What is a Likert Scale? Definition, Examples & Best Practices
Likert Scale is a term used in survey research and market analysis. Understanding likert scale helps researchers design better surveys, collect higher-quality data, and draw more reliable conclusions. It is relevant across customer experience, employee research, brand tracking, and academic studies.
Likert Scale Definition
Likert Scale is a concept that plays an important role in survey methodology and market research. The primary keyword for this term is 'likert scale'.
Researchers and practitioners use likert scale when designing studies, collecting responses, or interpreting data. A solid understanding of likert scale helps avoid common errors and improves the reliability of research findings.
Secondary search queries for this topic include: likert scale examples, 5 point likert scale, likert scale questions. Each reflects a slightly different angle on the same core concept.
Why Likert Scale Matters
Getting likert scale right has a direct impact on data quality. When researchers misapply or overlook this concept, it often shows up later as unexplained variance in results, lower response rates, or findings that cannot be confidently acted on.
According to Dillman, Smyth, and Christian in Internet, Phone, Mail, and Mixed-Mode Surveys (4th ed., 2014), many of the most persistent sources of error in survey research trace back to decisions that touch on concepts like likert scale.
How Likert Scale Is Used in Practice
In customer experience programmes, likert scale informs how surveys are structured and distributed. In employee research, it shapes question design and result interpretation. In brand tracking and market research, it determines how data is collected and compared over time.
MindProbe supports researchers working with likert scale through purpose-built features in the survey builder and analytics dashboard, reducing the manual work involved in applying best practices.
Types & Variants
5-Point Likert Scale
The most common format. It's fast to complete, easy for respondents to understand, and produces data that is simple to analyse and benchmark. Ideal for commercial surveys where completion rate is a priority.
7-Point Likert Scale
Adds two additional response options (e.g. "Somewhat Disagree" and "Somewhat Agree") for greater granularity. Preferred in academic research and studies where subtle attitude differences are important. The trade-off is slightly higher cognitive load for respondents.
4-Point Likert Scale (Forced Choice)
Removes the neutral midpoint, forcing respondents to lean one way or the other. Useful when you need to eliminate fence-sitting, but it can frustrate respondents who genuinely feel neutral about a topic.
Semantic Differential Scale
A close cousin of the Likert scale that places bipolar adjective pairs at each end (e.g. "Modern" vs "Old-fashioned") rather than agreement labels. Often used in brand and product perception research.
Common Mistakes Related to Likert Scale
The most common errors involving likert scale in survey research tend to fall into three categories: misidentifying when the concept applies, applying it inconsistently across a study, and failing to account for it when interpreting results.
Piloting surveys before full distribution and checking results against known benchmarks are the two most reliable ways to catch problems before they affect your data.
Likert Scale and Survey Quality
Survey quality depends on consistent application of methodological principles, and likert scale is one of those principles. Whether you are running a one-off customer feedback study or a continuous brand tracking programme, understanding and correctly applying this concept improves the reliability of what you collect.
Frequently Asked Questions
A Likert scale is a psychometric rating scale that asks respondents to indicate their level of agreement with a statement, typically on a 5 or 7-point scale from "Strongly Disagree" to "Strongly Agree."
Likert Scale affects survey results by influencing how respondents engage with questions and how the resulting data can be interpreted. Applied correctly, it improves measurement accuracy. Overlooked, it can introduce systematic errors that skew findings in ways that are hard to detect after data collection has closed.
A 5-point scale offers less granularity but is quicker to complete, while a 7-point scale captures more nuance and is preferred in academic research. For most commercial surveys, a 5-point scale is sufficient.
Technically, Likert scale data is ordinal — the intervals between points are not guaranteed to be equal. However, in practice many researchers treat averaged Likert data as interval data for the purposes of statistical analysis, which is generally acceptable when scales have 5+ points.
The decision to focus on likert scale depends on your research objective and the population you are studying. It is most relevant when designing surveys for scale, when comparing results across groups or time periods, or when the accuracy of measurement is critical to a business or academic decision.
Common mistakes include misapplying the concept to unsuitable research designs, using inconsistent definitions across survey waves, and failing to account for likert scale when interpreting results. Piloting surveys and reviewing methodology with a second set of eyes before launch catches most of these errors.
Good starting points include Dillman, Smyth, and Christian's Internet, Phone, Mail, and Mixed-Mode Surveys, Pew Research Center's methodology documentation, and AAPOR's best practice standards. For practical application, MindProbe's help documentation covers how the platform's features relate to concepts including likert scale.