Designing and analyzing Likert scales

A Likert scale is a rating scale used to assess opinions, attitudes, or behaviors. Likert scales are popular in survey research because they allow you to easily operationalize personality traits or perceptions.

To collect data, you present participants with Likert-type questions or statements and a continuum of possible responses, usually with 5 or 7 items. Each item is given a numerical score so that the data can be analyzed quantitatively.

Designing Likert-type questions

A Likert scale is made up of 4 or more questions that assess a single attitude or trait when response scores are combined. Each question may measure a separate component of that overall 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.

Phrasing as questions vs statements

Both statements and questions are often used in Likert scales. Using a mix of both can keep your participants engaged and attentive during your survey.

When deciding how to phrase questions and statements, it’s important to ensure that they are easily understood and do not bias your respondents in one way or another.

Positive vs negative framing

Use both positive and negative frames in your questions. If all 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 negatives

Double negatives can lead to confusion and misinterpretations as respondents will be unsure of what they are agreeing to.

Ask about only one thing at a time

If you use double-barreled questions, your respondents may selectively answer about one topic but ignore the other, or try to pick a neutral but inaccurate answer.

Selecting the response items

Likert scales commonly have 5 or 7 items, and the items on each end are called response anchors. The midpoint is often a neutral item with positive items on one side and negative items on the other. Each item is given a score from 1–5 or 1–7.

Number of items

More items give you deeper insights but make it harder for participants to decide on answers because there are more choices. Fewer items mean you capture less detail, but the scale is more user-friendly.

Types of items

You can measure a wide range of perceptions, motivations, and intentions using Likert scales.
Some of the most common types of items include:

• Agreement: Strongly agree, Agree, Neither agree nor disagree, Disagree, Strongly disagree
• Quality: Very poor, Poor, Fair, Good, Excellent
• Likelihood: Not at all likely, Somewhat likely, Extremely likely
• Experience: Very negative, Somewhat negative, Neutral, Somewhat positive, Very positive

Unipolar vs bipolar items

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

Use mutually exclusive items

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

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Example of a Likert scale

Analyzing Likert scale data

Before you analyze data from Likert-type questions and Likert scales, it’s important to consider what type you’re dealing with.

Ordinal vs interval level data

Your data can be divided into these two different types because they are associated with separate analysis procedures.

• data from individual Likert-type questions are treated as ordinal level.
• data from the overall Likert scale are treated as interval level.

In ordinal scales, each item has a rank that is higher or lower than others, but the exact differences between the items aren’t evenly spaced or clearly defined.

For example, you can’t be sure that the difference between “very poor” and “poor” is the same as the difference between “good” and “excellent”.

Interval scales also have a clear order, but the difference between each point is evenly spaced. For example, non-Likert rating scales from 1 to 10 can assume that the difference between 2 and 4 is the same as the difference between 5 and 7.

Overall Likert-scale data is often treated as interval because it is a composite score made from adding answers to 4 or more questions.

Descriptive statistics

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

Likert-type questions can be individually analyzed for deeper insights into specific attributes.

If the questions all measure a single trait or attitude when combined, they can also be grouped together and analyzed as a Likert scale. You can code the answers to each question into numbers and then add up the numbers to get an overall attitude score for each participant.

Inferential statistics

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

Whether you treat your data as ordinal or interval impacts your choice of a parametric or non-parametric statistical test. Parametric tests make stricter assumptions, such as even spacing, of data than non-parametric tests.

• For ordinal data (individual Likert-scale questions), use non-parametric tests such as Spearman’s correlation or chi-square test for independence.
• For interval data (overall Likert scale scores), use parametric tests such as Pearson’s r correlation or t-tests.

Strengths and limitations of Likert scales

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

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

Problems with Likert scales often come from inappropriate design choices.

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

Frequently asked questions about Likert scales

What 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.