfied do you feel in your current position?

• Extremely happy
• Somewhat happy
• Neither happy nor unhappy
• Somewhat unhappy
• Extremely unhappy

Education engagement survey

How would you rate your satisfaction with your child’s education?

• Completely satisfied
• Moderately satisfied
• Neither satisfied nor unsatisfied
• Unsatisfied
• Moderately unsatisfied
• Completely unsatisfied

Marketing engagement survey

A business’ social responsibility score is more important than price

• Completely agree
• Somewhat agree
• Neither agree or disagree
• Somewhat disagree
• Completely disagree

Go beyond standard likert scale questions with Qualtrics

Understanding engagement or sentiment towards your products or services is an essential part of collecting data to improve your business. And with Qualtrics CoreXM — you can go even further.

Designed to empower everyone to gather experience insights and take action, Qualtrics CoreXM is an all-in-one solution to carry out customer, product, marketing and brand research, and then implement effective strategies.

From customer satisfaction surveys and event feedback to product concept testing and simple polls, create and deploy the research projects you need to enhance every aspect of your business.

Listen to everyone wherever they are providing feedback — whether directly in surveys and chatbot windows or indirectly via online reviews. Capture experience data across more than 125 resources and use that data for more targeted research and highly personalized experiences.

Use advanced analytics to interpret the feedback data and then automatically alert the right people to tell them what actions to take. All in real-time, with no legwork required. It’s time to go from measuring to acting and start closing experience gaps across your business.

Related resources

Survey research 15 min read, survey bias types 24 min read, post event survey questions 10 min read, best survey software 16 min read, close-ended questions 7 min read, survey vs questionnaire 12 min read, response bias 13 min read, request demo.

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Likert Scale

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• What Is a Likert Scale? | Guide & Examples

What Is a Likert Scale? | Guide & Examples

Published on 6 May 2022 by Pritha Bhandari and Kassiani Nikolopoulou. Revised on 16 January 2023.

A Likert scale is a rating scale used to measure opinions, attitudes, or behaviours.

It consists of a statement or a question, followed by a series of five or seven answer statements. Respondents choose the option that best corresponds with how they feel about the statement or question.

Because respondents are presented with a range of possible answers, Likert scales are great for capturing the level of agreement or their feelings regarding the topic in a more nuanced way. However, Likert scales are prone to response bias , where respondents either agree or disagree with all the statements due to fatigue or social desirability .

Likert scales are common in survey research , as well as in fields like marketing, psychology, or other social sciences.

Download Likert scale response options

Table of contents

What are likert scale questions, when to use likert scale questions, how to write strong likert scale questions, how to write likert scale responses, how to analyse data from a likert scale, advantages and disadvantages of likert scales, frequently asked questions about likert scales.

Likert scales commonly comprise either five or seven options. The options on each end are called response anchors . The midpoint is often a neutral item, with positive options on one side and negative options on the other. Each item is given a score from 1 to 5 or 1 to 7.

The format of a typical five-level Likert question, for example, could be:

• Strongly disagree
• Neither agree nor disagree
• Strongly agree

In addition to measuring the level of agreement or disagreement, Likert scales can also measure other spectrums, such as frequency, satisfaction, or importance.

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Researchers use Likert scale questions when they are seeking a greater degree of nuance than possible from a simple ‘yes or no’ question.

For example, let’s say you are conducting a survey about customer views on a pair of running shoes. You ask survey respondents ‘Are you satisfied with the shoes you purchased?’

A dichotomous question like the above gives you very limited information. There is no way you can tell how satisfied or dissatisfied customers really are. You get more specific and interesting information by asking a Likert scale question instead:

‘How satisfied are you with the shoes you purchased?’

• 1 – Very dissatisfied
• 2 – Dissatisfied
• 4 – Satisfied
• 5 – Very satisfied

Likert scales are most useful when you are measuring unobservable individual characteristics , or characteristics that have no concrete, objective measurement. These can be elements like attitudes, feelings, or opinions that cause variations in behaviour.

Each Likert scale–style question should assess a single attitude or trait. In order to get accurate results, it is important to word your questions precisely. As a rule of thumb, make sure each question only measures one aspect of your topic.

For example, if you want to assess attitudes towards environmentally friendly behaviours, you can design a Likert scale with a variety of questions that measure different aspects of this topic.

Here are a few pointers:

Include both questions and statements

Use both positive and negative framing, avoid double negatives, ask about only one thing at a time, be crystal clear.

A good rule of thumb is to use a mix of both to keep your participants engaged during the survey. When deciding how to phrase questions and statements, it’s important that they are easily understood and do not bias your respondents in one way or another.

If all of your questions only ask about things in socially desirable ways, your participants may be biased towards agreeing with all of them to show themselves in a positive light.

• Positive framing
• Negative framing

Respondents who agree with the first statement should also disagree with the second. By including both of these statements in a long survey, you can also check whether the participants’ responses are reliable and consistent.

Double negatives can lead to confusion and misinterpretations, as respondents may be unsure of what they are agreeing or disagreeing with.

• Bad example
• Good example

Avoid double-barrelled questions (asking about two different topics within the same question). When faced with such questions, your respondents may selectively answer about one topic and ignore the other. Questions like this may also confuse respondents, leading them to choose a neutral but inaccurate answer in an attempt to answer both questions simultaneously.

The accuracy of your data also relies heavily on word choice:

• Pose your questions clearly, leaving no room for misunderstandin.
• Make language and stylistic choices that resonate with your target demographic.
• Stay away from jargon that could discourage or confuse your respondents.

When using Likert scales, how you phrase your response options is just as crucial as how you phrase your questions.

Here are a few tips to keep in mind.

Decide on a number of response options

Choose the type of response option, choose between unipolar and bipolar options, make sure that you use mutually exclusive options.

More options give you deeper insights but can make it harder for participants to decide on one answer. Fewer options mean you capture less detail, but the scale is more user-friendly.

Usually, researchers include five or seven response options. It’s a good idea to include an odd number so that there is a midpoint. However, if you want to force your respondents to choose, an even number of responses removes the neutral option.

You can measure a wide range of perceptions, motivations, and intentions using Likert scales. Response options should strive to cover the full range of opinions you anticipate a participant can have.

Some of the most common types of items include:

• Agreement: Strongly Agree, Agree, Neither Agree nor Disagree, Disagree, Strongly Disagree
• Quality: Very Poor, Poor, Fair, Good, Excellent
• Likelihood: Extremely Unlikely, Somewhat Unlikely, Likely, Somewhat Likely, Extremely Likely
• Experience: Very Negative, Somewhat Negative, Neutral, Somewhat Positive, Very Positive

Some researchers also include a ‘don’t know’ option. This allows them to distinguish between respondents who do not feel sufficiently informed to give an opinion and those who are ‘neutral’ on the topic. However, including a ‘don’t know’ option may trigger unmotivated respondents to select that for every question.

On a unipolar scale, you measure only one attribute (e.g., satisfaction). On a bipolar scale, you can measure two attributes (e.g., satisfaction or dissatisfaction) along a continuum.

Your choice depends on your research questions and aims. If you want finer-grained details about one attribute, select unipolar items. If you want to allow a broader range of responses, select bipolar items.

Unipolar scales are most accurate when five-point scales are used. Conversely, bipolar scales are most accurate when a seven-point scale is used (with three scale points on each side of a truly neutral midpoint.)

Avoid overlaps in the response items. If two items have similar meanings, it risks making your respondent’s choice random.

Before analysing your data, it’s important to consider what type of data you are dealing with. Likert-derived data can be treated either as ordinal-level or interval-level data . However, most researchers treat Likert-derived data as ordinal: assuming there is not an equal distance between responses.

Furthermore, you need to decide which descriptive statistics and/or inferential statistics may be used to describe and analyse the data obtained from your Likert scale.

You can use descriptive statistics to summarise the data you collected in simple numerical or visual form.

• Ordinal data: To get an overall impression of your sample, you find the mode, or most common score, for each question. You also create a bar chart for each question to visualise the frequency of each item choice.
• Interval data: You add up the scores from each question to get the total score for each participant. You find the mean , or average, score and the standard deviation , or spread, of the scores for your sample.

You can use inferential statistics to test hypotheses , such as correlations between different responses or patterns in the whole dataset.

• Ordinal data: You hypothesise that knowledge of climate change is related to belief that environmental damage is a serious problem. You use a chi-square test of independence to see if these two attributes are correlated.
• Interval data: You investigate whether age is related to attitudes towards environmentally friendly behaviour. Using a Pearson correlation test, you assess whether the overall score for your Likert scale is related to age.

Lastly, be sure to clearly state in your analysis whether you treat the data at interval level or at ordinal level.

Analysing data at the ordinal level

Researchers usually treat Likert-derived data as ordinal . Here, response categories are presented in a ranking order, but the distances between the categories cannot be presumed to be equal.

