Creating Likert scale score categories is essential to answer one of the research objectives, particularly in descriptive statistical analysis. We can categorize non-parametric variables that use the Likert scale into high, medium, and low categories. This information will significantly enrich the research findings.
In research, especially in social studies, the Likert scale is often used to measure respondents’ perceptions, attitudes, or behaviors. We already know that these variables are non-parametric, meaning their numerical values cannot be directly obtained.
Therefore, scoring techniques are needed, one of which uses the Likert scale. It is important to understand that variables measured using the Likert scale are, in principle, similar to ordinal scale data collection techniques.
However, one challenge faced by researchers is how to transform Likert scale scores into meaningful categories, making the results easier to interpret. This article from Kanda Data will discuss how to create categories based on Likert scale scores.
Measurement of Non-Parametric Variables (Likert Scale)
Initially, we need to understand that in statistics, there are parametric and non-parametric variables. So, what is the difference between parametric and non-parametric statistics?
Variables measured using the Likert scale are called non-parametric variables. Non-parametric variables are generally measured using nominal and ordinal scales. Essentially, Likert scale measurement is conceptually similar to ordinal scale measurement, making it a non-parametric variable.
Non-parametric variables typically have data characteristics that do not follow a normal distribution. Their measurement is based on rank or order, not absolute numbers. Thus, the Likert scale, which is conceptually similar to ordinal scale measurement, can be used to measure non-parametric variables such as attitudes, motivation, and other non-parametric variables.
Definition of Likert Scale
The Likert scale is a commonly used tool to measure non-parametric variables, as it allows respondents to express their level of agreement or disagreement with specific statements. In this case, several statement items are created, representing the indicators of the variable being measured.
From the respondents’ responses, scores can be obtained for each statement item. Likert scale scores are then interpreted as ordinal data, where researchers do not only consider the numerical values but also their order.
The Likert scale is a type of rating scale consisting of several levels, for example, from 1 (strongly disagree) to 5 (strongly agree). This scale is commonly used to measure the level of agreement or attitudes towards a statement.
Example of Likert Scale Variable Case Study
For example, in economic behavior research, researchers can use the Likert scale to measure consumer satisfaction with a particular product. Respondents are asked to rate various aspects of the product, such as quality, price, and service, using a scale from 1 to 5.
Based on the respondents’ answers, scores for each variable indicator are obtained. These scores are then processed to gain a general view of consumers’ perceptions of the product.
The Importance of Determining Categories from Likert Scale Scores
Grouping Likert scores into specific categories helps in easier data analysis. Without categorization, drawing clear conclusions from the data can be difficult.
For example, scores of 1-5 on a Likert scale can be grouped into “low,” “medium,” and “high” categories. In a study on employee motivation, researchers might use a 5-point Likert scale to assess motivation. After collecting the data, scores can be categorized as follows:
Score 1–2: Low motivation
Score 3: Medium motivation
Score 4–5: High motivation
Alternatively, if the indicator consists of 10 statements, we can determine that if the respondent answers all the statements, the minimum score is 10, and the maximum score is 50. We then categorize the scores as follows:
Score 10–23: Low motivation
Score 24–37: Medium motivation
Score 38–50: High motivation
By grouping scores into categories like this, we can interpret the data and more easily draw conclusions about employee motivation.
Conclusion
Using the Likert scale to measure non-parametric variables is very useful in social and economic research. However, transforming scores into meaningful categories is an essential step to aid in data analysis and interpretation.
With the right approach, we can gain clearer insights from the data collected. That concludes this article from Kanda Data, and we hope it is useful for you. Stay tuned for next week’s article update.
