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Types of data

It is important to be aware of the types of data that are available so that the appropriate analytic techniques are used, and inappropriate ones are avoided.

Nominal data

Nominal data are data that cannot meaningfully be placed in rank order – they are typically labels (names) for categories. Some examples are eye colour, biological species, students’ ethnicity or gender, educational course of study. There is no underlying progression in the data – no order of merit or size.

Ordinal data

Ordinal data are data that can be placed in rank order, but for which the magnitudes of distances on the underlying scale cannot meaningfully be compared.

Some examples are rating scales (Likert Scale, satisfaction ratings) socioeconomic status, educational level, some educational test scores.

A teacher may construct an informal end of topic test containing 20 items. Because the difficulty of each question is not necessarily equal, the marks cannot be meaningfully compared, one against the other, but the test scores can be used to place students in rank order. 

Interval data

Interval data are data that can not only be placed in rank order but can also be compared in terms of magnitudes of difference.

Some examples are time, weight, some educational test scores.

A difference on a scale corresponds to the same change regardless of its location on the scale. For example, weight is an interval-scale measurement; the difference between 80kg and 90kg is the same as the difference between 90kg and 100kg. This is because the change in the property underlying the measurement (mass) is the same in each case.

Educational test scores which are weighted and scaled for difficulty fall into this category. Examples are e-asTTle and PAT data.

It is important that educators understand the difference between ordinal and interval data so that they do not make false assumptions when reading data.