Published on July 3, 2020 by Pritha Bhandari and Kassiani Nikolopoulou. Revised on November 30, 2022. A Likert scale is a rating
scale used to measure opinions, attitudes, or behaviors. It consists of a statement or a question, followed by a series of five or seven answer statements. Respondents choose the option that best corresponds with how they feel about the statement or question. Because respondents are presented with a range of possible answers, Likert scales are great for capturing the level of agreement or their feelings regarding the topic in a more nuanced way. However, Likert scales are prone to
response bias, where respondents either agree or disagree with all the statements due to fatigue or social desirability or have a tendency toward extreme responding or other
demand characteristics. Likert scales are common in survey research, as well as in fields like marketing, psychology, or other social sciences. Download Likert scale response options What are Likert scale questions?Likert scales commonly comprise either five or seven options. The options on each end are called response anchors. The midpoint is often a neutral item, with positive options on one side and negative options on the other. Each item is given a score from 1 to 5 or 1 to 7. The format of a typical five-level Likert question, for example, could be:
In addition to measuring the level of agreement or disagreement, Likert scales can also measure other spectrums, such as frequency, satisfaction, or importance. When to use Likert scale questionsResearchers use Likert scale questions when they are seeking a greater degree of nuance than possible from a simple “yes or no” question. For example, let’s say you are conducting a survey about customer views on a pair of running shoes. You ask survey respondents “Are you satisfied with the shoes you purchased?”
A dichotomous question like the above gives you very limited information. There is no way you can tell how satisfied or dissatisfied customers really are. You get more specific and interesting information by asking a Likert scale question instead: “How satisfied are you with the shoes you purchased?”
Likert scales are most useful when you are measuring unobservable individual characteristics, or characteristics that have no concrete, objective measurement. These can be elements like attitudes, feelings, or opinions that cause variations in behavior. Receive feedback on language, structure and formattingProfessional editors proofread and edit your paper by focusing on:
See an example How to write strong Likert scale questionsEach Likert scale–style question should assess a single attitude or trait. In order to get accurate results, it is important to word your questions precisely. As a rule of thumb, make sure each question only measures one aspect of your topic. For example, if you want to assess attitudes towards environmentally friendly behaviors, you can design a Likert scale with a variety of questions that measure different aspects of this topic. Here are a few pointers:
Include both questions and statementsA good rule of thumb is to use a mix of both to keep your participants engaged during the survey. When deciding how to phrase questions and statements, it’s important that they are easily understood and do not bias your respondents in one way or another. Use both positive and negative framingIf all of your questions only ask about things in socially desirable ways, your participants may be biased towards agreeing with all of them to show themselves in a positive light.
Respondents who agree with the first statement should also disagree with the second. By including both of these statements in a long survey, you can also check whether the participants’ responses are reliable and consistent. Avoid double negativesDouble negatives can lead to confusion and misinterpretations, as respondents may be unsure of what they are agreeing or disagreeing with.
Ask about only one thing at a timeAvoid double-barreled questions (asking about two different topics within the same question). When faced with such questions, your respondents may selectively answer about one topic and ignore the other. Questions like this may also confuse respondents, leading them to choose a neutral but inaccurate answer in an attempt to answer both questions simultaneously.
Be crystal clearThe accuracy of your data also relies heavily on word choice:
How to write Likert scale responsesWhen using Likert scales, how you phrase your response options is just as crucial as how you phrase your questions. Here are a few tips to keep in mind.
Decide on a number of response optionsMore options give you deeper insights but can make it harder for participants to decide on one answer. Fewer options mean you capture less detail, but the scale is more user-friendly. Usually, researchers include five or seven response options. It’s a good idea to include an odd number so that there is a midpoint. However, if you want to force your respondents to choose, an even number of responses removes the neutral option.
Choose the type of response optionYou can measure a wide range of perceptions, motivations, and intentions using Likert scales. Response options should strive to cover the full range of opinions you anticipate a participant can have. Some of the most common types of items include:
Some researchers also include a “don’t know” option. This allows them to distinguish between respondents who do not feel sufficiently informed to give an opinion and those who are “neutral” on the topic. However, including a “don’t know” option may trigger unmotivated respondents to select that for every question. Choose between unipolar and bipolar optionsOn a unipolar scale, you measure only one attribute (e.g., satisfaction). On a bipolar scale, you can measure two attributes (e.g., satisfaction or dissatisfaction) along a continuum.
