can qualitative research use likert scale

Likert Scale Questionnaire: Designing Effective Surveys

Introduction

A quick review of survey research, what is the likert scale, likert scale questions with examples, which type of question is a likert type question, when to use a likert scale question, likert scale advantages and disadvantages, what are the limitations of likert scales, how to write strong likert scale questions.

Likert scales are an important aspect of survey research . While it is typically used in quantitative data analysis , Likert scale responses have useful applications as a complement to qualitative analysis as well.

Whether you measure attitudes or measure opinions through collecting data from surveys, understanding when and how to employ a Likert scale survey question is essential for your research.

Surveys are an essential tool in qualitative research and social science research that allows researchers to capture perspectival data from potentially large numbers of respondents quickly and efficiently. It is quick because surveys can be distributed at scale, and it is efficient because the data collected from surveys can be easily organized for quantitative and qualitative analysis .

Surveys are also standardized because they ask the same questions to all respondents. This has a variety of implications, including the ability to compare responses and make assertions about the perspectives of certain groups of people.

Types of survey questions

Likert questions are just one type of question commonly found in surveys. Different survey items include multiple-choice questions, ranking questions, demographic questions, and open-ended questions.

In theory, any type of question can be posed in a survey. However, survey research tends to regress to a limited number of commonly employed question types that have been found to be easy to understand to keep the survey respondent engaged and willing to submit their responses.

Likert scale questions are one such type of question that is accessible to most respondents. Like most other commonly used survey question types, Likert scale questions also capture data easily and quickly in the form of numerical responses.

Another consideration is the type of data collected in surveys. Statistical data is typically classified into four groups: discrete data, continuous data, nominal data, and ordinal data. While the former two are collected for quantitative analysis (e.g., income level, body weight), nominal data and ordinal data are qualitative in nature (e.g., favorite sports, film ratings).

Likert scale questions are concerned with capturing ordinal data, as they allow for a qualitative understanding of respondents' perspectives. When numbers in Likert scale questions are used to represent perspectives as "exceptional," "good," "fair," "poor," or other terms, a researcher can provide additional, useful context to other responses in their survey.

At the same time, care should be taken when statistically analyzing and interpreting data from ordinal scales. The numerical values in such questions represent subjective perspectives; reducing opinions down to numbers can be problematic without sufficient contextualization of those opinions.

Likert scale survey questions collect data about respondents' perspectives in the form of numerical values.

In particular, Likert scale questions conceptualize perspectives on a continuum where respondents look at a statement and decide the extent to which they agree or disagree.

Using this kind of scale allows researchers to capture, at minimum, a general sense of what their respondents think about a particular concept or phenomenon.

Market research, for example, can employ a customer satisfaction survey with Likert scale options to gain a broad view of whether a product or service is accepted by a particular customer base.

What do Likert scale survey questions look like?

A typical Likert scale question looks like a multiple choice survey item. An item that employs a Likert scale commonly has a statement that people might agree or disagree with, followed by a set of numbers meant to represent the extent that a respondent aligns with the statement.

You have likely come across a set of 5-point Likert scale questions. These are questions that ask you about the extent of something, like whether you agree or disagree with a particular opinion. Other examples include indicating the extent to which each participant does something (e.g., ranging from "always" to "never") or likes something (e.g., ranging from "hate" to "love"). Each opinion is followed by the numbers 1 to 5 to represent a rating scale.

With these survey items, you select 1 if you strongly disagree, 5 if you strongly agree, or 3 if you neither agree nor disagree. The scale points 2 and 4 are there to provide additional choices about differing degrees of agreement.

Note that a Likert scale question need not have 5 values. Depending on your research question and your research design, you might employ a 3- or 10-point Likert scale question, for example.

That said, you may find that a 5-point Likert scale is typical in survey design. Ultimately, researchers should consider what scale strikes the right balance between giving respondents enough possible degrees of opinion without getting bogged down in too many possibilities and slowing down survey response time in data collection.

Likert scale questionnaires can take on many forms. Ultimately, all forms of Likert scale items ask respondents to give a numerical representation of their perspective.

Likert scale examples include survey items that often ask whether they agree or disagree with a particular statement. These statements might look like "I am being paid fairly for the work that I do" or "I am satisfied with the help the customer service team provided."

Another kind of survey item that uses Likert scales might ask respondents to rate their level of approval with a particular product or service. The rating scale in this kind of question establishes a continuum between high approval and high disapproval of a product or service.

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Questions that employ Likert scales collect data using rating scales where the answers are ordered but do not correlate to absolute or equally spaced values. It is useful to think of Likert scale responses as as a continuum stretching between two extremes or opposite poles.

Using this idea, there are two types of scales that are used when collecting data with Likert scales.

Bipolar scale

When you are looking to measure sentiments or opinions along a continuum of two opposites, bipolar scales are useful to acknowledge that both opposites are valid. In many cases, the middle response in a bipolar Likert scale question is zero while the positive and negative values correspond to each of the two opposites.

Researchers may want to provide a middle point in Likert scale surveys to emphasize that respondents have a means to signal indifference or indecision about a particular topic or phenomenon.

Unipolar scale

A survey question that adopts a unipolar scale does not have such a middle point. In many cases, the type of question that survey researchers ask is the same (e.g., "Do you agree or disagree?"). Instead, unipolar scales place less emphasis on a middle ground or neutral value and more emphasis on the extent of agreement or approval.

When you need respondents to be more decisive in their choices, you may consider using a unipolar scale in Likert scale questions.

As common as Likert scale questions are in surveys , they are more appropriate in certain aspects of a research inquiry than in others.

Lists of items

Likert scale responses are often sought for a series of items or topics at one time. For example, market researchers may use a Likert scale to assess opinions on a series of products or services.

Grouping these items together makes it easier for the respondent to quickly provide their opinions about a list of items, particularly when the process of answering these questions is the same throughout the series.

Defined values

Respondents' perspectives can be either open-ended (in which case, free-response questions are more appropriate) or confined to a particular continuum like agreement or approval. A Likert scale is useful for capturing the latter.

A well-crafted series of Likert scale questions places respondents' answers within a range defined by two opposite ends. If a range of opinions cannot be conceptualized in a linear fashion (e.g., political ideologies defined by multiple issues), consider using a different type of survey item.

Defined points

When opinions and perspectives can be assessed on an ordinal scale, a Likert scale survey helps bring clarity to where respondents stand by giving them defined points to choose (e.g., "strongly agree", "strongly disagree").

While care should be taken not to overly reduce respondents' answers to mere numbers, a Likert scale helps visualize a continuum of agreement or approval by providing respondents with a range of choices to choose from.

Likert scale surveys have the advantage of collecting responses at scale, enabling the researcher to quickly collect a large number of Likert scale responses at once.

This is useful for statistical analysis and data visualizations in papers and presentations. With Likert scales, researchers can easily aggregate perspectival data and gain a general sense of where respondents' answers lie.

It's important to recognize that despite the convenience of reducing perspectival data down to numbers, there are caveats to a statistical analysis of data collected from Likert scales. Data from an ordinal scale cannot be assumed to have uniform numerical values.

In other words, a 5 is greater than a 4, but the difference may not be the same as that between a 4 and a 3, since respondents may have different opinions about what the numbers might mean.

As a result, data from Likert scale questions might be able to tell researchers the general perspective of respondents. Insights that indicate a slight approval of a product or service, for example, are going to look consequentially different from those that show widespread approval.

That said, researchers should not overestimate the precision that Likert scales can provide. The aggregated numbers from Likert scale data give a broad sense of perspectives more than they provide fine-grained detail about those opinions.

In qualitative research , critiques about Likert scale data apply to surveys in general. Survey items with predefined answer options (e.g., points on a scale, lists of choices, etc.) might give researchers the temptation to reduce opinions by making generalizations through statistical analysis.

Oftentimes, the best remedy for this limitation is to collect other data to further contextualize survey responses. Qualitative researchers can follow up on survey responses through open-ended questions, interviews , or focus group discussions to examine respondents' answers more deeply.

There is a discipline to writing questions that involve a rating scale. Researchers should consider their survey design carefully, particularly with respect to survey questions that reduce respondents' opinions to simple values.

Two issues that researchers should consider are the consistency of items and the number of options they provide for each item.

Consistency of items

As respondents answer Likert scale items quickly, any inconsistency in the list of items might slow down response times or create confusion.

Market researchers, for example, should only group similar items together in a set of Likert scale questions. Be sure to group items into similar categories (e.g., products, services, etc.) to ensure that respondents can easily answer survey questions.

Number of values

Researchers should also consider whether their Likert scale questions have a middle value. In other words, should the response scale have an odd number or even number of responses? This determines if there is a middle value that represents indifference or indecision.

This decision essentially affects how respondents answer survey questions. Ultimately, this also informs data analysis and how data should be interpreted .

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A Guide to Using the Likert Scale in Research Papers

The Likert scale is an important tool for researchers as it can provide valuable data and insight into the opinions of respondents. This article provides a comprehensive guide to using the Likert scale in research papers, from understanding its principles to crafting effective questions that maximize results. It outlines how this scale can be used effectively in both quantitative and qualitative studies and will help researchers gain a better understanding of how they should utilize this powerful tool. The discussion also covers key considerations when analyzing responses, such as potential bias or limitations due to particular survey design features. Finally, tips on creating accurate interpretative frameworks are provided so readers may apply their own knowledge base to develop meaningful insights from collected data sets.

1. Introduction to the Likert Scale in Research Papers

2. advantages of utilizing the likert scale, 3. designing a questionnaire using the likert scale, 4. administration and collection of data from surveys incorporating the likert scale, 5. analyzing data obtained through use of the likert scale, 6. limitations and challenges associated with implementation of the likert scale in research studies, 7. conclusion: an overview on how to effectively utilize the liket scale in your study.

The Likert Scale is an essential tool used by researchers to assess attitudes, opinions, or behaviors of a population. It provides valuable insight into how people feel about a certain topic and can be the foundation for sound research papers. In this section we will discuss what it means to use the Likert scale in your research paper.

• What is a Likert Scale?

