Quantitative Data Definition, Types, and Methods (With FAQs)
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A wide range of professionals, including analysts, healthcare providers, and business managers, rely on data to influence their decisions. Quantitative data refers to information that has numerical values. Understanding quantitative data can help you use statistics and numbers to communicate information objectively. In this article, we review the quantitative data definition, explore several types of quantitative data, examine various analysis methods, and answer a few frequently asked questions.
Quantitative data definition
You may benefit from learning the quantitative data definition if you work in a research-oriented profession, such as statistics, psychology, or data science. The term quantitative data refers to any form of quantifiable information. You can count or measure it and calculate its value numerically, such as its length and width in centimetres, or revenue and expenses in dollars. Using quantitative data, individuals can answer questions such as "How many?" "How often?" and "How much?" By using mathematical techniques, individuals can verify and evaluate the quantitative data.
Quantitative data can be discrete, continuous, or interval compared to ratio data. Discrete data are collections of information that only have certain numerical values and most commonly appear in tally charts, bar graphs, or pie charts. Continuous data can take any value, infinitely decompose into smaller parts, and can change its value. Data intervals and ratios differ in their ability to fall below zero. A data interval scale can represent a value below zero, while ratio variables cannot represent a variable equal to or below zero. Here are some examples of quantitative data:
Height in centimetres
Mileage in kilometres
Sales in dollars
Weight in pounds
Time in months or years
Types of quantitative data
After reviewing the quantitative data definition, you may ask about the different types of quantitative data. The following are a few common forms of quantitative data:
Counters: By using counters, you can count various subject matters within an area. For example, the number of apples in a bowl or the number of cars in a parking garage.
Measurements: Measuring provides you with quantitative data in units of measurement. For example, you may measure the speed of an airplane, the temperature of a room, or the distance of a football field.
Sensors: A numerical measurement of sensory information is a quantitative measurement. For example, you can use the volume of a sound in decibels.
Projections: Based on previous data, a projection is a forecast for a future count or measurement. For example, if a plant is 12 inches tall, you may project it to grow to three inches in five weeks to total a future projection of 15 inches.
Quantification: To perform mathematical operations on qualitative data, quantification can assign numerical values to qualitative data. For example, a survey that asks customers to rate a product on a scale.
Quantitative data analysis methods
Here are a few common analysis methods for quantitative data:
Trend analysis is a statistical analysis method that allows people to examine quantitative data that was collected over an extended period. Methods like this help track data change over time and allow an understanding of how one variable changes as compared to another variable. For example, a survey may ask residents of a state what their opinion is about their governor, then ask that same question every month for a year.
Cross-tabulation allows individuals to examine relationships between multiple variables. Most quantitative data analysis methods use cross-tabulation because they use a basic tabular format to draw conclusions between different data sets in the research study. As an example, a political campaign may conduct a survey to see which candidate voters are likely to choose in an election. By analyzing cross-tabulation data, the campaign can see how respondents from different demographics responded to the survey in comparison to the overall vote total.
Conjoint analysis analyzes the parameters behind a decision. It's capable of collecting and analyzing advanced metrics that offer a deeper understanding of complex decisions. Conjoint analysis assigns value to various characteristics and asks respondents to evaluate those attributes. For example, a computer business can use conjoint analysis to determine which features to include in their products. The company selects various characteristics, such as storage space, screen size, and memory, and adds realistic numerical values to each characteristic. The business can then offer customers a variety of laptop choices with unique combinations of capabilities and assess which features customers value.
Gap analysis involves presenting quantitative data as a side-by-side matrix to measure the difference between expected and actual performance. Using gap analysis can be valuable in identifying performance gaps and determining the measures necessary to close the imbalances. For example, if a manufacturer's production numbers don't meet their expectations, a gap analysis can help determine what process to fix.
Text analysis is a method of extracting meaning and information from text. The goal of text analysis is to quantify or translate qualitative data into more easily understandable information using clever statistical methods. Quantitative text analysis refers to analyzing textual data using statistical procedures. The analysis of quantitative text involves the use of automated software and systematic methods to process large amounts of text. Text analysis turns data sources, such as call center transcripts, online reviews, and customer surveys, into actionable information.
Strengths, weaknesses, opportunities, and threats (SWOT) analysis
SWOT analysis is a quantitative method of analyzing data to identify an organization's strengths, weaknesses, opportunities, and threats. SWOT analysis provides a holistic view of the competition and a guide to developing effective business strategies. For example, if a market forecast suggests a decrease in economic activity, a company can conduct a SWOT analysis to reduce unnecessary expenditures and reassign its most effective employees.
Total unduplicated reach and frequency (TURF) analysis
TURF analysis is a method of analyzing quantitative data that estimates the total market reach of one or more products and services. It helps organizations improve their marketing strategies by establishing how often and through what channels their messaging reaches existing and potential customers. For example, if a chocolate manufacturer wants to launch a new chocolate flavour, it can benefit from understanding the current market conditions. By using TURF analysis, the manufacturer can determine the potential market share of their new chocolate and where to promote it to reach the greatest number of consumers.
Quantitative data FAQs
Here are a few frequently asked questions about quantitative data:
What are the benefits of quantitative data?
The objectivity of quantitative data is one of its major benefits. It's based on concrete numbers and contains fewer variables than qualitative data. This can help remove bias and improve the accuracy of research findings. Qualitative data research involves more personal interactions with people, while quantitative data research relies only on numerical responses. Quantitative data research is typically quicker and more cost-effective than qualitative data research. For example, using quantitative analysis methods, such as sending a survey to thousands of people, is typically more convenient than conducting qualitative analysis methods, like individual interviews.
What is qualitative data?
By using qualitative data, individuals can uncover descriptive and conceptual findings. Qualitative data can help individuals understand quantitative results and explore ideas. You can use qualitative data to determine what, how, and why a phenomenon occurs using methods such as observation, one-on-one interviews, and focus groups. Qualitative research involves collecting, analyzing, and interpreting non-numerical data, such as languages, text, videos, photographs, or audio recordings. Qualitative research can provide insights into how an individual subjectively perceives and interprets social interactions and phenomena.
Related: What Is Quantitative Analysis?
Is quantitative data more reliable than qualitative data?
Both qualitative and quantitative research methods have advantages. Quantitative methods use large populations, which can lead to more reliable results than qualitative data research. Statistics enable researchers to generate and analyze quantitative data, which enhances the credibility of the results. The credibility of qualitative research depends on both the researcher's ability and effort.
Quantitative research uses precise measurements and analysis to answer inquiries accurately. Researchers can compare numerical results to each other by making the same measurement or observation on the same sample, then calculating the correlation among their results. If the researchers rate the test similarly, the researchers can confirm that the results are highly reliable.
What are some example quantitative research questions?
The following are a few examples of quantitative research questions:
How much time do you typically spend shopping in our store?
How often does your supervisor provide you with constructive feedback?
How often do you use our products or services?
How many times have you ordered from our company in the past year?
How often do you fill out the optional survey after completing a purchase?
How much money do you typically spend during a single visit to our store?
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