Data professionals work with two types of data: Quantitative and qualitative. In simple terms, quantitative data is measurable while qualitative data is descriptive—think numbers versus words.
If you plan on working as a data analyst or a data scientist (or in any field that involves conducting research, like psychology), you’ll need to get to grips with both. In this post, we’ll focus on quantitative data. We’ll explain exactly what quantitative data is, including plenty of useful examples. We’ll also show you what methods you can use to collect and analyze quantitative data.
By the end of this post, you’ll have a clear understanding of quantitative data and how it’s used.
- What is quantitative data? (With examples)
- What’s the difference between quantitative and qualitative data?
- What are the different types of quantitative data?
- How is quantitative data collected?
- What methods are used to analyze quantitative data?
- What are the advantages and disadvantages of quantitative data?
- Key takeaways
So: What is quantitative data? Let’s find out.
1. What is quantitative data?
Quantitative data is, quite simply, information that can be quantified. It can be counted or measured, and given a numerical value—such as length in centimeters or revenue in dollars. Quantitative data tends to be structured in nature and is suitable for statistical analysis. If you have questions such as “How many?”, “How often?” or “How much?”, you’ll find the answers in quantitative data.
What are some examples of quantitative data?
Some examples of quantitative data include:
- Revenue in dollars
- Weight in kilograms
- Age in months or years
- Length in centimeters
- Distance in kilometers
- Height in feet or inches
- Number of weeks in a year
2. What is the difference between quantitative and qualitative data?
It’s hard to define quantitative data without comparing it to qualitative data—so what’s the difference between the two?
While quantitative data can be counted and measured, qualitative data is descriptive and, typically, unstructured. It usually takes the form of words and text—for example, a status posted on Facebook or an interview transcript are both forms of qualitative data. You can also think of qualitative data in terms of the “descriptors” you would use to describe certain attributes. For example, if you were to describe someone’s hair color as auburn, or an ice cream flavor as vanilla, these labels count as qualitative data.
Qualitative data cannot be used for statistical analysis; to make sense of such data, researchers and analysts will instead try to identify meaningful groups and themes.
You’ll find a detailed exploration of the differences between qualitative and quantitative data in this post. But, to summarize, the differences between quantitative and qualitative data are as follows:
- Quantitative data is countable or measurable, relating to numbers; qualitative data is descriptive, relating to words.
- Quantitative data lends itself to statistical analysis; qualitative data is grouped and categorized according to themes.
- Examples of quantitative data include numerical values such as measurements, cost, and weight; examples of qualitative data include descriptions (or labels) of certain attributes, such as “brown eyes” or “vanilla flavored ice cream”.
Now we know the difference between the two, let’s get back to quantitative data.
3. What are the different types of quantitative data?
There are two main types of quantitative data: Discrete and continuous.
Discrete data is quantitative data that can only take on certain numerical values. These values are fixed and cannot be broken down. When you count something, you get discrete data. For example, if a person has three children, this is an example of discrete data. The number of children is fixed—it’s not possible for them to have, say, 3.2 children. Another example of discrete quantitative data could be the number of visits to your website; you could have 150 visits in one day, but not 150.6 visits. Discrete data is usually visualized using tally charts, bar charts, and pie charts.
Continuous data, on the other hand, can be infinitely broken down into smaller parts. This type of quantitative data can be placed on a measurement scale; for example, the length of a piece of string in centimeters, or the temperature in degrees Celsius. Essentially, continuous data can take any value; it’s not limited to fixed values. What’s more, continuous data can also fluctuate over time—the room temperature will vary throughout the day, for example. Continuous data is usually represented using a line graph.
Continuous data can be further classified depending on whether it’s interval data or ratio data. Let’s take a look at those now.
Interval vs. ratio data
Interval data can be measured along a continuum, where there is an equal distance between each point on the scale. For example: The difference between 30 and 31 degrees C is equal to the difference between 99 and 100 degrees. Another thing to bear in mind is that interval data has no true or meaningful zero value. Temperature is a good example; a temperature of zero degrees does not mean that there is “no temperature”—it just means that it’s extremely cold!
Ratio data is the same as interval data in terms of equally spaced points on a scale, but unlike interval data, ratio data does have a true zero. Weight in grams would be classified as ratio data; the difference between 20 grams and 21 grams is equal to the difference between 8 and 9 grams, and if something weighs zero grams, it truly weighs nothing.
Beyond the distinction between discrete and continuous data, quantitative data can also be broken down into several different types:
Measurements: This type of data refers to the measurement of physical objects. For example, you might measure the length and width of your living room before ordering new sofas.