A Likert scale is a type of rating system commonly found in surveys that allows respondents to rate their feelings towards something on either end of a spectrum from strongly disagree/positively agree (or vice versa). This type of survey includes multiple questions with five-point scales ranging from “Strongly Agree” at one end up to “Strongly Disagree” at the other, along with options like “Somewhat Agree” and “Neither Agree nor Disagree” located between them. The results generated through these surveys help researchers gain an understanding of where individuals stand on particular topics, allowing them to better tailor their research accordingly.

• Utilizing the Likert Scale in Research Papers:

Leveraging data collected via questionnaire using the liker scale as part of primary methodology has been seen increasingly more frequently within academic circles recently. To illustrate; A 2018 study published in ‘International Journal Of Environmental Studies’ sought out teacher’s perceptions concerning global warming using 8 items measured on 5-point liker scales which were then subjected statistical analysis – leading us towards meaningful conclusions being drawn around said issue.(1) As evidenced here — incorporating liker scale data into academic literature can allow for detailed exploration into pertinent issues that may have otherwise gone undiscovered.

(1) Aggelakos T., Politis D., Vlachogianni K., Samara P.: Teachers’ Perceptions Concerning Global Warming: An International Comparison Using Data From Greece And Cyprus – Int J Environ Stud 75(6): 741–756 (2018), https://doi/org/10.1080/00207233

The Likert scale is a reliable and widely used technique for assessing people’s opinions, beliefs or behaviors. It offers several advantages that have encouraged its widespread use in many areas of research and assessment.

First, the Likert scale provides an effective way to measure attitude towards something due to its flexibility; it can be customized according to individual needs by altering the number of items in each category as well as their content. This allows researchers the ability to tailor their questions based on what they want responses from participants on. Additionally, individuals are free to express more than one opinion when completing these surveys because there is no limit imposed on how many choices someone has available within any given question or section. Consequently, this ensures greater accuracy in results since respondents will be able provide information which reflects their genuine stance regarding whatever topic is being surveyed about rather than having only two answer options (i.e., yes/no).

In addition, utilizing a likert-type survey makes data analysis easier because all answers are rated along a continuum – thus making comparison between groups relatively simple through numerical measurement techniques such as mean values. For example, research done using the methodology found higher levels of customer satisfaction amongst medical students after implementing specific recommendations into practice [1]. In other words, analyzing trends becomes much faster with this type of tool compared to open-ended questions where time must be spent manually going through qualitative responses provided by participants before drawing any meaningful conclusions from them.

• Understand the basics of designing and developing a questionnaire using the Likert scale.
• Consider how to use rating scales effectively in research studies.

The most widely used type of rating scale is undoubtedly the Likert Scale , developed by American psychologist Rensis Likert in 1932 as part of his doctoral dissertation on employee morale at The University of Michigan Institute for Social Research. This simple yet effective tool has since been adopted across numerous disciplines, from psychology to market research and public opinion polling, providing researchers with an accurate means to collect data on attitudes towards products or services, perceived levels of customer satisfaction or overall customer experience trends. As such, learning how to design and develop surveys incorporating this technique can provide invaluable insight into any field involving subjective opinions. To get started you need only decide upon your key question(s) then select suitable responses – typically multiple-choice answers ranging from ‘Strongly Disagree’ through ‘Neutral’ up to ‘Strongly Agree’. These may also be augmented with open-ended questions where required. Then once participants have completed their survey results will indicate whether statements were accepted (positive), rejected (negative) or neither one nor other (neutral). Examples of actual questionnaires employing this approach can be found within published research papers such as those conducted by Lu & Tang [2015] into attitudes towards online shopping behavior among Malaysian university students.

Data Collection The Likert Scale is an effective tool to measure attitudes, beliefs and opinions. Its use can significantly simplify the task of collecting data from surveys as it allows respondents to answer quickly with minimal effort. An example of a research paper that utilized the scale was carried out by Kontostathis (2013), where he investigated public opinion on immigration policies in Greece. He asked participants questions such as “Do you think foreigners should be allowed access to health care services?” Each question had a five-point rating system – strongly agree, agree, undecided, disagree or strongly disagree – for respondents to choose from which enabled him to draw meaningful conclusions about how Greek citizens felt about immigration policy at the time.

When utilizing this method of data collection there are several best practices one must follow:

• Ensure each option within the Likert scale has a clear meaning.
• Avoid using words that carry different interpretations such as good/bad.
• Minimize bias in survey design and wording.

Data obtained through the use of a Likert Scale can be analyzed in numerous ways. A research paper published by Zeller and Carstensen (2019) provides an excellent example of how to proceed with such analysis. In their study, they conducted a survey using the five-point Likert Scale to assess participants’ attitudes towards new technology.

• Frequency Analysis: Using descriptive statistics, frequency counts were determined for each category within the scale ranging from “strongly disagree” to “strongly agree” in order to identify patterns among participant responses.
• Reliability Tests: To measure internal consistency across questions, Cronbach’s alpha was used; it showed that all items are reliably related.

Investigating Social Factors Using the Likert Scale When it comes to research studies focused on social factors, using a Likert scale is often advantageous. This approach gives researchers an easy way of quantifying responses and collecting useful data without having to rely solely on qualitative measures. For example, a 2012 study published in BMC Medical Research Methodology , which aimed to investigate stress levels among medical students at two different universities in Italy, used a seven-point likert scale for participants’ self-reported answers about their emotions.

Despite these advantages, however, there are still several limitations and challenges associated with implementation of the Likert scale that must be taken into account by researchers prior to its use. To begin with, validity may become an issue if respondents interpret items differently or rate them inconsistently due to bias or confusion over how they should evaluate each item; such issues can lead to inaccurate results being reported.

• In addition, response fatigue may occur when long lists of questions have been asked; this means that survey takers start becoming less engaged as time goes on and thus give unenthusiastic answers.

Furthermore , because surveys are usually completed online nowadays (e.g., via email), it becomes more difficult for researchers to ensure honest responses from those taking part – something which could potentially skew results further away from accuracy. Consequently careful consideration needs always be given before selecting any particular methodologies within one’s project design – lest it fail due generate meaningful outcomes

The Likert scale is a powerful tool for any research study looking to measure the attitude, opinion or behavior of an individual. By utilizing this method effectively, researchers can gain valuable insight into their subjects and make well-informed decisions on how best to proceed with their project. It is important that researchers understand all aspects of the Likert Scale before implementing it in their study.

• Gather information : Before beginning your research paper using the Likert scale you should gather as much knowledge about it as possible; its uses, benefits and drawbacks.
• Set up questions: Once familiar with its use and effects create a set of effective questions tailored specifically for your desired outcomes from the survey responses.
• Testing: Test out each question by running through them yourself prior to administering surveys so that respondents are able to accurately answer questions without confusion.

In conclusion, when utilizing the likert scale in research studies one must be mindful about every step taken along its usage; from creating suitable questions for your desired outcome down correctly formatting each response option – taking such measures will ensure positive results. A great example of successful application would be Johnson et al’s (2019) “Effects of College Athletics Participation on Academic Performance” which utilized both 5-point & 7-point scales depending upon difficulty level [1]. With thorough preperation comes success!

[1] Johnson B., Mokhtari J., Ghosh S., Ray N.(2019). Effects Of College Athletics Participation On Academic Performance Using The Liker Scale To Assess Student Attitude And Behaviour Towards Sports Involvement. International Journal Of Science & Research Education 8(3), 1535 – 1545

This article has provided an in-depth guide to using the Likert Scale in research papers. By implementing these strategies, researchers can ensure that their findings are as accurate and meaningful as possible. With this knowledge, authors have a comprehensive understanding of how to use the Likert Scale effectively within their studies. Ultimately, it is hoped that this article will serve as a valuable resource for those interested in utilizing the power of surveys and questionnaire data more fully.

Educational resources and simple solutions for your research journey

What Is a Likert Scale? Definition, Types, and Examples 

The Likert scale was devised by the American social scientist Rensis Likert in 1932 as a method for measuring attitudes in his publication “A Technique for the Measurement of Attitudes.”    

We all have come across these scales in various surveys and research studies, where we are asked to indicate our level of agreement or disagreement on a spectrum. Likert scales are used to measure opinions and attitudes in more depth, unlike simple “yes/no” questions.   

Psychometrics is a field in psychology involving the development and validation of assessment instruments such as surveys, scales, and questionnaires. A psychometric scale is a scale commonly used in questionnaires and is the most widely used scale in survey research. The most widely used psychrometric scales are Likert scales , which comprise a number of response points, usually 4 to 9, with accompanying verbal anchors.  

In this article, you will find everything you want to know about this scale, including topics like Likert scale options , advantages and characteristics of Likert scales , and even how to analyze data from a Likert scale .  

Table of Contents

What is a Likert scale?  

Likert scale definition : A Likert scale is a quantitative analysis data collection tool used in surveys and research to assess individuals’ attitudes, opinions, or perceptions. This scale presents a series of statements or questions to respondents. The responses are assigned numerical values, allowing for quantitative analysis of the data. Likert scales are widely employed in fields such as psychology, sociology, and market research to quantify subjective experiences and gather quantitative insights into people’s attitudes or opinions.  

Respondents express their attitudes, opinions, or perceptions on a predetermined scale. For example, the typical Likert scale would range from an option of “strongly agree” to an option of “strongly disagree,” with varying degrees of agreement or disagreement in between. The categories of response may be coded numerically. If so, the numerical values must be defined for that specific study, e.g., 1 = strongly agree, 2 = agree, etc.  

What are Likert scale questions?  

Coming to the application of Likert scale in research , let’s understand what Likert scale questions are. These questions are a type of research instrument that employs the Likert scale to measure people’s opinions, attitudes, or perceptions. The questions present a statement or assertion, and respondents are asked to indicate their level of agreement or disagreement with that statement. Usually, five or seven options are provided for respondents to choose from. An example of such a question is “How satisfied are you with the results of the dental appliance?” And typical response items would be as follows:  

• Very dissatisfied  
• Dissatisfied  
• Satisfied  
• Very satisfied  

As you can see, positive options appear on one side and negative options on the other, and the midpoint is typically neutral. The options on the extreme ends are called response anchors .  

