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The ABCs of Quantitative Data: What It Is, How to Collect It, and Why You Should
Quantitative data is all around us. In the form of surveys and market research, this data type helps businesses make decisions about everything from product development to marketing strategy. But what exactly is quantitative data? Simply put, it's information that can be expressed and measured numerically and used to track and analyze changes over time. When quantitative data collection and analysis are at the core of business decisions, outcomes improve.
Quantitative Data Examples
The number of Twitter followers or LinkedIn "likes" that a business has or the amount of time or money a customer spends on a website or product are all examples of quantitative data. Such information is useful to enterprises seeking to gain detailed insights and make data-backed decisions. The good news is that quantitative data is there for the taking if you understand what to target, how to collect it, and how to analyze it.
Discrete vs Continuous Quantitative Data
Not all data is created equal. In particular, there's a big difference between discrete and continuous data. To make the most of quantitative data, it's essential to understand the differences between the two:
Discrete data is data that you can count. This data is helpful for understanding things like the number of products sold or how many people are interested in your product. However, it doesn't give you much insight into trends or patterns over time. For example, the number of people who respond to a survey, the number of visitors to a website in a day, or the number of Tweets that mention your brand.
Major Characteristics of Discrete Data
- Made up of non-negative whole numbers or values
- Can't be divided into parts
- Easily visualized with histograms, bar graphs, and box plots
Continuous data is data that is measured rather than counted, and it never sleeps. It's in a perpetual state of flux. Continuous data provides insight into what's happening with your market, product, or service. Continuous data is helpful for understanding customer behavior. When gathered and tracked correctly, preferences from buying habits and product interests to personality, values, and lifestyle shine a light on business opportunities. In short, continuous data is an invaluable asset for businesses of all sizes. Collecting and analyzing continuous data in the computer age is easier than ever. So if you're not already taking advantage of this powerful tool, now is the time to start.
Major Characteristics of Continuous Data
- Determined by the way it's measured
- Is divisible
- Is precise down to decimals
- Best visualized on a line graph
So Which Type of Data Is More Beneficial for Businesses?
While continuous data is endlessly valuable for enterprises, discrete data has an important role. Ultimately the two go hand in hand. Continuous data is critical for understanding trends and patterns, while discrete data is helpful for understanding overall values. Combined, they provide a well-rounded picture for informing strategies and decisions.
Quantitative vs Qualitative Data
As we mentioned above, quantitative data is information that can be expressed in numbers and used to generate statistical analysis. This data type, collected through closed-ended questions like multiple choice, rating scales, and ranking, is helpful for objectives like understanding customer satisfaction levels or identifying areas for improvement. On the other hand, qualitative data is descriptive information that is beyond numeric expression. Collected through open-ended questions like free-text responses and comments, this data type helps enterprises gain a highly nuanced understanding of customer sentiment, motivations, and needs.
Both quantitative and qualitative data have their place in market research. While each is vital, quantitative data can be less susceptible (although not immune) to bias. It's also easier to process and analyze. Qualitative data is often used to fine-tune quantitative questions and can aid in decision-making around what to measure with quantitative data.
Quantitative Data Collection Methods
There are several methods for collecting quantitative data. The following are among the most often used.
Surveys are among the most popular and effective methods for collecting quantitative data and are administered online, by phone, or in person.
This involves observing and recording people's behavior. Observational studies are conducted in naturalistic settings or controlled environments like laboratories. Specific behaviors can be tracked and translated into numbers that can be quantitatively analyzed.
3. Secondary Data Collection
The gathering of existing data that is collected by someone else and made publicly available. For example, sources like census reports, government documents, and published research studies contain secondary data.
4. Scientific Observation
Involves observing and recording people's behavior during observational studies conducted in controlled environments like laboratories.
5. Transactional Data Collection
Transactions are incredibly rich in valuable quantitative data such as, what was purchased, at what price, where, and by whom.
The Advantages of Quantitative Data
By its very nature, quantitative data is often more reliable than other data types because it's easier to measure numeric variables precisely and because it can be analyzed using statistical methods. This allows researchers to draw actionable conclusions about relationships between variables and datasets. In addition, bias is more manageable and easier to minimize when gathering and analyzing quantitative data. Quantitative data helps enterprises determine customer needs, brand performance, customer satisfaction, ad testing, new product development, pricing optimization, and more.
