Quantitative research is the most common research framework in the social sciences. While other research frameworks, such as qualitative research, can be subjective, quantitative research is more objective. It studies processes through numbers, and often, the goal of the research is to produce statistics. The information researchers collect using quantitative methods allows them to perform analyses that can be simple or complex, such as showing the connections made between data, calculating averages or percentages, or using inferential statistics to make generalizations about a large population. Researchers use specific methodologies, such as experiments, questionnaires, and structured observations, to collect the data they use for quantitative research.
Let’s take a closer look at quantitative research methods and data analysis to understand what quantitative research is and how you can use it.
What is the purpose of quantitative research?
Quantitative research aims to help scientists, marketers, and researchers better understand society and people. It often involves the exploration or examination of events or occurrences that affect individuals. An example of quantitative research could involve a hospital that conducts surveys after patients have been admitted and discharged. The surveys’ purpose could be to discover how much time doctors who work at the hospital actually spend with their patients.
Surveys could ask patients research questions about how long their visits with a doctor lasted and how long they spent waiting to be seen. The surveys could also ask patients to rank their overall satisfaction with the care they received on a rating scale from one to 10. The surveys’ goal might be to discover if doctors are spending enough time with patients and how that correlates with the patients’ overall care. Using the data collected in the surveys, the hospital can estimate how much time each physician spends with patients. The hospital may also see a connection between higher patient satisfaction — such as patients giving scores of eight, nine or 10 — and increased patient-physician interaction or shorter wait times.
Types of quantitative research methods and techniques
Quantitative research design describes how a researcher arranges a study in an attempt to control the variables. At one end of the spectrum is a method to observe and describe data rather than control or manipulate variables. At the opposite end are methods that aim to control variables and establish clear connections between them. The method you might use depends on your overall goals and what you hope to get from your research.
1. Descriptive research
Descriptive research describes situations, circumstances, or variables. The focus of descriptive statistics is the “what” instead of “why.” Usually, descriptive research involves a fair amount of observation. A researcher might ask children to describe how they spent their summer vacation or watch a teacher during a class to see how they explain concepts to students.
When using descriptive research, a scientist isn’t likely to start from a hypothesis. If they develop one at all, it will be after they have collected data. They can then use the data to test the hypothesis through synthesis and analysis. As they collect data, they need to be careful about the variables they measure and the items they study. Descriptive research typically doesn’t end with an explanation of the cause and effect between variables.
Some examples of descriptive research include:
- A description of teenagers’ alcohol habits: Researchers can issue surveys asking teens how much they drink, when they drink, and who they drink with. They can conduct the surveys over a period of years to see how teenage alcohol consumption changes with time.
- A description of how people in assisted-living facilities spend their time: Researchers can conduct surveys asking people who live or work at assisted-living facilities how much time they spend on certain activities. A researcher might also visit a facility to observe residents and workers, timing their activities.
- A description of how the housing market in a particular city has changed over the years: A researcher can collect data on housing prices, sales volume, and time-on-the-market to see how real estate in a city has changed over a defined period, such as the past 10 years. Since the researcher aims to describe the data, they aren’t looking for factors that could have affected homes’ prices, such as economic recessions or new amenities in the city.
- A description of how opinions on a subject have changed over a period: Researchers can describe how opinions on a subject, such as climate change or driving while intoxicated, have changed over time. They can collect data by conducting surveys that ask people to rank their feelings or beliefs on a subject on a scale from one to 10. They can follow up with the same people year after year to describe the evolution of opinions.
2. Correlational research
The goal of correlational research is to examine and determine the relationship between multiple variables or data points. Like descriptive research, correlational research doesn’t attempt to identify a reason for the connection between variables or find a cause for the relationship. Instead, the intent is to discover patterns or trends between the variables by making comparisons. A researcher performing correlational research shouldn’t manipulate or adjust the data.
Examples of correlational research include:
- An examination of the relationship between depression and diet: A researcher might ask people to rate their depression on a scale from one to 10 and provide details about what types of food they eat and how much of each food. For example, a researcher might notice a correlation between eating less than one serving of vegetables or fruit each day and more severe depression. They might notice that people who rate their depression as a one or two tend to eat multiple servings of sweets during the day.
