The purpose of quantitative research is to develop systematic knowledge about the social world by measuring variables and analyzing numerical data. Researchers use quantitative methods to describe patterns, estimate relationships, test hypotheses and, when the design supports it, evaluate causal claims.^{(See disclaimer 1)}

Quantitative research does not automatically produce objective findings; its credibility depends on valid measurement, appropriate sampling and analysis, transparent reporting and attention to potential sources of bias. These practices can make a study more reproducible or replicable by other researchers.

The data is primarily used to:^{(See disclaimer 2)}

• Find patterns and averages
• Make predictions
• Test causal relationships
• Generalize results to wider populations

Quantitative research is structured around the scientific method. After observing the situation to be investigated, the researcher forms a hypothesis and then uses deductive reasoning by predicting how the data should look if the hypothesis is true. The researcher collects the data and analyzes them to confirm or reject the hypothesis.

The basic procedure of a quantitative research design is as follows:^{(See disclaimer 3)}

1. Make observations about something unknown, unexplained or new. Investigate current theories or trends about the problem or issue.
2. Formulate a hypothesis to explain your observations.
3. Predict outcomes based on your hypothesis and develop a plan to test your predictions.
4. Collect and process your data using one or more of the methods below.
5. Interpret the results in light of the study design, measurement quality, statistical uncertainty and alternative explanations. Report findings transparently whether or not they match the prediction. Replication and additional studies can assess the robustness and generalizability of the findings, while unexpected results may motivate revisions to theories or hypotheses.

GCU supports four main types of quantitative research approaches: descriptive, correlational, experimental and comparative.^{(See disclaimer 3)} Understanding how these approaches differ can help researchers choose the methodology that best aligns with their research question, study objectives and desired outcomes.

The four main types of quantitative research include:^{(See disclaimer 3)}

1. Descriptive Research 

This method is focused on the “what” question. It describes the current status or circumstance of the factor being studied. Researchers using this method do not generally begin with a hypothesis; rather, they develop one after collecting the data. 

Variables are not manipulated in this type of research, and researchers do not use the law of probability. Survey research with large data sets falls into this design category.

2. Correlational Research

This method is used to study relationships between factors, which are measured and recorded as research variables. It is used to detect trends and patterns in data.

In a correlational study, variables are not manipulated. Thus, the results are not sufficient to establish the causes of the patterns and trends discovered. We can have correlational associated or correlational predictive designs.

3. Experimental Research

The experimental method studies whether there is a cause-and-effect relationship between the research variables. The researcher controls or manipulates an independent variable to measure its effect on one or more dependent variables. This kind of research is commonly used in science fields such as sociology, psychology, chemistry and physics.

Experimental research includes a variety of designs such as pre-experimental, quasi-experimental and true experimental, and it typically includes parameters such as random or non-random assignment, manipulation of variables and use of a control group.

4. Comparative Research 

This method focuses on comparisons. Researchers study preexisting groups rather than creating new groups for the study. As in experimental research, the focus is the effect of an independent variable on a dependent variable.

The difference is that there is no control group, no random selection or no active manipulation variable. Comparative research can also include ex post facto study designs in which archival data is used.

What is a quantitative researcher’s most valuable tool? Quantitative researchers use a variety of software tools to collect, analyze and visualize data. The best tool depends on the research question, dataset and analytical needs. Commonly used quantitative research tools include R, Python, SPSS and SAS, each of which offers capabilities for statistical analysis, data management and data visualization.^{(See disclaimer 4)}

Other helpful quantitative research tools include:^{(See disclaimer 4)}

• R: An open-source programming language commonly used for statistical modeling and data visualization
• SPSS: A statistical analysis platform that supports data preparation, data management, predictive modeling and advanced statistical analysis
• SAS: A software suite used for statistical analysis, data management, analytics and artificial intelligence applications
• Python – A programming language widely used for data analysis, data cleaning, statistical computing and data visualization through libraries such as pandas, NumPy and Matplotlib

Quantitative researchers aim to create a general understanding of behavior and other phenomena across different settings and populations. Quantitative studies are often fast, focused, scientific and relatable.^{(See disclaimer 5)}

Why is quantitative research important for furthering academic progress? The speed and efficiency of the quantitative method are often attractive to researchers. Data computing equipment makes it possible to process and analyze data quickly, even with large sample sizes. Surveys, polls, statistical analysis software and weather thermometers are all examples of instruments used to collect and measure quantitative data.

Researchers can clearly communicate quantitative results using unbiased statistics. When performed correctly, an analysis allows researchers to make predictions and generalizations to larger, more universal populations outside the test sample.^{(See disclaimer 1)} This is particularly useful in social science research.

Quantitative studies are focused. The goals and design of the study are determined from the beginning, and the research serves to test the initial theory and determine whether it is true or false. Researchers may appreciate the control and simplicity.

If well-designed, quantitative studies are relatable in the sense that they are designed to make predictions, discover facts and test existing hypotheses. Researchers use these studies to test theories about how or why certain events occur by finding evidence that supports or disproves the theories.

Experienced researchers know that all study methods have their flaws. Reliable quantitative research requires the knowledge and skills to scrutinize your findings thoroughly. This means carefully considering and reporting on your test variables, predictions, data collection and testing methods before developing your final conclusion.

Findings can be generalized if your selection process is well-designed and the test sample is an appropriate representation of the population being studied.

In addition to situations where the above advantages apply, quantitative research is helpful when you collect data from a large group of diverse respondents. It is also a good method to use when your audience is more receptive to results in the form of facts, graphs, charts and statistics.

Quantitative research is often performed by professionals in the social science disciplines, including sociology, psychology, public health and politics.^{(See disclaimer 2)} According to PsyForU Research International, it is also widely used in the fields of education, marketing and healthcare.^{(See disclaimer 6)}

Researchers typically use quantitative data when the objective of their study is to assess a problem or answer the “what” or “how many” of a research question. In essence, the goal of quantitative research studies is to understand the relationship in a population between an independent variable and one or more dependent variables.

Ideally, when developing a study, researchers should review their goals as well as the claims they hope to make before deciding whether the quantitative method is the best approach.

A quantitative researcher is responsible for managing and analyzing data for an organization. The goal is to develop insights based on data analysis that can help the organization create forward-looking and more effective business decisions and business models.

To do the job, a quantitative researcher first needs to understand what type of insights the organization needs. They then look for or compile data related to that area and use quantitative research tools to analyze the data. Based on their analyses, researchers develop insights intended to drive informed business decision-making. They may present their findings and insights in reports or presentations.

Most researchers are introduced to the various study methodologies while in school, particularly as learners in an advanced degree program. During the course of their doctoral journeys and careers, some researchers develop a preference for one particular form of study. One benefit of a quality education is exposure to the purposes and advantages of the various methodologies and how to apply them in research.

Gaining experience in quantitative research enables professionals to go beyond existing findings to explore their area of interest through their own sampling, analysis and interpretation of the data. Professionals may be known as a statistician or a quantitative researcher consultant.

If you are interested in conducting research or enhancing your skills in a research field, earning a doctoral degree can teach advanced research skills.

In a quantitative degree program, you will be taught how to:

• Design a quantitative study
• Develop skills in quantitative data collection and work with statistical formulas
• Produce results and findings using quantitative analysis

At Grand Canyon University, you can choose from a diverse range of quantitative doctoral degrees brought to you by the College of Doctoral Studies. Pursue your academic interests and conduct original research in your field. Fill out the form on this page to speak with a university counselor about earning your doctoral degree at GCU.