For example, consider a scale where 1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, and 5 = strongly disagree.

In this scale, 4 is more negative than 3, 2, or 1. However, it cannot be inferred that a response of 4 is twice as negative as a response of 2.

Treating Likert-derived data as ordinal, you can use descriptive statistics to summarise the data you collected in simple numerical or visual form. The median or mode generally is used as the measure of central tendency . In addition, you can create a bar chart for each question to visualise the frequency of each item choice.

Appropriate inferential statistics for ordinal data are, for example, Spearman’s correlation or a chi-square test for independence .

Analysing data at the interval level

However, you can also choose to treat Likert-derived data at the interval level . Here, response categories are presented in a ranking order, and the distance between categories is presumed to be equal.

Appropriate inferential statistics used here are an analysis of variance (ANOVA) or Pearson’s correlation . Such analysis is legitimate, provided that you state the assumption that the data are at interval level.

In terms of descriptive statistics, you add up the scores from each question to get the total score for each participant. You find the mean , or average, score and the standard deviation , or spread, of the scores for your sample.

Likert scales are a practical and accessible method of collecting data.

• Quantitative: Likert scales easily operationalise complex topics by breaking down abstract phenomena into recordable observations. This enables statistical testing of your hypotheses.
• Fine-grained: Because Likert-type questions aren’t binary ( yes/no , true/false , etc.) you can get detailed insights into perceptions, opinions, and behaviours.
• User-friendly: Unlike open-ended questions, Likert scales are closed-ended and don’t ask respondents to generate ideas or justify their opinions. This makes them quick for respondents to fill in and ensures they can easily yield data from large samples.

Problems with Likert scales often come from inappropriate design choices.

• Response bias: Due to social desirability bias , people often avoid selecting the extreme items or disagreeing with statements to seem more ‘normal’ or show themselves in a favorable light.
• Fatigue/inattention: In Likert scales with many questions, respondents can get bored and lose interest. They may absent-mindedly select responses regardless of their true feelings. This results in invalid responses.
• Subjective interpretation: Some items can be vague and interpreted very differently by respondents. Words like ‘somewhat’ or ‘fair’ don’t have precise or narrow definitions.
• Restricted choice: Since Likert-type questions are closed-ended, respondents sometimes have to choose the most relevant answer even if it may not accurately reflect reality.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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Likert Scale Surveys: Why & How to Create Them (With Examples)

Among many survey types that either offer you qualitative or quantitative insights, the Likert Scale gives you the best of both worlds. 

To best describe the Likert scale in brief, it’s a 5 or 7 point scale that collects qualitative data in the form of options that say“ I agree ” or “ I disagree ” and represents these insights as easy to analyze quantitative data reports. 

Here are the eye-opening points we’ll cover in this article: Let’s start with developing a clear understanding of the Likert scale, how it’s different from other surveys and more.

Likert Scale: Definition

Likert scale is a psychometric and unidimensional scale from which respondents pick the best option representing their views on a topic. Generally, researchers use this survey scale to gauge people’s attitudes and perspectives towards different things, which cannot be polarized.

Companies use the Likert scale in their surveys to understand the target market and how satisfied customers are with them, what they think of the brand and product, and more. 

It is not the best option to assess attributes such as age, gender, and demographic parameters but works excellent when gathering opinions using agree or disagree statements. 

Since opinions and views are not always polar, using this scale with five to seven options ranging from ‘ Strongly Agree ’ to ‘ Strongly disagree ’ allows respondents more room to answer honestly and not feel restricted to choose any definite answer. 

For example, one person may be liberal on some topics but may hold conservative thoughts on others. So, offering them statements with ‘ Agree’ and ‘ Disagree’ options is the right way to understand their viewpoint. 

This scale was developed by psychologist Rensis Likert and had several variants like the Guttman scale, Bogardus scale, and Thurstone scale.

The Likert scale operates on the assumption that the intensity and strength of an experience are linear, meaning it can go from total agreement to total disagreement. It also assumes that attitudes are measurable. 

The most popular types of Likert scale are 5-point and 7-point scales with one neutral and equal positive and negative options. Here’s an example of a 5-point scale:

Difference Between Likert Scale and Likert Item

Likert item is the statement used in the survey that respondents evaluate using dichotomous options. 

A Likert scale is the total of all the responses a survey gathers. There can be more than one Likert item in a Likert scale survey. 

Here is an example:

In this survey, four statements related to a topic come one by one as the respondent answers each question. Each of these statements is a Likert item.

5‐point and 7‐point Scale: Does It Matter?

The two most widely used scales are the 5-point (also known as the unipolar Likert Scale) and the 7-point (also known as bipolar Likert scale) scales. A lot of research on the scales confirms that these two scales are the most effective in collecting plausible and accurate feedback data.

Factually speaking, researchers have confirmed that the information from Likert items on such a scale tends to become less apt as the number of points goes beyond 7 or drops below 5 .

Here are some arguments on why each one is preferred and what they bring to the table.

Why consider a 5-point scale

• These are less confusing.
• Less effort and time-consuming.
• Highly mobile-responsive as it fits the screens better.
• Some researchers reported that 5-point scales are more reliable.
• 5-point scales can be easily understood by people taking surveys.
• If you want to gauge an idea that can have responses ranging from the maximum amount of something to a minimum, then a unipolar or 5-point scale is an apt option. 
• Many researchers use a 5-point scale to increase response rate and quality and reduce the respondents’ “frustration level”.
• A good option when you do not want to measure negative effectiveness through your statement. 
• A good option for Likert-scale surveys with multiple Likert items.
• The quantitative data from the 5-point scale is easy to analyze as there are fewer points to consider.
• It’s a common and universally used scale, so it’s easy to compare data gathered through the 5-point scale.
• The results from the 5-point scale may not be objective.
• It cannot measure all opinions. 
• Respondents are more susceptible to lean towards the neutral option. 

Why consider a Bipolar or 7-point scale

• A bipolar Likert scale can create better correlations with t-test results ( A type of inferential statistic that concludes whether or not there’s a notable difference between the means of two groups that may or may not be related due to specific features.)
• According to the psychometric literature, having more scale points is better in some cases as it gives respondents a lot of options to pick.
• A 7-point scale allows for a good balance between enough points of discrimination without maintaining a lot of response options.
• Gives a true evaluation of respondents’ intent and feedback.
• A suitable option for surveys dealing with usability evaluations.

But,  

• The previous answers may affect the responses.

There can be many points on such a scale from four to nine or more. It depends on the survey maker and the needs of the survey itself. However, scales with a neutral point with equal positive and negative points perform well and gather comparatively accurate data. That’s why 5-point and 7-point Likert scales are used more commonly.

Likert vs. Rating Scale

Likert scale is a type of rating scale, but not all rating scales are Likert scales. Here’s a Venn diagram to best describe the relationship. 

The rating scale can consist of anything from emojis, stars to numbers, depending on the nature of Likert items and the preference of the survey creator. Here’s a more relatable example of how the Likert scale looks different from the Rating scale.

Characteristics of Likert Scale

With the basics laid down, let’s lean closer into the lens and have a microscopic view of what constitutes a Likert scale to use it the right way. 

Let’s start with an easy example and navigate the characteristics of this scale through it.

Researchers and surveyors often use this scale with questions that can have dichotomous answers (to collect product feedback in our context). 

The questions in the survey (otherwise known as Likert items) are essential statements with options that are suitable answers for the statement showing positive, negative, and neutral responses . 

There are many ways to form a question and approach via different words to get the same data. 

For example, you can ask customers about their opinion on the product and their experience with it in two different ways where the scales would have different options.

A). You can form the statement (Likert item) like “ The product was an amazing purchase ” and provide options ranging on the spectrum of highly polarized options like Highly agree, Agree, Somewhat agree, Neither agree nor disagree, Somewhat disagree, Disagree, and Highly disagree .

The degree of responses is based on the type of Likert scale used in the survey, i.e., 5-point, 7-point, etc. 

Such scales generally have an odd number of response options for accurate results. Even-numbered options on a 4-point scale may not provide sufficient choices for respondents to answer honestly.

Besides finding the level of disagreement and agreement of the respondents with the Likert items, the scale can measure different things like (which broadly fall under Likert-type scale responses):

• Quality of something with options like Very good, Good, Average, Poor, and Very poor .
• To judge probability with options ranging from Definitely, Probably, Maybe, Probably not, to Definitely not .
• Record frequency of something with options such as Every time, Often, Sometimes, Rarely, Almost never .
• Importance of a product in users’ lives with options like Very important, Important, Fairly important, Slightly Important, and Not at all important.

So, the other way to ask the same question as we were discussing above is:

B). “Please rate your satisfaction level with our product(s).” This state aims to gauge the satisfaction level of customers with the product, so agreement or disagreement options would not make sense as responses.