- An examination of the relationship between SAT scores and grades during the first year of college: A researcher might be curious to see if there is a connection between standardized test scores and grades once a student is in college. They might look at the grades of first-year college students who received scores of 2200 or higher on the SATs to see if there is a relationship.
- An examination of people’s lifestyle habits and the prevalence of certain diseases: Researchers can ask people about their lifestyle habits, such as how much alcohol they drink daily or weekly or how many cigarettes they smoke, to see if there is a connection between habits and the prevalence of diseases. People who smoke more might have higher lung cancer rates or more respiratory issues than people who don’t smoke.
- An examination of types of classroom exercises and the grades students receive: A researcher might be interested in discovering if there is a connection between the types of exercises a teacher leads in a classroom setting and the grades students earn on tests. The research might measure how much time students spend listening to lectures, performing group work, or working individually, then compare that information to the grades students receive.
3. Causal-comparative research
Causal-comparative research is also called quasi-experimental research. It has aspects in common with experimentation but can’t be considered a true experiment. The goal of causal-comparative research is to create a cause-and-effect relationship among multiple variables. What sets this type of research apart from true experimentation is that the researchers cannot manipulate the independent variable. Researchers also need to use naturally occurring or pre-existing groups as part of the study and randomly assign individuals to particular groups. Unlike a true experiment, there is no control group during quasi-experimental research.
Examples of quasi-experimental research include:
- Examining the effect taking vitamins has on children’s school attendance: A researcher might examine the attendance record of a group of elementary school students who take a multi-vitamin each day to see if the students taking the vitamin are more likely to go to school regularly compared to a group of students who don’t take the vitamin.
- Examining the connection between gender and scores on math tests: A researcher might ask students in the same grade to take a math test, then examine the scores the students received to see if one gender scored higher than the other overall.
- Examining the effect exercise habits have on heart health: A researcher compares blood pressure levels, cholesterol levels, and resting heart rates of people who exercise daily and people who don’t exercise to see if there is a connection between exercise and heart health.
- Examining the effect going to preschool has on high school graduation rates: A researcher might look at preschool attendance rates and compare them to graduation rates later on to see if students who started their education earlier were more likely to finish high school.
One thing to keep in mind with quasi-experimental research is that many other variables, not just the ones studied, can impact the results. Students who take multivitamins might also have other things going on in their lives that cause them to go to school more often, for example. People who exercise and have lower cholesterol and blood pressure levels might have other factors, such as a healthier diet or family history, that make them more likely to have better heart health.
4. Experimental research
An experimental research approach relies on the scientific method to manipulate and control variables to determine the cause-and-effect relationship. Experiments can take place in lab settings, but researchers can also conduct them in workshops or classrooms. To be considered a true experiment, research needs to have a control group and control over all possible variables except the independent variable, which the researchers manipulate. Participants in the experiment are assigned to a random group rather than self-selected, as they usually are in a quasi-experimental research method.
Examples of experimental research include:
- Examining the effect of a new medication on chronic illness: Researchers divide patients with the same illness into three groups. One group receives no treatment, one receives a placebo, and the third receives a new medication. At the end of the experiment, the researchers assess the patients to determine if the new medication was more effective than a placebo or no treatment at all.
- Examining the effect of personalized instruction on students’ grades: Researchers divide a class of students into two. Both groups receive in-class instruction. One group also gets an hour of one-on-one tutoring three times a week. At the end of the trial, the students take a test. The researchers examine the students’ grades to see if those who received tutoring performed better than those who didn’t.
- Examining the effect of race or gender on crime: Researchers hire actors to pretend to commit a crime in broad daylight, such as stealing a bike or breaking into a car. The experiment’s goal is to determine if bystanders react differently based on the gender or race of the actor.
Data collection methodologies
Data collection is a critical part of any research study. When you perform quantitative research, you use one or more methods to gather your data.
1. Surveys or questionnaires
A survey or questionnaire asks participants questions to help researchers collect the data they need. Survey questions can be open-ended, such as, “How do you feel after drinking a cup of coffee?” or closed, such as, “How many cups of coffee do you drink daily?: 0-1, 2-3, 4 or more.” Quantitative research surveys usually use closed questions that provide a numerical value as an answer.