When to use Likert scale questions?  

Likert scale questions are versatile and can be tailored to various topics, making them a popular and effective tool in survey research to measure subjective experiences and opinions. Likert scale questions are appropriate to use in situations where one wants to obtain nuanced responses rather than binary yes/no-type answers. The responses to the former are far more informative and specific.  

It is important to ensure that these scales are used appropriately and that the questions are clear, unbiased, and relevant to the research objectives. Here are some common scenarios when such questions might prove useful in research:  

• Psychological research: Measuring attitudes, personality traits, and emotional states.  
• Social science research: Quantifying attitudes and opinions on societal issues, political views, cultural preferences, etc  
• Healthcare research: Assessing patient satisfaction or treatment efficacy. 
• Market research: Gauging consumer preferences, brand perceptions, and product satisfaction, aiding businesses in making informed decisions.  

Examples of a Likert scale  

Here are some examples of Likert scales used in surveys and questionnaires.   

Likert scale example for agreement  

• Strongly agree  
• Undecided  
• Strongly disagree  

Likert scale example for likelihood  

• Definitely  
• Probably not  
• Definitely not

Likert scale example for frequency  

• Very frequently  
• Frequently  
• Occasionally  
• Very rare  

Likert scale example for importance  

• Very important   
• Important  
• Fairly important  
• Slightly important  
• Not important  

Note that the above examples are all 5-point scales . Keep reading to know more about the types of Likert scales .  

Types of Likert scales  

When using a Likert scale survey , a researcher must consider issues such as categories of response and size and direction of the scale. Broadly, there are two types of Likert scales : odd and even.  

Odd Likert scale  

The odd Likert scale (e.g., 3-point, 5-point, or 7-point scale ) includes a middle point, representing a neutral response. This midpoint can be interpreted differently by different respondents, but it avoids bias.  

Odd Likert scales for satisfaction are as follows:  

3-point scale  

5-point scale  

7-point scale  

• Moderately dissatisfied  
• Slightly dissatisfied  
• Slightly satisfied  
• Moderately satisfied  

Even Likert scale  

Even Likert scale questions have options without a central point. This means that respondents have to choose from the provided answer options (“forced choice” survey scale). Therefore, this scale is used when obtaining insights on a neutral option is not essential or when biased feedback is expected.  

Even Likert scales for satisfaction are as follows:  

2-point scale  

• Dissatisfied   

4-point scale  

• Very dissatisfied

Characteristics of Likert scales  

Here are some essential characteristics of a Likert scale that make it a widely used and effective research tool.  

• Each response option on a Likert scale is accompanied by unambiguous labels reflecting the intended level of agreement or disagreement.  
• Likert scales are ordinal in nature, meaning that the response options have a clear order or ranking. Items have two extreme positions, with gradation between the extremes. The most common type has five items, but the use of more items increases precision and reliability in the results.  
• Responses can be assigned numerical values to facilitate quantitative analysis of the data.   
• These scales are usually symmetrical, with an equal number of positive and negative response options. This helps maintain balance and reduce response bias.  
• These scales are easy to administer in surveys or questionnaires, making them a practical choice for collecting data even on a large scale.  
• These scales can be adapted to various topics and contexts, making them versatile for measuring attitudes and opinions in different fields, such as psychology, sociology, business, and education.  

How to write Likert scale questions?  

• In your Likert scale survey , try to include both questions and statements. This makes the survey engaging.   
• Use a mix of positive and negative framing in your questions to avoid bias in any one direction.  
• Use concise and clear writing. Avoid ambiguity by avoiding complex syntax, double negatives, and jargon. Use proper word choice and do not include different topics within the same question .  

How to write Likert scale responses?

• Decide the number of response options by balancing ease of answering with informativeness. Most researchers include five options.  
• Decide between offering unipolar and bipolar options, i.e., measuring a single attribute (e.g., agreement) versus two attributes (e.g., agreement or disagreement).  
• Ensure that the options provided are mutually exclusive.  
• Avoid confusion by omitting overlapping response items or items with similar meanings.

How to analyze data from a Likert scale ?  

To analyze data derived from the Likert scale , you first need to determine the data type. Likert-type data might be considered ordinal-level or interval-level data. An ordinal scale is one where the order matters but not the difference between values, e.g., socio-economic status, income level, satisfaction rating. Meanwhile, an interval scale is one where the difference between two values is meaningful, e.g., temperature, pH, exam scores. Note that Likert-derived data is typically treated as ordinal under the assumption of unequal distance between responses.  

Next, you need to choose the descriptive statistics and/or inferential statistics to be used to describe and analyze the Likert-derived data. Descriptive statistics can be used to summarize the collected data in a simple numerical form or a graphical form. Alternatively, inferential statistics may be used to test hypotheses, such as correlations between different responses or patterns in the whole dataset.  

Descriptive statistics  

• For ordinal data, the mode is identified for each question to provide an overall assessment of the sample. For visualization, bar charts may be created, displaying the frequency of the choices.
• In the case of interval data, scores from each question are totaled for individual participants. The mean and standard deviation of scores across the sample are calculated, which indicate the average and spread, respectively

Inferential statistics  

• For ordinal data, a hypothesis may be formed, such as exploring the connection between social media use and awareness of current affairs. A chi-square test of independence is employed to examine the correlation between these attributes.
• For interval data, consider an investigation into the relationship between IQ scores and social media use. Pearson’s correlation is used to determine whether the overall Likert scale score correlates with IQ. The analysis specifies whether data are treated at the ordinal or interval level.

Analysis at the ordinal level  

Researchers commonly treat Likert-derived data as ordinal, where response categories are ranked, but equal distance between categories is not assumed. Descriptive statistics, such as the median or mode, are used to summarize data numerically or visually. Bar charts illustrate the frequency of each choice. Appropriate inferential statistics for ordinal data include Spearman’s correlation or a chi-square test for independence.  

Analysis at the interval level  

Likert-derived data can also be treated at the interval level, presuming equal distances between response categories. Appropriate inferential statistics, like analysis of variance (ANOVA) or Pearson’s correlation, are used, provided the assumption of interval-level data is stated. Descriptive statistics involve totaling scores, calculating the mean, and determining the standard deviation across the sample.  

Advantages and disadvantages of Likert scale  

The advantages of Likert scale include (i) granular results, allowing for a more detailed understanding of individuals’ perspectives on a given topic, and (ii) quantitative nature, which allows handling and statistical analysis of complex topics. Further, Likert scales are (iii) user-friendly and time-saving as they are closed-ended. Therefore, large samples can be used to obtain data.  

The disadvantages of this scale are that it is prone to (i) response bias (i.e., respondents either agree or disagree with statements due to fatigue or social desirability) and (ii) subjective interpretation (owing to variation in interpretation of the items). Moreover, being closed-ended, (iii) Likert-type questions restrict the choices of the respondents.  

Frequently asked questions  

• What is the 5-point Likert scale ?  

The 5-point scale is a commonly used rating scale in social science research and survey questionnaires. Respondents are asked to indicate their level of agreement or disagreement with a statements based on five response items.  

• What is the best Likert scale for research?  

The choice depends on the nature of the research question, target audience, and study objectives. However, the 5-point Likert scale is most commonly used; it is versatile owing to its balanced response options and ease of interpretation. Many researchers are familiar with it, and it is widely accepted in academic and professional settings for analyzing Likert scale data .   

• How can I use the Likert scale in research?  

Here are step-by-step guidelines on how to use a Likert scale in research :   

Begin by defining the objectives of your research. Identify the specific attitudes, opinions, or perceptions you intend to measure using the Likert scale . Select an appropriate scale format, and create clear and unbiased statements and a balanced set of response items. Next, conduct a pilot test or pretest of the scale with a small sample to identify any issues with clarity or response patterns. Use the feedback to refine and improve your scale. Finally, present your findings in a clear and organized manner in your research report or article.   

• Are Likert scales quantitative or qualitative?  

Likert scales are quantitative in nature. While the data collected from Likert scales are ordinal (meaning there is a clear order or ranking of responses), they are treated as quantitative for statistical analysis purposes because the scales involve assigning numerical values to responses, allowing for the application of various quantitative statistical techniques.  

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Using a Likert Scale in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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What a Likert Scale Looks Like

Creating items on a likert scale.

• Disadvantages

A Likert scale is a type of psychometric scale frequently used in psychology questionnaires. It was developed by and named after organizational psychologist Rensis Likert. Self-report inventories are one of the most widely used tools in psychological research.

On a Likert scale, respondents are asked to rate the level to which they agree with a statement. Such scales are often used to assess personality , attitudes , and behaviors.

At a Glance

While you might not have known what they were called, you've probably encountered many different Likert scales. Simply put, a Likert scale is a type of assessment item that asks you to rate your agreement with a statement (often from "Strongly Agree" to "Strongly Disagree.") Such scales can be a great way to get a nuanced look at how people feel about a particular topic, which is why you'll often see this type of item on political surveys and psychological questionnaires.

On a survey or questionnaire, a typical Likert item usually takes the following format:

• Strongly disagree
• Neither agree nor disagree
• Strongly agree

It is important to note that the individual questions that take this format are known as Likert items, while the Likert scale is the format of these items.

Other Items on a Likert Scale

In addition to looking at how much respondents agree with a statement, Likert items may also focus on likelihood, frequency, or importance. In such cases, survey takers would be asked to identify:

• How likely they believe something to be true (Always true, Usually true, Sometimes true, Usually not true, Never true)
• How frequently they engage in a behavior or experience a particular thought (Very frequently, Frequently, Occasionally, Rarely, or Never)
• How important they feel something is to them (Very important, Important, Somewhat important, Not very important, Not important)

A Note on Pronunciation

If you've ever taken a psychology course, you've probably heard the term pronounced "lie-kurt." Since the term is named after Rensis Likert, the correct pronunciation should be "lick-urt."

In some cases, experts who are very knowledgeable about the subject matter might develop items on their own. Oftentimes, it is helpful to have a group of experts help brainstorm different ideas to include on a scale.

