Types of quantitative research: Examples + tips
You need quantitative research data, conducted on a statistically significant sample to get the most informative results for your business.
You may already use quantitative research, or you may be new to this research type. Join us as we explore quantitative research, how to use it, and the best ways to collect quantitative data.
Types of quantitative research
Quantitative research uses numerical data to identify patterns, averages, and cause-and-effect relationships. Researchers choose from four primary types of quantitative research designs based on the specific goals and variables of their study.
The four main types of quantitative research include:
- Descriptive: Used to identify current characteristics, trends, or frequencies within a population.
- Correlational: Used to find statistical relationships or associations between two or more variables.
- Causal-comparative: Used to determine the potential cause-and-effect relationship between groups where variables cannot be manipulated.
- Experimental: Used to test a specific hypothesis through controlled testing and variable manipulation.
Below is a detailed breakdown of how each type works.
Descriptive quantitative research
Descriptive research is a non-experimental type of quantitative research that measures the current status of a specific variable or topic. This methodology answers questions of what, where, when, and how, but it does not explain why (which is the goal of qualitative research). In this research type, the researcher observes and measures variables without manipulating or controlling them.
Common methods for this type
- Surveys are often used to gather a large amount of data that can be analyzed for frequencies, averages, and patterns. For example, surveys can be used to describe the demographics of a given region, gauge public opinion on political topics, and evaluate customer satisfaction with a company’s products.
- Observations are often used to gather data without relying on survey respondents' honesty or accuracy. This method of descriptive research is used to understand how individuals act in real-life situations.
- Case studies can also be used to gather detailed information to identify characteristics of a narrowly defined subject. They are frequently used to generate hypotheses and theories.
When to use this type
Choose this methodology when your goal is to:
- Identify market trends or categories.
- Form a hypothesis for later testing.
- Describe the characteristics of a specific sample group.
- Confirm if an existing phenomenon is still occurring.
Descriptive research examples:
- Market trends: An athletic shoe brand surveys New York customers to identify their purchasing habits.
- Media habits: A researcher tracks which websites adults aged 16–20 visit to find online news.
- Lifestyle patterns: A study measures how many vacation days working professionals actually use per year.
Correlational quantitative research
Correlational research is a type of quantitative research that measures the statistical relationship between two or more variables. In this research type, the researcher examines how variables interact without controlling or manipulating any of them. The primary focus is to determine the direction and strength of a relationship between fixed variables.
Common Methods for This Type
- Secondary data utilizes existing datasets (such as historical records or public statistics) to find correlations. While cost-effective, researchers must verify that the original data is relevant to their specific study.
- Surveys measure variables of interest. To ensure data integrity, questions must be structured to avoid bias and lead to objective numerical data.
- Naturalistic observation collects data in a natural environment. Researchers measure the frequency, duration, or scale of specific behaviors as they occur spontaneously.
When to use this research type
Choose this methodology when your goal is to:
- Identify if a change in one variable is associated with a change in another.
- Gather data from natural settings to generalize findings to real-life situations.
- Test a hypothesis where experimental manipulation is impossible or unethical.
Correlational research examples
- Digital marketing: A team analyzes whether an increase in Facebook shares correlates with higher search engine rankings.
- Education: A researcher observes college seminars to see if there is a relationship between gender and the frequency of class participation.
- Web performance: An analyst measures if adding video content to a landing page correlates with an increase in average dwell time and conversion rates.
Note: Correlational research identifies relationships, but it does not prove that one variable causes the other.
Causal-comparative (quasi-experimental) research
Causal-comparative research is a type of quantitative research that identifies cause-and-effect relationships between independent and dependent variables. While this methodology mimics an experiment, it is not a "true" experiment because the researcher does not randomly assign participants to groups. Instead, the researcher examines groups that already exist or are formed by external circumstances.
Common designs for this type
- Nonequivalent groups: Researchers compare two similar groups where only one receives a treatment or possesses a specific trait.
- Regression discontinuity: Researchers assign a cutoff point (like a test score) to participants. Those just above the line receive the treatment, while those just below serve as a control group.
- Natural experiments: An external event—like a policy change or a natural disaster—creates a situation where people are assigned to groups by "nature" rather than by the researcher.
When to use this research type
Choose this methodology when your goal is to:
- Determine if a specific factor causes a change in outcome.
- Conduct research when a true randomized experiment is unethical, too expensive, or impossible to perform.
- Analyze the impact of real-world changes (like a new law) on different populations.
Causal-comparative research examples
- Public policy: The Oregon Health Insurance Experiment analyzed the impact of Medicaid by comparing people who won a random enrollment lottery to those who did not.
- Education impact: A researcher compares test scores between two similar schools—one that offers an after-school program and one that does not—to measure the program's effect.
- Workplace equity: A study evaluates the wage gap by comparing salaries between men and women across the same industries and locations.
