Reliable data is a critical element for many major decisions in business. Whether you want to create a highly targeted product, elevate your brand, or better understand your customers, data collection is the ultimate starting point. Quantitative data serves you with many concrete, numerical facts that can be used to drive some of the most vital business initiatives.
What is quantitative data? What are the best quantitative data collection methods? Find out the answers to all this and more below.
What is Quantitative Data?
Quantitative data collection is the act of collecting numerical consumer insight data that translates easily into statistical or quantifiable information. Quantitative data collection methods include surveys, questionnaires, observations, and more. Further, this type of data collection typically involves a substantial number of respondents. This larger pool of people provides a large enough sample of extractable data to be statistically relevant for a more sizable population.
While quantitative data is focused on extrapolating numerical data, the purpose of qualitative data is more about collecting contextual or descriptive information from a pool of people. In simpler terms, quantitative data is usually objective and answers questions about who, what, when, and where by looking at measurable numbers. However, qualitative data extracts more subjective information, such as why.
Why is Quantitative Data Important?
Quantitative data is important for researchers due to its objectivity and ability to convey concrete numerical data. It generates reliable, factual data that can be used to answer important questions and quantify specific problems, theories, or hypotheses. Quantitative data bears importance in numerous arenas, including medicine, socio-economic studies, and experimental research. However, it is often used to guide a brand's product development efforts, deny or confirm hypotheses about product reception, or tailor marketing messages to a target audience.
By answering specific questions about its target market, a brand has verifiable datasets that can be used in innumerable ways. For instance, an up-and-coming electric vehicle (EV) manufacturer would greatly benefit from broad data points like only 3% of car owners in the US drive EVs or that 35% of people in the UK are considering buying an EV.
Quantitative data collection also allows brands to garner insights from large sample sizes. Quantitative data can be easier to collect from large groups of people. This affords the ability to break down information into smaller datasets based on important demographics like age groups, geographic locations, or other attributes of interest for comparison and analysis.
Quantitative vs Qualitative Data
Both qualitative and quantitative data are important, but the two forms of data are significantly different. As mentioned above, quantitative data is used to collect concrete numbers that can translate to reliable, statistical information. By contrast, qualitative data is more fluid and contextual.
A few important differentiations between quantitative and qualitative data include:
- Research Methods - Quantitative data collection methods involve a more close-ended approach, while qualitative data collection methods tend to be more open-ended, time-consuming, and interactive.
- Scope - Because quantitative data collection is more straightforward and quicker, the scope of respondents can be vast. For example, one multiple-choice survey may garner responses from thousands of people. Qualitative data is most often conducted with smaller sample sizes and may only be relevant to a narrower audience.
- Types of Data Yielded - Quantitative data delivers clear, objective data like specific counts or numerical measurements. Qualitative data is more subjective, and based on less-concrete concepts like behaviors, perceptions, and motivations.
- Analysis Methods - Quantitative data is simple to interpret, analyze, and graph because it is based on numerical values. Qualitative data, however, requires a more subjective analysis process, which can generate room for bias or human error.
Quantitative and qualitative data can be exceptionally valuable when paired together. For example, an online marketing firm could benefit from knowing how many people prefer websites with interactive content (quantitative). Yet, they can also make valuable decisions if they know why people prefer to interact (qualitative). In many cases, research efforts combine quantitative and qualitative data because combined, the two forms of data provide a fuller picture.
Quantitative Data Collection Methods
Quantitative data is undeniably important for concrete, numerical information. The next question is, how is quantitative data collected? Check out five unique quantitative data collection methods below.
Surveys are the most common method for quantitative data collection. These basic questionnaires are a simple, effective method for collecting quantitative data and generally have a high rate of completion. Additionally, surveys can be deployed both online and offline to reach a broad spectrum of participants.
For example, a panel survey may be sent to a targeted group or general population of online panelists. These surveys generally have a high rate of response because they have opted into consumer panels for the sole purpose of taking part in research. And these surveys shouldn't take much time. Offline surveys are often more time-intensive but can be valuable for collecting data from targeted individuals or customers. Offline surveys may be in the form of a mailed questionnaire or a survey conducted over the phone.
Panel surveys involve a variety of question types, including open-ended questions, multiple-choice questions, single-choice or yes/no questions, grids and scales. Surveys are highly effective for collecting quantitative data because they are efficient, often inexpensive to conduct, versatile, flexible, and offer reliable, measurable data from large groups.
Virtual (remote) or in-person interviews can be used for collecting quantitative data, although interviews are more commonly used for qualitative data collection purposes. Essentially, interviews are like written surveys but in verbal form. The process simply involves an interviewer going over the questions with the survey respondent.
While interviews take more time and can be more difficult to arrange, they do have their merits. For example, a face-to-face interview injects a human touch into the experience, which can be important for certain research topics. Also, interviews are more guided and controlled than surveys. The interviewer can effectively pace questions and responses to make sure the respondent is not dashing through just to finish as quickly as possible.
Qualitative research often uses observational studies, which tend to involve observing people in certain situations or scenarios. However, observation can be a valuable quantitative data collection method as well. The difference is observation for quantitative data collection purposes is wholly focused on numbers.
Generally, quantitative observation studies involve an observer who watches people in a certain situation. However, observations may also be automated, especially online. For example, observation may involve seeing how many products a shopper considers before making a selection. Or, a website analysis tool may measure how many minutes people spend evaluating furniture on a website before making a purchase.
Observation data collection is valuable because it provides specific, highly reliable insight into certain human behaviors. The collected data may be more reliable when observed in real life. For example, people may not always know precisely how many products they considered before making a purchase. However, an observational study would show a researcher exactly how many products a person considered.
Document review is a type of secondary research commonly used in quantitative data collection. You are basically reviewing existing documentation from other data sources to find information to answer or understand a new question. For example, a researcher may analyze a collection of case studies or public records to generate broader datasets on a particular topic.
Secondary research methods like document review can be valuable because new knowledge can often be drawn from collective data sources. Further, evaluating data that already exists can be a good starting point for heightened research efforts. Ins some cases, researchers can identify knowledge or data gaps by reviewing what is already available. This is why literature reviews are such a crucial pillar of the medical research community.
Connected data is an innovative quantitative data collection method that combines insights from survey-derived data and other data sources. For example, through the use of consented personal data like address and email, you can establish deterministic connections between survey and first-party, second-party and third-party datasets. This builds a more robust fusion of quantitative datasets for a deeper, holistic analysis of target audiences.
Connecting and enriching quantitative data provides valuable insights for businesses. Motivation and attitudinal data can be connected with different data sources, such as customer transaction data, purchase behavior categories, interests, and social media affinity. This allows businesses a deeper understanding of customer preferences.
Businesses can use this to improve their consumer intelligence and make more informed decisions. For instance, connected data insight is valuable for product development, marketing strategies, and overall business direction. Additionally, linking survey data with third-party data sources and platforms delivers brands the competitive advantage needed to build stronger relationships with their target audience.
The most successful modern business is built using data as its driving force. However, data used to support your business must be measurable, reliable, and suitable for driving true action and results. Therefore, using the best quantitative data collection methods possible is undeniably important.
Kantar is the trusted source for the most meaningful data. Validated by industry-leading anti-fraud technology, access 170 million people in more than 100 global markets for reliable, quality data. Reach out to learn more about how Kantar can make support the data collection that drives your critical business decisions.