Not all data is created equal: the hidden hierarchy of information
In short: Before you can analyze a trend, you must understand what kind of information you are actually holding. From the siblings you have to the height of a mountain, data is categorized by how it is collected, what it describes, and how many variables are at play.
Data classification begins with the source of collection. Primary data is gathered specifically by the entity that intends to use it. In contrast, secondary data is information gathered by others. While secondary data is often more efficient because it saves time and money—and is useful for researching past events—it requires a higher level of scrutiny. Users must evaluate the reliability and validity of the source, specifically looking for underlying motives or biases that might skew the facts.
Once collected, data is split into two broad categories: qualitative and quantitative. Qualitative data is descriptive and non-numerical, covering things like opinions, religions, or favorite colors. Quantitative data, however, is numerical and can be counted or measured. This category is further refined into discrete and continuous types. Discrete data consists of distinct, separate values—such as the number of siblings one has, where a decimal like 2.63 is impossible. Continuous data can take any value within a range, such as height, where a person can be any measurement between two points.
Finally, data is classified by the number of variables being observed. Univariate data involves a single variable, while bivariate data involves two. When more than two variables are involved, the data is considered multivariate. The specific question being asked dictates which type of data is necessary to collect and which statistical tools will be most effective for the analysis.
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Sources
- Summary by Intellect · source: Types Of Data | Statistics & Probability | Maths | FuseSchool
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