Quantitative Data Definition
Quantitative data is any set of information that can be numerically recognized and analyzed. Quantitative data is the most relevant form of data for use in both mathematics and statistics, as it is the primary type of data that can be measured objectively.
Some examples of quantitative data include:
- The number of employees in an organization’s C-Suite.
- The weight of a puppy over the course of its first year.
- Total revenue generated over a quarter.
- How many times your sister went to the gym last month.
- The total percent of the world’s population found in China.
Types of Quantitative Data: Discrete vs. Continuous
Quantitative data is data that can be quantified, but quantifying that data looks different depending on the type of data set at hand. The two main categories of quantitative data are discrete and continuous. Here are the primary differences between the two:
Discrete data are whole numbers that can be counted and don’t change within a given time period. Some examples include:
- The number of kittens up for adoption at your local shelter on a given day.
- Your body temperature when you first woke up this morning.
- The number of students in your third grade class.
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 include:
- The number of kittens up for adoption at your local shelter over the course of a year.
- Your body temperature throughout the day.
- The number of third graders that you have taught since you started teaching 10 years ago.
Discrete data can only be one numeric value, but continuous data are made up of several different numeric values within a range. Think of discrete data as separate, individual points of data, whereas continuous data looks more like connected dots or lines that mark each subtle transition in the numerical value over time.
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How is Qualitative Data Different?
Qualitative data is different from quantitative data because it analyzes data qualities that can have subjective interpretations or values. Whereas quantitative data is obtained via methods with only select numeric options or ranges for a response, qualitative data is intentionally collected in an open-ended way that leaves the data open for human response and interpretation.
Thematic analysis can be applied to qualitative data responses, allowing the researcher to group the resulting data into categories. This type of data is known as categorical data, and although the categories and the number of items in each can be counted, the data is still considered qualitative because the groups are measured by their open-ended responses, or “words” rather than numbers.
It’s also important to note that just because data contains a number or numbers, that does not necessarily qualify it as quantitative data. Consider a person’s phone number. While the data is numeric, it is subjective in that it relates to only one person and can fluctuate or be interpreted differently by a different person.
In the phone number example, someone might identify Joe’s phone number by his cell phone number, his office number, his home phone number, or an outdated phone number that he no longer uses, meaning that the number is up for subjective interpretation as qualitative data. There would also be little reason to analyze phone numbers in a structured data set, as finding the average or median of those numbers does not produce valuable insight from the data set.
How Can You Collect Quantitative Data?
You can collect quantitative data in several ways, but it’s important to set your measurement standards and parameters before you begin collecting data. What responses and numerical values are possible? What questions do you need to ask or build into an analytical dashboard to get those results? Some great methods to ensure that you avoid subjective data collection include:
- Using close-ended survey questions or questionnaires (i.e. multiple choice, dropdown selection, etc.).
- Setting up and reviewing analytics dashboards for numbers like daily web traffic (i.e. Google Analytics, Salesforce dashboards, etc.).
- Conducting experiments using the scientific method, or other strict procedure to quantify your results.
While researchers can and often do collect qualitative data at the same time as quantitative data, it’s important that your quantitative methods are direct and limit the range of responses to achieve objective measurements.
Why is Quantitative Data Important?
Quantitative data serves many different purposes across industries and personal uses. So much of its inherent value can be found in its name.
For starters, data is easily quantified, which means that data collection and analysis are quicker and involve less complicated processes than in qualitative research. Also, since this data is quantifiable and can only include a certain range of values, it allows the researcher to more easily collect unbiased data from the population.
Biased, subjective data certainly has its place, but quantitative data is especially important for researchers, as it enables them to apply statistical analysis to the data set. Predictive analytics is an exciting use case for quantitative data, because the quantities of yesterday and today can help researchers to predict the quantitative outcomes of the future, and perhaps the quantifiable steps needed to get there.
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