What we will cover in this article:
- The basic types of research
- The different types of research methods
- Study design in research
- The types of qualitative research and a research design in qualitative research
- The types of quantitative research and a research design in quantitative research
The research problem defines research design
According to American sociologist Earl Robert Babbie, “Research is a systematic inquiry to describe, explain, predict and control the observed phenomenon.”
The design of your research, on the other hand, provides your customized toolkit for a specific research problem. You need to make sure that the tools fit the problem. Research design represents the set of methods and procedures you utilize during the process of data collection and analysis specified in the research problem.
We create a research design as a framework to deliver answers to research questions. Based on the research problem, the design of a study defines defines:
- The right choice of study type (descriptive or experimental)
- Sub-type (e.g., descriptive-longitudinal case study)
- The hypotheses
- The independent and dependent variables
- The scope of experimental design
- Data collection methods and a statistical analysis plan, if applicable.
Types of research: Inductive and deductive research
You will find this familiar if you have ever written a thesis. Basically, you can start researching a subject from two ends.
To put it into practice: We either want to analyze why more people spend more time texting on weekends than on weekdays (inductive research), or assume that it results from them having more time on those days — and then we test this assumption (deductive research).
We associate inductive research approaches generally with qualitative methods and techniques, while deductive methods connect more to quantitative research.
Researching business and technology
The above holds true for any type of research, from physics to neurology, ornithology to user research. “Average people” don’t usually deal with all these fancy research-related expressions (other than that one time with your thesis paper back in college).
But businesses and tech companies do research all the time as well. In a business setting, researchers mainly ask:
- What do organizations or businesses really want to find out?
- What processes and mechanisms need analyzing to chase the idea?
- What arguments need building up around a concept?
- What evidence will people require to believe in the idea or concept?
Research serves three purposes, depending on prior knowledge and the context. We might not even know what will come out in the end (exploratory research). We might want to structure already existing information in a newer / better way (descriptive research) or to find explanations for a given phenomenon.
Let’s dive more into detail!
1. Exploratory research
If we want to explore the phenomenon and research questions but don’t know for sure whether to offer a final conclusion, choose explanatory research. Conduct this type of research to take a look at new problem areas which no one has explored yet.
For example, we want to know what people use their phones for during the week and on weekends. We dive into what apps exist, how we can group them, how people choose, how their prioritize apps, etc.
Exploratory research proves essential for laying the foundation for more conclusive research and data collection.
2. Descriptive Research:
Descriptive research focuses on shedding light on specific issues through the process of data collection. Lead these studies to describe a behavior or phenomenon.
Descriptive research has three main goals: describing, explaining and validating research findings.
For example, we look at when people use apps and what for.
3. Explanatory Research:
Conduct explanatory research or causal research to understand the impact of certain changes in existing standard procedures. Conducting experiments represents the most popular form of casual research, such as research conducted to understand the effect of rebranding on customer loyalty.
For example, we look at why people seem to use their phones longer on average on weekends than on weekdays.
The research process
We broadly classify research methods as qualitative research and quantitative research.
Both methods have distinctive properties and data collection methods. In this segment, we will learn more about both.
Whichever research method you decide to go with, first evaluate the problem from an analytical point of view.
Qualitative research design: Types of qualitative research
As a research method, qualitative research collects data using conversational methods in which participants involved in the research answer open-ended questions. We collect the essentially non-numerical responses.
This method not only helps a researcher understand what participants think but also why they think in a particular way.
These qualitative research methods see wide usage:
- One-to-one Interviews
- Focus Groups
- Ethnographic Research
- Text Analysis
- Case Study Research
Quantitative research design: Types of quantitative research
Quantitative research methods deal with numbers and anything that can deal with a measurable form in a systematic way of investigating the phenomenon. We use it to answer questions in terms of justifying relationships with measurable variables to explain, predict or control a phenomenon.
Researchers often use three methods to conduct this type of research
- Survey Research
- Descriptive Research
- Correlational Research
What makes up research design? Identifying the ideal research methodologies
To choose the appropriate research methods, you must clearly identify the research objectives. Take into consideration this example of research objectives you may have for your business:
- First, find out your clients’ needs.
- Know their preferences and understand what they find important.
- Find an appropriate way to make them aware of your products and services.
- Find ways to improve your products or services to suit your customers’ needs.
After identifying what you need to know, ask which research methods will offer you that information.
Organize your questions within the framework of the 7 Ps of marketing, which influences your company – product, price, promotion, place, people, processes and physical tests.
Research methods in psychology
Psychologists use many different methods for conducting research. Each has advantages and disadvantages that make it suitable for certain situations and unsuitable for others.
Case studies, surveys, naturalistic observation and laboratory observation exemplify descriptive or correlational research methods. Using them, researchers can describe different events, experiences or behaviors, and look for links between them. However, they do not enable researchers to determine causes of behavior.