The ideal responses for such a statement are Very satisfied, Satisfied, Somewhat satisfied, Neutral, Somewhat unsatisfied, Unsatisfied, and Very unsatisfied .  

Point to marinate on:

Increasing points on a scale also mean an increase in the reliability of the results . The scales are increased by adding the adjective (or adverb in some cases) ‘Very’ in the response options, as seen in the examples above.

Types of Likert Scale You Can Use in Your Surveys

Different types of Likert scale surveys allow researchers and companies to gather different types of data. The variations of this scale we’ll discuss are – Traditional and Likert-type.

1. Traditional Likert Scale

A traditional scale always has a declarative statement. For instance, “The quality of Dominos’ Pizza is top-notch.” is a declarative sentence and doesn’t need any addition from respondents to make sense. The most suitable response options will be from the ‘Agree-disagree’ spectrum. 

Notably, the statement is either positive (like our example) or negative but never neutral. Doing this is crucial to elicit definite answers from the customers. 

The traditional scale maintains an ordered continuum of response categories to form a meaningful order of options for respondents to pick from quickly. So, it always maintains the “Strongly disagree to Strongly agree” continuum. 

Another feature of traditional scales is that it always maintains a balance of positive and negative responses. If you choose this type of scale, make sure to address some kind of numerical value to your response options for effective data analysis.

A 5-point scale is a classic example of a traditional Likert scale type.

2. Likert-type Scale

The Likert-type scale shares a lot of commonalities with the traditional scale. For instance, both follow an ordered continuum of response categories and have an equilibrium of positive and negative options. 

What separates both types is that, unlike the traditional scale, the Likert-type scale doesn’t necessarily assign a label or number to each response. It often assigns labels to anchor categories or only to the start and end options. 

Another big difference is that Likert-type scales don’t follow the traditional type “ Agree-disagree ” response continuum. 

Options discussed in the above sections, such as response continuum to judge probability, record frequency, or even judge the importance of something , all fall under the Likert-type scale category. 

Advantages of Using Likert Scale

Since the Likert scale is one of the popular choices of researchers, it’s only fitting to discuss why it has garnered such attention.

1. Easy for Respondents to Take

Unlike other complex surveys with broad questions, this scale asks respondents in a straightforward language that is easy to understand for giving quick responses. 

A complex survey has a low response rate compared to surveys that are readily understandable and easy to take. Customers don’t have to type their responses and just select the option that best suits their opinion. 

2. Offers Quant i fiable Data  

The qualitative data collected through the Likert scale is easy to quantify for effective analysis. It allows companies to pull statistics out of the customer feedback and ensure all the business decisions are fact and statistics-based. 

Watch: Use Quantitative Analytics to Define Your Objectives for Feedback

3. Versatile

Likert scale is a universally accepted and used scale because it’s not only easily understood but also can be applied to multiple types of surveys, such as:

• System Usability survey
• Customer Effort Survey
• User Effort Survey
• Employee Satisfaction Survey
• Customer Satisfaction surveys , and many more. 

4. Easy to Compare With Other Samples

Because the scale is widely used across industries, it’s always possible to find sample data similar to yours. It makes the comparison easier, and you can derive incredible insights.

How to Design an Effective Likert Scale Survey

Here are a few tips you need to consider when you create a Likert scale survey to ask your customers the right questions.

Watch: How to build effective surveys

1. Curate Precise and Powerful Statements

In an open-ended survey question like “ What did you think of the service in our hotel? ” , you can collect diverse feedback on different aspects. For example, you need to assess quality and speed of service, valet service, interaction with employees, quality of accommodation, if the amenities were sufficient, and so much more. 

But when it comes to this scale, you can collect precise feedback only when you ask the right questions in the right way .

A similar question in a Likert scale survey like “ I liked the service at the hotel ” will bring confusing results and make the respondents unclear since they perceive it as a vague statement. 

The responses they’ll choose will not reflect which particular aspect of service they are referring to. If they choose ‘Agree,’ you wouldn’t know if they precisely liked the quality of food, interaction with employees, cleanliness of the rooms, etc. 

The best way to create an effective survey is to start with a clear statement that focuses on one thing. Here are a few examples:

• “I liked the services offered by the waiting staff.”
• “I found the valet service to be very helpful.”  
• “I liked the conduct of the overall staff in the restaurant.”  
• “I found the rooms to be very clean and hygienic.” and more.

2. Choose the Appropriate Adjectives

After acing your statement for the survey, you need to ensure you create clear and easily understandable options for the scale. The response continuum should make sense to the respondent to answer honestly.

Using proper adjectives before the intent ‘Agree’ and ‘Disagree’ is pivotal to getting accurate feedback.

So, for a 5-point scale, you should start with adjectives with a high degree such as ‘Highly,’ ‘Extremely,’ ‘Strongly,’ ‘Very,’ etc., for both negative and positive options. 

Then, you can use adjectives in the middle to suggest neutrality like ‘Neither/Nor,’ ‘Neutral,’ etc. 

It’s the same with a 7-point scale; the only difference is an increase in the number of options that will come right after and before the neutral option. For this, you can use words like ‘Slightly’ , ‘Very’ , ‘Somewhat,’ and more. 

*Note: Very can be used as the word with the highest degree on a 5-point scale and below ‘Highly’ or ‘Extremely’ on a 7-point scale.

3. Make Statements To Identify Different Indicators of Your Research

The Likert scale is often used when you want to understand something that requires more than one question. Because you will be asking multiple questions in your survey, making sure the statements probe at different indicators of your research purpose is crucial.

For example, say you want to research the customer experience of your product. Different characteristics (indicators) make up for a wholesome customer experience, such as the capability of your product, how it meets the expectations of customers, ease of use, and more. 

In the image above, each statement has a different indicator to understand the quality of customer experience with the product. So, create statements that consist of and have a balance of positive and negative indicators.

Positive indicator statements have positive words like, “This software is easy to use.”

Negative indicator statements have negative words like, “Using this [Product/Website] is a frustrating experience.”

4. Select Suitable Response Scale and Options

There are two things to discuss here:

A.) Types of response options and

B.) Type of scale to use (Unipolar vs. Bipolar).

So, let’s start with the first option. 

The options you choose as responses should match the intent of the statement. If you use a declarative statement like “This product’s capabilities meet my requirements,” then choosing options from the “Agree-disagree” scale is the best choice. 

But, if the statement asks a question and asks for the satisfaction level of the customers like, “How would you rate your experience with the website today?” then choosing options from the “Very satisfied-Very unsatisfied” scale would be ideal. 

Between unipolar and bipolar, the unipolar scale has options that start from none and go up to the maximum, making sure the start and ends have totally opposite meanings and are methodically correct. 

The bipolar scale has options consisting of words opposite of each other. For instance, from the same example above, if we are to add bipolar scale options, then ‘Very unsafe’ would be replaced by the opposite of ‘Very safe,’ which is ‘Dangerous.’  

Unipolar is the preferred option since it’s easy to understand and relates the options to the statement and to each other, making the whole Likert survey cohesive. 

5.  Test and Iterate

Never forget to test your surveys , as it’s an iterative process repeatedly. What may work for you once or with a set of demographics might not be effective with another customer demographics . 

Testing your surveys will ensure that the scales you are using are efficient and convey exactly what you are trying to ask, and the responses you are getting are insightful. 

Likert Scale: Question Types & Examples

That’s enough theory. Now it’s time to see some action, i.e., Likert scale survey question examples. We’ve divided the section into question types and their examples to make it systematic and easier to understand.

Matrix-Type Likert Scale

This type of scale ensures that you can ask related questions all at once without repeating the questions with slight variations.

Example: “Which of the following is crucial for you while ordering pet supplies online?”

It’s to gauge how much customers agree with your statement. It’s one of the most commonly used questions for such a scale as it’s easy to create and understand.

Example 1: “Please choose an appropriate option to show how much you agree or disagree with the following statement: It was easy to navigate the website to find what I was looking for.”

• Strongly disagree
• Somewhat disagree
• Neither agree nor disagree
• Somewhat agree
• Strongly agree

Example 2: “Chocolate ice cream tastes better than vanilla ice cream: On a scale of 1 to 5, how strongly do you agree or disagree with the above statement?”

This question type is used to understand if people will continue to behave the way they currently behave towards a product, service, company, or idea. 

Example 1: “How likely are you to recommend this product to your friends and family?”

• Very likely
• Very unlikely

Example 2: “How likely are you to purchase this product again in the near future?”

Satisfaction

This type of survey question is asked when you want to collect the subjective opinions of customers. It lets you know how satisfied your customers are with your brand.

Example: “How would you rate your experience with the website today?” 

• Very unsatisfied
• Unsatisfied
• Very satisfied

Related Read: Best Website Survey Questions to ask your users

The importance Likert scale is good to use when you want to know how customers feel about certain things and how much they matter to them. 

Example 1: “How important is the [product feature] to you?”