Managing the Limitations of Quantitative Data
Quantitative data can provide valuable insights but doesn't always paint a complete picture of a phenomenon, especially when inexpertly collected. For example, while it's true that respondents easily misinterpret poorly thought out survey questions, even the best survey questions only capture how a respondent feels about a specific subject at the moment they answer the survey question. If survey responses aren't assessed with the possibility of misinterpretation factored in, the resulting data may lead those attempting to gain actionable insight astray. In addition, quantitative data can be biased if the sample group surveyed is not representative of the population as a whole. To compensate, enterprises need carefully executed surveys designed with a comprehensive understanding of survey bias. Working with an expert partner like IntelliSurvey mitigates the above effects to the full extent possible and yields datasets that are as accurate and representative as possible.
Quantitative Data Clean Up
Once gathered, raw quantitative data must be cleaned and prepared before proceeding with the analysis. Why? Because real-world data is chaotic and full of errors, which can lead to inaccurate results if left uncorrected. Cleaning and preparing data is no small task, but it's just one of many things streamlined by IntelliSurvey's powerful proprietary software. When cleaning up data, eliminating bad-quality respondents and cheaters is critical. Our CheatSweep™ algorithm identifies and flags data provided by bad actors so it doesn't appear in your reports or count towards quotas. At IntelliSurvey, we own all of the software we use and have finetuned our technology over 20+ years for the express purpose of quality market research. As a result, our data clean-up is second to none, ensuring that we generate only top-quality, actionable datasets for our clients.
Research Methodologies For Quantitative Data Analysis
There are a variety of ways to analyze quantitative data, below is an overview of some of the most common methodologies.
MaxDiff: MaxDiff involves creating a scale, or "difference," between two or more items to understand how respondents perceive them. Often used in marketing research, MaxDiff helps brands understand consumer preferences.
Cluster analysis: This statistical technique groups data points into clusters. Cluster analysis is helpful for market research to segment a population into various groups.
Factor analysis: This methodology uses observed data to identify the most important underlying factors influencing a specific phenomenon. Factor analysis helps market researchers identify the key drivers of consumer behavior.
Correlations: Correlations measure the relationship between two variables.
TURF: TURF stands for "total unduplicated reach and frequency." It is a market research technique used to determine the optimal mix of products.
Brand Equity: This is the value of a brand. Companies need to quantify their brand equity to track the brand strength over time and to help make sound marketing decisions.
Importance drivers: Importance drivers are the factors that influence how vital a product or service is to consumers.
We Put Advanced Statistical Analysis at Your Fingertips
The essence of quantitative data analysis is processing raw numbers into relevant, actionable insight. IntelliSurvey’s reporting capabilities feature a variety of easy-to-use do-it-yourself analytics functionalities. In addition, our research team is poised to offer guidance and support to clients who need even more advanced analytics.
Summarize and describe the main features of a dataset so you can understand the details. While this type of analysis presents solid numbers, it doesn't necessarily demonstrate their reasoning and is best suited for single-variable analysis. For example, you might use descriptive statistics to calculate your survey respondents' average age or income.
Glean relevant, actionable insight from datasets. In other words, inferential statistics allow you to make accurate predictions about your area of focus by enabling the use of sample data to make assumptions about larger populations. For example, let's say you're a marketer trying to understand how your target audience feels about your brand. With inferential statistics, you can use data from a small sample of people and make predictions about how a larger population feels, allowing you to focus your marketing efforts on the most impactful areas.
Allows you to understand how different variables interact with each other and how they affect the outcome of interest. For example, you might use multivariate statistical methods to examine the relationship between income, education, and job satisfaction.
Data Collection and Analysis Expertise In Your Corner
At IntelliSurvey, we understand the importance of high-quality data. We have the tools and expertise to help you collect, clean, and analyze your datasets to make sound decisions for your business that increase sales, customer satisfaction, and the efficiency of your marketing spend.
From scoping advisory and questionnaire design to sample sourcing, field management, and advanced analytics, our teams have over 1,000 years of combined experience. We've got you covered so you can get to the root of whatever you need to know, understand it fully, form actionable conclusions, and rise above the competition.
Contact us today if you're ready to get started or want to learn more about how we can help you gather the vital data you need and make the most of it.
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