# Scales of Measure

## Scales of Measure

Measurements or numerical data can be classified into four major categories referred to as scales of measure. The scales of measure are ordinal, nominal, interval, and ratio. Classification into these categories depends on salient features that distinguish each type of data.

## Ordinal

Ordinal scale involves the classification of data in a particular order. The order can be ascending or descending. For example, the result collected from the arrival of horses in a horse race. The data will indicate the horses in their order of arrival from first to last. Mode, median, and average can be calculated from an ordinal data (Rubin, 2012).

## Interval

Interval measurements give great priority to the difference or distance between values. For example, the range 4 – 6 is 2, similarly, the range 8-10 is 2. It is notable that even though the numerical differ, the interval is still the same. Units such as temperature measurement and the Likert scale involve the use of intervals. Interval measurements may involve the use of a 0 such as thermometers and Likert scale (Blankenship, 2010). Mode median and mean can be obtained from an interval data.

## Ratio

Ratio measurements have an arbitrary 0 value, which is not a representation of an absence of a value but a measure of quality. With ratio data, one can calculate the measures of central tendency. Ratio measurements can be used to arrive at additive and multiplicative inferences (Furr, 2017).

## Nominal

Nominal classification involves the use of labels as a representative. For example, M can be used to represent Males and F to represent Females. The classification of data as nominal allows for the determination of equivalence or membership of a set (Chadha, 2009). For example, if a data had several Fs and Ms. Someone can proceed to determine the members of the set F. alternatively, one may determine who falls in the category of M, which is membership.

References

Blankenship, D. (2010). Applied research and evaluation methods in recreation. Human Kinetics.

Chadha, N. K. (2009). Applied psychometry. SAGE Publications India.

Furr, R. M. (2017). Psychometrics: an introduction. Sage Publications.

Rubin, A. (2012). Statistics for evidence-based practice and evaluation. Cengage Learning.

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