Sensors: A sensor is a device or system which detects changes in the surrounding environment and sends this information to another electronic device, usually a computer. This information is then converted into numbers—that’s your quantitative data. For example, a smart temperature sensor will provide you with a stream of data about the temperature of the room throughout the day.
Counts: As the name suggests, this is the quantitative data you get when you count things. You might count the number of people who attended an event, or the number of visits to your website in one week.
Quantification of qualitative data: This is when qualitative data is converted into numbers. Take the example of customer satisfaction. If a customer said “I’m really happy with this product”, that would count as qualitative data. You could turn this into quantitative data by asking them to rate their satisfaction on a scale of 1-10.
Calculations: This is any quantitative data that results from mathematical calculations, such as calculating your final profit at the end of the month.
- Projections: Analysts may estimate or predict quantities using algorithms, artificial intelligence, or “manual” analysis. For example, you might predict how many sales you expect to make in the next quarter. The figure you come up with is a projection of quantitative data.
Knowing what type of quantitative data you’re working with helps you to apply the correct type of statistical analysis. We’ll look at how quantitative data is analyzed in section five.
4. How is quantitative data collected?
Now we know what quantitative data is, we can start to think about how analysts actually work with it in the real world. Before the data can be analyzed, it first needs to be generated or collected. So how is this done?
Researchers (for example, psychologists or scientists) will often conduct experiments and studies in order to gather quantitative data and test certain hypotheses. A psychologist investigating the relationship between social media usage and self-esteem might devise a questionnaire with various scales—for example, asking participants to rate, on a scale of one to five, the extent to which they agree with certain statements. If the survey reaches enough people, the psychologist ends up with a large sample of quantitative data (for example, an overall self-esteem score for each participant) which they can then analyze.
Data analysts and data scientists are less likely to conduct experiments, but they may send out questionnaires and surveys—it all depends on the sector they’re working in. Usually, data professionals will work with “naturally occurring” quantitative data, such as the number of sales per quarter, or how often a customer uses a particular service.
Some common methods of data collection include:
- Analytics tools, such as Google Analytics
- Probability sampling
- Questionnaires and surveys
- Open-source datasets on the web
Data analysts and data scientists rely on specialist tools to gather quantitative data from various sources. Google Analytics, for example, will gather data pertaining to your website; at a glance, you can see metrics such as how much traffic you got in one week, how many page views per minute, and average session length—all useful insights if you want to optimize the performance of your site.
Aside from Google Analytics, which tends to be used within the marketing sector, there are loads of tools out there which can be connected to multiple data sources at once. Tools like RapidMiner, Knime, Qlik, and Splunk can be integrated with internal databases, data lakes, cloud storage, business apps, social media, and IoT devices, allowing you to access data from multiple sources all in one place.
Sampling is when, instead of analyzing an entire dataset, you select a sample or “section” of the data. Sampling may be used to save time and money, and in cases where it’s simply not possible to study an entire population. For example, if you wanted to analyze data pertaining to the residents of New York, it’s unlikely that you’d be able to get hold of data for every single person in the state. Instead, you’d analyze a representative sample.
There are two types of sampling: Random probability sampling, where each unit within the overall dataset has the same chance of being selected (i.e. included in the sample), and non-probability sampling, where the sample is actively selected by the researcher or analyst—not at random. Data analysts and scientists may use Python (the popular programming language) and various algorithms to extract samples from large datasets.
Questionnaires and surveys
Another way to collect quantitative data is through questionnaires and surveys. Nowadays, it’s easy to create a survey and distribute it online—with tools like Typeform, SurveyMonkey, and Qualtrics, practically anyone can collect quantitative data. Surveys are a useful tool for gathering customer or user feedback, and generally finding out how people feel about certain products or services. To make sure you gather quantitative data from your surveys, it’s important that you ask respondents to quantify their feelings—for example, asking them to rate their satisfaction on a scale of one to ten.
Open-source datasets online
In addition to analyzing data from internal databases, data analysts might also collect quantitative data from external sources. Again, it all depends on the field you’re working in and what kind of data you need. The internet is full of free and open datasets spanning a range of sectors, from government, business and finance, to science, transport, film, and entertainment—pretty much anything you can think of! We’ve put together a list of places where you can find free datasets here.
5. How is quantitative data analyzed?
A defining characteristic of quantitative data is that it’s suitable for statistical analysis. There are many different methods and techniques used for quantitative data analysis, and how you analyze your data depends on what you hope to find out.
Before we go into some specific methods of analysis, it’s important to distinguish between descriptive and inferential analysis.
What’s the difference between descriptive and inferential analysis of quantitative data?
