Doctoral Studies: Types of Quantitative Research Methods and Designs

Each PhD student’s motivations for seeking a terminal degree are unique. Some people are driven by better job opportunities, while others enjoy the thought of becoming known as an authority in their profession or are passionate about educating leaders about new information. Whatever your personal reasons are for pursuing a doctorate, you will undoubtedly improve your analytical and critical reasoning skills. This is largely due to the methodological strategy you used for your research.

You should consider constructing your study design as you are ready to conduct your quantitative dissertation research. The experimental, comparative, or predictive correlational designs are just a few examples of the various types of quantitative research designs. Your research goals will determine the strategy you should take in the main. You should talk with your dissertation advisor about your alternatives before deciding which of these quantitative research approaches to use.

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What is the Quantitative Research Design?

Dissertations’ main goal is to provide research-related answers. They might add to the corpus of knowledge in an area by creating new theories, extending ones that already exist, or doing something else. Research issue statements, the core of a dissertation, are addressed by research questions, regardless of their intended use.

For instance, doctorate candidates may try to find the answers to questions like, Do teacher practices affect the motivation of special education pupils, and if so, to what extent? or Do employee benefits at work have an impact on output?

Your research findings will aid in the development of fully supported responses to your inquiries. However, you’ll need to create your dissertation’s research plan in order to obtain these results.

The term “research design” describes the method you will use to respond to your main research questions. The gathering and analysis of numerical data will be the main focus of your research plan if your dissertation is quantitative in nature.

Decide if your dissertation will be exploratory or conclusive in nature before you can decide on the specifics of your quantitative research design. Exploratory research aims to produce general insights by thoroughly examining the topic. Contrarily, conclusive research seeks to get a firm conclusion regarding the subject.

Types of Quantitative Research Design

Your plan for doing your PhD research is outlined in your quantitative research design. In order to develop your research design, you will need to provide answers to questions like the ones below:

• What are the main objectives of your strategy?
• Which techniques for gathering data will you employ?
• Which methods of data collection will you employ?
• What standards do you use to pick samples or vet research participants?
• How will you guard against the danger of unintentional bias that can distort your findings?
• How will you examine your information?

Additionally, think about whether you’ll need main or secondary data. “Primary data” are details you get directly from sources like study participants. The term “secondary data” refers to material that was initially gathered by other researchers; it’s critical to check the quality and dependability of these sources.

Examples of Quantitative Research Designs

Think about the primary categories of quantitative research design as you analyze the responses to the questions above:

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

Descriptive Quantitative Research Design

If you want to measure variables and maybe establish relationships between variables, this kind of quantitative research design is acceptable. The quantitative descriptive study design, however, is unable to identify causal links between variables.

Since your only function in descriptive research is to observe, it is often known as “observational studies.” When writing your dissertation, you might conduct the following sorts of descriptive studies:

• Case Study: This is an illustration of a reasonably straightforward quantitative research design, or a case study. It entails gathering information from just one research topic.
• Case Series: A study is referred to as a “case series” if the researcher analyzes data from a small number of research subjects.
• Cross-sectional study: Researchers examine variables in their sample population in a cross-sectional study. The non-causal links between them are then established.
• Prospective Study: A prospective research, often known as a “cohort study” or “longitudinal study,” involves the initial analysis of a number of factors. At the end of the study, researchers conduct additional analysis of the findings. These studies could go for a very long time (for instance, examining food patterns of people and then calculating heart disease incidences after 30 years).
• Case Control Study: Researchers can contrast cases or subjects who possess a specific attribute with cases who do not (the controls) in a case-control research. These investigations are frequently referred to as “retrospective studies.”

Although some researchers might formulate a hypothesis before starting their investigation, because the researcher’s job is simply observational, they might not. Instead, the hypothesis is created by the descriptive researcher after gathering and analyzing the data for their quantitative dissertation.

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Correlational Quantitative Research Design

Correlational research is quite similar to the design of quantitative descriptive research in that it also makes no attempt to affect the variables. The researcher performing the study measures or evaluates the variables, which is another commonality. Descriptive and correlational investigations vary primarily in that the latter aims to understand the relationship between the variables.

A correlational analysis can also determine if the direction of the association is positive or negative. When two variables move in the same direction, there is a positive correlation. A negative correlation, on the other hand, indicates that the variables move in the opposite directions.

