What is the Likert scale?
Likert scale is defined as a unidimensional scale used to collect the respondent attitudes and opinions. This scale is often used to understand respondent ratings and agreement levels with the topic in-hand. Different variations of Likert scale are focused directly on measuring the attitudes of people, such as Guttman scale, Bogardus scale, Thurstone scale etc.
An example of the Likert Scale is if an organization would like to collect feedback on a product from a respondent, they could deploy a Likert Scale question in the form of a dichotomous option question framed as “The product was a good purchase” with the options listed as agree or disagree. The other way to frame this question is “Please state your satisfaction level with the products” and the options ranging from very dissatisfied to very satisfied.
Psychologist, Rensis Likert established a distinction between a scale which materializes from a collection of responses to a group of items (may be 8 or more), and responses are measured in a range of values. Technically, a Likert Scale makes reference to the last-mentioned. The difference between these two concepts has to do with the distinction that Likert made between the phenomenon that is being investigated and the variables of the means of capture.
Likert Scale is a psychometric scale used mainly in market research to understand the opinions and attitudes of a consumer towards a brand, product or target market. It serves organizations to make measurements and know about the degree of conformity of a person or respondent towards a certain affirmative or negative sentence.
When responding to an item on the Likert Scale, the user responds specifically based on their level of agreement or disagreement. Frequency scales with Likert implement fixed response formats to measure attitudes and opinions.These scales allow determining the level of agreement or disagreement of the respondents. Likert scale assumes that the strength and intensity of the experience are linear, therefore it goes from a total agreement to a total disagreement, assuming that attitudes can be measured.
The answers can be offered in different levels of measurement, allowing scales of 2, 4, 5, 6, 7, 8 or 9 elements previously configured. In most cases, it is better to have a neutral element for those users who neither agree nor disagree.
Learn more: Thurstone vs Guttman Scale
Types of Likert Scale with Examples
- Even Likert Scale
- Odd Likert Scale
Even Likert Scale: In case the researcher intends to gain extreme feedback without providing a neutral option.
- 2-Point Likert Scale of Agreement: The most easy to use Likert scale question which has only two answer options.
- 4-Point Likert Scale for Importance: This type of Likert scale allows researchers to include 4 extreme options without the provision of a neutral option. Here the various degrees of importance are represented in a 4-Point Likert Scale.
- 8-Point Likelihood of recommendation: This is a variation of the previously explained 4-point Likert scale, the only difference being, this scale has 8 options to collect feedback about likelihood of recommendation.
Odd Likert Scale: In case the researcher intends to give the respondents the choice of responding in a neutral manner, the scale will be considered to be Odd Likert Scale.
- 5-Point Likert Scale: With 5 answer options, this odd Likert scale question is used to gather information about a topic by including a neutral answer option for respondents to select in case they don’t wish to answer from the extreme choices.
- 7-Point Likert Scale: With 7 answer options, this odd Likert scale question is used to gather information about a topic by including a neutral answer option for respondents to select in case they don’t wish to answer from the extreme choices.
- 9-Point Likert Scale: With 9 answer options, this odd Likert scale question is used to gather information about a topic by including a neutral answer option for respondents to select in case they don’t wish to answer from the extreme choices.
Learn more about: Odd Likert Scale Surveys
Characteristics of Likert Scale
The Likert scale came into existence in 1932 in the form of the 5-point scale which these days, is extensively used. These scales range from a group of generic topics to the most specific ones that ask people to indicate how much do they agree or disagree, approve or disapprove, or believe it is true or false. Some major charactersitics of the likert scale, are:
- Multiple-choice questions: Likert scale questions are usually multiple-choice questions which have minimum two or three measurable categories.
- Related answers: Items should be easily related to the answers in the sentence, regardless of whether the relationship between item and sentence is evident.
- Scale type: The items must always have two extreme positions as well as an intermediate item that serves as a graduation between the extremes.
- Number of answer options: It is important to mention that although the most common Likert scale is that of 5 items, the use of more items helps to generate greater precision in the results.
- Increasing reliability of the scale: The ends of the scale are often increased to create a seven-point scale by adding “very” to the top and bottom of the five-point scales. It has been shown that the seven-point scale reaches the upper limits of the reliability of the scale.
- Using wide scales: As a general rule, Likert and others recommend that it be better to use a scale as wide as possible. One can always collapse the answers into concise groups, if appropriate, for analysis.
- Lack of a neutral option: By considering these details, scales are sometimes curtailed to an even number of categories (usually four) to eliminate the “neutral” option on a “forced choice” survey scale.
- Intrinsic variable: The primary Likert record clearly states that there could be an intrinsic variable whose value marks the feedbacks or attitudes of the respondents and this underlying variable is the interval level, at best.
- Reliable scales: The items on the scale should always be safe and reliable. To achieve reliability it is sometimes necessary to sacrifice the accuracy of the scale.
Likert Scale Data and Analysis
Surveys are constantly used to measure quality. For example, surveys can be used to measure the client’s perception of the quality of the product or the performance of quality in the provision of services. Likert scales are a common classification format for surveys. The respondents provide their opinion about quality of a product/service from high to low or better to worse using two, four, five or seven levels.
Researchers and auditors generally group collected data into a hierarchy of four fundamental measurement levels – nominal, ordinal, interval and ratio measurement levels:
- Nominal data: Data in which the answers are classified into variables need not necessarily have a quantitative data or order is called nominal data.
