Speaker 1: In this video, we're going to look at research design for quantitative studies. We'll start by first explaining what research design is, and then we'll explore the four most common research designs for quantitative studies. Speaking of which, if you are currently working on a dissertation or a thesis, be sure to grab our free chapter templates. These are going to help you fast track your write-up. These tried and tested templates provide a detailed roadmap to guide you through each chapter step by step. If that sounds helpful, you can find the link in the description. So let's start with the basics and ask the question, what exactly is research design? Well, simply put, research design refers to the overall plan or strategy that guides a research project, from its conception to the final analysis of data. A good research design serves as a blueprint for how you, as the researcher, will collect and analyze data while ensuring consistency, reliability, and validity throughout your study. Within quantitative research, the four most common research designs are descriptive, correlational, experimental, and quasi-experimental. Having a good understanding of the different research design options available to you is essential. Without a clear, big-picture view of how you'll design your research, you run the risk of making misaligned choices in terms of your methodology, I mean, especially the data collection and analysis-related decisions. In this video, we will look specifically at research design for quantitative studies, but if you're interested in the qualitative side of things, we've got a video covering that too. You can find the link in the description. So now that we've defined research design, let's dive into the four most popular design options for quantitative studies. First up is descriptive research design. As the name suggests, descriptive research focuses on describing existing conditions, behaviors, or characteristics. Importantly, this is achieved by systematically gathering information without manipulating any variables. In other words, there's no intervention on the researcher's part, only data collection. For example, if you were studying the prevalence of smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens, asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be. In other words, it would describe the situation. The key defining attribute of this type of design is that it purely describes the characteristics of the data. In other words, descriptive research generally doesn't explore relationships between different variables, nor the causes that underlie those relationships. This doesn't mean that descriptive research is inferior to other research design types. Actually, on the contrary, descriptive research is perfect for addressing what, who, where, and when type research aims and research questions. By doing so, it can deliver valuable insights and can also be used as a precursor to other research design types, which is coming up next. Next up, we've got correlational research design. This type of design is a popular choice for researchers looking to identify and measure relationships between two or more variables without manipulating them. In other words, this research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing. For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants' exercise habits along with records of their health indicators, such as blood pressure, heart rate, or body mass index. You could then use a statistical test to assess whether there's a relationship between the two variables, exercise frequency and health. As you can see, correlational research design is useful when you want to explore potential relationships between variables that can't be manipulated or controlled, whether that's because of ethical, practical, or logistical reasons. Also, since correlational design doesn't involve the manipulation of variables, it can be implemented at a larger scale more easily than experimental design types, which we'll look at soon. That being said, it's important to keep in mind that correlational research design does have limitations, just like any design type. Most notably, it cannot be used to establish causality. In other words, correlation does not equal causation. So, be sure to exercise caution when you interpret correlational findings and don't make the mistake of drawing casual inferences based solely on correlational research. To establish causality, you need to move into the realm of experimental design, up next. Experimental research design is used to determine if there's a causal relationship between variables. With this type of research design, you, as the researcher, manipulate one variable, the independent variable, while controlling others, the dependent variables. Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality. For example, if you wanted to measure how different types of fertilizer affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertilizer, as well as one with no fertilizer at all. You could then measure how each plant group grew, on average, over time and compare the results from the different groups to see which fertilizer was most effective. Naturally, experimental research design provides researchers with a powerful way to identify and measure causal relationships and their directionality between variables. However, developing a rigorous experimental design can be challenging, as it's not always easy to control all of the variables in a study. This often results in smaller sample sizes, which can reduce the statistical power and generalizability of the results. Another challenge with experimental research design is that it requires random assignment. This means assigning participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group. Note that this is not the same as random sampling. You can learn more about that in our sampling video up here. Assigning participants randomly helps reduce the potential for bias and confounding variables, but it can lead to ethics-related challenges. For example, withholding a potentially beneficial medical treatment from a control group of patients may be considered unethical in certain situations. So, as with any research design option, experimental design comes with its unique set of pros and cons. Hey, if you're enjoying this video so far, please help us out by hitting that like button. You can also subscribe for loads of plain language actionable advice. If you're new to research, check out our free dissertation writing course, which covers everything that you need to get started on your research project. As always, you can find the link in the description. Last but not least, we've got quasi-experimental research. This type of design is used when the research aims involve investigating causal relationships, but the researcher cannot or does not want to randomly assign participants to different groups, whether it's for practical or ethical reasons. Instead, with a quasi-experimental design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison. For example, if you were studying the effects of a new teaching method on students' achievement in a particular school district, you might not be able to randomly assign students to different classes using different teaching methods. In that case, you'd have to choose classes or schools that already use different teaching methods. This way, you'd still achieve separate groups without having to assign the participants to specific groups yourself. Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it's more difficult to confidently establish causality between variables. Moreover, you have less control over other variables that may impact findings, which increases the risk of confounding variables. All that said, quasi-experimental designs can still be incredibly valuable in research contexts where random assignment just isn't possible. Notably, this design type can often be undertaken on a much larger scale than experimental research, which means greater statistical power. What's important is that you, as the researcher, understand the limitations and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables. All right, so there you have it. In this video, we've explored four popular quantitative research designs, descriptive, correlational, experimental, and quasi-experimental. If you got value from this video, please hit that like button. That way, more students can find this content. For more videos like this, check out the Grad Coach channel and be sure to subscribe for plain language, actionable research tips, and advice. Also, if you're looking for one-on-one support with your dissertation, thesis, or research project, be sure to check out our private coaching service where we hold your hand throughout the research process step by step. You can learn more about that and book a free consultation at gradcoach.com.