Levels of Measurement

When psychologists conduct their research, understanding the measurement variables in statistics is one of the most critical steps. Thus, in statistics, researchers use measurement variables to describe and classify the variable type and how to measure it. In psychology, there are four levels of measurement: nominal, ordinal ratio, and interval.

What are the four levels of measurement?

Now we define the levels of measurement used in statistics and apply them to example scenarios from psychological research to help you learn.

Nominal

The nominal level of measurement in psychology consists essentially of measurements of ‘named’ or ‘labelled data’. It can also be referred to as categorical data.

Nominal data is characterised by:

• No order between values– one answer in a questionnaire is as vital as the others, and this is because these data tend not to provide numerical value.
• Nominal values do not overlap– respondents can select only one answer (data that can take only specific values are called discrete data).

• They are not usually used for evaluation calculations but rather for grouping data or participants;

  • The common calculations used to represent nominal data are percentages and mode.

Ordinal data

Ordinal data have the following characteristics:

• There is no way to measure the numerical value of one response to the next, e.g. researchers cannot determine how much the respondents who answered 3 differ in importance from respondents who answered 5.
• Data based on ranking – there is a difference between the ratings based on the order, but we cannot measure how big the difference is.
• The order of the data is essential, e.g. 1 may reflect a weaker response than 5.
• Data collected using a Likert scale, for example.

Ordinal data is usually qualitative because we cannot determine numerical significance between values. It is used typically for data reflected in categories, i.e., ordinal data has limited use for quantitative data.

Ratio data

Ratio data is characterised by:

• There is an absolute zero, i.e., the data collected cannot be 0 or less than 0.
• The measured data is continuous (data that can have any value).
• The distance between the values is the same, e.g. the distance between 3 and 5 and 7 and 9 is the same.
• Researchers can measure the difference between the values, e.g., the researcher can identify and quantitatively measure the difference between participants who responded to 1 and 50.
• The direction of change in numerical values is important, e.g., 14 to 24 indicates an increase, while 30 to 17 indicates a decrease.

Ratio data are used typically when quantitative data are involved because researchers can identify the quantifiable difference between the measured values.

Interval data

Interval data are characterised by:

• The value 0 is not absolute; the collected data can be 0 or less.
• The measured data is continuous (the data can have any value).
• The interval between the values is equal, e.g. the intervals between 3 and 5 and 7 and 9 are identical.
• Researchers can measure the difference between the values, e.g., the researcher can identify and quantitatively measure the difference between participants who responded to 1 and 50.
• The direction of change in numerical values is essential, e.g., 14 to 24 indicates an increase, while 30 to 17 indicates a decrease.

Similar to ratio data, interval levels are typically used to measure quantitative data because researchers can determine the quantifiable difference between the measured values.

How do we know when to use nominal, ordinal, ratio, or interval levels of measurement?

When conducting research, it is crucial to determine the data’s level of measurement because this helps us understand how to interpret the data, what statistical test should be used, and what information the data can give us.

Identifying nominal, ordinal, interval, and ratio data

Have a look at the diagram below to see how we identify the type of data to use.