Scale refers to an instrument or tool of measurement of any kind that can quantify events or objects. The school ruler used to draw measured straight lines is a common example of a scale. However, when it comes to comprehensive research, the meaning of scale becomes more advanced, introducing measurements like ordinal scales.
In research methodology, scales are used to measure various variables in a study on multiple parameters. There are four major types of scales in research methodology, which we will discuss below.
Top Benefits of Ordinal Scale
A sort of scale used in research that ranks data according to a certain order or sequence is called an ordinal scale of measurement. The scale’s main benefit is that it enables us to quantify the size of the variable under investigation. While nominal scale helps measure tagged qualities, ordinal scale considers a specific order for measurement. Utilizing ordinal scales has a number of advantages:
Ease of Comparison
As the data is already sorted, ordinal scales make it simple to compare variables. This suggests that analyzing the data and identifying patterns and trends can be done quickly.
Using an ordinal scale in a customer satisfaction survey can help to identify areas for improvement. This approach allows the detection of improvements over time.
Grouping Variables
After variables have been ordered, grouping them is incredibly convenient with ordinal scales. This enables a simpler and more effective analysis of the data.
Grading students based on their scoring sequence can provide insights into their performance. This method allows for the identification of students who did well, those who did poorly, and those who may require additional support.
Such kind of grouping is highly effective in an academic performance evaluation.
Simple Analysis and Categorization
Because of their simplicity, ordinal scales are widely employed in statistical analysis and questionnaire formulation. The obtained data allows for easy inferences about the intended audience.
Responses can be ranked based on how strongly respondents agree or disagree with a particular proposition using an ordinal scale. This allows for easy analysis and interpretation of the survey data.
You can create attractive surveys using trusted tools like SurveyPoint and collect data on a large scale. To gain conclusive insights, you can utilize an ordinal scale in this situation.
Informative Results
The results of the ordinal scale are more informative than the nominal scale since it uses a linear rating system. A greater grasp of the size of the variable being measured is made possible by the ranking system.
In a poll about how satisfied people are with a product, an ordinal scale could be used to rate responses from “extremely satisfied” to “extremely unhappy.” This provides more information than a nominal scale, which only indicates whether the respondent is satisfied or not.
ALSO READ: Nominal VS Ordinal Scale: Explore The Difference
How to Use an Ordinal Scale?
An ordinal scale records the higher and lower value of different variables in a set of variables. It is used in measuring those choices in a survey questionnaire that need to be ranked according to an individual’s preferences.
Suppose you are conducting a survey to measure diverse voting behavior. And you ask users about their satisfaction level with the current ruling government using a scale that includes
- extremely satisfied,
- somewhat satisfied,
- no reaction, somewhat dissatisfied, and
- highly dissatisfied as answer choices.
In this scale, if a respondent selects somewhat satisfied, it indicates they are not extremely satisfied or dissatisfied with the government.
ALSO READ: Internal Validity: Making Your Survey Research More Beneficial
The Mode of Collected Responses For Ordinal Scale
Once the researcher records the data on an ordinal scale, they can interpret it. They can analyze ordinal data in several ways, ranging from descriptive to inferential.
Inferential data analysis uses methods such as the Mann-Whitney U test, Kruskal-Wallis H test, and Spearman’s rho, or rank correlation coefficient. These approaches all infer findings using techniques like median, mean, correlation, distribution, and others. Yet, the frequency distribution table offers a straightforward understanding of the findings.
A frequency distribution table contains two columns. The first column lists all the variables, while the second column shows the number of times each variable was recorded during the survey. This allows for easy analysis and interpretation of the data.
Conclusion
The ordinal scale is useful for the easy comparison of variables and even easier interpretation of results. The close-ended question usually has a maximum of 5 options, and people have to choose one. Hence, it is also easy for the respondent to pick an answer that he or she likes. Interpretation is easy because the ordinal scale does not produce a vague variable with negligible frequency.
Once ordered, the variables on an ordinal scale can also be easily grouped. Experts suggest that a questionnaire should include an open-ended question followed by an ordinal scale question that records why respondents have selected a particular answer.
Ordinal scales are frequently used in research, but they can also be useful in regular life. The ability to compare items quickly and in the sequence is helpful.
An ordinal scale can be used to rank items, such as when we need to rate a restaurant or prioritize our tasks. Therefore, we can make better decisions and achieve better results.
Making better decisions and being more organized in daily life can both result from understanding how to use the ordinal scale.
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