research design marketing research

The Four Types of Research Design — Everything You Need to Know

Updated: December 11, 2023

Published: January 18, 2023

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?

The four types of research design, research design examples.

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.

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.

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Experimental

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).

Correlational

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

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.

Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.

Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.

The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.

This helped uncover key reasons for poor performance and guided changes in packaging.

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying 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 research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of 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, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d 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 cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (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 if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign 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 ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research 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 student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign 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, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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10.2 Steps in the Marketing Research Process

Learning objective.

• Describe the basic steps in the marketing research process and the purpose of each step.

The basic steps used to conduct marketing research are shown in Figure 10.6 “Steps in the Marketing Research Process” . Next, we discuss each step.

Figure 10.6 Steps in the Marketing Research Process

Step 1: Define the Problem (or Opportunity)

There’s a saying in marketing research that a problem half defined is a problem half solved. Defining the “problem” of the research sounds simple, doesn’t it? Suppose your product is tutoring other students in a subject you’re a whiz at. You have been tutoring for a while, and people have begun to realize you’re darned good at it. Then, suddenly, your business drops off. Or it explodes, and you can’t cope with the number of students you’re being asked help. If the business has exploded, should you try to expand your services? Perhaps you should subcontract with some other “whiz” students. You would send them students to be tutored, and they would give you a cut of their pay for each student you referred to them.

Both of these scenarios would be a problem for you, wouldn’t they? They are problems insofar as they cause you headaches. But are they really the problem? Or are they the symptoms of something bigger? For example, maybe your business has dropped off because your school is experiencing financial trouble and has lowered the number of scholarships given to incoming freshmen. Consequently, there are fewer total students on campus who need your services. Conversely, if you’re swamped with people who want you to tutor them, perhaps your school awarded more scholarships than usual, so there are a greater number of students who need your services. Alternately, perhaps you ran an ad in your school’s college newspaper, and that led to the influx of students wanting you to tutor them.

Businesses are in the same boat you are as a tutor. They take a look at symptoms and try to drill down to the potential causes. If you approach a marketing research company with either scenario—either too much or too little business—the firm will seek more information from you such as the following:

• In what semester(s) did your tutoring revenues fall (or rise)?
• In what subject areas did your tutoring revenues fall (or rise)?
• In what sales channels did revenues fall (or rise): Were there fewer (or more) referrals from professors or other students? Did the ad you ran result in fewer (or more) referrals this month than in the past months?
• Among what demographic groups did your revenues fall (or rise)—women or men, people with certain majors, or first-year, second-, third-, or fourth-year students?

The key is to look at all potential causes so as to narrow the parameters of the study to the information you actually need to make a good decision about how to fix your business if revenues have dropped or whether or not to expand it if your revenues have exploded.

The next task for the researcher is to put into writing the research objective. The research objective is the goal(s) the research is supposed to accomplish. The marketing research objective for your tutoring business might read as follows:

To survey college professors who teach 100- and 200-level math courses to determine why the number of students referred for tutoring dropped in the second semester.

This is admittedly a simple example designed to help you understand the basic concept. If you take a marketing research course, you will learn that research objectives get a lot more complicated than this. The following is an example:

“To gather information from a sample representative of the U.S. population among those who are ‘very likely’ to purchase an automobile within the next 6 months, which assesses preferences (measured on a 1–5 scale ranging from ‘very likely to buy’ to ‘not likely at all to buy’) for the model diesel at three different price levels. Such data would serve as input into a forecasting model that would forecast unit sales, by geographic regions of the country, for each combination of the model’s different prices and fuel configurations (Burns & Bush, 2010).”