Descriptive analysis does exactly what it says on the tin; it describes the data. This is useful as it allows you to see, at a glance, what the basic qualities of your data are and what you’re working with. Some commonly used descriptive statistics include the range (the difference between the highest and lowest scores), the minimum and maximum (the lowest and highest scores in a dataset), and frequency (how often a certain value appears in the dataset). You might also calculate various measures of central tendency in order to gauge the general trend of your data. Measures of central tendency include the mean (the sum of all values divided by the number of values, otherwise known as the average), the median (the middle score when all scores are ordered numerically), and the mode (the most frequently occurring score). Another useful calculation is standard deviation. This tells you how representative of the entire dataset the mean value actually is.
While descriptive statistics give you an initial read on your quantitative data, they don’t allow you to draw definitive conclusions. That’s where inferential analysis comes in. With inferential statistics, you can make inferences and predictions. This allows you to test various hypotheses and to predict future outcomes based on probability theory.
Quantitative data analysis methods
When it comes to deriving insights from your quantitative data, there’s a whole host of techniques at your disposal. Some of the most common (and useful) methods of quantitative data analysis include:
Regression analysis: This is used to estimate the relationship between a set of variables, and to see if there’s any kind of correlation between the two. Regression is especially useful for making predictions and forecasting future trends.
Monte Carlo simulation: The Monte Carlo method is a computerized technique used to generate models of possible outcomes and their probability distributions based on your dataset. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will occur. It’s used by data analysts to conduct advanced risk analysis, allowing them to accurately predict what might happen in the future.
Cohort analysis: A cohort is a group of people who share a common attribute or behavior during a given time period—for example, a cohort of students who all started university in 2020, or a cohort of customers who purchased via your app in the month of February. Cohort analysis essentially divides your dataset into cohorts and analyzes how these cohorts behave over time. This is especially useful for identifying patterns in customer behavior and tailoring your products and services accordingly.
Cluster analysis: This is an exploratory technique used to identify structures within a dataset. The aim of cluster analysis is to sort different data points into groups that are internally homogenous and externally heterogeneous—in other words, data points within a cluster are similar to each other, but dissimilar to data points in other clusters. Clustering is used to see how data is distributed in a given dataset, or as a preprocessing step for other algorithms.
- Time series analysis: This is used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time, such as weekly sales figures or monthly email sign-ups. By looking at time-related trends, analysts can forecast how the variable of interest may fluctuate in the future. Extremely handy when it comes to making business decisions!
Above is just a very brief introduction to how you might analyze your quantitative data. For a more in-depth look, check out this comprehensive guide to some of the most useful data analysis techniques.
6. What are the advantages and disadvantages of quantitative data?
As with anything, there are both advantages and disadvantages of using quantitative data. So what are they? Let’s take a look.
Advantages of quantitative data
The main advantages of working with quantitative data are as follows:
Quantitative data is relatively quick and easy to collect, allowing you to gather a large sample size. And, the larger your sample size, the more accurate your conclusions are likely to be.
Quantitative data is less susceptible to bias. The use of random sampling helps to ensure that a given dataset is as representative as possible, and protects the sample from bias. This is crucial for drawing reliable conclusions.
- Quantitative data is analyzed objectively. Because quantitative data is suitable for statistical analysis, it can be analyzed according to mathematical rules and principles. This greatly reduces the impact of analyst or researcher bias on how the results are interpreted.
Disadvantages of quantitative data
There are two main drawbacks to be aware of when working with quantitative data, especially within a research context:
Quantitative data can lack context. In some cases, context is key; for example, if you’re conducting a questionnaire to find out how customers feel about a new product. The quantitative data may tell you that 60% of customers are unhappy with the product, but that figure alone will not tell you why. Sometimes, you’ll need to delve deeper to gain valuable insights beyond the numbers.
- There is a risk of bias when using surveys and questionnaires. Again, this point relates more to a research context, but it’s important to bear in mind when creating surveys and questionnaires. The way in which questions are worded can allow researcher bias to seep in, so it’s important to make sure that surveys are devised carefully. You can learn all about how to reduce survey bias in this post.
7. Key takeaways and further reading
In this post, we explained what quantitative data is, how it differs from qualitative data, and how it’s collected and analyzed. To recap what we’ve learned:
- Quantitative data is data that can be quantified. It can be counted or measured, and given a numerical value.
- Quantitative data lends itself to statistical analysis, while qualitative data is grouped according to themes.
- Quantitative data can be discrete or continuous. Discrete data takes on fixed values (e.g. a person has three children), while continuous data can be infinitely broken down into smaller parts.
- Quantitative data has several advantages: It is relatively quick and easy to collect, and it is analyzed subjectively.
Collecting and analyzing quantitative data is just one aspect of the data analyst’s work. To learn more about what it’s like to work as a data analyst, check out the following guides. And, if you’d like to dabble in some analytics yourself, why not try our free five-day introductory short course?