• Start by creating a large pool of potential items to draw from.
• Select a group of judges to score the items.
• Sum the item scores given by the judges.
• Calculate intercorrelations between paired items.
• Eliminate items that have a low correlation between the summed scores.
• Find averages for the top quarter and the lowest quarter of judges and do a t-test of the means between the two. Eliminate questions with low t-values, which indicates that they score low in the ability to discriminate.

After weeding out the questions that have been deemed irrelevant or not relevant enough to include, the Likert scale is then ready to be administered.

Experts suggest that when creating Likert scale items, survey creators should pay careful attention to wording and clearly define target constructs.

Some researchers have questioned whether having an even or odd number of response options might influence the usefulness of such data. Some research has found that having five options increases psychometric precision but found no advantages to having six or more response options.

Advantages of a Likert Scale

Because Likert items are not simply yes or no questions, researchers are able to look at the degree to which people agree or disagree with a statement.

Research suggests that Likert scales are a valuable and convenient way for psychologists to measure characteristics that cannot be readily observed.

Likert scales are often used in political polling in order to obtain a more nuanced look at how people feel about particular issues or certain candidates.

Disadvantages of a Likert Scale

Likert scales are convenient and widely used, but that doesn't mean that they don't have some drawbacks. As with other assessment forms, Likert scales can also be influenced by the need to appear socially desirable or acceptable.

People may not be entirely honest or forthright in their answers or may even answer items in ways that make themselves appear better than they are. This effect can be particularly pronounced when looking at behaviors that are viewed as socially unacceptable.

What This Means For You

The next time you fill out a questionnaire or survey, notice if they use Likert scales to evaluate your feelings about a subject. Such surveys are common in doctor's offices to help assess your symptoms and their severity. They are also often used in political or consumer polls to judge your feelings about a particular issue, candidate, or product.

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Likert Scale

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Likert-type scale ; Rating scale

Likert scaling is one of the most fundamental and frequently used assessment strategies in social science research (Joshi et al. 2015 ). A social psychologist, Rensis Likert ( 1932 ), developed the Likert scale to measure attitudes. Although attitudes and opinions had been popular research topics in the social sciences, the measurement of these concepts was not established until this time. In a groundbreaking study, Likert ( 1932 ) introduced this new approach of measuring attitudes toward internationalism with a 5-point scale – (1) strongly approve, (2) approve, (3) undecided, (4) disapprove, and (5) strongly disapprove. For example, one of nine internationalism scale items measured attitudes toward statements like, “All men who have the opportunity should enlist in the Citizen’s Military Training Camps.” Based on the survey of 100 male students from one university, Likert showed the sound psychometric properties (i.e., validity and...

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Yamashita, T., Millar, R.J. (2021). Likert Scale. In: Gu, D., Dupre, M.E. (eds) Encyclopedia of Gerontology and Population Aging. Springer, Cham. https://doi.org/10.1007/978-3-030-22009-9_559

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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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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.

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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 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.

About the author

Qualaroo editorial team.

The Qualaroo Editorial Team is a passionate group of UX and feedback management experts dedicated to delivering top-notch content. We stay ahead of the curve on trends, tackle technical hurdles, and provide practical tips to boost your business. With our commitment to quality and integrity, you can be confident you're getting the most reliable resources to enhance your user experience improvement and lead generation initiatives.

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Likert scale interpretation: How to analyze the data with examples

• January 10, 2022
• 10 min read
• Best practice

What are Likert scale and Likert scale questionnaires?

Likert scale examples: the types and uses of satisfaction scale questions, likert scale interpretation: analyzing likert scale/type data, how to use filtering and cross tabulation for your likert scale analysis, 1. compare new and old information to ensure a better understanding of progress, 2. compare information with other types of data and objective indicators, 3. make a visual representation: help the audience understand the data better, 4. focus on insights instead of just the numbers, how to analyze likert scale data, likert scale interpretation example overview, interpreting likert scale results, explore useful surveyplanet features for data analyzing.

Likert scaling consists of questions that are answerable with a statement that is scaled with 5 or 7 options that the respondent can choose from.

Have you ever answered a survey question that asks to what extent you agree with a statement? The answers were probably: strongly disagree, disagree, neither disagree nor agree, agree, or strongly agree. Well, that’s a Likert question.

Regardless of the name—a satisfaction scale, an agree-disagree scale, or a strongly agree scale—the format is pretty powerful and a widely used means of survey measurement, primarily used in customer experience and employee satisfaction surveys.

In this article, we’ll answer some common questions about Likert scales and how they are used, though most importantly Likert scale scoring and interpretation. Learn our advice about how to benefit from conclusions drawn from satisfaction surveys and how to use them to implement changes that will improve your business!

A Likert scale usually contains 5 or 7 response options—ranging from strongly agree to strongly disagree—with differing nuances between these and a mandatory mid-point of neither agree nor disagree (for those who hold no opinion). The Likert-type scale got its name from psychologist Rensis Likert, who developed it in 1932.

Likert scales are a type of closed-ended question, like common yes-or-no questions, they allow participants to choose from a predefined set of answers, as opposed to being able to phrase their opinions in their own words. But unlike yes-or-no questions, satisfaction-scale questions allow for the measurement of people’s views on a specific topic with a greater degree of nuance.

Since these questions are predefined, it’s essential to include questions that are as specific and understandable as possible.

Answer presets can be numerical, descriptive, or a combination of both numbers and words. Responses range from one extreme attitude to the other, while always including a neutral opinion in the middle.

A Likert scale question is one of the most commonly used in surveys to measure how satisfied a customer or employee is. The most common example of their use is in customer satisfaction surveys , which are an integral part of market research .

Are satisfaction-scale questions the best survey questions?

Maybe you’ve answered one too many customer satisfaction surveys with Likert scales in your lifetime and now consider them way too generic and bland. But, the fact is they are one of the most popular types of survey questions.

First of all, they are pretty appealing to respondents because they are easy to understand and do not require too much thinking to answer.

And, while binary (yes-or-no) questions offer only two response options (i.e., if a customer is satisfied with your products and services or not), satisfaction-scale questions provide a clearer understanding of customers’ thoughts and opinions.

By using well-prepared additional questions, questions about particular products or service segments can be asked. That way, getting to the bottom of customer dissatisfaction is possible, making it easier to find a way to address their complaints and improve their experience.

Such surveys enable figuring out why customers are satisfied with one product but not another. This empowers the recognition of products and service areas that customers are confident in while helping to find ways to improve others.

When it comes to analyzing and interpreting survey scale results, Likert questions are helpful because they provide quantitative data that is easy to code and interpret. Results can also be analyzed through cross-tabulation analysis (we’ll get back to that later).

Likert questions can be used for many kinds of research. For example, determine the level of customer satisfaction with the latest product, assess employee satisfaction, or get post-event feedback from attendees after a specific event.

Questions can take different forms, but the most common is the 5-point or 7-point Likert scale question. There are 4-point and even 10-point Likert scale questions as well.

How to choose from these options?

The most common is the 5-point question. Most researchers advise the use of at least five response options (if not more). This ensures that respondents have enough choices to express their opinion as accurately as possible.

Some researchers suggest always using an even number of responses so respondents are not presented with a neutral answer, therefore having to “choose a side.” This is to avoid a tepid response even when respondents have an opinion, which is one of the most common types of errors in surveying .

Likert scale interpretation involves analyzing the responses to understand the participants’ attitudes toward the statements.

It’s important to note that Likert scales provide a quantitative representation of attitudes but do not necessarily capture underlying reasoning or motivations. Qualitative methods, such as interviews or open-ended questions, are often used in conjunction with Likert scales to gain a deeper understanding of participants’ perspectives.

Overall, Likert scale interpretation of data involves analyzing the numerical ratings, considering the directionality of the scale, examining central tendency and variability, identifying response patterns, and conducting comparative analyses to draw meaningful conclusions about people’s attitudes or opinions.

How to analyze satisfaction survey scale questions

For a survey to be its best , how gathered information is analyzed is as important as the gathering itself. That’s why we’ll now turn to the most effective ways of analyzing responses from satisfaction survey scales.

When using Likert scale questions, the analysis tools used are mean, median, and mode. These help better understand the information collected.

The mean (or average) is the average value of data, calculated by adding all the numbers and dividing this sum by the total number of values offered to respondents. The median is the middle value of a data set, while the mode is the number that occurs most often.

Some other useful ways of analyzing information are filtering and cross tabulation.

Using a filter, the responses of one particular group of respondents are focused upon and the rest filtered out. For example, how female customers rate a product can be determined by filtering out male respondents, while concentrating on customers aged 20 to 30 can be gleaned by filtering out older respondents.

Cross tabulation, on the other hand, is a method to compare two sets of information in one chart and analyze the relationship between multiple variables. In other words, it can show the responses of a particular subgroup while it can also be combined with other subgroups.

Say you want to look at the responses of unemployed female respondents aged 20 to 30. By using cross tabulation, all three parameters—gender, age, and employment status—can be combined and correlation calculated.

If this all sounds confusing, SurveyPlanet luckily doesn’t just offer great examples of surveys and the ability to create custom themes , but also the power to export survey results into several different formats, such as Microsoft Excel and Word, CSV, PDF, and JSON files.

How to interpret Likert scale data?

When information has been gathered and analyzed, it’s time to present it to stakeholders. This is the final stage of research. Analyzing the results of Likert scale questionnaires is a vital way to improve services and grow a business. Presenting the results correctly is a key step.

Here’s how to develop a clear goal and present it understandably and engagingly.

Compare the newly obtained information with data gathered from previous surveys. Sure, information gathered from the latest research is valuable on its own, but not helpful enough. For example, it tells you if customers are currently satisfied with products or services, but not whether things are better or worse than last year.

The key to improving customer service—and thus developing a business—is comparing current responses with previous ones. This is called longitudinal analysis. It can provide valuable insights about how a business is developing, if things are improving or declining, and what issues need to be solved.

If there is no previous data, then start collecting feedback immediately in order to compare results with future surveys. This is called benchmarking. It helps keep track of progress and how products, services, and overall customer satisfaction changes over time.