Now do you understand why defining the problem is complicated and half the battle? Many a marketing research effort is doomed from the start because the problem was improperly defined. Coke’s ill-fated decision to change the formula of Coca-Cola in 1985 is a case in point: Pepsi had been creeping up on Coke in terms of market share over the years as well as running a successful promotional campaign called the “Pepsi Challenge,” in which consumers were encouraged to do a blind taste test to see if they agreed that Pepsi was better. Coke spent four years researching “the problem.” Indeed, people seemed to like the taste of Pepsi better in blind taste tests. Thus, the formula for Coke was changed. But the outcry among the public was so great that the new formula didn’t last long—a matter of months—before the old formula was reinstated. Some marketing experts believe Coke incorrectly defined the problem as “How can we beat Pepsi in taste tests?” instead of “How can we gain market share against Pepsi?” (Burns & Bush, 2010)

New Coke Is It! 1985

(click to see video)

This video documents the Coca-Cola Company’s ill-fated launch of New Coke in 1985.

1985 Pepsi Commercial—“They Changed My Coke”

This video shows how Pepsi tried to capitalize on the blunder.

Step 2: Design the Research

The next step in the marketing research process is to do a research design. The research design is your “plan of attack.” It outlines what data you are going to gather and from whom, how and when you will collect the data, and how you will analyze it once it’s been obtained. Let’s look at the data you’re going to gather first.

There are two basic types of data you can gather. The first is primary data. Primary data is information you collect yourself, using hands-on tools such as interviews or surveys, specifically for the research project you’re conducting. Secondary data is data that has already been collected by someone else, or data you have already collected for another purpose. Collecting primary data is more time consuming, work intensive, and expensive than collecting secondary data. Consequently, you should always try to collect secondary data first to solve your research problem, if you can. A great deal of research on a wide variety of topics already exists. If this research contains the answer to your question, there is no need for you to replicate it. Why reinvent the wheel?

Sources of Secondary Data

Your company’s internal records are a source of secondary data. So are any data you collect as part of your marketing intelligence gathering efforts. You can also purchase syndicated research. Syndicated research is primary data that marketing research firms collect on a regular basis and sell to other companies. J.D. Power & Associates is a provider of syndicated research. The company conducts independent, unbiased surveys of customer satisfaction, product quality, and buyer behavior for various industries. The company is best known for its research in the automobile sector. One of the best-known sellers of syndicated research is the Nielsen Company, which produces the Nielsen ratings. The Nielsen ratings measure the size of television, radio, and newspaper audiences in various markets. You have probably read or heard about TV shows that get the highest (Nielsen) ratings. (Arbitron does the same thing for radio ratings.) Nielsen, along with its main competitor, Information Resources, Inc. (IRI), also sells businesses scanner-based research . Scanner-based research is information collected by scanners at checkout stands in stores. Each week Nielsen and IRI collect information on the millions of purchases made at stores. The companies then compile the information and sell it to firms in various industries that subscribe to their services. The Nielsen Company has also recently teamed up with Facebook to collect marketing research information. Via Facebook, users will see surveys in some of the spaces in which they used to see online ads (Rappeport, Gelles, 2009).

By contrast, MarketResearch.com is an example of a marketing research aggregator. A marketing research aggregator is a marketing research company that doesn’t conduct its own research and sell it. Instead, it buys research reports from other marketing research companies and then sells the reports in their entirety or in pieces to other firms. Check out MarketResearch.com’s Web site. As you will see there are a huge number of studies in every category imaginable that you can buy for relatively small amounts of money.

Figure 10.7

Market research aggregators buy research reports from other marketing research companies and then resell them in part or in whole to other companies so they don’t have to gather primary data.

Source: http://www.marketresearch.com .

Your local library is a good place to gather free secondary data. It has searchable databases as well as handbooks, dictionaries, and books, some of which you can access online. Government agencies also collect and report information on demographics, economic and employment data, health information, and balance-of-trade statistics, among a lot of other information. The U.S. Census Bureau collects census data every ten years to gather information about who lives where. Basic demographic information about sex, age, race, and types of housing in which people live in each U.S. state, metropolitan area, and rural area is gathered so that population shifts can be tracked for various purposes, including determining the number of legislators each state should have in the U.S. House of Representatives. For the U.S. government, this is primary data. For marketing managers it is an important source of secondary data.

