Types of Quantitative Research Methods and Designs

Professor giving lecture on quantitative methods

Every doctoral student has their own reasons for pursuing a terminal degree. Some are motivated by enhanced career prospects, while others like the idea of being recognized as an expert in their field or have a passion for bringing new knowledge to leaders. Regardless of your own motivations for earning a doctoral degree, you are sure to develop stronger critical thinking and analytical reasoning abilities along the way. This is thanks in large part to your strategic research design. 

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 or predictive correlational designs. The approach you should choose depends primarily on your research aims. Before you decide which of these quantitative research methods to choose, you should have a conversation with your dissertation advisor about your options.

In This Article:

What Is Quantitative Research Design?

At the core, 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.

“Research design” refers to your approach for answering your fundamental research questions. If you are writing a quantitatively based dissertation, your research design will center on numerical data collection and analysis.

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 seeks to develop general insights by exploring the subject in depth. In contrast, conclusive research aims to arrive at a definitive conclusion about the topic.

Taking a Closer Look at the Types of Quantitative Research Designs

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:

  • What are your overall aims and approach? 
  • Which data collection methods will you use? 
  • Which data collection procedures will you use? 
  • What are your criteria for selecting samples or screening research subjects? 
  • How will you prevent the possibility of inadvertent bias that may skew your results? 
  • How will you analyze your data?

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.

Quantitative Research Design Examples

While reflecting upon the answers to the above questions, consider the main types of quantitative research design:

  • Experimental research design 
  • Quasi-experimental research design
  • (Causal) comparative
  • Correlational including predictive quantitative design
  • General correlation

Descriptive Quantitative Design for Your Research

This type of quantitative research design 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:

  • Case or case study: This is a fairly simple quantitative research design example. It involves the collection of data from only one research subject. 
  • Case series: If the researcher evaluates data from a few research subjects, the study is called a “case series.” 
  • Cross-sectional study: In a cross-sectional study, researchers analyze variables in their sample of subjects. Then, they establish the non-causal relationships between them. 
  • Prospective study: Also called a “cohort study” or “longitudinal study,” this involves analyzing some variables at the beginning of the study. Then, researchers conduct further analyses on outcomes at the conclusion of the study. These studies may take place over a long period of time (e.g., researchers analyzing individuals’ diet habits and then determining incidences of heart disease after 30 years). 
  • Case-control study: Researchers can compare cases or subjects with a certain attribute to cases that lack that attribute (the controls). These are also called “retrospective studies.”

Because the role of the researcher is solely observational, they may not develop a hypothesis beforehand, though 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.

Correlational Quantitative Research Design

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 or evaluates 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.”

Note that a correlational study can also produce findings of zero correlation. For example, the presence of muscular waiters might not be correlated with tips.

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.

Quasi-Experimental Quantitative Research Design

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.

Experimental Quantitative Research Design

Experimental quantitative research design utilizes 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:

  1. The researcher measures the variables. 
  2. The researcher influences or intervenes with the variables in some way. 
  3. The researcher measures the variables again to ascertain how the intervention affected the variables.

An experimental quantitative study has the following characteristics:

  • The nature and relationship of the variables 
  • A specific hypothesis that can be tested 
  • Subjects assigned to groups based on pre-determined criteria 
  • Experimental treatments that change the independent variable 
  • Measurements of the dependent variable before and after the independent variable changes

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 within their groups.

(Causal) Comparative Research Design

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:

  • Exploring the effects of participating in a group 
  • Exploring the causes of participating in a group 
  • Exploring the consequences of a change on a group

Though causal comparative research designs can provide insight into the relationships between variables, researchers can’t use it to define why an event took place. This is because the event already occurred, so researchers can’t be sure what caused it and what the effects are.

Causal comparative studies include the same general steps:

  • Identify phenomena and think about the causes or consequences of that phenomena 
  • Create a specific problem statement 
  • Create one or more hypotheses 
  • Select a group to study
  • Match the group with one or more variables to control the variables and eliminate differences within the group (this step may differ depending on the type of causal comparative study done) 
  • Select instruments to use in the study 
  • Compare groups using one or more differing variables

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.

Aspiring doctoral students at Grand Canyon University (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. Complete the form on this page to explore your doctoral degree options at GCU. 


Approved by the assistant dean of the College of Doctoral Studies on April 21, 2023. 

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.