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Approved and verified accurate by the Dean of the College of Doctoral Studies on Jan. 8, 2026.
The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.
Quantitative research design involves developing scholarly studies focused on quantitative data collection and analysis. Some types of quantitative research design include correlational, descriptive, longitudinal, experimental and quasi-experimental.
While reflecting upon the answers to the above questions, consider the main types of quantitative research design:
This type of quantitative research is appropriate if you intend to measure variables and perhaps establish associations between variables. However, the quantitative descriptive research design cannot establish causal relationships between variables.
Descriptive research is also referred to as observational studies because your role is strictly that of an observer. The following are some of the types of descriptive studies you might engage in when writing your dissertation:(See disclaimer 2)
Because the role of the researcher is solely observational, they may not develop a hypothesis beforehand. (Although some researchers might develop one before beginning their research.) Rather, the descriptive researcher develops the hypothesis after collecting the data and analyzing it for their quantitative dissertation.
Because it likewise makes no attempt to influence the variables, correlational research is very similar to quantitative descriptive research design. Another similarity is that the researcher conducting the study measures the variables involved. The main difference between descriptive and correlational studies is that a correlational study seeks to understand the relationship between the variables.
A correlational study can also establish whether this relationship has a positive or negative direction. A positive correlation means that both variables move in the same direction. In contrast, a negative correlation means that the variables move in opposite directions.
For example, a positive correlation might be expressed as follows: “As a person lifts more weights, they grow greater muscle mass.” A negative correlation, meanwhile, might be expressed as follows: “As a waiter drops more trays, their tips decrease.”
The fact that correlational research cannot be used to establish causality is a common point of confusion among new researchers. After all, it certainly seems to be causal in nature that a waiter who drops trays frequently would receive smaller tips.
However, the key is that correlational studies do not provide definitive proof that one variable leads to the second variable. While the two variables may be linked, further study with a different type of quantitative research would be required to establish a causal link.
A longitudinal quantitative research study does not involve manipulating any sort of variables, including environmental variables. Rather, it’s purely an observational study.
In a longitudinal study, researchers observe the same group of study participants over a period of time. There is no set period of time that a study may last, and some of these studies may be as short as a few weeks. However, it’s also not uncommon for these studies to last at least a year or even several decades.
Longitudinal studies are particularly helpful for observing long-term effects, trends and changes over time. They can be applicable to a variety of fields, including education, healthcare and social sciences. Researchers can use longitudinal studies to track outcomes or development over time, such as the lifetime earnings of people who were enrolled in an early childhood education program or the life expectancy of people who live below the federal poverty line.
One common type of quantitative research uses surveys. This method is useful for collecting vast amounts of numerical data from a target demographic at one point in time, rather than over a period of time. It’s an appropriate method of data collection for different types of studies, such as descriptive and correlational studies, as well as experimental and quasi-experimental.
Some examples of data collection tools for this type of research design include:
In a quasi-experimental quantitative research design, the researcher attempts to establish a cause-effect relationship from one variable to another. For example, a researcher may determine that high school students who study for an hour every day are more likely to earn high grades on their tests. To develop this finding, the researcher would first measure the length of time that the participants study each day (variable one) and then their test scores (variable two).
In this study, one of the variables is independent, and the other is dependent. The value of the independent variable is not influenced by the other variables; the value of the dependent variable, however, is wholly dependent on changes in the independent variable. In the example above, the length of study time is the independent variable, and the test scores are the dependent variable.
A quasi-experimental study is not a true experimental study because it does not randomly assign study participants to groups. Rather, it assigns them to groups specifically because they have a certain attribute or meet non-random criteria. Control groups are not strictly mandatory, although researchers still often use them to substantiate their findings.
Experimental quantitative research design uses the scientific approach. It establishes procedures that allow the researcher to test a hypothesis and to systematically and scientifically study causal relationships among variables.
All experimental quantitative research studies include three basic steps:
An experimental quantitative study has the following characteristics:
A scientific experiment may use a completely randomized design in which each study participant is assigned randomly to a group. Alternatively, it may use the randomized block design in which study participants who share a certain attribute are grouped together. In either case, the participants are randomly given treatments (or interventions) within their groups.
Causal comparative research, or ex post facto research, studies the reasons behind a change that has already occurred. For example, researchers might use a causal comparative design to determine how a new diet affects children who have already begun it. This type of research is especially common in sociological and medical circles.
There are three types of causal comparative research designs, including:
Although causal comparative research designs can provide insight into the relationships between variables, researchers can’t use it to define why an event took place.
Causal comparative studies include the same general steps:
Causal comparative studies are similar to correlational studies, but whereas both explore relationships between variables, causal comparative studies compare two or more groups, and correlational studies score each variable in a single group. Though correlational studies include multiple quantitative variables, causal comparative studies include one or more categorical variables.
A doctoral degree is a terminal degree program that allows you to plan, execute and analyze your own original research in your field. If you’re pursuing a doctoral degree with a quantitative research focus, you’ll be expected to conduct quantitative research.
As you prepare for your quantitative dissertation research, you’ll need to think about structuring your research design. There are several types of quantitative research designs, such as the experimental, comparative and predictive correlational designs.
The approach you should choose depends primarily on your research aims. Before you finalize which of these quantitative research methods to choose, you should have a conversation with your dissertation advisor about your options.
The College of Doctoral Studies at Grand Canyon University provides opportunities for learners to develop advanced quantitative research skills using various data collection methods. You can add to the body of knowledge in your field as you strive to develop a greater understanding of your chosen research area. Here, you’ll benefit from faculty mentorship and other doctoral student support resources, including the Center for Innovation in Research and Teaching and our Doctoral Community (DC) Network™.
Aspiring doctoral students at GCU can choose from a wide range of programs in various fields from the College of Doctoral Studies. These include the Doctor of Philosophy in General Psychology: Performance Psychology (Quantitative Research) degree and the Doctor of Education in Organizational Leadership (Quantitative Research) degree.
Research design refers to your approach for answering your fundamental research questions. If you are writing a dissertation for a quantitative degree, your research design will require numerical data collection and analysis.
Dissertations seek to answer research questions. They may develop new theories, expand upon existing theories or otherwise add to the body of knowledge in a field. Whatever the purpose, research questions address a research problem statement, which is the heart of a dissertation.
For example, doctoral students may seek to answer questions such as, If and to what extent do teacher practices influence special education students’ motivation? or Do office perks affect workers’ productivity?
The findings you glean from your research will help you develop fully substantiated answers to your questions. To acquire these findings, however, you’ll need to develop your dissertation’s research design.
Before you can settle on the details of your quantitative research design, you must decide whether your dissertation will be exploratory or conclusive in nature. Exploratory research aims to discover new insights, ideas or opportunities, while conclusive research aims to test hypotheses, measure outcomes or evaluate alternatives for marketing decisions.
Your quantitative research design is your strategy for carrying out your doctoral research. In the process of establishing your research design, you will need to answer questions such as the following:
You should also consider whether you will need primary or secondary data. Primary data refer to information that you collect firsthand from sources such as study participants. Secondary data refer to information that was originally collected by other researchers; importantly, you will need to verify these sources’ reliability and validity.