Experimental quantitive research
Experimental research is the most rigorous type of quantitative research because it uses the scientific method to prove or disprove a specific hypothesis. In this research type, the researcher manipulates one or more independent variables to measure their effect on a dependent variable. This methodology establishes a definitive cause-and-effect relationship through strict control and randomization.
Common designs for this type
- Pre-experimental: Researchers observe a group after introducing a change to see if further, more expensive study is warranted. This design lacks a control group.
- True experimental: This design relies on random sampling and statistical analysis to validate a hypothesis. It is the most accurate form of research because it eliminates outside bias.
- Quasi-experimental: This type tests a hypothesis without random assignment. It is often used when the researcher cannot control how groups are formed.
When to use this research type
Choose this methodology when your goal is to:
- Compare how two or more groups respond to different conditions.
- Make data-driven decisions where the stakes (or costs) are high.
- Move beyond simple correlation to prove exactly why a change occurred.
Experimental research examples
- Product development: Engineers create three different prototypes of a tool and test each under identical stress conditions to determine which design is the most durable.
- A/B testing: A marketing team runs two different versions of a digital advertisement simultaneously. By tracking which version generates more clicks, they identify the most effective creative strategy.
- Demographic testing: A company shows the same product video to two distinct age groups to measure which demographic has a higher intent to purchase.
What is quantitative research?
Research in which collected data is converted into numbers or numerical data is quantitative research. It is widely used in surveys, demographic studies, census information, marketing, and other studies that use numerical data to analyze results.
Primary quantitative research yields results that are objective, statistical, and unbiased. These results are often used as benchmarks.
Characteristics of quantitative research:
- Data is numerical
- Analysis is from a statistical perspective
- Conducted on a statistically significant sample size that is representative of the target market
- Uses structured tools, such as surveys, to gather data
- Uses closed-ended questions focused on the end goal of the research
- Can provide generalized results that represent an entire population
- Can be used to find patterns and averages
- Can be used to make predictions
- Can test causal relationships
The pros and cons of quantitative research
The difference between quantitative and qualitative research is quantitative research collects numerical data. It is statistical and structured, and its results are objective and conclusive.
Qualitative research collects non-numerical data to gain insights. It is performed with the goal of gaining a deeper understanding of a topic, issue, or problem from an individual perspective. Data is meant to describe rather than predict. Information is gathered through focus groups, observation, and open-ended survey questions.
Qualitative research data is not numerical. Because of its exploratory nature, answers are descriptive text or statements rather than choices from a structured answer set. This makes qualitative research more time-consuming to analyze than quantitative research, though it is equally valuable in a well-structured survey.
Refer to this article for further information about the difference between quantitative and qualitative research.
Pros of using quantitative research
There are several advantages to quantitative research. Some of the most salient advantages are:
- Reliable data: data collected in quantitative research is reliable and accurate because it is collected, analyzed, and presented in numerical form.
- Study can be replicated: standardized collection allows the study to be performed again to directly compare results.
- Fast and easy collection of data: quantitative research data can be collected quickly and the process of conducting a survey with the quantitative research method is straightforward and less time-consuming than qualitative research.
- Wider scope of data analysis: quantitative research provides a wider scope of analysis with the use of statistics.
- Eliminates bias: there is no scope for personal opinions or biasing of results in the numerical data.
- Less interpretation of results: accept or reject your hypothesis based on numerical data.
Limitations of using quantitative research
No research method is perfect. These are some of the main limitations of quantitative research:
- Superficial representation: complex concepts such as feelings and opinions cannot be expressed
- Data can be over-manipulated: missing data, imprecise measurements, or inappropriate sampling are biases that can lead to inaccurate conclusions
- Difficult to analyze without a tool: statistical analysis can be challenging to perform without statistics knowledge and experience or a tool that performs statistical analysis
Data collection methods for quantitative research
Data collection, the process of gathering and measuring information on variables of interest, is critical in any type of research. How the information is collected and used and what insights it can generate are determined by the methodology and analytical approach of the researcher.
In quantitative research, you’ll use one or more of these methods to collect data.
Questionnaires or surveys ask questions to help researchers collect data. There are several types of survey questions. For quantitative surveys, closed-ended questions that yield numerical values and answers are typically used.
Cross-sectional surveys
This type of survey gathers data from multiple demographic groups during the same time period. With cross-sectional surveys, you can compare data across demographics and track multiple variables.
Longitudinal surveys
These surveys gather data from one demographic group at multiple time periods. A longitudinal survey may be used to follow up with participants at, for example, one month, two months, one year, and five years later. The goal of a longitudinal survey is to see how habits change over time or what impact habits have on a group of people over the course of months or years.
Interviews
Similar to surveys, participants are asked a series of questions in interviews. Instead of answering online or on paper, the researcher asks questions face-to-face with the participant. Interviews may be structured, where each participant is asked the same questions in the same order, or unstructured, where questions are asked as the researcher thinks of them or in response to what a participant says.
Observation
In observation, a researcher watches people and notes their behaviors, actions, and habits. Observation is most often used in qualitative research, but can also be used in quantitative research.
What type of quantitative research do you need?
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