Understanding Levels of Measurement

Summary:

The text discusses the levels of measurement for data, dividing them into qualitative and quantitative categories. The four categories mentioned are nominal, ordinal, interval, and ratio scales. The nominal scale represents data with labels or categories like eye colour or country. The ordinal scale has ordered categories, like rankings or Likert scales. The interval scale is for numerical data where the distance between values can be measured but lacks a true zero point. The ratio scale is similar to the interval scale but has a true zero point, representing the absence of the measured characteristic.

The text provides a cheat sheet summarizing the characteristics and examples of each level of measurement. It also includes practice questions to test understanding, such as identifying the data type for each level, differentiating between nominal and ordinal data, and classifying variables into their respective scales.

In summary, the text explains the levels of measurement, provides examples, and offers practice questions to reinforce understanding.

Excerpt:

Understanding Levels of Measurement

Insight

Data can be divided into qualitative (labels, categories and attributes) and quantitative (measures and counts). Those types of data can also be divided into 4 categories:

1. Nominal Scale: here, the data are just labels like eye colour, country, language, or name. No value is greater than any other.
2. Ordinal Scale: We are still dealing with categories at the ordinal scale, but this time they have an order. For example, the traffic lights can be ordered like green(continue) < yellow (slow) < and red(stop)
3. Interval Scale: The interval scale is for numerical data (quantitative). its main characteristic is that we can tell the distance between any pair of values; also, it doesn’t have a “true zero”. For example, Year 0 doesn’t mean that time started there, so it is not a “true zero”.
4. Ratio Scale: The ratio scale is almost the same as the interval scale, it deals with numerical values, and we can tell the distance between any pair of values. But in this case, the zeros represent the “lack of” the measured characteristic. For example, a speed of 0 mph means that you are not moving.