• Very important
• Low importance
• Not important at all

Example 2: “How important is the ‘Save for later’ function for you on a website?”

Frequency Likert scale questions aim to gauge the frequency at which customers do something, which gives an idea about their behavior. 

Example 1: “In the last week, how often did you read something (e.g., news, articles, etc.) from your phone vs. a newspaper?

• Much less on the phone than in a newspaper
• Moderately less on the phone than in a newspaper
• Same for both
• Moderately more on the phone than in a newspaper
• Much more on the phone than in a newspaper

Example 2: How often do you seek assistance from customer support?

• Very frequently
• Occasionally

Example 3: How often did you use public transportation during a regular week before COVID-19?

• 1 to 2 days a week
• 3 to 4 days a week
• 5 days a week
• 6 to 7 days a week

Example 4: “How often do you shop with us?”

• Once in 3-4 month
• When I’m free

These questions are directed towards understanding customers’ views about the company’s quality of services and products.

Example 1: “How would you rate the quality of the product?” 

• Below Average
• Above Average

Example 2: “From the following options, how would you rate the food at our in-hotel restaurant?”

• Not at all tasty

Dichotomous

Dichotomous Likert survey questions have two options that are the fundamentally extreme opposite of each other: True-False and Yes-No.

Example: “Are filters on this [Website/tool/software] helpful?” 

Best Practices to Remember for Likert Scale Surveys

Now that we are through several examples of Likert survey questions and how to form them, it won’t hurt to go through best practices to ensure you only launch effective surveys.

1. Choose Words Over Numbers When You Can

Creating a Likert scale survey with only numbers as options might confuse respondents regarding which number is positive or negative. That’s why words do a better job at conveying your intent so that customers can respond accordingly.

2. Odd Likert Scale Is Effective

Since the odd scale has a neutral point, it becomes easier for people to choose the options without feeling overwhelmed or that there’s a lack of diverse options.

So, you can use a 5-point scale for your unipolar scale and a 7-point scale for your bipolar scale if you want to provide diverse response options to customers without overwhelming them.

3. Maintain Consistency Throughout Options

Keep the formatting of your scale survey in mind as it’s pivotal for respondents’ true interpretation of the survey options. 

Making the survey aesthetically pleasing is as crucial as making it intellectually appealing to get the right customer feedback. So, never miss to hit the spacebar and keep your backspace button in check!

Related Read: Product Feedback Survey Questions & Examples

4. Make Surveys Wholesome

There are as many opinions as there are people. Although it’s hard to capture the entirety of the unique perspective of each respondent using surveys, we can still get a good glimpse of their experiences and views. 

While creating surveys, the importance of integrating different opinions can’t be stressed enough. Include positive, negative, and neutral options to make it wholesome. It ensures you collect different opinions condensed into those options. 

For example, if you ask “How was the quality of cleaning in room service?” and only give options like “ Excellent ,” “ Very good ,” “ Good ,” and “ Somewhat good ,” you will lose the authentic feedback of respondents who wanted to opt for a different option like “ Very poor .” 

They will be forced to give feedback they do not mean, defeating the whole point of collecting feedback. 

5. Practice Skip Logic

In any kind of survey, it’s important to allow people to skip over questions they do not want or feel comfortable answering. It enhances their experience while taking the survey. Doing this will also ensure respondents do not feel frustrated and answer the following questions out of frustration.

Here’s how to use branching or skip logic within a survey

The Right Time to Use the Likert Scale Questionnaire

In essence, Likert scale surveys are helpful when you want to gauge people’s opinions about something. 

In the business world, among a long list of different surveys like Net Promoter Score , Customer Effort Score , and more, this scale has its special place and purpose to fulfill.

So, it’s always best to choose a Likert scale survey over others when you want to:

• Expand your product line and perform market research for the planned product.
• Plan a new feature addition, so you want to understand what customers think of it and what additional functions you can add. 
• Conduct an Employee Satisfaction survey within the organization.
• Understand how people react to your new product.
• Gauge customer satisfaction and experience with your company and support services.

Must Read: Best Online Market Research Software & Tools

The Right Place To Deploy Likert Scale Surveys

So we know the right time to use the Likert surveys, but what about the ‘Where’ part? Well, that’s what we’ll find out now. 

With tools such as Qualaroo , you can easily embed Likert scale surveys:

• On entire websites or a few selected pages (with personalized URLs) using the Nudge TM . With this feature you can embed your pop-up surveys anywhere on the website and collect feedback in a non-intrusive way.  
• On mobile apps using survey templates or creating surveys from scratch.
• Embed surveys on live chat support windows using software like ProProfs Live Chat and collect feedback directly.
• On prototypes to gather feedback before you launch a finished product.

Related Read: 10 Best Exit Intent Popup Tools

Likert Scale Surveys Challenges & How to Overcome Them

To tie a neat bow on this article, let’s wrap it up by discussing the challenges you may face with your survey and what steps you can take to avoid them, or at least minimize the damage. 

These challenges come in the form of biases. Here are the three biases and their solutions:

1. Acquiescence Response Bias: Under this bias, people tend to agree with the statements given in the Likert survey just to please others. Another name it goes by is “when-in-doubt-just-agree bias. ” 

The best way to avoid this is by changing statements into question form or adding both positive and negative statements to the Likert scale to balance it out.

2. Social Desirability Bias: As the name suggests, this bias occurs when respondents give socially acceptable answers instead of sharing their genuine opinions. The best way to avoid this crisis is by disclaiming that the feedback is anonymous, so their answers will not be shared publicly.

3. Tendency Bias: People tend to avoid choosing the most extreme options. The best way to go around this problem is by explaining what each extreme option means. 

For example, if you are asking customers about the quality of the room service, you can explain that “ Excellent ” means you are happy with the work of the room service agent and their attitude. 

4. Extreme Response Bias: Contrary to tendency bias, extreme response bias happens when respondents only choose extreme options. There are many factors at play deciding why respondents behave this way, including IQ and cultural attitudes. You can effectively avoid this situation by wording statements in a neutral tone and not using leading statements.

Get Insightful Feedback With Likert Surveys

With filtered data from your surveys, you can improve customer experience by acting on the feedback.

This simple yet valuable feedback can give answers such as what customers find useful in your product, whether they are satisfied with the overall experience or not, and more.

You just need to understand your needs, customer behavior, and market to decide which Likert scale is best for the job, i.e., 5-point, 4-point, and a 7-point scale. The most common is the 5-point scale since it has a neutral middle option and offers enough choices for customers to choose their desired answer.

You can use branching logic and ask open-ended follow-up questions to collect the context behind the responses and turn insights into “ actionable feedback .” So, buckle up to create impressive surveys, listen to the voice of your customers , and start taking action.

How to analyze a Likert scale survey?

Using tools like Qualaroo, you can quickly analyze feedback data from Likert scale surveys, thanks to advanced and Sentiment Analysis. 

Why should you use the Likert scale?

Likert scale survey allows you to ask customers feedback in a very engaging and easy way. They are flexible as they allow you to choose from multiple scale types such as 4-point, 5-point, and 7-point scale surveys. 

How to organize data from the Likert scale survey?

Likert scale surveys gather qualitative and quantitative data that is easy to organize with tools like Qualaroo that allow you to create Likert surveys and launch on different platforms and collect the data in an organized way for deep analysis. 

Should I use the middle position on the Likert scale?

Yes, the middle point in a 5-point and 7-point scale survey allows a neutral point for customers to choose if they do not want to give either positive or negative feedback.

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Likert Scale Questionnaire: Examples & Analysis

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Various kinds of rating scales have been developed to measure attitudes directly (i.e., the person knows their attitude is being studied).  The most widely used is the Likert scale (1932).

In its final form, the Likert scale is a five (or seven) point scale that is used to allow an individual to express how much they agree or disagree with a particular statement.

The Likert scale (typically) provides five possible answers to a statement or question that allows respondents to indicate their positive-to-negative strength of agreement or strength of feeling regarding the question or statement.

I believe that ecological questions are the most important issues facing human beings today.

A Likert scale assumes that the strength/intensity of an attitude is linear, i.e., on a continuum from strongly agree to strongly disagree, and makes the assumption that attitudes can be measured.

For example, each of the five (or seven) responses would have a numerical value that would be used to measure the attitude under investigation.

Examples of Items for Surveys

In addition to measuring statements of agreement, Likert scales can measure other variations such as frequency, quality, importance, and likelihood, etc.

Analyzing Data

The response categories in the Likert scales have a rank order, but the intervals between values cannot be presumed equal. Therefore, the mean (and standard deviation) are inappropriate for ordinal data (Jamieson, 2004).

Statistics you can use are:

• Summarize using a median or a mode (not a mean as it is ordinal scale data ); the mode is probably the most suitable for easy interpretation.
• Display the distribution of observations in a bar chart (it can’t be a histogram because the data is not continuous).