- Ordinal data: Data in which it is possible to sort or classify the answers, but it is not possible to measure the distance is called ordinal data.
- Interval data: In general, whole data in which measurements of orders and distances can be made is called interval data.
- Ratio data: Ratio data is similar to interval data with the only difference being an equal and definitive ratio between each data and absolute “zero” being a treated as a point of origin.
Data analysis using nominal, interval and ratio data is generally simple and transparent. Ordinal data analyzes data particularly in regards to Likert or other scales in the surveys. This is not a new problem. The effectiveness of handling ordinal data as interval data continues to be debatable in survey analysis of a variety of applied fields. Some of the major points to keep in mind, are:
- Statistical tests: The main reason why ordinal data is sometimes treated as interval data is the claim that parametric statistical tests are more powerful than nonparametric alternatives. Moreover, inferences from parametric tests are easy to interpret and provide more information than non-parametric alternatives.
- Concentration on likert scales: However, the treatment of ordinal data as interval data without examining the values of the data set and the objectives of the analysis can mislead and misrepresent the results of a survey. To appropriately analyze scalar data, it is preferable to consider ordinal data as interval data and concentrate on Likert scales.
- Median or range for inspecting data: A universal guideline suggests that the mean and the standard deviation are baseless parameters for detailed statistics when the data are on ordinal scales, just like any parametric analysis based on the normal distribution. The non-parametric test is done on the basis of appropriate median or range for inspecting data.
- Continuous measurement: The Kruskall-Wallis models can provide similar results as an analysis of variance, but based on the ranges and not on the means of responses. Since these scales are representative of an underlying measurement continues, a recommendation is to analyze them as interval data as a pilot before collecting the continuous measurement.
Learn more: Increase Survey Response Rates
Best practices for analyzing the results of the Likert scale
Likert five-point scales are commonly associated with surveys and are used in a wide variety of settings. If a person has come across the Likert scale in case options such as – totally agree, agree, disagree or disagree, disagree or strongly disagree, are asked about a particular topic.
Because the Likert element data is discrete, ordinal, and limited in scope, there has been a long dispute over the most valid way to analyze Likert data. The fundamental option is between parametric and non-parametric tests. The advantages and disadvantages for each type of test are generally described as the following:
- Parametric tests assume a normal and uninterrupted division.
- Non-parametric tests do not assume a normal or uninterrupted division. However, there are concerns about a lesser ability to detect a difference when one actually exists.
Which is the best option? This is a real decision that a researcher has to make when they decide to analyze information received from a survey where Likert Scale questions were asked.
- Over the years, a series of studies that have tried to answer this question. However, they have been inclined to look at a limited number of potential distributions for Likert data, which causes the generalization of the results to suffer. Thanks to increases in the power of computing, simulation studies can now thoroughly evaluate a wide range of distributions.
- The researchers identified a diverse set of 14 distributions that are representative of the actual Likert data. The computer program extracted self-sufficient pairs of samples to test all possible combinations of the 14 distributions.
- In total, 10,000 random samples were generated for each of the 98 distribution combinations. The pairs of samples are analyzed using both the two-sample t-test and the Mann-Whitney test to compare the efficacy of each test. The study also evaluated different sample sizes.
- The results show that for all pairs of distributions the Type I error rates (false positive) is very close to the target quantities. In other words, if an organization uses any of the analysis and results are statistically significant, it does not need to be too worried about a false positive.
- The results also show that for most pairs of distributions, the difference between the power of the two tests is trivial. In other words, if there really is a difference at the population level, any of the analyzes is equally likely to detect it.
- There are some pairs of specific distributions where there is a power difference between the two tests. If an organization performs both tests on the same data and do not agree (one is significant and the other is not), this difference in power affects only a small minority of cases.
- In general, the choice between the two analyzes is a loop. If an organization needs to compare two groups of five-point Likert data, it usually does not matter which analysis method is used.
- Both, parametric and non-parametric tests, consistently provide the same security against fallacious negatives and also provide the same security against fallacious positives. These patterns are valid for sample sizes of 10, 30 and 200 per group.
Advantages of Likert Scale
There are manage advantages of using a Likert Scale in a survey for market research. They are:
- Ease of implementation: This universally accepted scale can be easily understood and applied to various customer satisfaction or employee satisfaction surveys.
- Quantifiable answer options: Quantify Likert items that have no clear relation to the expression and conduct statistical analysis on the received results.
- Analyze the rank of opinions: There may be a sample which has varied opinions about a particular topic. Likert scale offers a ranking of the opinion of these people surveyed.
- Simple to respond: Respondents can understand the intent of this scale and easily answer to the question.
Disadvantages of the Likert Scale
There are also a few disadvantages of the Likert Scale. They are:
- Lack of scientific proof: There are scientific studies that indicate that there is a bias in the scale since the positive answers always overcome the negative ones.
- Scope of bias: There are also studies that prove that respondents tend to answer “in agreement” since it implies less mental endeavor at the time of answering an online survey.
- Hard to precisely evaluate results: Difficulty in establishing precision with the amount of positive and negative responses.
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The Likert-scale survey is a comprehensive technique for gauging feedback and information, which makes it significantly easy to understand and respond. This is a critical question to measure the opinion or attitude of a respondent towards a specific topic, so it will be of great help in the next step of an investigation.
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