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Levels of Measurement

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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.

Levels of Measurement Data Graphs StudySmarter

Data concept art, Flaticon

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.

‘What is your gender?

The nominal data here could be ‘male’, ‘female’, or ‘other’.

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 overlaprespondents 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.

Most nominal data is used for qualitative data, as this type of data has limited use for quantified data. Finally, we cannot use nominal data to show differences between data because there is no significance in the order of nominal data.

Ordinal data

The ordinal level of measurement in psychology is categorical data, and the values have a fixed set or order. The intervals between these data points are not equal.

Examples of questions in a questionnaire that collect ordinal data are:‘On a scale of 1 to 5, rate how happy this video makes you’.

OR,‘What socioeconomic status is most representative of you?’The only possible answers participants can give are '1', '2', '3', '4' and '5'.

OR‘Working class’, ‘Middle class’ or ‘Upper class’. Although the order of the data collected is important, the differences between the values are not.

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

The ratio level of measurement in psychology is classified and ranked data. This is measured with continuous data.

Examples of data where ratio measurement is used are participants’ height, age, speed travelling. None of the examples listed can have a value of 0, and the data is continuous because the values reported can have an infinite number of values.

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

The interval level of measurement in psychology is a type of data that is essentially the same as ratio data, except that the values can have a value of 0 or below (0 is not absolute). The data are ordered somehow, but the interval data are equivalent.

An example of collected data that can be classified as interval data measurement is temperature since the temperature can be 0 or below.

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.

Levels of Measurement Tree diagram ordinal nominal ratio interval data StudySmarter

Tree diagram to show how to identify ordinal, nominal, ratio and interval data - StudySmarter Originals

Levels of Measurement - Key takeaways

  • The nominal level of measurement in psychology consists essentially of measurements of ‘named’ or ‘labelled data’.
  • The ordinal level of measurement in psychology is categorical data, and the values have a fixed quantity or order.
  • The ratio level of measurement in psychology is a type of data that is classified and ranked; this is measured with continuous data.
  • The interval level of measurement in psychology is a type of data that is essentially the same as ratio data, except that the values can have a value of 0 or below (0 is not absolute).

Frequently Asked Questions about Levels of Measurement

Research can investigate the level of consciousness via EEG's (electroencephalograms), the type of data this gives is ratio data.

The nominal level of measurement in psychology is measurements of ‘named’ or ‘labelled data’ and can also be referred to as categorical data. Examples of questionnaires used to collect nominal data are ‘What is your gender?’ or ‘What is your ethnicity?’

  • Nominal data – measurements of ‘named’ or ‘labelled data’, e.g., gender, ethnicity.
  • Ordinal data is a type of categorical data, and the values have a fixed set or order, e.g., ‘on a scale of 1-5, rate how angry does this statement makes you?’
  • Ratio data is classified and ranked, which is measured using continuous data, and this type of data has an absolute 0, e.g., height, speed.
  • Interval data is essentially the same as ratio data except that values can have a value of 0 or below (0 is not absolute, e.g., temperature.)

  • Nominal.
  • Ordinal.
  • Ratio.
  • Interval.

We can determine the level of measurement by identifying the characteristics of the data and identifying which level of measurements the characteristics correspond to, e.g., continuous data that can measure an absolute 0 would be recognised as a ratio level of measurement.

Final Levels of Measurement Quiz

Question

For the following question, what is the appropriate level of measurement that characterises the data: ‘What is your gender’?

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Answer

Nominal.

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Question

What are the characteristics of nominal data?

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Answer

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.

Show question

Question

What levels of measurement are used for qualitative data?

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Answer

Nominal.

Show question

Question

What levels of measurement are used for quantitative data?

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Answer

Ordinal.

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Question

What level of measurement is typically used for questionnaires that measure responses using Likert scales?

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Answer

Ordinal data.

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Question

What are the characteristics of ordinal data?

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Answer

  • 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 the difference.
  • The order of the data is essential, e.g. 1 may reflect a weaker response than 5.

Show question

Question

What type of data is usually available when using a ratio level of measurement?

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Answer

Data that is classified and ranked and that can have an absolute zero.

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Question

Why is ratio data quantitative?

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Answer

Researchers can identify the quantifiable difference between the values measured.

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Question

What is the difference between ratio and interval data?

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Answer

The value of 0 is not absolute in interval data, but it is in ratio data.

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Question

What is continuous data?

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Answer

Data that can be of any value.

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Question

What is discrete data?

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Answer

Data that can only have certain values is called discrete data.

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Question

When carrying out research, why is it important to identify the appropriate level of measurement of data?

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Answer

Because it helps us understand:

  • how to interpret the data.
  • the appropriate statistical test to use.
  • the information that the data can give us.

Show question

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