The Four Types of Research Design — Everything You Need to Know
When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.
In this blog post, we’ll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.
Table of Contents
What is research design?
Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.
Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.
Marketers use different types of research design when conducting research.
There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.
The Four Types of Research Design
Researchers use different designs to accomplish different research objectives. Here, we’ll discuss how to choose the right type, the benefits of each, and use cases.
Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.
An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).
In other words, the researcher wants to test a causal relationship between two or more variables.
In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.
Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don’t — and measure their productivity.
The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.
One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.
Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).
A correlational design examines the relationship between two or more variables without intervening in the process.
Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.
For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.
Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.
Best for: Understanding the extent to which two or more variables are associated with each other in the real world.
Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.
Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.
SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.
For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.
Best for: Gathering unbiased data that reveals behaviors or recurring phenomena.
Diagnostic research determines the root cause of a problem and finds the most effective solution. It’s often used in marketing to identify areas of improvement or potential opportunities for growth.
The diagnostic research design consists of three steps:
Inception, which includes data collection and problem definition.
Diagnostics, which comprises data analysis, hypothesis testing, and setting objectives.
Solutions, which define the best possible solution.
In product teams, a diagnostic design would involve analyzing customer feedback and reviews to identify areas where a company can improve. This would help identify where a product offering needs to change — pricing, missing features, customer service, etc.
Diagnostic research provides an accurate diagnosis of a problem and identifies areas of improvement.
Best for: Understanding the underlying causes of a problem and how to address it.
Research Design Examples
Let’s explore how leading brands employ different types of research design. In most cases, companies combine several methods to reach a comprehensive overview of a problem and find a solution.
UnderArmour doubled its market share among running shoes by referring to diagnostic and descriptive research.
The team aimed to design a breakthrough product by constantly improving their shoes in response to athletes’ real-time feedback. To do so, the company shipped free shoes to over 10,000 athletes. Using Qualtrics, the company surveyed participants for their feedback.