The most crucial information to compare new findings with is previous surveys. But it is highly recommended to constantly compare findings with other types of information, such as Google Analytics, sales data, and other objective indicators.

Another good practice is comparing qualitative with quantitative data . The more information, the more accurate the research results, which will help better convey findings to stakeholders. This will also improve business decision-making, strengthening the experiences of customers and employees.

Numbers are easier to understand when suitable visual representation is provided. However, it is essential to use a medium that adequately highlights key findings.

Line graphs, pie charts, bar charts, histograms, scatterplots, infographics, and many more techniques can be used.

But don’t forget good old tables. Even if they’re not so visually dynamic and a little harder on the eyes, some information is simply best presented in tables, especially numerical data.

Working with all of these options, more satisfactory presentations can be created.

When presenting findings to stakeholders, don’t just focus on the numbers. Instead, highlight the conclusions about customer or employee satisfaction drawn from the research. That way, everyone present at the meeting will gain a deeper understanding of what you’re trying to convey.

A valuable and exciting piece of advice is to focus on the story the numbers tell. Don’t simply list the numbers collected. Instead, use relevant examples and connect all the information, building on each dataset to make a meaningful whole.

Define and describe problems that need to be solved in engaging and easy-to-understand terms so that listeners don’t have a hard time understanding what is being shared. Include suggestions that could improve, for example, customer experience outcomes. It is also important to share findings with the relevant teams, listen to their perspectives, and find solutions together.

An example of Likert scale data analysis and interpretation

Let’s consider an example scenario and go through the steps of analyzing and interpreting Likert scale data.

Scenario: A company conducts an employee satisfaction survey using a Likert scale to measure employees’ attitudes toward various aspects of their work environment. The scale ranges from 1 (Strongly Disagree) to 5 (Strongly Agree).

Item 1: “I feel valued and appreciated at work.”

Item 2: “My workload is manageable.”

Item 3: “I receive adequate training and support.”

Item 4: “I have opportunities for growth and advancement.”

Item 5: “My supervisor provides constructive feedback.”

Step 1: Calculate mean scores by summing up the responses and dividing by the number of respondents.

Item 1: Mean score = (4+5+5+4+3)/5 = 4.2

Item 2: Mean score = (3+4+3+3+4)/5 = 3.4

Item 3: Mean score = (4+4+5+4+3)/5 = 4.0

Item 4: Mean score = (3+4+3+2+4)/5 = 3.2

Item 5: Mean score = (4+3+4+3+5)/5 = 3.8

Step 2: Assess central tendency by looking at the distribution of responses to identify the most frequent response or central point.

Item 1: 4 (Agree) is the most frequent response.

Item 2: 3 (Neutral) is the most frequent response.

Item 3: 4 (Agree) is the most frequent response.

Item 4: 3 (Neutral) is the most frequent response.

Item 5: 4 (Agree) is the most frequent response.

Step 3: Consider Variability by assessing the range or spread of responses to understand the diversity of opinions.

Item 1: Range = 5-3 = 2 (relatively low variability)

Item 2: Range = 4-3 = 1 (low variability)

Item 3: Range = 5-3 = 2 (relatively low variability)

Item 4: Range = 4-2 = 2 (relatively low variability)

Item 5: Range = 5-3 = 2 (relatively low variability)

Step 4: Identify response patterns By looking for consistent agreement or disagreement across items or patterns of response clusters.

Step 5: Comparative analysis of responses among different groups, such as other departments or job positions, to identify attitude variations.

In this example, there is a pattern of agreement on items related to feeling valued at work (Item 1), receiving training and support (Item 3), and receiving constructive feedback (Item 5). However, there is a relatively neutral response pattern for workload manageability (Item 2) and growth opportunities (Item 4).

For example, you could compare responses between different departments to see if there are significant differences in employee satisfaction levels.

Based on the analysis, employees feel valued and appreciated at work (Item 1) and perceive adequate training and support (Item 3). However, there may be room for improvement regarding workload manageability (Item 2), opportunities for growth (Item 4), and the provision of constructive feedback (Item 5).

The relatively low variability across items suggests moderate agreement within the group. However, the neutral response pattern for workload manageability and opportunities for growth may indicate areas that require attention to enhance employee satisfaction.

Likert scales are a highly effective way of collecting qualitative data. They help you gain a deeper understanding of customers’ or employees’ opinions and needs.

Make this kind of vital research easier. Discover our unique features —like exporting and printing results —that will save time and energy. Let SurveyPlanet take care of your surveys!

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What is a Likert Scale? Definition, Examples, and How To Use One

 Two thumbs up! Five stars! There are many types of scales designed to let the public know the quality of something. But what about when you want to get the public’s opinion on something? Whether you’re a business surveying customers or a market researcher, a popular way to survey people is using a Likert scale. 

Create your Likert-based survey, form, or poll now!

Likert Scale Definition: History and Usage

What is a Likert scale and how do you use one? Firstly, the Likert scale is named after American social scientist Rensis Likert. Likert devised the psychometric approach in 1932 for conducting social and educational research. Today, Likert-type scales are considered some of the best survey tools for researching popular opinions. As a result, they’re often used for customer satisfaction surveys or marketing research surveys .

On a Likert scale , a person selects one option among several that reflects how much they agree with a statement. In other words, t he scale generally consists of five or seven balanced responses that people can choose from, with a neutral midpoint. However, there can be as few as two responses (with no neutral response) or as many as ten. Now, i t’s very common for companies to use these scales when they want to evaluate a customer’s level of satisfaction during a recent experience.

Later on, you’ll see many examples of Likert survey scales . However, to be sure we’re on the same page here’s a typical question using 5 points :

SurveyLegend provides quality support to its customers:

• Strongly Disagree
• Somewhat Disagree
• Neither Agree Nor Disagree
• Somewhat Agree
• Strongly Agree

Hint: The correct answer is 5…at least that’s what our customers are saying!

Are Likert Scales Ordinal or Interval?

Almost everyone agrees that the Likert rating scale provides ordinal data (data which is measured along a scale, but the distances between each point are unknown). However, many believe this scale also provides interval data ( data that is measured along a scale, with each point placed at equal distances from one another). 

Here’s what we think: Likert survey scales provide ordinal data, as the results naturally represent someone’s preferences. For example, we know that a 4 is better than a 3. However, it’s not interval data because we don’t know exactly what constitutes a 4 versus a 3 in someone’s mind. 

That said, when a survey has enough questions, many researchers use the data to come up with reliable averages. This means Likert scales “approach” the definition of interval data. We think Dr. David L. Morgan from the Portland State University’s Sociology Department summed up the interval nature of the scale well. He says, “ The key point here is to use multiple items, where any one of them may be too weak to provide an adequate measure, but the combination of them is much stronger.”

Are Likert Scales Quantitative or Qualitative?

Likert scales give quantitative value to qualitative data. For example, it may be designed to measure how much a person agrees with a statement regarding a product’s value and assigns a data point to it. This is one reason why the scale is almost universally loved. Researchers appreciate that Likert rating scale questions use a universal method of collecting data, so the results can be easily understood. Additionally, CEOs and marketers like that they can say someone thinks their product is “excellent” because they assigned a “10” to it.

Why Use a Likert Scale? Advantages and Disadvantages

There are Likert scale advantages and disadvantages. However, there are many more pros than cons!

Likert scales are easy for people to understand and complete. B ecause questions using the Likert method follow a scale, respondents don’t have to answer yes or no, or either-or. Instead, they can choose to be neutral. All of this means that they wind up delivering better response rates! In addition, questions are easier to analyze and report on than open-ended or fill-in-the-blank questions, which are more difficult to analyze because the answers haven’t been configured in advance. 

The one drawback is that you don’t always get in-depth feedback. Consider a restaurant. Sure, you may know someone is dissatisfied with your restaurant because they only gave you one star. But, you won’t know why they were dissatisfied (Was it the food quality? The service? Cleanliness?).  To solve this, it’s important to ask multiple questions about different aspects of the customer’s visit. After that, ask for their overall satisfaction level.

Difference Between 5 Point Likert Scale and a 7 Point Scale

Most researchers agree that the best Likert scales are the 5-point and 7-point varieties. This simply refers to how many responses the person has to choose from. Most Likert scales you see are going to be odd-numbered. They will have an equal number of positive and negative responses on either side of a neutral response. Poor to excellent Likert scales are popular as you’ll see in one of our 5 point Likert scale templates below.

So why are 5 point and 7 point considered ideal? Because if you offer less than five options, online survey takers may be limited in their responses; they may resort to picking the “most” applicable answer. As a result, you’re deprived of their true opinion. On the other hand, if you go above seven, respondents may feel overwhelmed or annoyed; they may just pick a random answer to move along quickly.

Other Point Variations in Likert Scales

While the 5 point and 7 point Likert scale are the most popular, there are other variations.

2-Point Likert Scale

The simplest form of this scale provides no neutral option (yes/no).

3-Point Likert Scale

The 3 point scale is used similarly to the 2 point version, but introduces a neutral option (yes/unsure/no).

4-Point Likert Scale

This version forces respondents to make a choice. Consider this 4 point smiley face Likert scale, where the faces represent: Extremely Dissatisfied, Somewhat Dissatisfied, Satisfied, and Extremely Satisfied:

There is no neutral choice. However, if a researcher wants to find a neutral point, they can average together Somewhat Dissatisfied and Satisfied.

6-Point Likert Scale

The 6 point scale is meant to provide more options for respondents. Because it is an even point scale, there is no neutral option.

9-Point Likert Scale

This Likert scale is used to offer respondents a wider variety of choices. Additionally, it provides a higher degree of measurement precision, with a neutral option. However, it’s not commonly used as it can take respondents longer to make their selections which can result in less accurate responses as participants speed through the survey.

10-Point Likert Scale

This provides a greater Likert scale level of measurement precision, like the 9 point Likert scale. However, it does not include a neutral option.

5 Tips for Creating a Likert Scale

1. ask multiple questions.

It’s often not enough to ask one general question about a particular topic, opinion, or experience. If you do, you won’t see the full picture. Asking multiple questions remedies this, taking into consideration all of the factors that could have contributed to this response.

