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In this customer-centric era gauging customer satisfaction and experience is paramount. Whether it is for market research or healthcare, researchers need effective tools that make it easy to quantify the subjective aspect of human behavior. This is where Likert Scale can be of value.
The systematic approach allows respondents to express their agreement, disagreement, or neutrality toward a statement. It gives you a standardized method to evaluate subjective responses and delve deeper into human behavior and perspectives.
In this blog, we will share types of Likert Scale questions and use cases.
Likert scale is generally used for market research and surveys in relation to people’s opinions and attitudes on a subject matter. It is a psychometric scale.
Rensis Likert, in 1932, developed the Likert scale because of his interest in measuring people’s opinions and attitudes on different items.
A Likert scale has two endpoints with the highest and lowest variables and a mid-section with a neutral variable. The scale ranges from poor to good, depending on the subject matter of the survey. The data obtained with a Likert scale is beneficial in measuring your target audience or participants’ attitude towards your company’s service or product.
A Likert scale is used for various surveys and research purposes. You can use a Likert scale in offline and online surveys to gain insight into a customer or employee satisfaction and the likelihood of recommending or understanding the customer’s issue.
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You can use the agreement-disagreement scale, frequency scale, satisfaction scale, quality scale, importance scale, or likelihood scale to gather data from your audience.
The odd Likert scale contains a midpoint in the scale, which allows the responders to opt for the neutral option. The neutral option helps a responder who does not want to bias toward any answer.
The even Likert scale does not have a neutral option. Hence, the responders are forced to choose from the given choices. These can be implemented using online survey tools.
Now let’s explore the two types of Likert scales and some examples of response options.
Let’s look at some examples of odd scales and the kinds of response options you can use with it.
High, Neutral, and Low options. Not enough choices are given to the responders.
➤ Would you participate in the seminar?
Two extremities are connected with a neutral option. This type of scale produces a smooth distribution of data.
➤ The company provides an appropriate amount of holiday for employee welfare.
➤ How was our waiter service?
➤ Would you recommend our Café to your friend?
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This scale is known to provide the most accurate data. It gives the responders many options to choose from making it easy to use.
➤ Do you agree with the new property law?
➤ How satisfied are you with our helpline?
➤ How important do you think is the legal 40-hour work per week?
This serves in the same way as a 7-point scale. Providing more options for the responders to choose from gives them flexibility and freedom to answer. The scale can have a range of “strongly agree” on one end to “strongly disagree” on the other.
Let’s see the examples of even scales and the different response types you can use with it.
This is the simplest scale. A yes or no type option follows the question. This is usually used for measuring agreement.
➤ Did you graduate from school?
It is a better option when you require accurate data. The lack of a neutral option forces the responders to choose an option from the extremities.
➤ Do you agree with the new flight instruction?
➤ How satisfied/dissatisfied are you with the range of colors provided?
➤ How often do you use our car service?
➤ How likely would you be to recommend home decore products to your friend?
It falls under the example of an even scale. However, even with the lack of neutral choice, you can always add more intermediate options such as “slightly agree” or “slightly disagree”. This also serves the same purpose.
In this section, we’ll look into two use cases – market research and healthcare. This type of rating scale helps you evaluate customer preference, satisfaction, perception, and quality of service/product. In both MR and healthcare, understanding people’s perceptions and experiences can help you make informed decisions and deliver excellent experiences.
Let’s look at Likert scale examples for both use cases.
2. Evaluate product satisfaction and usability:
2. Evaluate care provider performance:
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A Likert scale is gaining a lot of popularity in offline and online survey platforms. The questionnaire is easy to create, and the choice of options is easy to understand.
Unlike descriptive questions, which may frustrate the responder and may prove to be time-consuming. The range provided in a Likert scale helps the participants to answer without having to think.
Its versatility and ability to quantify the respondents’ responses make it a popular tool for researchers. By implementing it properly, you can uncover valuable insights and enhance customer experience.
In this blog, we have mentioned various Likert scale examples that you can use to design a survey. Leverage survey software that enables you to use this scale with other question types so you can explore deeper into customer behavior and gather insightful data.
It is a type of rating scale developed by psychologist Rensis Likert. The scale response ranges from ‘strongly agree’ to ‘strongly disagree.’ and asks respondents to select their level of agreement with a statement.
The Likert Scale offers a quantifiable method to analyze respondents; subjective responses. It allows you to evaluate data on survey participants’ opinions, attitudes, and preferences. This enables you to understand various perceptions and make informed decisions.
Ease of administration, standardized response options, comparability across respondents, quantitative data analysis, and ease of capturing data are some common advantages of using this rating scale.
Yes, you can perform quantitative data analysis on Likert scale data. By assigning numerical values to the responses, you can use statistical techniques like means, medians, and correlations to analyze the data.
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