Nominal and Ordinal Scales vs Ratio and Interval

Scaling in Test Development

Scaling is a process in test development that refers to assigning numbers to different levels of a characteristic that is being measured. The purpose is to be able to quantify psychological attributes for statistical analysis. Scaling is helpful because it allows one to more easily compare differences in psychological traits with statistically meaningful test scores that can be interpreted in accordance with basic statistical analysis models (Cohen et al., 2021). In other words, it produces a more objective and less subjective approach to psychological testing and evaluation.

Types of Scales Used in Test Development

There are different types of scales that can be used in test development: they are nominal, ordinal, interval, and ratio scales. Each serves its own purpose.

Nominal Scales

Nominal scales put data into categories so that there is no implied quantitative relationship between the data/categories. Essentially, they are used as a way to label variables that have no quantitative value. For example, assigning numbers to different psychological disorders (i.e,, 1 for depression, 2 for anxiety) is a basic type of nominal scaling (Cohen et al., 2021). The number is used to label but not to signify anything else.

Ordinal Scales

Ordinal scales order data based on a hierarchical structure. However, these scales do not give a sense of the magnitude of differences between the categories. For example, one way to use ordinal scales would be to rank people based on their performance in a psychological test, where the rank indicates who performed better, but not by how much (Cohen et al., 2021).

Interval Scales

Interval scales tell the order of items as well as the exact differences between them. An example would be the Celsius temperature scale, where the difference between degrees is consistent. In psychological testing, interval scales can be used in tests that want to measure attributes such as intelligence, where the intervals between scores will say something meaningful (Cohen et al., 2021).

Ratio Scales

Ratio scales have all the properties of interval scales, the only difference being that they have a true zero point (interval scales do not), which lets them convey data in absolute quantities. For example, the measurement of reaction time in seconds would be a way to ratio scale in psychological tests (Cohen et al., 2021).

Factors to Consider When Choosing the Type of Scale

To choose the right type of scale for test development, one needs to think of the nature of the measurement. For example, the nature of the construct being measured will signify the type of scale to use. For categorical constructs, nominal scale would be appropriate. For constructs that are categorized by differences, such as intelligence, interval or ratio scales would be appropriate (Cohen et al., 2021).

The purpose of the test or intended use of the test scores also will dictate the choice of scale. For diagnostic purposes, nominal or ordinal scales may be appropriate. However, if one is conducting research where detailed statistical analysis is required, interval or ratio scales are going to be the preferred scale simply because they are more precise and yield a clearer, more detailed and objective measurement (Cohen et al., 2021). At the same time, the type of statistical analysis planned for the test data will also affect what type of scale is selected. Nominal and ordinal scales limit the types of statistical analyses that can be conducted (non-parametric). Interval and ratio scales allow for more analyses and parametric methods (Cohen et al., 2021).

Scale type also will affect the reliability and validity of a test. Interval and ratio scales generally improve the reliability of a test because they provide more detailed and consistent data and support the validation process of establishing a test's accuracy in measuring the intended construct (Cohen et al., 2021). Another factor in selecting the scale type is the ease with which test results can be interpreted by users (clinicians, educators). Simple scales like nominal and ordinal scales are easier for non-specialists to understand. However, interval and ratio scales give deeper insights—so one has to know one’s audience and what the audience wants (Cohen et al., 2021).

Overall, measurements need to be precise, and the precision required in measurement affects the scale choice. Interval and ratio scales are more precise and are thus chosen when exact measurements are needed. An example would be in psychological experiments where precise measurement of reaction times or physiological responses is needed; in such cases, ratio scales are appropriate (Cohen et al., 2021). On top of all this are practical considerations, such as the feasibility of scale implementation. Some scales might require more sophisticated administration and scoring procedures, which could be a limitation in certain settings (Cohen et al., 2021).