2. Avoid using different scales

Mixing different scales within your surveys can cause respondent confusion. Bonus: Using only one scale will also make your final analysis that much easier.

3. Label the numeric responses

Do not simply attach a number to possible responses. Always include wording on your scale question , otherwise online survey takers may confuse which numbers are positive and which are negative.

4. Create unbiased responses

To improve Likert scale validity and reliability, stay away from survey questions that may lead people to answer a certain way. For example, you don’t want to force them to choose between extremes as it can skew your results.

5. Keep it simple

Creating a Likert scale survey doesn’t need to be complicated. In fact, the best survey questions are concise and to the point. For example, l ong, complex questions tend to lose readers or test their patience. Additionally, inadvertently asking two questions in one can leave respondents unsure of how to answer.

Likert Scale Examples and Questions

Below are a number of examples of Likert scales using different numbers of points that may provide inspiration when developing your survey. However, please note that the numbers in the answers indicate the relative position of items, but not the magnitude of difference. Additionally, you do not have to include numbers in your survey questions if you prefer not to.

Comparing 2 Products

Support / opposition, familiarity level, awareness level, knowledge of action, affect on …, acceptability level, appropriateness level, importance level, concern level, difficulty level, influence level, probability level, overall impression, agreement level, level of agreement, satisfaction level, level of satisfaction, how to analyze likert scale data.

You will find many ideas out there about how to analyze Likert scale data . For example, many people want to know how to calculate mean score for Likert scale data, which would simply be the sum of all numbers divided by the count. However, as mentioned earlier, these scales provide ordinal data, and the values between points cannot be considered equal. Therefore, using a mean (the average of all the numbers) is not appropriate for analysis.

Instead, it is recommended that you use a mode score for easy inappropriate for ordinal data. First, to calculate the mode score for Likert scale data, simply determine the number that appears the most. So, let’s say you surveyed ten people on the quality of a product on a 5 point scale and the results were as follows:

5, 5, 4, 1, 4, 4, 4, 4, 4, 3

In this example, the mode would be 4, as it appears the most. You’ll note that in this example, the mean would also have been 4. However, in highly skewed distributions, these numbers would be different as you’ll see below:

1, 1, 1, 1, 5, 5, 5, 1, 1, 1

Here, the mode would be 1. Most people clearly don’t like the product. However, the mean would have been 3, but concluding that most people think the product is average is clearly not the case, which is why a mode score is most appropriate when analyzing Likert scale data.

Live Likert Scale Example

Want to see a Likert scale in action? Below is a Likert scale example created using SurveyLegend’s opinion scale option. Go ahead and try it for yourself, it’s live!

A Likert scale is a great option for businesses and researchers wanting an easy way to survey customers or the general public. When it’s time to create your survey, choose SurveyLegend for easy design and analysis. SurveyLegend offers the best online survey software, with countless survey samples and templates. So, do you want to switch languages to have your Likert scale in Spanish or another language? We let you easily create Likert scale questions that are responsive, beautifully adjusting even to the small mobile phone screens. 

Have you used a Likert scale in your research? Did you find it easy to use and analyze? Let us know in the comments!  

Frequently Asked Questions (FAQs)

Yes.  It’s named after American social scientist Rensis Likert, who created the scale.

A Likert scales measure how much a person agrees with a statement, providing five or seven balanced responses that people can choose from with a neutral midpoint. 

A Likert scale provides ordinal data (data that is measured along a scale, but the distances between each point are unknown).

Likert scales give quantitative value to qualitative data, assigning a data point to a statement.

A mode score should be used, to determine which number appears the most.

Jasko Mahmutovic

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• Open access
• Published: 24 April 2024

Development of nursing handoff competency scale: a methodological study

• Jiyoung Do 1 &
• Sujin Shin   ORCID: orcid.org/0000-0001-7981-2893 2  

BMC Nursing volume  23 , Article number:  272 ( 2024 ) Cite this article

Metrics details

Nursing handoff competency is the ability of the nurse performing the handoff to select and interpret the necessary information for patient care and to convey it efficiently to the nurse accepting the handoff. Nursing handoff is an important nursing task that ensures nursing care continuity, quality and patient safety. This study aimed to develop a scale to measure nursing handoff competency and verify its validity and reliability.

This study adopted a methodological design. A research process included three phases: (1) scale development (literature review and interviews); (2) scale validation (validity and reliability); (3) standard setting. Data were collected from 496 clinical nurses currently working in hospital wards, intensive care units, and emergency rooms, and who independently perform a handoff in South Korea.

The final scale comprises a self-reported 4-points Ilert scale with 25 items based on four factors: knowledge on handoff methods, identification of patient information, judgment and transfer of nursing situation, and “formation of supportive relationships. Construct validity, criterion-related validity, and discrimination validities were verified and the fitness of the scale revealed good results in confirmatory factor analysis. The Cronbach’s α of the whole tool was.912 and the cut-off score for satisfied/unsatisfied was.72.

Conclusions

The developed scale can evaluate the nurse’s handoff competencies and determine whether training is necessary. The measurement results of the scale can be used to select training subjects and compose the contents of the education program.

Peer Review reports

Introduction

Nursing Handoff is communication between nurses sharing patient information [ 1 ]. The Joint Commission on Accreditation of Healthcare Organizations defines a handoff as an interactive process of transferring patient-specific information to ensure patient care continuity and patient safety [ 2 ]. The nursing care of inpatients is transferred between nurses at least 2–3 times every 24 h, but nurses must consistently provide proper care [ 3 ]. Therefore, nursing handoff is one of the most important nursing tasks in ensuring patient safety and quality of care.

The nurse who is accepting the handoff (receiving nurse) understands the patient’s situation based on the content and delivery method of the information provided by the nurse performing the handoff (sending nurse) [ 4 ]. In particular, the information content and delivery method affect the receiving nurse’s clinical judgment, which is directly linked to patient care. Hence, the nurse must identify significant information and effectively deliver it [ 5 ]. Competencies required for this can be divided into two categories: the ability to comprehensively understand the patient’s health problems by analyzing patient information and the ability to explain things so that nurses can easily understand them.

Previous studies used clinical reasoning competency [ 6 ], critical thinking disposition [ 7 ], clinical judgment [ 4 ], communication ability [ 8 ], and communication clarity [ 9 ] to indirectly measure the handoff competency. These tools do not reflect the characteristics of the nursing handoff, such as accurately grasping important patient information, presenting it logically, and delivering it promptly and accurately. Therefore, these instruments have limitations in accurately assessing handoff competency.

The Handoff Clinical Evaluation Exercise [ 10 ] and Handover Evaluation Scale [ 11 ] were developed to not contain items to assess the specific abilities required for a handoff. The Korean versions of the Perceived self-efficacy of hand-off reporting scale [ 12 ] and scale to evaluate communication in nursing handoff [ 13 ] reflect the handoff characteristics but only focus on specific areas such as communication standards. Therefore, an instrument that addresses the limitations of existing scales and comprehensively assesses nursing handoff competency is needed.

This study aimed to develop a nursing handoff competency scale for assessing the overall handoff competencies required of nurses to identify meaningful patient information and deliver it effectively. The assessment results can be used to identify nurses who need additional training and shed light on their areas of weakness to develop education programs specific to their needs. Providing tailored educational support can enhance the nursing handoff competencies, which would ultimately improve nursing care efficiency and quality.

Study design

This methodological study developed a scale to measure nursing handoff competency and to verify its validity and reliability.

Study procedure

This study was conducted in three phases: developing nursing handoff competency measurement scale, testing its validity and reliability, and standard setting for the scale.

Phase 1: Development of the scale

The conceptual framework was derived through literature and interviews. From this, preliminary items were formed.

Conceptual framework

Anayzing the literature review and interview results generated the conceptual framework. We searched using the following search criteria. Literature published from 2011 to 2021 was searched and searches were performed on PubMed, CINAHL, RISS, KISS. The terms used in the search were a combination of handoff, handover, nursing, competency, and competence. A literature review was conducted using 26 studies on intervention studies evaluating the effectiveness of handoff education programs, systematic literature review studies, and studies on the development of patient handoff scales.

Personal interviews were conducted with 2 nursing managers and 10 clinical nurses to derive qualitative data. To confirm suitability with the clinical department, interviews were conducted with a total of 12 people, including nurse managers, new nurses, and clinical nurses with preceptor experience. To specifically analyze handoff capabilities, intensive care unit nurses who intensively care for patients with complex health problems were included, and ward and emergency room nurses were also included. The main questions of the interview were open-ended: “What does it mean to be good at handoff?” and “What are the characteristics of a nurse who is good at handoff?” The inductive content analysis method of Elo and Kyngäs [ 14 ] was used for data analysis. The analysis classified the conceptual framework of nursing handoff competency into two dimensions: nursing judgment and nursing delivery.

Content validation of the preliminary items

Content validity was conducted in two rounds for 2 dimensions, 8 attributes, and 69 preliminary items derived from the conceptual framework phase. The first round was conducted by 9 experts, including nursing college professors, nursing managers, and nurses with > 5 years of nurse education experience. The evaluation results were analyzed whether the Item-Content Validity Index (I-CVI) of the item was > 0.80 and the S-CVI/Ave was > 0.90 [ 15 ]. S-CVI/Ave was 0.92 in the first round, which satisfied the validity standard. The I-CVI distribution was 0.67  ∼  1.00 and with 10 items having < 0.80.

Six items that overlapped with other items or were inappropriate for measuring handoff competence were deleted among the items with an I-CVI of < 0.80. Additionally, four items with unclear meanings were modified. The second content validity was qualitatively evaluated by three nursing professors. Items with similar contents were deleted, and items were organized to include only the core contents. Finally, 2 dimensions, 8 attributes, and 49 preliminary questions were derived.

Phase 2: Evaluation of the scale

In the scale verification stage, construct validity, convergent validity, discriminant validity, criterion validity, and reliability were verified.

Samples and data collection

Data collection was performed twice for tool evaluation. The first survey was conducted for exploratory factor analysis (EFA) in the scale development stage, and the second survey was conducted for confirmatory factor analysis (CFA), convergent, discriminant, criterion validities, and reliability verification in the scale verification stage.