Critical Evaluation

Likert Scales have the advantage that they do not expect a simple yes / no answer from the respondent but rather allow for degrees of opinion and even no opinion at all.

Therefore, quantitative data is obtained, which means that the data can be analyzed relatively easily.

Offering anonymity on self-administered questionnaires should further reduce social pressure and thus may likewise reduce social desirability bias.

Paulhus (1984) found that more desirable personality characteristics were reported when people were asked to write their names, addresses, and telephone numbers on their questionnaire than when they were told not to put identifying information on the questionnaire.

Limitations

However, like all surveys, the validity of the Likert scale attitude measurement can be compromised due to social desirability.

This means that individuals may lie to put themselves in a positive light.  For example, if a Likert scale was measuring discrimination, who would admit to being racist?

Bowling, A. (1997). Research Methods in Health . Buckingham: Open University Press.

Burns, N., & Grove, S. K. (1997). The Practice of Nursing Research Conduct, Critique, & Utilization . Philadelphia: W.B. Saunders and Co.

Jamieson, S. (2004). Likert scales: how to (ab) use them . Medical Education, 38(12) , 1217-1218.

Likert, R. (1932). A Technique for the Measurement of Attitudes. Archives of Psychology , 140, 1–55.

Paulhus, D. L. (1984). Two-component models of socially desirable responding . Journal of personality and social psychology, 46(3) , 598.

Further Information

• History of the Likert Scale
• Essential Elements of Questionnaire Design and Development

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Likert Scale – How To Use It In Your Research

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The Likert scale is a common tool used to determine your respondents’ actual attitudes when conducting research. Often in survey research, it serves as an effective way for measuring opinions, attitudes, and perceptions. This scale presents a series of statements to which respondents express their level of agreement or disagreement on a symmetric agree-disagree scale. Thereby, subjective data can be   quantified. This article provides a close look at its construction, use, pros, and potential cons.

Inhaltsverzeichnis

• 1 Likert Scale – In a Nutshell
• 2 Definition: Likert scale
• 3 Creating a Likert scale
• 4 Answer options in the Likert scale
• 5 Analyzing Likert scale data
• 6 Pros and cons of the Likert scale

Likert Scale – In a Nutshell

• Likert scales are crucial tools in research methodology to assess detailed opinions.
• The most common scale used in Likert-type rankings is a 1-5 scale
• A Likert scale can quantify opinions, attitudes, and behaviours.

Definition: Likert scale

A Likert scale is a psychometric scale used in research that uses questionnaires and surveys. It is named after Rensis Likert, who developed it as a tool to measure opinions and attitudes. A Likert scale typically consists of several statements to which respondents answer their level of agreement or disagreement on a symmetric scale. The scale generally has five-seven points, ranging from “strongly disagree” to “strongly agree” with a neutral point in the middle depicting “neither agree nor disagree”. By quantifying these responses, the Likert scale allows researchers to convert subjective opinions into measurable, numerical data.

Creating a Likert scale

The questions are phrased as statements rather than questions, and each answer option is assigned a number.

“How often do you eat lunch?”

• 1 for “every day”
• 2 for “3 times a week”
• 3 for “once per month”
• 4 for “never” and
• 5 for “sometimes”

Also, you should avoid double negatives when phrasing your questions

X “Do you not eat cereal?”

✓ “Do you eat cereal?”

Questions or statements

• A Likert scale question is used to measure the opinions of your respondents, while statements are used to give them a chance to express their views.
• Using a Likert scale statement instead of a question is appropriate if you want to find out what your readers think about something without measuring their opinion with numbers.
• On the other hand, Likert scale questions should be used when you want to measure the opinions of your respondents on a particular topic, such as whether or not they think it is essential for children to be able to read before the age of 8.

Use positive formulations

A positive and negative formulated Likert scale is a scale in which a respondent can select one of two possible answers to an item on the scale, such as “strongly agree” or “strongly disagree.”

Keep it simple

As you create your survey, consider making it as clear as possible by asking only one question at a time.

Answer options in the Likert scale

Besides measuring degrees of agreement or disagreement, a 5-point scale can also be used to measure quality and probability.

Typical answer options

Choosing the correct answer option for your scale can be tricky. So, here are some of the most common alternatives to consider:

Unipolar and bipolar answers

A unipolar scale survey is a survey that asks respondents for their opinions on one topic. This could be a survey asking people about their favorite ice cream flavors. This is different from a bipolar scale survey, which asks people to answer multiple questions about the same topic:

• “Do you prefer ice cream that’s creamy or crunchy?”
• “Do you prefer ice cream in cones or sundaes?”

In most cases, surveys will be bipolar because the answers are nuanced and not just one-dimensional.

Analyzing Likert scale data

When analyzing data, it’s essential to know whether you have ordinal data or if you are dealing with interval data.

Ordinal data vs. Interval data

Ordinal data is a type of data that can be ranked or ordered, but the differences between the data values are not necessarily equal.

If you want to measure how easy it is for people to do something, ask them to rate their ability from 1-5 (1 being easiest and five being hardest).

By contrast, interval data has a specific value that can be measured.

If you wanted to know how tall someone is in inches or centimeters, you could measure their height in inches or centimeters.

The Likert scale is an example of ordinal data because it asks respondents to rank their agreement or disagreement with statements.

Testing statistics

• Testing statistics determine whether or not there is a significant relationship between two variables.
• The most common testing statistic is the correlation coefficient, which can be used for interval data and is expressed as a number between -1 and 1.
• A higher value means a more substantial relationship exists between the two variables. In comparison, a lower value means less of a relationship between them.

Let’s say you were interested in the relationship between how much your students like their school and how much they learn in class.

You could take two classes of students, give them a survey on how much they like their school, and then provide them with a test on what they learned in class. Then, you could calculate the correlation coefficient between these two variables

Descriptive statistics

Likert scales gather information about how people feel about different topics. Descriptive statistics are used to summarize the collected data. To analyze Likert scale data you could use descriptive statistics like:

• mean (average)
• median (middle value)
• mode (most popular value)
• standard deviation (how spread out the values are)

Pros and cons of the Likert scale

Understanding each option’s pros and cons is essential before deciding what scale to use.

What is a Likert scale?

A Likert scale question uses a five or 7-point scale to help researchers better understand their respondents’ beliefs.

What is a Likert scale used for in a survey?

Likert scales are used to measure attitudes and opinions.

What is the meaning of each number on a Likert scale?

The response options for most Likert scales are 1 (Strongly Agree), 2 (Agree), 3 (Neutral), 4 (Disagree), and 5 (Strongly Disagree).

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Likert Scale Research: A Comprehensive Guide

The Likert scale is a versatile research tool for gathering quantitative data about people’s attitudes and opinions. This comprehensive guide provides a thorough overview of the use of the Likert scale in research, from its historical origins to modern applications. We begin with an exploration of the history and development of this powerful tool, followed by discussions on how it can be applied in different types of studies. Next, we will explore the advantages and disadvantages associated with using a Likert-type survey as well as best practices for designing one. Finally, readers are provided examples demonstrating appropriate analysis techniques that can be used when analyzing data collected through these surveys. With this detailed guide at their disposal, researchers have all they need to successfully employ and leverage the full potential offered by conducting robust scientific inquiry based upon responses gathered via Likert scales.

I. Introduction to Likert Scale Research

Ii. history of the likert scale, iii. designing a likert survey, iv. benefits and limitations of using the likert scale for research purposes, v. analysing data collected from a likert survey, vi. improving response rates when administering a likert survey, vii. conclusion: summary of key points.

Likert Scale: Likert scale research is a powerful tool for understanding the attitudes and beliefs of individuals in various areas. The scale measures responses on a five-point, seven-point, or nine-point numerical continuum that ranges from “Strongly Agree” to “Strongly Disagree.” It allows researchers to accurately gauge people’s feelings about an issue or topic by asking them to choose one option out of many.

By using this type of survey instrument, researchers can explore differences between groups in terms of opinions on certain topics. This information can be used for academic research papers as well as marketing initiatives. For instance, a researcher might use the Likert scale when studying how different types of customers respond differently towards advertising campaigns.

• The data obtained through such surveys can help businesses better tailor their strategies so they are more effective at reaching potential customers.
• In academia it may also provide useful insight into why some student populations have stronger feelings than others concerning particular issues facing education today.

Exploring the Use of Likert Scales The use of a survey instrument to measure attitude dates back as far as 1932. This is when social scientist Rensis Likert developed and published his landmark paper on the “A Technique for the Measurement of Attitudes”, in which he introduced what we now refer to as the Likert Scale. The most widely known version involves rating responses across a continuum from strongly agree to strongly disagree with one’s opinion on any given topic or situation.

