Quantitative data is any set of numerical data, typically used for mathematics or statistics, that can be analyzed and measured objectively. Some examples of quantitative data may include the number of employees in an organization’s C-suite or the total revenue generated over a quarter. Quantitative data forms the core of information handled in a data analytics setting.
The way data is quantified depends on the dataset’s type, which can be categorized as either discrete or continuous.
Discrete data are whole numbers that can be counted and don’t change within a given time period. Some examples can include body temperature on waking up in the morning, the number of students in a third-grade class.
On the other hand, continuous data include numbers within a range that don’t necessarily have to be whole and may change more frequently in a given time period. Some examples can include body temperature throughout the day, or the number of students a third-grade teacher has taught over the course of 10 years.
While discrete data can only be one numeric value, continuous data are made up of a range of numerical values. Think of discrete data as separate, individual points of data, whereas continuous data looks more like connected dots or lines that mark each transition in the numerical value over time.
Where qualitative data involves analyzing data qualities that can have subjective interpretations or values, quantitative data involves measuring select numeric options or ranges to obtain an objective result.
Compared to quantitative data, qualitative data allows for a variety of data types to be analyzed and interpreted based on their context. Further, quantitative can be limiting in that not even all numerical data can be considered quantitative data.
For example, while categorical data, a type of thematic analysis, can be measured and counted, the data is still considered qualitative because the groups are measured by their open-ended responses, or words, rather than numbers.
Phone numbers are also an example of numerical data that wouldn’t be considered quantitative data. The meaning of the data is considered subjective and fluctuates depending on who collects the data and when. Moreover, a phone number contains no real “value” to count, measure, or include in statistical analysis, so analyzing the data from a quantitative perspective won’t necessarily produce valuable insight. Quantitative data related to phone numbers would be how many numbers are within a given area code.
Quantitative data can be collected in several ways; however, it’s important to set measurement standards and parameters before collecting data in order to avoid subjective data collection. While it’s not uncommon to collect qualitative data at the same time, it’s important that quantitative methods limit the range of responses to obtain objective measurements. Ask questions like What responses and numerical values are possible? and What questions need to be asked or built into an analytical dashboard to get those results?
Other methods to avoid subjective data collection include:
Read on about quantitative data’s role in decision-making at the enterprise level at Datamation.com.