The inclusion criteria for this study are as follows: (1) nurses currently working in hospital wards, intensive care units, and emergency rooms, and (2) clinical nurses performing handoff independently. The exclusion criteria of this study are as follows: (1) nurses currently on duty at the hospital but not performing patient handoff, and (2) new nurses who do not perform independent nursing due to the training period. Participants in the first main survey were excluded from the second main survey.

Based on previous studies, the number of samples for EFA is required to be 5–10 times greater than the number of items in the scale [ 16 ], which was calculated as 273, considering the dropout rate of 10%. The number of samples for CFA was calculated to be 223, considering the appropriate research results of at least 200 people and a dropout rate of 10%. The second main survey was conducted with the scales modified as an EFA result with the data collected in the first main survey.

For data collection, the researcher explained the purpose of the study and the personal information protection of research participants on the social network system and online cafes, where clinical nurses are the main visitors, and posted the link address of the recruitment documents and questionnaires for research participants. The first survey included 273 participants, while the second included 223, with a total of 496 participants.

Measurement

The general characteristics of the participants, including gender, age, type and region of the institution, department, total clinical career, clinical career at the current hospital, and current working type were investigated. The first survey consisted of 57 questions, including 49 preliminary items and 8 general characteristic items. The second survey consisted of 65 items, including 27 items modified after EFA, 30 for criterion validity, and 8 for general characteristics.

The Korean version of the “Nurses Clinical Reasoning Scale (NCRS)” [ 17 ] and the “Global Interpersonal Communication Competence” measuring communication ability (GICC-15) scale were used to verify the criterion validity [ 18 ]. NCRS consists of 15 items on a 5-point Likert scale, wherein higher scores indicate higher clinical reasoning competencies. The scale’s reliability was Cronbach’s α = 0.94; in this study, Cronbach’s α was 0.87. The GICC-15 consists of 15 items on a 5-point Likert scale, wherein higher scores indicate higher communication abilities. The scale’s reliability was Cronbach’s α = 0.72; in this study, Cronbach’s α was 0.77.

Data analysis

The collected data were analyzed using SPSS 25.0 and AMOS 22.0 programs. The specific analysis method is as follows.

The general participant characteristics were analyzed using descriptive statistics such as frequency, percentage, mean, and standard deviation.

EFA and CFA were conducted to verify construct validity. Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test were conducted to confirm that the items were suitable for EFA. The maximum likelihood method was used for factor extraction, and the promax rotation method was used for factor rotation. The criteria for deleting items were a factor loading value of less than 0.30 [ 19 ]. Model fit in CFA was judged through the \( {x}^{2} \) test ( p  >.05), Standardized Root Mean-square Residual (SRMR < 0.05), Root Mean Square Error of Approximation (RMSEA < 0.05), Comparative Fit Index (CFI > 0.9), and Tucker-Lewis Index (TLI > 0.9).

The standardized factor loading values derived from CFA and the correlation coefficient between factors and standard error values were used to confirm the confidence interval of the correlation coefficient between factors to verify convergent and discriminant validities.

Pearson’s correlation coefficient was used to verify the criterion validity.

the reliability of the developed scale was evaluated using Cronbach’s α value and Spearman-Brown’s coefficient.

Phase 3: Standard setting of the scale

Finally, the cut-off score of the developed scale was set. The extended Angoff method estimates the score expected for a minimum competency holder [ 20 ]. The criteria for sufficiency/insufficiency in this study were established using the extended Angoff method. Criteria setters consisted of 6 people, including 2 new nurses and 4 experienced nurses who had experience in educating new nurses. The first round score was calculated by assigning a score that a nurse with a minimum competency could obtain out of four points for each item on the scale. A discussion was conducted between the criteria setters in the second round, based on the evaluation results of the first round. The cut-off score was derived through a total of two rounds.

Ethical consideration

Ethical approval.

for this study was obtained from the institutional review board of Ewha Womans University (Approval no. ewha-202105-0028-02). The researcher provided sufficient information to the participants about the purpose of the study, the use of data, and the protection of information before the survey. Consent was obtained from all participants in the online survey by presenting a consent form and having them participate in the survey after agreeing to the study.

Development of the initial items

Based on the literature, a concept analysis resulted in the derivation of two dimensions: nursing judgment and nursing delivery, as components for assessing nursing handoff competencies. In previous studies, handoff competencies of nursing judgment dimension include clinical reasoning, problem-solving ability, critical thinking tendency, clinical judgment, handoff performance ability, clinical performance ability, handoff self-efficacy, communication self-efficacy, handoff confidence, reporting confidence. Nursing delivery dimensions were evaluated as communication clarity, communication ability, information clarity, SBAR skills and knowledge, and handoff time. Notable indicators from scales utilized in prior research to measure nursing handoff capabilities were analyzed. Additionally, characteristics and items were derived through studies on factors influencing handoff, studies on the development of handoff scale, and qualitative studies on nurses’ experiences with handoff. Thus, in the nursing judgment dimension, 5 attributes and 17 indicators were generated, while in the nursing delivery dimension, 3 attributes and 17 indicators were derived.

Furthermore, through the analysis of interview content, two dimensions, nursing judgment, and nursing delivery, were developed, yielding 8 attributes and 48 indicators. Within the nursing judgment dimension, 5 attributes and 23 indicators were derived. These attributes encompass possessing substantial knowledge related to patient conditions, understanding and applying hospital-suggested handoff methods, and comprehending meaningful information. Moreover, an attribute not previously explored in the literature, “utilizing various resources for information gathering,” was identified. The attribute “reasoning health problems by considering contextual situations” includes indicators for task prioritization, comprehensive understanding of patient health issues, and identification of contextual factors influencing patient condition changes.

In the developmental phase of the conceptual framework, attributes and indicators derived from literature and interviews were analyzed, similar content was grouped, and duplicated indicators were eliminated. Consequently, the initial scale comprised two dimensions, eight attributes, and 69 preliminary items.

Participant characteristics

The general characteristics of the participants in this study are shown in Table  1 . The first survey included 228 (83.5%) female participants and an average age of 29.7 (± 5.0) years. The tertiary general hospital was the most common type of hospital where the participants worked 148 (54.2%), and the average clinical career was 4.8 (± 3.9) years. Additionally, 85.7% of the participants in the second main survey were female, the average age was 29.9 (± 4.8) years, and the average clinical career was 4.4 (± 3.5) years.

Validity and reliability

Construct validity, exploratory factor analysis.

The correlation coefficient for the item “I do not talk about things that are not related to work” between the item and total score was r  =.14. The reliability of all items increased from Cronbach’s α of 0.943 to 0.944 when this item was deleted. Therefore, EFA was performed on 48 questions except for this one. The KMO measurement value was 0.92 and Bartlett’s sphericity was statistically significant ( \( {x}^{2}\) = 4908.858, df = 1128, p  <.001), thus the collected data were suitable for factor analysis. Communality was set at 0.30 to construct items that can express the meaning of factors well. The eigenvalue of ≥ 1.0, which means the explanatory power of the factors, and the scree plot were referred to for the number of factors. Items with a factor loading of ≥ 0.30 were considered meaningful [ 19 ], so items with a factor loading of < 0.30 were deleted. This study extracted 12 factors with an eigenvalue of ≥ 1.0, but 4 factors were suitable for the point where the eigenvalue of the scree plot rapidly decreases as a result of checking the “elbow” point (Fig.  1 ). Therefore, the number of factors was fixed at 4, and the first factor analysis was performed. Finally, the scale was confirmed with 4 factors and 27 items derived from the results of 4 EFA rounds, with 87.52% cumulative explanatory power of the scale (Table  2 ). The difference between the content of the item and the factor load was considered for items with overlapping loads on both factors. Hence, item 5 was deleted, and items 21 and 23 were placed in factor 3 with a greater factor load. Item 2 was maintained factor 2 by reviewing the contents. Factor 1 has 22.02% explanatory power with 4 items, factor 2 has 24.06% explanatory power with 8 items, factor 3 has 20.88% explanatory power with 7 items, and factor 4 has 20.55% explanatory power with 8 items.

Scree plot eigenvalues of exploratory factor analysis

Confirmatory factor analysis

CFA was conducted to confirm the structural suitability of 27 questions and 4 factors derived through EFA. Maximum likelihood estimation was used for CFA, and a standard of ≥ 0.50 was applied for the standardization coefficient [ 21 ]. The contents were reviewed among the five items with a standardized coefficient of ≤ 0.50 for the first CFA, wherein two items (Items 27 and 33) were deleted. The second CFA result is shown in (Table  3 ). The fitness indices of the model were as follow: \( {x}^{2}\) = 349.335 ( p =.001), SRMR of 0.05, RMSEA of 0.04, TLI of 0.95, and CFI of 0.96.

Criterion-related validity

The criterion validity test revealed a significant correlation between the total score of the Nursing Handoff Competency Scale developed in this study and the scores of the NCRS and GICC-15 ( r  =.60, p  <.01; r  =.42, p  <.01) The criterion validity of the scale developed in this study was verified (Table  4 ).

Convergent and discriminant validity

The construct reliability of each factor greater than 0.7 was taken as an indicator to evaluate convergent validity [ 22 ]. In this study, the construct reliability of each factor was 0.73 (factor 1), 0.85 (factor 2), 0.84 (factor 3), and 0.69 (factor 4). Additionally, discriminant validity proves the degree of difference between factors. If the confidence interval of the correlation coefficient between factors does not include 1.0, discriminant validity is judged to have been secured [ 23 ]. In this study, discriminant validity was verified because the confidence interval of the correlation coefficient between factors did not include 1.0, so the discriminant validity was verified (Table  5 ).

Reliability

Cronbach’s α of the whole scale was 0.91 and the Cronbach’s α of the factors ranged from 0.688 to 0.848. Cronbach’s α of each factor was as follows: knowledge on handoff methods 0.72, identification of patient information 0.85, judgement and transfer of nursing situation 0.84, and formation of supportive relationships 0.69. The correlation coefficient for split-half reliability was calculated by dividing the items into odd and even numbers and then applying them to the Spearman-Brown formula, and was calculated as 0.85, thereby verifying the reliability of the scale.