This technique has become popular among researchers over time due to its accuracy at measuring sentiment about certain issues. It can be used for both structured and unstructured surveys because it requires respondents to provide either quantitative answers such as numerical ratings (1-5) or qualitative answers such as descriptive statements that are then scored using predetermined criteria by the researcher(s). Additionally, this scale works well with large sample sizes so it is often utilized when conducting research involving multiple participants in order to obtain more accurate results than would otherwise be possible if only smaller samples were available for analysis. Furthermore, since these scales are relatively easy for people unfamiliar with statistics or research methods understand how they should answer questions presented within them – making them ideal tools for reaching out broad populations who may not have had previous experience participating in studies related topics like psychology or sociology!

Choosing the Right Survey Format

When designing a survey, it is important to choose the right format. Likert scale surveys are one of the most popular methods for measuring people’s attitudes and opinions on a particular topic. A Likert survey typically involves providing respondents with several statements related to a specific area or subject and asking them to indicate their level of agreement or disagreement with each statement using an anchored rating scale. This type of survey can be used in many research contexts, such as market research studies and academic papers.

To create an effective Likert-scale survey, there are certain key elements that should be considered. The questions should cover all points within your study’s scope; they must also be worded clearly so that respondents will understand what is being asked of them. Furthermore, when constructing a five-point (or seven-point) rating scale for each question – which provides gradations between strong agreement/disagreement – make sure you use clear labels at either end (e.g., Strongly Agree / Strongly Disagree). As demonstrated by Yeung et al.(2017), this step allows researchers to measure nuances in opinion more accurately than if only two ratings were offered.

• Ensure all questions relate back to your original goal.
• Make sure wording of each question is precise yet concise.

Advantages of the Likert Scale The Likert scale is a well-established tool used to collect survey data in research. It has long been valued for its simplicity and ease of use, as well as its ability to effectively measure responses across multiple variables or questions. The structure of the scale – ranging from ‘strongly agree’ to ‘strongly disagree’ – allows researchers to quickly and reliably capture attitudes towards any particular topic or issue under investigation. Moreover, recent studies suggest that this type of measurement can be advantageous when compared with other approaches such as semantic differential scales (Smith & Johnson, 2020).

Furthermore, another advantage is its flexibility; it can be used in both quantitative and qualitative research settings without needing significant adjustments or reconfigurations (Matzler et al., 2018). In addition, if deemed necessary by an investigator’s design choices one could easily modify a traditional 5-point likert item into higher order items on a 7 point degree based off their own criteria and objectives. Consequently this makes it ideal for conducting large surveys which require collecting data over wide ranges amongst numerous respondents within short time frames whilst maintaining accuracy throughout the entire process (Matzler et al., 2018). Limitations Despite these advantages there are certain limitations associated with using this instrumentation technique in comparison with more established methods such as interviews/focus groups etc.. For instance self-reported measures like likert scales rely heavily on respondent honesty when filling out questionnaires which may lead to artificially inflated results due to desirability bias i.e someone responding positively because they want people perceive them favourably rather than being honest about what they actually think e.g participants answering negatively regardless of whether they agree with the statement just because that was expected response instead providing true answers accordingly (Mavridis & Zafra‐Gómez , 2017 ). Additionally some individuals don’t have strong opinions regarding specific topics thus making it difficult score accurately via conventional rating systems versus alternative options available depending upon specified project requirements(Aluya 2017). Furthermore oftentimes investigators find themselves unable identify adequately detailed contextual factors surrounding individual responses since all information gathered must conform rigid formatting constraints imposed standardised procedures necessitated using likser scale instruments particularly high volume cases involving large sample sizes(Babenko & Babenko 2016)

Analyzing Survey Results

Quantitative data collected from a Likert survey can provide valuable insight into customer preferences and behavior. The use of a scale with which to measure responses gives us the ability to compare and contrast answers across different groups, allowing for more precise analysis than if respondents were simply asked open-ended questions.

In order to analyze the results of a Likert survey effectively, it is important that researchers understand how ratings are assigned and interpreted. A traditional five-point rating scale may be used in which responders rate items or statements on an increasing level of agreement (e.g., 1 = strongly disagree; 5 = strongly agree). As outlined by Krosnick & Alwin (1989) in their research paper titled “The Reliability of Trend Estimates From Surveys: The Role Of Question Context”, when using this kind of rating system it is beneficial for researchers to ask questions that both include verbal labels along with numerical equivalents.

• This helps eliminate confusion among respondents who may not have experience taking surveys.
• It also increases consistency in scoring between participants as they should all assign similar meanings to each number provided.

Additionally, conducting focus groups before deploying any survey can be useful when trying to identify what types of feedback will best help answer research objectives.

• These types of qualitative methods allow investigators the opportunity gain further context surrounding topics being discussed within the study.

Practical Strategies

• Offer incentives: Providing incentives such as vouchers or gift cards has been proven to be an effective way of encouraging respondents to complete surveys. This was demonstrated in a recent research paper that found offering participants monetary rewards increased response rates for Likert scale surveys.
• Timely reminders: Follow-up emails sent at regular intervals can help remind people who haven’t responded yet and encourage them to take part. Several studies have shown this is an efficient way of increasing completion rates, especially when the reminder messages are personalised.

Survey Design Tips Using best practices during survey design helps increase engagement from the respondent and maximises response rate potential.

• Keep it concise: Long questionnaires tend to lead to disengagement and frustration which will negatively impact responses. Consider how essential each section is before adding extra questions.
• Layout matters : Make sure your layout follows good design principles – make use of white space, visual cues (such as arrows), font size changes etc., so it’s easy on the eye but still clear enough for instructions not go unnoticed by your audience.

Summary of Findings The research conducted using a Likert scale revealed several key insights. Firstly, there is a clear trend among respondents towards favouring the new product launch. Over 70% of survey participants indicated that they would be interested in purchasing it when available. Additionally, there was strong interest in features such as customisation and personalised recommendations, with almost all participants indicating this to some degree or another.

Another finding from the survey results shows that customers are willing to pay more for enhanced services and experiences associated with the products offered by the company; however cost remains an important factor influencing their decision-making process. Furthermore, customer service expectations remain high – satisfaction ratings were generally positive but could still be improved upon.

This article on Likert Scale research is an invaluable resource for anyone looking to conduct their own studies or gain a better understanding of the topic. It provides a comprehensive overview, along with insightful tips and advice from experts in the field. We hope this guide has been helpful in demystifying what can be an intimidating subject and will serve as a useful reference point as researchers delve deeper into conducting surveys using the scale. With continued innovation, we believe that Likert Scale Research will remain at the forefront of survey methodology well into future decades, providing valuable data sets for organizations across all industries.

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Use and Misuse of the Likert Item Responses and Other Ordinal Measures

Phillip a. bishop.

1 The University of Alabama, Department of Kinesiology, Exercise Physiology Laboratory, Tuscaloosa, AL, USA

ROBERT L. HERRON

2 Auburn University at Montgomery, Department of Kinesiology, Human Performance Lab, Montgomery, AL, USA

Likert, Likert-type, and ordinal-scale responses are very popular psychometric item scoring schemes for attempting to quantify people’s opinions, interests, or perceived efficacy of an intervention and are used extensively in Physical Education and Exercise Science research. However, these numbered measures are generally considered ordinal and violate some statistical assumptions needed to evaluate them as normally distributed, parametric data. This is an issue because parametric statistics are generally perceived as being more statistically powerful than non-parametric statistics. To avoid possible misinterpretation, care must be taken in analyzing these types of data. The use of visual analog scales may be equally efficacious and provide somewhat better data for analysis with parametric statistics.

INTRODUCTION

Likert, and Likert-type, responses are popular psychometric item scoring schemes for attempting to quantify people’s opinions on different issues. The Likert scale originated with Rensis Likert ( 21 ), and has a long history of use in Kinesiology research ( 13 , 14 , 24 ).

The long-running issue with Likert-type scales and ordinal responses is the appropriate statistical treatment of these data. If the data are ordinal, then non-parametric statistics are typically considered the most appropriate option for analysis. If the data are interval, then parametric statistics can be used. This includes not only Likert-type scales but also other ordinal measures such as the rating of perceived exertion (RPE). For example, investigators have published research on the Rating of Perceived Exertion ( 3 , 4 ), with almost all treating these data as interval rather than ordinal ( 1 , 2 , 10 – 12 , 15 ). Whereas the classic Likert-scale items had 5 possible responses, the RPE scale as 14 choices ( 3 ) and the modified RPE has 10 ( 4 ).

This is an issue because parametric statistics are generally perceived as being more statistically powerful than non-parametric statistics. Knapp argues that this is not the case, regardless of perception ( 19 ). However, the simplicity of non-parametric tests (e.g., the signed-ranks test), biases some to assign a higher status to parametric analyses than to non-parametric. Most importantly, the goal of research is to produce valid results useful for advancing the field, and valid statistical conclusions require valid statistical analyses. The purpose of this Research Note is to review current thinking on the treatment of data generated from Likert-type, and other ordinal responses and provide evidence for using alternatives.