The following sentence could be used to show a positive correlation: “As a person lifts more weights, they grow greater muscle mass.” In contrast, a negative correlation could be stated as follows: “As a waiter drops more trays, their tips decrease.”

Keep in mind that a correlational analysis can also yield results showing no correlation. For instance, tips may not be connected with the presence of muscular waiters.

One area where beginning researchers frequently get confused is the fact that correlational research cannot be utilized to prove causality. After all, it would seem to be coincidental that a waiter who frequently drops trays would get fewer gratuities. The important thing to remember is that correlational research cannot prove beyond a reasonable doubt that one variable causes the other.

Quasi-Experimental Quantitative Research Design

A cause-and-effect connection between one variable and another is sought after in a quasi-experimental quantitative research design. For instance, a study may find that high school students are more likely to achieve good test scores if they study for an hour each day. In order to arrive at this conclusion, the researcher would first calculate the subjects’ daily study time (variable 1) and then their test scores (variable 2).

One of the variables in this study is independent, while the other is dependent. The dependent variable’s value, however, is entirely dependent on changes in the independent variable and is not affected by the other variables in any way. The amount of study time is the independent variable in the example above, and the test results are the dependent variable.

Because participants in a quasi-experimental study are not divided into groups at random, it is not a true experimental study. Instead, it places them in particular groups based on a characteristic or other non-random criterion. Although they are frequently used by researchers, control groups are not necessarily required.

Experimental Quantitative Research Design

The scientific method is applied in the design of experimental quantitative research. It creates processes that enable the researcher to put a hypothesis to the test and conduct methodical, scientific studies of causal correlations between variables.

Three fundamental steps are present in all experimental quantitative research studies:

• The variables are measured by the researcher.
• The variables are affected or altered in some way by the researcher.
• To determine how the intervention altered the variables, the researcher remeasures them.

Observable traits of an experimental quantitative study include:

• The variables’ characteristics and connections
• An applicable testable hypothesis
• Using pre-established criteria, subjects were divided into groups.
• Treatments that alter the independent variable in experiments
• Measurements of the dependent variable both prior to and following changes to the independent variable

A completely random design, in which each study subject is assigned to a group at random, may be used in a scientific investigation. The randomized block design, on the other hand, groups study participants according to shared characteristics. In either scenario, the participants’ groups get treatments at random.

Comparative Research Design (Causal)

Ex post facto research, also known as causal comparative research, examines the causes of a change that has already taken place. Researchers might, for instance, employ a causal comparative design to find out how a new diet impacts kids who have previously started it. In the social and medical communities, this kind of research is very prevalent.

Three different causal comparative research design types exist, including:

• investigating the impacts of group participation
• investigating the reasons for joining an organization
• investigating how a change may affect a group

Researchers cannot utilize causal comparative study designs to explain why an event occurred, despite the fact that they can provide light on the correlations between variables. This is due to the fact that the event has already happened, making it impossible for researchers to determine its exact cause and effects.

The same fundamental procedures are used in causal comparative studies:

• Think about the causes or effects of the phenomenon you’ve chosen.
• Form a clear problem statement.
• Make one or more theories
• Pick a study group.
• To eliminate variations within the group and control the variables, pair the group with one or more variables (this step may vary depending on the type of causal comparative study conducted).
  choosing the tools to be used in the research
• Using one or more different variables, compare the groupings.

Similar to correlational research, causal comparative studies compare two or more groups, whereas correlational studies score each variable in a single group. Both types of studies study links between variables. While causal comparative studies use one or more category variables, correlational investigations typically include numerous quantitative variables.

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Frequently Asked Questions

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Why choose a quantitative research approach?

With quantitative research, you can extrapolate the findings from a sample group to the total population. Quantitative research gives you the tools to come to conclusions and choose an appropriate course of action because it is structured and statistically sound.

Questionnaires can be either quantitative or qualitative.

Both quantitative and qualitative questions are frequently included in surveys (questionnaires). The qualitative questions would show a box where users may write in their own words, whilst the quantitative questions might present a yes/no or rating scale (1 to 5).

What kind of study design is most frequently employed?

The correlational research design is another popular one, along with the causal research design. Similar to the causal style, the correlational research design format identifies links between variables. In a correlational study approach, variables are measured but not changed.