Final measurement scale

The nursing handoff competency scale was finally confirmed through the validity and reliability verification of the developed scale. The scale consists of 25 items with a total of 4 factors: “knowledge on handoff methods (4 items),” “identification of patient information (8 items),” “judgment and transfer of nursing situation (7 items),” and “formation of supportive relationships (6 items).” The scale has a 4-point Likert scale consisting of strongly disagree (1), disagree (2), agree (3), and strongly agree (4), and the measurement range is 25–100 points. The finally confirmed nursing handoff competency scale is shown in Appendix 1 .

Cut-off score

The extended Angoff method was used to set the criterion score. The standard setters consisted of new nurses and nurses with experience as preceptor. In the first round, each standard setter assigned the score (0–4 points) needed for a minimumally competent examinee to pass, resulting in a score of 64.83. In the second round, scores were assigned again after discussion between standard setters based on the scores derived in the first round. The final score of 71.25 was derived by multiplying the average of the final assigned scores by the number of questions. The total score of the scale developed in this study was derived as an integer, but the criterion score was calculated as 71.25. When determining the criterion score, the result of applying the criterion score should be considered [ 20 ]. Since the nursing handoff competency scale developed in this study was to select nurses who need nursing handoff education, selecting a high score can provide many subjects with educational opportunities. Therefore, the sufficient/insufficient criterion score of this scale was determined as 72 points.

In total, 17.5% of them have insufficient nursing handoff competency with a score of < 72.0 (Table  6 ). Among the participants with < 1 year of clinical career, 56.0% had insufficient nursing handoff competencies, which was higher than 44.0% of participants who had sufficient competencies, considering the difference according to the clinical career, whereas 12.6% among the participants with ≥ 1 year of clinical career had insufficient nursing handoff competencies. In particular, Six of the participants with 1–3 years of clinical career had a very low nursing handoff competency score of < 50 points. This indicates that nurses who need handoff education should include not only new nurses but also experienced nurses.

This study developed a scale to measure nurses’ handoff competency and verified its reliability and validity. For handoff, a preparation process is needed to determine the patient’s current condition. In this process, the patient’s health-related data, nursing care performed, current treatment status, and information on treatment plans are selected and organized, and this convey through clinical judgment. Another characteristic of nursing handoff is to efficiently convey the information so that the receiving nurse understands the patient’s situation within the given time. Therefore, in this study, in the process of deriving the conceptual framework of the scale, the items were derived by integrating the results of the content analysis of interviews with clinical nurses as well as the literature review of 26 studies. Through this, we derived items to evaluate understanding ability, such as ‘Identify the overall changes in the patient’s health problems (item 13)’, ‘Explain the patient’s health problem by identifying the contextual factors related to the change in the patient condition (item 16)’. In addition, when deriving items, items about communication skills to convey information efficiently were derived. However, through exploratory factor analysis, only two items were retained. This was not classified as a separate factor, but was combined with items about judgment of the nursing situation and organized into the factor ‘judgment and communication of the nursing situation’. So, the final scale consisted of 4 factors and 25 items.

The first factor “Knowledge on handoff methods” includes four items pertinent to the type of information transferred during handoff, the method of handoff, documentation, and the use of an electronic medical record system. These items were derived based on the statements of new graduate nurses, who find handoff challenging, and experienced nurses with preceptor experience. Only 24.7% of facilities have written guidelines or a checklist for handoff within the unit although nurses receive handoff training after being hired [ 24 , 25 ]. Consequently, most nurses receive only informal handoff training. Nurses who lack knowledge about handoff may omit important pieces of information or present irrelevant information [ 26 , 27 ]. Unsystematic handoff causes prolonged handoff time and hinders follow-up nursing tasks [ 28 ]. Therefore, systematic handoff education and tools for identifying handoff education needs are required to enhance the efficiency of nurses in nursing tasks and help new graduate nurses adjust to clinical practice.

The second factor, “Identification of patient information” consists of eight items that assess whether important information about the patient’s health problems, treatment, and care was delivered during the handoff. The transfer of essential information about the patient’s health problems, treatment, and care between nurses at the change of shift is important to ensure nursing care continuity [ 29 , 30 ]. Inadequate understanding of patient information engenders problems that threaten patient safety [ 31 ], and information omission and ambiguity hinder the follow-up care provided by the receiving nurse and thwart the provision of continuous care because the receiving nurse lacks knowledge about the current treatment status [ 32 , 33 ]. Therefore, assessing whether a nurse has identified essential information and delivered them is important to measure handoff competency.

The third factor, “Judgment and transfer of nursing situation” comprises seven items about the clinical inference and judgment based on required patient information for a nursing handoff and the ability to transfer patient information. Critical thinking to understand the clinical situation is required during nursing handoff [ 34 ]. New graduate nurses experience difficulties to think critically regarding patient health problems even after a year in clinical practice [ 7 ]. Additionally, the item assessed to be the most difficult for the minimally competent person was “Comprehensively understanding the patient’s health problem by connecting relevant data” during the establishment of criteria for the scale in this study. The critical thinking process required for clinical judgment is essential to perform a handoff. However, existing handoff assessment tools only evaluate the handoff task itself and disregard the process of understanding the patient’s health problems before the handoff. Nurses’ handoff performance is assessed by determining their ability to analyse and understand the information in a given situation, and the developed scale is important because it contains items for this purpose. Moreover, the method of information delivery is an important part that affects the clinical decision-making of the receiving nurse [ 35 ]. This scale did not separate the competency to communicate effectively as a factor. However, the items “Explain the patient’s health problem by identifying the contextual factors (cause, effect) related to the change in patient condition (item 16)”, “Explain information related to health problems according to a causal relationship (item 18)”, and “Structured by integrating data related to health issues, rather than listing information (item 19)” are important items that measure the competency to deliver the context of the nursing process, rather than simply listing information.

The final factor, “Formation of supportive relationships” comprises six items pertinent to maintaining a collaborative and positive relationship between the transferring and receiving nurses. The quality of care is negatively affected by interpersonal problems among nurses, and nurses with better communication skills can communicate actively to promote patient safety [ 36 ]. A handoff can be categorized into technical communication, which involves the structuring and explanation of clinical information, and relational communication, which pertains to the interactions between the nurses [ 37 ]. The handoff quality is also influenced by relational factors, thus considering both aspects of communication are important. One of the items of our tool pertains to allowing the receiving nurse to ask questions and discuss difficult clinical situations together. The individual interviews with clinical nurses conducted in this study confirmed that the receiving nurse should check the patient’s condition and treatment status during handoff, and in the process, the receiving nurse could make up for the lacking parts of the sending nurse. Therefore a successful transfer of information requires a mutually respectful attitude between the two involved parties as well as evaluation and feedback to foster a supportive relationship.

As previously discussed, a key strength of the tool developed in this study is that it reflects the interpersonal relationship between nurses during a handoff as well as the nursing handoff. Existing instruments to evaluate the effects of handoff training do not reflect the purpose of a handoff, which is to help the receiving nurse understand the patient’s situation. Therefore, these instruments could not assess the process of summarizing and structuring patient data to transfer this information. The tool developed in this study contains items to assess the ability to understand and deliver data, reflects the nature of the nursing handoff based on field data obtained through interviews, and assesses the competencies required for a handoff comprehensively. However, Although this scale is suitable for inter-shift handoffs, further research is needed to determine whether it is also valid for interdepartmental handoffs.

In this study, we provided evidence on how well this scale reflects the concept it is intended to measure through verification of content validity, construct validity, and criterion validity. In scale validation research, the basis for validity cannot be sufficiently supported with just one approach, so various logical grounds must be presented [ 38 ]. This study is significant in that it presented a rational basis through various test of the validity of the scale. Moreover, another significant aspect of our scale is that we set cut-off scores to determine adequate/inadequate handoff competency. The cut-off score was set to 72 points. This score can be used to determine whether a nurse has adequate competency to perform a handoff. Among the second round study participants, 17.5% scored below 72, and the differences in the scores were significantly associated with the length of their clinical career. Also the results indicate that some experienced nurses also need additional handoff training, as identified by the cut-off score. This cut-off score is believed to expand the applicability of this tool. Healthcare facilities could determine nurses’ current handoff competencies and identify those in need of further training using this tool. The results of the tool will highlight the areas of weakness among nurses and hence can be useful for structuring relevant handoff training. Furthermore, handoff competency changes based on different career lengths and can be used as evidence data for handoff training for clinical nurses and as foundational data for developing a systematic handoff education system.

Limitations

The nursing handoff competency measurement scale developed in this study is a self-assessment scale, thus the measurement result and the objective nursing handoff competency may be different depending on the participant’s perception. Therefore, the evaluation results of the receiving nurse, peers, nursing educators, and nursing managers should be considered together to objectively utilize the results of the nursing handoff competency measurement scale developed in this study. Additionally, more than 70% of the participants in this study were nurses from tertiary general hospitals and general hospitals and the number of care hospital nurses is low at 2.2%. Therefore, further study is needed to confirm whether it is appropriate to apply to care hospital nurses. Finally, the convergent validity verification results for this tool did not meet the validity criteria. Therefore, further research is needed to address this issue.

The nursing handoff competency scale developed in this study is a consistent and valid evaluation tool. It consisted of 4 factors and 25 items and was a self-assessment tool on a 1–4 point Likert scale. The scores range from 25 to 100, and higher scores indicate higher nursing handoff competency. A score of ≥ 72 can be interpreted as sufficient nursing handoff competency. Therefore, we recommend this scale to evaluate the competency level to perform nurse handover, determine the need for educational support, and check the effectiveness of education.

Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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J.Do: Conceptualization, Methodology, Validation, Investigation, Formal analysis, Data Curation, Writing - Original Draft; S. Shin: Conceptualization, Methodology, Validation, Writing - Review & Editing, Supervision.

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Do, J., Shin, S. Development of nursing handoff competency scale: a methodological study. BMC Nurs 23 , 272 (2024). https://doi.org/10.1186/s12912-024-01925-w

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