What is the Likert Scale, What Are Its Features and Advantages?

The Likert scale is a one-dimensional scale that researchers use to collect participants’ attitudes and opinions. Researchers often use this psychometric scale to understand views and perspectives on a brand, product, or target market. Different variations of Likert scales, such as the Guttman scale, the Bogardus scale, and the Thurstone scale, have focused directly on measuring people’s ideas. Psychologist Rensis Likert made a distinction between a scale of responses to a group of items (maybe 8 or more). Responses are measured at a range of values. In this article, there are examples as well as information about what Likert scale is and its types.

Likert Scale Example

For example, to collect product feedback, the researcher uses a Likert Scale question in the form of a binary option question. With options listed as agree or disagree frames the question as the product is a good buy. Another way of framing this question is by stating the level of satisfaction with the products and options ranging from not at all to very satisfied.

When responding to an item on a Likert Scale, the user responds expressly with his own consent or level of understanding. These scales allow to determine the level of agreement or disagreement of the participants. The Likert scale assumes that the strength and intensity of the experience is linear and therefore measures either full agreement or complete disagreement, assuming attitudes can be measured.

Likert Scale Types with Examples

The Likert scale has become a favorite among researchers who collect opinions about customer satisfaction or employee satisfaction. This scale can basically be divided into two main types:

• Even Likert Scale
• Odd Likert Scale
• Even Likert Scale

Researchers even use Likert scales to gather excessive feedback without providing a neutral option.

• 4-Point Likert Scale for Importance: This type of Likert scale allows researchers to include four extreme options without making a neutral choice. Here, various degrees of importance are represented on the 4-Likert Scale.

• 8-Point Proposition Probability: This is a variation of the 4-point Likert scale described earlier, the only difference being that this scale has eight options to collect feedback on a recommendation possibility.

Odd Likert Scale

Researchers use the strange Likert scale to give participants the option to respond impartially.

• 5-point Likert scale: With five answer options, researchers use this strange Likert-scale question to gather information on a topic by adding a neutral answer option for participants to choose what they don’t want to answer from extreme choices.

• 7-point Likert scale: The 7-point Likert scale adds two more answer options to the extreme points of a 5-point Likert-scale question.

• 9-point Likert scale: The 9-point Likert scale is quite rare, but it can be used by adding two more answer options to the 7-point Likert scale question.

Properties of the Likert Scale

The Likert scale emerged in 1932 as a commonly used 5-point scale. These scales range from a set of general topics to the most specific topics asking participants to indicate both their level of consensus, approval, or belief. Some important features of the Likert scale are:

• Related answers: Regardless of whether the relationship between the item and the sentence is clear, the items should be easily associated with the sentence’s answers.

• Scale type: Items should always have two extreme positions and an intermediate response option that serves as a scale between the ends.

• Number of answer choices: Although the most common Likert scale is the 5-item scale, it is important to note that using more items helps create greater precision in results.

• Increased reliability of the scale: Researchers often increase the ends of the scale to create a seven-point scale by adding “many” above and below the five-point scales. The seven-point scale reaches the upper limits of the reliability of the scale.

• Using large scales: As a general rule, Likert and others recommend that it is better to use as broad a scale as possible. If appropriate, responses can always be divided into short groups for analysis.

• Lack of a neutral option: Given these details, the scales are sometimes reduced to an even number of categories (usually four), eliminating the possibility of being impartial in the mandatory election survey scale.

• Internal variable: The primary Likert record can be a natural variable that determines respondents’ feedback or attitudes, and this underlying variable is at best the range level.

Likert Scale Data and Analysis

Researchers regularly use surveys to measure and analyze the quality of products or services. It is a standard classification format for Likert scale studies. Participants provide their opinions (data) about the quality of a product / service using two, four, five or seven levels from high to low or better to worse. Researchers and supervisors often group the collected data into a hierarchy of four basic measurement levels, with nominal, rank, range, and ratio measurement levels for further analysis as follows:

• Nominal data: Responses classified as variables do not necessarily have a quantitative data or rank called nominal data.