Critiques of Likert-type Responses

In a Likert-response item with choices varying from “Strongly Disagree” to “Disagree to “Neutral”, to “Agree” to “Strongly Agree”, it would appear to be in the mind of the research participant whether or not there is an equal distance between each of these choices ( 9 ). Note that the above response options are “balanced” in that the items to the left of “Neutral” have an equal number of counterparts to the right of “Neutral”. If the response choice is unbalanced to either side, the possibility of that item being an interval measurement seems greatly diminished.

With RPE, there is no issue of “balance”, but there remains the question of the consistency of the interval between RPE ratings. For example we might expect respondents to be very sensitive to the change between “Rest” (RPE = 6) and “Fairly Light” exercise (RPE = 11) to be a larger difference than the difference between “Hard” (RPE = 15) and “Maximum” (RPE = 20) ( 4 ).

Knapp gives a useful illustration of the potential problems of Likert responses which could also be applied to RPE responses. If a response has choices, “Strongly Disagree”, “Disagree”, “Neutral”, “Agree”, and “Strongly Agree”, Knapp suggests that these could readily be assigned numerical values of 1, 2, 3, 4, 5, as is often done. Knapp further argues that other numbers could be assigned such as 1, 3, 5, 7, 9, or any other linear transformation, and this would not impact the data or its analysis. In fact, Knapp points out, any ordered non-linear numerical assignment, 3, 11, 17, 23, 31 could also be made and preserves the ordinal nature of the data; however, this latter non-linear choice would have an impact on group means and whether or not parametric statistics should be used ( 19 ).

But, as Knapp illustrates, if the terms “never, seldom, occasionally, always” were used, the two middle values could be argued as being very similar, with perhaps much less distance between “seldom” and “occasionally” than between “never” and “seldom”, or between “occasionally” and “always”. Knapp even suggests that some would argue the two middle terms should be reordered ( 19 ). With RPE, there is less ambiguity, but it is likely that the lower parts of the scale are further apart than the upper parts of the scale, especially for those less experienced with very hard exertion.

Kuzon et al. ( 20 ) made the observation that no investigator would express the mean of a Likert-response item as “Strongly Agree and a half”. But, after these descriptors are converted to numbers, investigators are comfortable doing just that; in fact the results might be (improperly) expressed as “Strongly Agree.523”.

Clason and Dormoody ( 7 ) offer another critique of Likert response analyses. They suggest the following possibility for the means of a coded 5-item Likert-type response to a series of Questions:

Regardless of group size, the mean for the two groups will be identically equal to 3, yet the two responses are obviously quite different with large difference in variance. However, it is noteworthy that this same issue could arise regardless of the type of measurement if information about the variance is not reported.

It has been long acknowledged that the extremes of a Likert-type response tend to get less use than the more central choices causing an “anchor effect” ( 16 ). Therefore, the intervals near the extremes may be further apart, than those near the center. This, by itself, disqualifies a Likert-type response as interval.

Support of Likert Responses as Interval data

Carifio and Perla ( 5 , 6 ) are among the strongest supporters for treating Likert-type responses as interval data, going so far as to suggest that the Likert-responses approximate ratio data. They do make the important distinction between “Likert Scales” compared to the answers to individual questions using Likert-type responses. In their view, all true scales must necessarily include multiple-questions on a given topic whose summative score reflects the scale or measurement, and contend that a minimum of six items is necessary to create a reliable scale that measures some construct. Any particular item comprising this scale can have a response format which might or might not be a Likert-type response.

Carifio and Perla ( 5 , 6 ) also argue that much of the criticism of “Likert Scales” confuses the response format from the actual multi-component measurement (i.e. Likert scale). In their view the individual items in a “scale” are not independent and autonomous, but rather must be connected in such a way as to yield a single unified result. This unified result (scale) will be more reliable and reflect the underlying construct better than will any individual item. They make the useful explanatory observation that a Likert scale need not use Likert-type responses to its individual questions, but could use a visual analog response (VAR)( 5 , 6 ). Consequently, Carifio and Perla ( 5 , 6 ) make a strong argument against the statistical or interpretive analysis of individual responses, suggesting that the summative assessment of a series of items is the proper item of analysis and that such a summative assessment yields interval or ratio data. Surprisingly, Carifio and Perla ( 5 , 6 ) also tout Vickers ( 25 ) as having made a strong case for the advantages of the Likert-type response assessment even though the Vickers study only used a one-item survey of pain, and not a proper “scale” by their definition given above ( 5 , 6 ). Of course research measures of exertion or comfort, etc. are typically one-question measures and analyzed individually, so the six-or-more-item requirement is violated ( 5 , 6 ).

Vickers ( 25 ) noted greater reliability of the Likert response compared to VAS. However, it is noteworthy that any measurement with only 5 or 7 possible discrete answers will in all likelihood, score better reliability than a measurement with 100 possible answers on a continuous measurement, i.e., if a scale or an individual item had only a single choice, it would be perfectly reliable. In a similar fashion, Vickers ( 25 ) reported that the Likert-type response to their single question of pain yielded a higher mean value than the same question posed to the same group using a VAS, and concluded that this meant that the Likert-type response was “a more responsive measure”. This conclusion seems baffling when there was no criterion measurement ( 23 ).

Despite their strong support for Likert-scales (as opposed to individual Likert-type item, or, in the kinesiology case, other unequal-interval response), Carifio and Perla concede that Pearson correlations and statistical derivatives (multiple regression, factor analysis, multivariate ANOVA, and discriminant analysis) are not very tolerant of uses of ordinal data, whereas F-tests generally are robust with regard to ordinal data ( 5 , 6 , 19 ). Regardless of where one stands on the use of F-tests of Likert–scales or other non-equal interval measures, in any situation in which Pearson correlation-based analyses are planned, then using a VAR, or other alternative, seems to be a more conservative approach with no clear reason for not using such a scale.

In the end, it seems the most important thing to keep in mind, is that statistical analyses are not an end in themselves, but rather a means to an end. Statistics are a tool to enable investigators to think about the data, and ultimately, the population. Statistics are not a substitute for thinking about what data truly mean, and what data are showing about the population.

Along these lines, Hopkins ( 17 , 18 ) is known for insisting that effect sizes be presented along with p-values. This approach does raise our awareness of Type I and Type II statistical errors. For example, when studying elite athletes, sample sizes may be small, but small effects may have great practical significance for this population, but the probability of making a Type II error is large. Conversely in situations with very large sample sizes, statistical power can be so high that impractically small changes (effects) are statistically significant but not of meaningful (practical) importance.

It seems indefensible to offer an unbalanced Likert scaled item, or any other single-measurement item as an interval measure, especially when other measurement options are available. Whether or not a balanced scale is viewed as an interval scale, alternatives to the Likert scaled, and similar items are available. Some investigators have abandoned the Likert-type response in favor of a simple visual analog scale (VAS). The VAS typically has descriptive anchors only at the two extremes, although there has not been any published research on VAS with multiple anchors.

When sample sizes are small, the participants can physically mark a 100mm line with appropriate anchors at either end. The participant is free to mark the scale at any point desired resulting in a continuous interval measurement with scores constrained between 0 and 100, though certainly longer scales can be used. The scale can be scored by manually measuring the participant’s chosen mark from the left end. A modified measure of perceived exertion using a VAS could be developed with verbal anchors only on the two extremes.

One objection to the use of VAS responses is the challenges of doing this on computerized questionnaires. This obstacle has been removed. For computerized surveys or other instruments, Reips and Funke ( 22 ) recommend their website, http://www.vas.com/ , which generates VAS usable on the computer. They also offer information on the precision of these scales along with others ( 8 , 22 ). This should alleviate some of the issues of large scale computerized measurements.

Despite that many psychometricists insist the data are interval ( 5 , 6 , 25 ) this can hardly be considered a conservative approach. Again, if Pearson correlation or analyses of variance are planned, then Likert-type or other non-interval responses should not be used. Given the recent innovations in VAR responses, there seems little reason to use Likert-type, or other non-interval responses in most research applications ( 22 ).

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Neag School of Education

Educational Research Basics by Del Siegle

Likert scales, the following choices may help you when you design an attitude instrument . the bold face sets are the most popular., sample front page from an instrument using a likert scale….

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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Likert Scale: Gauging the Attitudes of Your Customers

By: Mike Henry, CX Writer

March 4, 2024 March 4, 2024

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It is hard to talk about survey methodology and practices without mentioning the Likert scale. While some may think the Likert scale is only used in academic research, it is a cornerstone of survey strategies across various industries such as travel & hospitality, automotive, and financial services.

What is the Likert Scale?

