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. Show
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. NominalThe 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:
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 dataThe 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:
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 dataThe 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:
Ratio data are used typically when quantitative data are involved because researchers can identify the quantifiable difference between the measured values. Interval dataThe 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:
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 dataHave a look at the diagram below to see how we identify the type of data to use.
Tree diagram to show how to identify ordinal, nominal, ratio and interval data - StudySmarter Originals Levels of Measurement - Key takeaways
Which level of measurement does not provide a numerical result?Nominal Data Levels of Measurement
For example, if we want to categorize male and female respondents, we could use a number of 1 for male, and 2 for female. However, the values of 1 and 2 in this case do not represent any meaningful order or carry any mathematical meaning. They are simply used as labels.
Which level of measurement provides the least sensitive analysis?There are four levels of measurement – nominal, ordinal, and interval/ratio – with nominal being the least precise and informative and interval/ratio variable being most precise and informative.
Which level of measurement does not provide objective information about the differences between each data point?Ordinal Scale is defined as a variable measurement scale used to simply depict the order of variables and not the difference between each of the variables.
Is true or false nominal or ordinal?The Ordinal Scale: Rank order (1st, 2nd 3rd), dichotomous data that has two choices like true/false or guilty/innocent and non-dichotomous data with choices like “completely agree” “somewhat agree” “neutral” and “disagree.”
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