• Sequential data: Data in which it is possible to sort or classify responses but impossible to measure distance is called sequential data.

• Spacing data: Collective data where orders and distances can be measured are called spacing data.

• Ratio data: Ratio data is similar to range data. The only difference is that there is an equal and precise ratio between each data and absolute zero is considered the starting point.

Data analysis using nominal, range, and ratio data is often transparent and easy. Ordered data analyze data specifically related to Likert or other scales in questionnaires. This is not a new problem. The effectiveness of processing sequential data as interval data continues to be discussed in survey analysis of various applied areas. Some of the important points to keep in mind include:

Statistical tests: Researchers sometimes treat sequential data as range data because they claim that parametric statistical tests are more powerful than nonparametric alternatives. Moreover, results from parametric tests are easy to interpret and provide more information than non-parametric options.

• Concentration on Likert scales: However, treating ordered data as range data without examining the values ​​of the data set and the purposes of the analysis can mislead and misrepresent the results of a survey. To analyze scalar data more appropriately, researchers prefer to consider sequential data as interval data and concentrate on Likert scales.

• Median or range to examine data: A universal guideline suggests that, like any parametric analysis based on normal distribution, when data are on ordered scales, mean and standard deviation are unfounded parameters for detailed statistics. Nonparametric testing is done based on the appropriate median or range to examine data.

Applications Used to Analyze the Results of Likert Scales

There is a long discussion of the most logical way to analyze Likert data, as the Likert item data is separate, ordered, and limited in scope. The first option is between parametric and nonparametric tests. The advantages and disadvantages of each analysis type are generally explained as follows:

• Parametric tests assume a regular and uninterrupted cleavage.

• Nonparametric tests do not assume a regular or continuous split. However, there are concerns that there is less ability to detect a difference when it exists.

This is a real decision a researcher has to make when deciding to analyze information from a questionnaire using Likert Scale questions.

• A series of studies over the years attempting to answer this question. However, they tended to look for a limited number of potential distributions for Likert data, which damages the generalization of the results. Thanks to increases in computing power, simulation studies can now comprehensively evaluate a wide variety of distributions.

• The researchers identified a variety of 14 distribution sets that represent true Likert data. The computer program made self-contained sample pairs to test all possible combinations of the 14 distributions.

• In total, 10,000 random samples were created for each of the 98 distribution combinations. Pairs of samples are analyzed using both the two-sample t-test and the Mann-Whitney test to compare the effectiveness of each test. The study also evaluated different sample sizes. What is Licert Scale, What are its Features and Advantages?

• The results show that Type I error rates (false positive) for all distribution pairs are very close to the target quantities. If an organization uses any of the analyzes and the results are statistically significant, they don’t need to worry too much about false positives.

• The results also show that for most distribution pairs, the difference between the strengths of the two tests is insignificant. If there is a difference at the population level, the probability that any of the analyzes will detect it is equal.

• There are some specific pairs of distributions where there is a difference in strength between the two tests. If an organization runs both tests on the same data and disagrees (one important, the other not), this difference in strength only affects a small proportion of cases.

• Generally, the choice between two analyzes is a loop. If an organization needs to compare two sets of five-point Likert data, the method of analysis is usually not important.

• Both parametric and non-parametric tests consistently provide the same security against false negatives and also offer the same protection against false positives. These models are valid for 10, 30 and 200 sample sizes per batch.

Advantages of the Likert Scale

There are many advantages to using the Likert Scale in a survey for market research. These advantages are as follows:

• Ease of application: This universal scale is easily understood and can be applied to various customer satisfaction or employee satisfaction surveys.

• Measurable answer options: Likert items that do not have a significant relationship with the expression should be measured and statistical analysis should be made on the results obtained.

• The order of views should be analyzed: There may be a sample with different views on a particular topic. The Likert scale provides a ranking of the views of these people surveyed.

• Simple to answer: Participants can understand the purpose of this scale and answer the question quickly.

Likert-scale survey is a comprehensive technique for measuring feedback and information, making it significantly easier to understand and respond. This is a critical question for measuring your opinion or attitude towards a particular topic so it will be of great help in the next step of a research.