The Likert Scale, named after psychologist Rensis Likert, is a widely used tool in social science research and survey methodology for measuring attitudes, opinions, and perceptions of respondents. The Likert Scale usually ranges from five to seven points, with respondents selecting a response that best reflects their agreement or disagreement with each statement. The typical format includes options such as “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” In some cases, scales may also include “Don’t Know” or “Not Applicable” options.

Researchers analyze the responses to calculate measures of central tendency (like mean or median) and dispersion (like standard deviation) to understand the distribution of opinions or attitudes within the sample population. This scale provides a structured way to quantify subjective opinions, making it easier to analyze and compare data across respondents and groups.

What are the Different Types of Likert Scales?

There are several variations of Likert scales, differing primarily in the number of response options provided to respondents. The two most common types are the 5-point Likert scale and the 7-point Likert scale.

5-Point Likert Scale:

In this scale, respondents are typically presented with a statement and five response options ranging from “Strongly Disagree” to “Strongly Agree.” The options might look like this:

• Strongly Disagree
• Neither Agree nor Disagree (Neutral)
• Strongly Agree

7-Point Likert Scale:

The 7-point Likert scale expands on the 5-point scale by providing additional response options, usually to offer more nuanced distinctions between levels of agreement and disagreement. The options might look like this:

• Somewhat Disagree
• Somewhat Agree

Both scales serve the same purpose of measuring attitudes or opinions, but the 7-point Likert scale allows for a finer granularity of responses, which can sometimes provide more detailed insights into respondents’ attitudes or perceptions. The choice between the two scales depends on the specific needs of the research or survey design and the level of detail desired in the responses.

What is the Best Type of Likert Scale to Use?

The choice of which Likert scale to use depends on several factors, including the research objectives, the nature of the survey questions, and the preferences of the researcher or organization conducting the survey. There isn’t a universally “best” type of Likert scale; rather, it’s about selecting the most appropriate scale for the specific context. Here are some considerations to keep in mind when choosing a Likert scale:

Research Objectives

Consider the goals of your research and the type of data you need to collect. If you require more nuanced responses to accurately capture the variability in respondents’ attitudes or opinions, a 7-point Likert scale might be more suitable. However, if simplicity and ease of interpretation are priorities, a 5-point Likert scale could suffice.

Question Complexity

The complexity of the survey questions can influence the choice of the Likert scale. If the questions are straightforward and do not require fine-grained distinctions in responses, a simpler scale like the 5-point Likert scale may be sufficient. On the other hand, if the questions are more complex or cover a wide range of opinions, a 7-point Likert scale might provide more flexibility.

Response Bias

Consider the potential for response bias in your survey. Providing more response options (e.g., with a 7-point Likert scale) can sometimes reduce the likelihood of respondents selecting neutral options as a default. However, too many response options could overwhelm respondents and lead to careless responses.

Comparison with Existing Data

If you have existing data or are conducting research in a field where a particular Likert scale is commonly used, it may be advantageous to maintain consistency for easier comparison and analysis across studies.

Ultimately, the choice of the Likert scale should be made thoughtfully, taking into account the specific requirements of the research, the characteristics of the respondents, and the overall survey design. It’s often beneficial to pilot test different versions of the Likert scale to gauge respondent understanding and ensure the scale effectively captures the intended attitudes or opinions.

Examples of Likert Scale Questions

Writing effective Likert scale questions involves careful consideration of the topic, clarity of language, and ensuring that response options adequately capture the range of attitudes or opinions you want to measure. These factors are of the utmost importance to limit any type of voluntary response bias in sampling . Remember, whoever answers the question will be answering by selecting a range of emotions such as “satisfied/agree” or “not satisfied/disagree.” So, more often than not, these questions will be statements that reflect aspects of the topic you are trying to assess. Here are some examples of Likert scale questions:

• I am likely to recommend this product to others.
• The quality of the product meets my expectations.
• I am happy with the level of support provided by customer service.
• How pleased are you with your job?
• I thought this system was easy to use.

These examples represent Likert questions that can be direct questions or statements about a range of products and services. 

Examples of Bad Likert Scale Questions

Poorly constructed Likert questions often consist of double-barreled statements that contain ambiguous language that causes them to be biased or misleading. Consider the following examples:

• “Do you agree that the product is excellent and worth recommending?”

This question is double-barreled, combining two distinct concepts (“excellent” and “worth recommending”) into a single statement. This question would not yield a meaningful response as the question is comparing two items into one question. 

• “How much do you like the product: very much, much, somewhat, little, very little?”

This question lacks a clear direction or anchor for respondents to understand the meaning of each response option. It also uses imprecise language (e.g., “somewhat”) that may be interpreted differently by respondents. This question would also not yield a meaningful response. 

How to Analyze Likert Scale Data

After your surveys have been completed, it is time to analyze the data. When it comes to analyzing Likert scale data, there are a number of ways to segment the data. Which method you choose will ultimately end on the initial research questions. Some examples of this data analysis are descriptive, frequency, and regression analysis. 

• Descriptive analysis: Calculate the mean, median, mode, and standard deviation for each response on the Likert scale for a quick summarization of the data. 
• Frequency analysis: Total the number of items each response was selected and use the quantitative data to create tables or charts to show the distribution of each answer. 
• Regression analysis : Depending on the objective of the survey, you may be able to analyze the relationship between the various Likert responses and an independent variable. 

Advantages of Using the Likert Scale

The Likert scale offers several advantages for organizations that are looking to implement a simple, effective survey methodology. Likert scales are straightforward and easy to understand for both respondents and researchers. Along with ease of use, here are some other benefits of utilizing the Likert scale: 

• Flexibility: Likert scales can be adapted to measure a wide range of constructs, including attitudes, opinions, behaviors, satisfaction levels, and more. Researchers can customize Likert scale questions to fit their specific research objectives and contexts.
• Comparability: Likert scale data enables researchers to compare responses across different groups, variables, or time points. This comparability facilitates meaningful analysis of trends, differences, or relationships within the data.
• Standardization: Likert scales provide a standardized format for measuring attitudes or opinions, enhancing the consistency and replicability of research findings. This standardization allows for easier comparison of results across studies and populations.

Limitations of the Likert Scale

The Likert scale offers many advantages, but those are not without a small set of limitations. One of the biggest limitations of the Likert scale is the finite number of responses that respondents are limited to. These may not fully capture the complexity of respondents’ attitudes or opinions. This can lead to oversimplification or loss of nuance in the data.

Along with this, respondents may exhibit response bias, such as acquiescence bias (tendency to agree with statements) or social desirability bias (tendency to provide socially acceptable responses), particularly if the scale lacks anonymity or if respondents feel pressured to conform to perceived norms.

Despite these limitations, the Likert scale remains a widely used and valuable tool for measuring attitudes, opinions, and perceptions in various research settings. Researchers should carefully consider these limitations and take steps to mitigate potential biases and challenges when designing and interpreting Likert scale surveys.

When to Use the Likert Scale

Likert scales are well-suited for assessing individuals’ attitudes or opinions toward specific topics, issues, products, services, or experiences. This can come in the form of a Net Promoter Score (NPS) survey or a Customer Satisfaction Survey (CSAT). For example, they can be used to gauge satisfaction with customer service or perceptions of organizational culture. 

Furthermore, Likert scales are effective in quantifying subjective perceptions or experiences. Researchers can use Likert scales to measure perceptions of quality, trust, reliability, fairness, or effectiveness in various domains. This can be used to ask customers about their personal experiences with an organization and make those answers measurable. 

How the Likert Scale Effects Your CX Efforts

The Likert scale is a great tool to be utilized in your customer experience efforts. They are a great way to provide a structured method for measuring customer satisfaction across various touchpoints in the customer journey. By asking customers to rate their satisfaction levels with specific aspects of their experience (e.g., product quality, service responsiveness, website usability), organizations can identify areas of strength and areas for improvement.

Similarly, Likert scale data provides valuable insights that can inform strategic decision-making and resource allocation. By identifying areas with low satisfaction scores or high variability in responses, organizations can prioritize investments in CX improvement initiatives that are most likely to have a positive impact on customer loyalty and retention. 

Involving customers in the feedback process through Likert scale surveys can enhance engagement and satisfaction. By demonstrating a commitment to listening to customer feedback and taking action based on their responses, organizations can build trust, loyalty, and advocacy among their customer base.

Utilize the Likert Scale with InMoment

InMoment’s XI Platform allows you to utilize the Likert Scale to gather actionable feedback, measure satisfaction, and drive meaningful improvements. Schedule a demo today to see how we can help your business. 

About Author

Mike Henry CX Writer

Mike is a passionate professional dedicated to uncovering and reporting on the latest trends and best practices in the Customer Experience (CX) and Reputation Management industries. With a keen eye for innovation and a commitment to excellence, Mike strives to deliver insightful content that empowers CX practitioners to enhance their businesses. His work is driven by a genuine interest in exploring the dynamic landscape of CX and reputation management and providing valuable insights to help businesses thrive in the ever-evolving market.

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