MEASUREMENT & MEASUREMENT LEVELS

Understanding measurement and measurement levels are important in the data collection phase of research. At this phase, the research variables must be operationally defined. An operational definition, not to be confused with a conceptual definition, denotes how variables will be observed and measured. Measurement is the process of assigning numbers or quantification of data. This allows the researcher to categorize the variables and assign them definite quantities which enable comparisons to be made. (Nieświadomy & Bailey, 2018).

The type of statistical analysis that can be done on data is determined by its level of measurement (measurement scale). There are 4 levels of measurement: nominal, ordinal, interval, and ratio.

The nominal measurement level entails data being categorized or named. Each category must be distinct from the other and include all possible data that fall into it. The number of categories depends on the data being collected, there could be 2 or 20 categories. Numbers are assigned to data in this measurement level by counting the frequency or percentage of objects within each category. An example of nominal data is gender. Gender can only be measured at the nominal level as there are only two options, male or female. A researcher would then calculate the number of males and females in his study and assign each a percentage. The nominal level is the lowest level of measurement and is the least precise for that matter. (Nieświadomy & Bailey, 2018)

Data that can be ranked in addition to being categorized is at the ordinal measurement level. At the ordinal level, while data can be ranked, the exact quantitative difference between each rank cannot be specified. For instance, stress levels of people can be categorized into mild, moderate, and severe. You could confidently conclude that individuals with severe stress levels are more stressed than those with moderate stress levels. However, you cannot determine the exact difference between stress levels. This measurement scale usually yields frequencies, percentages, and distributions of data. (Nieświadomy & Bailey, 2018)

Interval measurement scales include data that not only can be categorized and ranked but also the exact difference between ranks can be identified. Temperature and test scores are among the common interval data. If body temperature was being measured, each different reading would constitute a category, as 37.2 is a category while 37.4 is a different category. The researcher can then identify the exact difference between these two categories. (Nieświadomy & Bailey, 2018)

In the ratio measurement level, in addition to categorizing and ranking data and identifying exact differences between ranks, there is an absolute zero. The zero point on a scale indicates the absence of the variable being measured. For instance, if a researcher wanted to determine the number of certain medication requests by patients, the number of requests can be zero. This data would be considered ratio data. The ratio measurement scale is considered the highest and most precise. (Nieświadomy & Bailey, 2018)

Some data, while cannot be measured to be zero, are considered ratio data. Height and weight are prominent examples. Most researchers consider weight as a ratio, although you cannot weigh someone and get zero as a result.

Data can always be converted from a higher scale to a lower one but never the other way around. For example, the number of contractions a pregnant woman experiences during a specific time can be converted from the ratio scale, where it is an exact number and a zero point could be identified, to the ordinal scale, where it could be either few, a moderate amount, or plenty. However, researchers rarely convert data from a higher scale to a lower one as precision is lost. (Nieświadomy & Bailey, 2018)

Choosing the appropriate measurement scale for your study depends on two main points: the type and operational definition of data being collected, and the degree of precision required to answer the research question or test the hypothesis. If a researcher is concerned about the precision of the data, an interval or ratio measurement scale should be used. But, if ranked or categorized data will be sufficient to answer the research question or test the hypothesis, a nominal or ordinal measurement scale would be used. Moreover, some variables can only be measured at a certain level. For example, gender can only be measured at the nominal level, as a person can only be male or female. (Nieświadomy & Bailey, 2018)