A research design is a structure for using empirical data to answer your research topic. Choosing a research design necessitates deciding on:
A well-thought-out study design ensures that the data you collect is appropriate for the type of analysis you intend to do.
A research design could be required as a stand-alone assignment or as part of a broader research proposal. In either case, your goal is to demonstrate that you have carefully evaluated which methodology or methodologies are most appropriate for your research goals, as well as offering a plausible study plan.
You should have a clear idea of the research question you wish to investigate before you begin designing your study.
Example of a research question: How can teachers change their lessons to make remote learning more effective?
There are numerous approaches you might take to answering this question. Your study design decisions should be guided by your objectives and priorities—start by considering what you want to accomplish.
The first decision you must make is whether to use a qualitative or quantitative method for your research.
Qualitative research designs are more adaptable and inductive, allowing you to change your strategy based on what you learn during the research process.
Example of qualitative research: If you want to come up with fresh ideas for online teaching tactics, then a qualitative approach is the right choice. You can utilize this type of research to find out exactly what teachers and students in remote classes struggle with.
Quantitative research designs are more prescriptive and logical, with variables and hypotheses set before data collection.
Example of quantitative research: If you want to see how effective an online teaching method is, you should use a quantitative approach. This type of research can be used to assess learning outcomes such as grades and test scores.
A mixed-method design that incorporates parts of both methodologies is also an option. You can acquire a more complete view of the subject you are studying and increase the reliability of your conclusions by integrating qualitative and quantitative information.
There are various types of research designs to choose from in both qualitative and quantitative approaches. Each category serves as a foundation for the general shape of your study.
When planning your research, you must consider both scientific and practical factors.
Make sure that your choices are practical at each level of the research design process.
There are four different types of quantitative designs. You can evaluate cause-and-effect relationships with experimental and quasi-experimental designs, whereas descriptive and correlational designs can be used to measure variables and characterize relationships between them.
You can gain a good view of traits, trends, and relationships in the real world using descriptive and correlational designs. You cannot, however, draw any inferences regarding cause and effect.
Example of a correlational design: You might use a correlational design to see if the increase in online instruction over the last year is linked to any changes in test scores.
However, a causal relationship between the two variables cannot be established using this methodology. Many other factors, such as increasing stress and health difficulties among children and teachers, could have influenced any decrease in test scores.
Experiments are the most effective technique to test cause-and-effect relationships without risking the results being influenced by extraneous variables. Their controlled environments, on the other hand, may not always mirror how things work in the actual world. They are also frequently more complex and expensive to deploy.
In an experimental design, you could take a group of students and randomly assign half of them to be taught online and the other half to be taught in person, while controlling all other relevant variables.
You can be more convinced that any change in scores was driven by the technique of teaching (rather than other variables) by comparing their test results.
Qualitative research designs are less well-defined. This method focuses on acquiring a thorough understanding of a certain context or phenomenon, and it allows you to be more creative and adaptable in your research design.
Some common qualitative design types are illustrated below. As regards data gathering, it typically takes similar tactics, whereas data analysis focuses on distinct issues.
Grounded theory: It is a type of inductive theory that aims to create a theory by methodically studying qualitative evidence.
Phenomenology: Aims to comprehend a phenomenon or occurrence by explaining individuals’ real experiences.
Professional editors focus on the following when proofreading and editing your paper:
Your research design should clearly specify who or what will be the focus of your study, as well as how you will select your participants or subjects.
In research, a population refers to the total group from which you wish to draw conclusions, whereas a sample refers to the smaller number of people from whom you will actually collect data.
Plants, animals, organizations, texts, countries, and other objects can all be included in a population. It usually refers to a group of persons in the social sciences.
Will you, for example, target folks from a specific demographic, area, or background? Are you looking for people who work in a specific field or have a specific medical condition, or who use a specific product?
The easier it is to collect a representative sample, the more precisely you define your population.
It would be difficult to obtain a sample that is representative of all high school students in the UK if you were investigating the effectiveness of online teaching in the UK.
You may reduce the scope of the study to make it more manageable and draw more accurate conclusions—for example, 8th-grade children in low-income neighborhoods of London.
Even with a narrowly defined population, collecting data from every individual is rare. Instead, you will gather information from a sample.
There are two basic methods for selecting a sample: probability sampling and non-probability sampling. The sample method you select has an impact on how confidently you can generalize your findings to the entire population.
The most statistically correct approach is probability sampling, but it is typically difficult to execute unless you are dealing with a very small and accessible population.
Many studies utilize non-probability sampling for practical reasons, but it is crucial to be aware of the limits and examine potential biases. You should always try to collect a sample that is as representative of the population as feasible.
Sampling may or may not be necessary in some qualitative designs.
In an ethnography or a case study, for example, the goal is to gain a comprehensive understanding of a single situation rather than something generalized to a community. With regard to sampling, you may simply try to gather as much information as possible on the context you are researching.
You must still carefully evaluate your choice of case or community in these types of designs. You should be able to explain why this instance is appropriate for answering your research topic.
For example, you may choose a case study that highlights an unexpected or overlooked element of your research problem, or you could examine multiple cases that are very similar or very different.
The aforementioned is the first part of the methods that will help readers to frame the accurate form or research design. Subscribe to Author Assists to stay updated with the second part of the article as well as other useful articles related to academia.
2020 © copyright All rights reserved.