Your choice depends on your research questions and aims. If you want finer-grained details about one attribute, select unipolar items. If you want to allow a broader range of responses, select bipolar items. Unipolar scales are most accurate when five-point scales are used. Conversely, bipolar scales are most accurate when a seven-point scale is used (with three scale points on each side of a truly neutral midpoint.) Note Choosing between unipolar and bipolar questions is not the same thing as asking two things at once (double-barreled questions).Make sure that you use mutually exclusive optionsAvoid overlaps in the response items. If two items have similar meanings, it risks making your respondent’s choice random.
How to analyze data from a Likert scaleBefore analyzing your data, it’s important to consider what type of data you are dealing with. Likert-derived data can be treated either as ordinal-level or interval-level data. However, most researchers treat Likert-derived data as ordinal: assuming there is not an equal distance between responses. Furthermore, you need to decide which descriptive statistics and/or inferential statistics may be used to describe and analyze the data obtained from your Likert scale. You can use descriptive statistics to summarize the data you collected in simple numerical or visual form. Example: Descriptive statistics
You can use inferential statistics to test hypotheses, such as correlations between different responses or patterns in the whole dataset. Example: Inferential statistics
Lastly, be sure to clearly state in your analysis whether you treat the data at interval level or at ordinal level. Analyzing data at the ordinal levelResearchers usually treat Likert-derived data as ordinal. Here, response categories are presented in a ranking order, but the distances between the categories cannot be presumed to be equal. For example, consider a scale where 1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, and 5 = strongly disagree. In this scale, 4 is more negative than 3, 2, or 1. However, it cannot be inferred that a response of 4 is twice as negative as a response of 2. Treating Likert-derived data as ordinal, you can use descriptive statistics to summarize the data you collected in simple numerical or visual form. The median or mode generally is used as the measure of central tendency. In addition, you can create a bar chart for each question to visualize the frequency of each item choice. Appropriate inferential statistics for ordinal data are, for example, Spearman’s correlation or a chi-square test for independence. Analyzing data at the interval levelHowever, you can also choose to treat Likert-derived data at the interval level. Here, response categories are presented in a ranking order, and the distance between categories is presumed to be equal. Appropriate inferential statistics used here are an analysis of variance (ANOVA) or Pearson’s correlation. Such analysis is legitimate, provided that you state the assumption that the data are at interval level. In terms of descriptive statistics, you add up the scores from each question to get the total score for each participant. You find the mean, or average, score and the standard deviation, or spread, of the scores for your sample. Advantages and disadvantages of Likert scalesLikert scales are a practical and accessible method of collecting data.
Problems with Likert scales often come from inappropriate design choices.
Frequently asked questions about Likert scalesWhat is a Likert scale? A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined. To use a Likert scale in a survey, you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement. Are Likert scales ordinal or interval scales? Individual Likert-type questions are generally considered ordinal data, because the items have clear rank order, but don’t have an even distribution. Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them. The type of data determines what statistical tests you should use to analyze your data. What is operationalization? Operationalization means turning abstract conceptual ideas into measurable observations. For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations. Before collecting data, it’s important to consider how you will operationalize the variables that you want to measure. Cite this Scribbr articleIf you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... What is a 5 point rating scale?
What is rating scale data?A rating scale is a common method of data collection that is used to gather comparative information about a specific research subject. Specifically, a rating scale is a type of multiple-choice question and it allows survey respondents to assign a value to a product or service.
Is a rating scale qualitative or quantitative?Rating scales do not produce qualitative data, irrespective of what the end-point labels may be. Data from Likert scales and continuous (e.g. 1-10) rating scales are quantitative. These scales assume equal intervals between points.
What does the rating scale scores tell us?It consists of close-ended questions along with a set of categories as options for respondents. It helps gain information on the qualitative and quantitative attributes. The most common example is the Likert scale, star rating, and slider.
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