Your sampling methods, or how you choose the people you will survey, are a key part of data collection. You might want to collect surveys from people who fit a certain demographic description or a wider subset of the population. How frequently you collect survey information is also essential. Two general methods of conducting surveys exist:
- Cross-sectional surveys gather data from multiple demographic groups at the same point in time. The method lets you compare answers across demographics and also lets you track multiple variables.
- Longitudinal surveys gather data from one demographic group at multiple periods. A longitudinal survey might follow up with participants one month later, one year later, and five years later. This type of survey aims to see how habits can change over time or what impact habits have on a particular group of people over the course of months or years.
Interviews are similar to questionnaires. Instead of having a participant fill out a paper or digital form, the researcher asks the questions while sitting face-to-face with the participant or while they are on the phone. Interviews can be structured, meaning the researcher asks the same questions, in the same order, to everyone who participates. During an unstructured interview, the researcher might ask questions as they think of them or questions in response to what the participant says.
Observation involves watching people and taking note of their behaviors and habits. It’s commonly used to collect qualitative data but can be used to collect quantitative data. For example, a researcher might observe employees leaving work for the day and count those who leave right at 5 p.m., those who leave a bit early and those who stay late. The researcher might observe employees for several days to see if there are patterns or changes when people leave work.
Advantages of quantitative research
Quantitative research has many advantages for researchers and companies that need to get information on their customers. Some of the benefits of quantitative research include:
- Reliability: Quantitative research is objective, meaning the variables and data you collect are reliable and accurate. When you ask someone how many cups of coffee they drink every day, you get a direct, objective answer.
- Reproducibility: Since you are collecting numerical or computational data when you perform quantitative research, it’s easy to reproduce the surveys or interviews when needed. Replication is a key component of a quantitative approach.
- Impartiality: Numbers and statistics don’t have a bias. There’s no way for a research team to influence the results or otherwise make the results biased when using a quantitative approach.
- Scalability: You can scale quantitative research up or down as needed without affecting the quality of the surveys or the data’s validity.
Disadvantages of quantitative research
Quantitative research might not be the best option in all cases. Some possible drawbacks of the method include:
- Might not tell the whole story: The variables you collect through quantitative methods can be superficial or limited. For example, asking people how much coffee they drink doesn’t tell you very much. It can also be the case that other factors you are surveying affect the responses people give.
- Sample sizes can be small: Small sample sizes can limit the impact research has. Asking 10 people about their coffee drinking habits won’t give you a good idea of how coffee consumption plays out across the country, for example.
- Data can be over-manipulated: It’s possible for the setting of a research study to be manipulated and controlled to such an extent that it affects the accuracy of the results or for a range of other, unaccounted-for variables to affect the study.
Although there can be drawbacks to quantitative research, a well-designed study will account for those drawbacks and seek to eliminate them. For example, ensuring a large sample size and setting representational population parameters can help increase the accuracy of the results.
Secondary quantitative research methods
Secondary data is data someone else has already collected. Secondary research is sometimes called desk research since it involves collecting, summarizing, synthesizing and analyzing existing data rather than collecting data, often to strengthen the results from primary research. Secondary data is available from various sources, including:
- Universities and colleges
- Public and private libraries
- Government institutions
- Non-government organizations
- Commercial information sources
What’s the difference between quantitative and qualitative research?
Quantitative research and qualitative research are often compared. While quantitative research focuses on objectivity and numerical values, qualitative research is subjective and values descriptions of feelings or situations. Quantitative research focuses more on establishing measurable (quantifiable) data, while qualitative research is more interpretive and focused on qualities or characteristics.
Some of the data collection methods used during qualitative research are the same as those used during quantitative research, but the end goal is different. A qualitative research study might involve observation and surveys. The survey questions will generally be open-ended. Observation will be to see and describe how people approach situations, rather than counting how frequently they do something.
Another way to look at the difference between quantitative and qualitative research is that one often informs the other — in fact, they can be used together. Businesses typically perform qualitative research when they want to create a hypothesis. Asking people their opinions on a subject can help a business learn more about its customers’ concerns or the opportunities available to it. The company uses the information gathered to form a hypothesis, which it then tests using quantitative research.
Learn more about Cint’s quantitative research tools
Quantitative research can help you learn more about your company’s customers, potential customers, and the overall market. Cint connects brands and businesses to the right audience for quantitative research surveys. To learn more about our platform and what we do to ensure the data we collect is high-quality and accurate, contact us today.