KANDA DATA

In research activities, data serve as the main foundation for analysis and drawing conclusions. However, not all data have the same characteristics. One of the common challenges faced by novice researchers is understanding and applying data measurement scales. In fact, an understanding of data scales greatly determines the type of statistical analysis that can be used as well as the validity of research results.

Data measurement scales are generally divided into four types: nominal, ordinal, interval, and ratio. Each of these data scales has different characteristics, functions, and analytical implications. On this occasion, Kanda Data discusses the differences between nominal, ordinal, interval, and ratio data scales for research purposes.

The Importance of Understanding Data Scales for Researchers

Understanding data measurement scales is very important because it is directly related to the selection of statistical analysis methods, both descriptive and inferential. In addition, understanding data scales also determines appropriate data visualization techniques and the validity of interpreting research results.

On the other hand, the appropriateness of using statistical test tools, whether parametric or non-parametric, also depends on understanding data measurement scales. Errors in identifying data scales can lead to incorrect analyses, such as using mean tests on data that should only be analyzed using frequencies or rankings. Now, let us examine each of these data measurement scales in more detail.

Nominal Data Scale

The nominal data scale is the most basic measurement scale in statistics. Data on this scale function only as labels or categories, without indicating order or hierarchy. The characteristics of the nominal scale include: (a) no order (ranking); (b) mathematical operations cannot be performed; and (c) numbers function only as symbols.

Examples of nominal data include gender (male, female), type of occupation (farmer, fisher, trader), type of commodity (rice, maize, soybean), and respondent status (married, unmarried). Statistical analyses commonly used for nominal data are frequency, percentage, and mode.

Ordinal Data Scale

The ordinal data scale has categorical properties like the nominal scale, but it already has an order or ranking. However, the distance between categories is not necessarily equal. The characteristics of the ordinal scale are: (a) it has order or ranking; (b) the distance between categories cannot be measured precisely; and (c) mathematical operations are still limited.

Education level (elementary school, junior high school, senior high school, higher education), satisfaction scale (very dissatisfied – very satisfied), perception level (poor, moderate, good), and competition ranking (1st, 2nd, 3rd place) are examples of ordinal data scales. Commonly used analyses include non-parametric tests such as Spearman Rank or Mann–Whitney.

Interval Data Scale

The interval data scale has ordered categories with equal distances between values, but it does not have an absolute zero. The characteristics of the interval scale include: (a) clear ordering; (b) equal distances between values; (c) a relative zero point; and (d) addition and subtraction operations can be performed.

Examples of interval data include temperature measured in degrees Celsius or Fahrenheit, IQ scores, and test scores. For interval scales, a wider range of statistical analyses can be applied, such as mean, standard deviation, Pearson correlation, and linear regression, provided that statistical assumptions are met.

Ratio Data Scale

The ratio data scale is the most comprehensive measurement scale because it has all the properties of the previous scales, with the addition of a meaningful absolute zero. The characteristics of the ratio scale are: (a) it has order; (b) equal distances between values; (c) zero indicates the absence of a quantity; and (d) all mathematical operations can be performed.

Examples of ratio data include age (years), income (rupiah), body weight (kg), rice production (kg), and land area (hectares). The ratio scale allows the use of all statistical analysis techniques, both descriptive and inferential, including advanced parametric analyses.

Conclusion

A proper understanding of nominal, ordinal, interval, and ratio data measurement scales is a crucial initial step in research. Data scales not only determine how data are collected, but also influence analytical methods and the quality of the conclusions produced.

For researchers, especially in the social, economic, and agricultural fields, recognizing data scales from the research design stage will help avoid analytical errors and enhance the credibility of research results. By deeply understanding data scales, researchers can produce scientific work that is more accurate, valid, and accountable.