Remember: Correlation Is Not Causation! Two factors may have a connection without one causing the other to occur. Often, a third factor explains the correlation.
Why does it matter to know the basics of psychological research? Because in any situation when we deal with people, psychological occurrences might come into play.
Differences between research methods and research design
Generalized and established, research methods address research questions (e.g., qualitative vs. quantitative methods). Not all methods apply for all research questions, so the area of research that you want to explore limits the choice of method.
Research design involves determining how to apply your chosen method to answer your research question. Think of your study’s design as a blueprint detailing what to do and how to accomplish it.
Key aspects of research design include research methodology, participant/sample collection and assignment and data collection procedures and instruments.
Think of the choice of research methods, then design a reciprocal process extending well into your study. For example, a flaw in the design may arise over the course of your study.
Changing the design of the study may lead to the choice of a different method. In turn, this may lead to subsequent changes in the design to accommodate the new method(s).
UX research design
UX research design makes up the plan. It provides the logical structure of any scientific work. It helps you stay on track and systematize the research so to deliver valid data and confidence in decision making based on the results.
Research design functions to ensure the effectiveness and objectivity of your work by providing a blueprint of sorts for the collection, measurement and analysis of the data.
Assumptions and validation in practice: Experiment design
How it uses the assumptions and experiments below:
- Figure out which kind of assumption you have.
- Conduct an experiment like the one listed to see if you assumed correctly.
- If your team did, move forward to the next assumption..
- If they didn’t, evaluate other options.
Assumption 1: We think we have found a problem.
Experiment 1 — Online research: Let’s research whether people discuss this problem online. Google, Twitter, and Quora can help. Also check if a solution already exists.
Assumption 2: Based on our research, we still think Group X finds this a problem. This group consists of a lot of people, and they all experience the problem.
Experiment 2 — Census data and interviews: How many people actually comprise this group? Lead demographic research based on stats and numbers. If this group seems large, talk to some of them in person. See if they all mention the problem. If so, you seem to have proven your point.
Assumption 3: We think we have found a solution to this great problem.
Experiment 3 — Field research: Now sketch it and talk to some potential users. Then, get out of the building and show it to the target group because we want to make sure they think that your solution will help. If they do, we can move on to the next step.
Assumption 4: We now assume Group X will indeed pay for our solution to their problem.
Experiment 4 — Price before Product, Period: Ask potential customers how much they’d pay for this solution, if anything. If they do, figure out if we can actually make it happen.
Assumption 5: We find the solution feasible.
Experiment 5 — Feasibility testing: Chat with your engineers/devs. What do they think about building it? Establish if they find it not super hard to do. They will likely appreciate getting involved early on.
Assumption 6: We think adding Extra Feature Z will add a lot of value to our solution.
Experiment 6 — A/B testing with a mockup: Go and interview users to find out whether the feature makes its inclusion critical. Perhaps create a landing page with and without the feature listed and look at conversion. Don’t ask users if they’ll miss it; show them the product without it and check if they complain. (Useful tools: Invision, UserTesting.com, or AlphaHQ)
Assumption 7: We think people use what we designed to solve the problem.
Experiment 7 — Usability testing with prototypes: Create a paper or clickable mock and ask users to complete the task. Better yet, just see what happens without any prompt. Invision, UserTesting.com, AlphaHQ, Validately can help you out.
Assumption 8: We think we can build this in Time Period Y.
Experiment 8 — Project length estimation: At this point, get more people on board. First, get the engineers into a room, breaking down the product into high-level flows and features. Have them provide high-level point estimates (difficulty: 1-5 points) or T-shirt sizes (difficulty: S, M, L, XL) to get a better overview of how complex your product idea winds up, and how long it would take to build.
Assumption 9: Based on what we know, we think the product is running on the right track.
Experiment 9 — User testing: The time has come to involve some real users in the process. Talk to some customers about whether they value it enough to actually pay for.
Assumption 10: We think we might have reached the stage to kick it all off and launch.
Experiment 10 — Prepare the battlefield: Test the product within the organization. Ask the marketing and sales departments whether they all have what they need. A launch roadmap might also help. Here, we’re checking for internal feasibility and how it will all fit the given timeframe.
Assumption 11: We assume people will use the product we’re launching.
Experiment 11 — Setting up analytics: Setting up Google Analytics, Hotjar, Heap.io and/or other tracking tools. Set these up before launch.
Assumption 12: We assume people use our product to solve the problem.
Experiment 12 — Ask your customers: Go back to your target users and see how they use the tool you’ve built. Talk to random other users about what they use it for. You may learn of an additional market.
Assumption 13: We might miss another feature that we think might work.
Experiment 13: Return to Assumption 6.
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