The Survey Research Center at the University of Michigan also conducts periodic surveys and publishes information about trends in the United States. One research study the center continually conducts is called the “Changing Lives of American Families” ( http://www.isr.umich.edu/home/news/research-update/2007-01.pdf ). This is important research data for marketing managers monitoring consumer trends in the marketplace. The World Bank and the United Nations are two international organizations that collect a great deal of information. Their Web sites contain many free research studies and data related to global markets. Table 10.1 “Examples of Primary Data Sources versus Secondary Data Sources” shows some examples of primary versus secondary data sources.

Table 10.1 Examples of Primary Data Sources versus Secondary Data Sources

Gauging the Quality of Secondary Data

When you are gathering secondary information, it’s always good to be a little skeptical of it. Sometimes studies are commissioned to produce the result a client wants to hear—or wants the public to hear. For example, throughout the twentieth century, numerous studies found that smoking was good for people’s health. The problem was the studies were commissioned by the tobacco industry. Web research can also pose certain hazards. There are many biased sites that try to fool people that they are providing good data. Often the data is favorable to the products they are trying to sell. Beware of product reviews as well. Unscrupulous sellers sometimes get online and create bogus ratings for products. See below for questions you can ask to help gauge the credibility of secondary information.

Gauging the Credibility of Secondary Data: Questions to Ask

• Who gathered this information?
• For what purpose?
• What does the person or organization that gathered the information have to gain by doing so?
• Was the information gathered and reported in a systematic manner?
• Is the source of the information accepted as an authority by other experts in the field?
• Does the article provide objective evidence to support the position presented?

Types of Research Design

Now let’s look specifically at the types of research designs that are utilized. By understanding different types of research designs, a researcher can solve a client’s problems more quickly and efficiently without jumping through more hoops than necessary. Research designs fall into one of the following three categories:

• Exploratory research design
• Descriptive research design
• Causal research design (experiments)

An exploratory research design is useful when you are initially investigating a problem but you haven’t defined it well enough to do an in-depth study of it. Perhaps via your regular market intelligence, you have spotted what appears to be a new opportunity in the marketplace. You would then do exploratory research to investigate it further and “get your feet wet,” as the saying goes. Exploratory research is less structured than other types of research, and secondary data is often utilized.

One form of exploratory research is qualitative research. Qualitative research is any form of research that includes gathering data that is not quantitative, and often involves exploring questions such as why as much as what or how much . Different forms, such as depth interviews and focus group interviews, are common in marketing research.

The depth interview —engaging in detailed, one-on-one, question-and-answer sessions with potential buyers—is an exploratory research technique. However, unlike surveys, the people being interviewed aren’t asked a series of standard questions. Instead the interviewer is armed with some general topics and asks questions that are open ended, meaning that they allow the interviewee to elaborate. “How did you feel about the product after you purchased it?” is an example of a question that might be asked. A depth interview also allows a researcher to ask logical follow-up questions such as “Can you tell me what you mean when you say you felt uncomfortable using the service?” or “Can you give me some examples?” to help dig further and shed additional light on the research problem. Depth interviews can be conducted in person or over the phone. The interviewer either takes notes or records the interview.

Focus groups and case studies are often utilized for exploratory research as well. A focus group is a group of potential buyers who are brought together to discuss a marketing research topic with one another. A moderator is used to focus the discussion, the sessions are recorded, and the main points of consensus are later summarized by the market researcher. Textbook publishers often gather groups of professors at educational conferences to participate in focus groups. However, focus groups can also be conducted on the telephone, in online chat rooms, or both, using meeting software like WebEx. The basic steps of conducting a focus group are outlined below.

The Basic Steps of Conducting a Focus Group

• Establish the objectives of the focus group. What is its purpose?
• Identify the people who will participate in the focus group. What makes them qualified to participate? How many of them will you need and what they will be paid?
• Obtain contact information for the participants and send out invitations (usually e-mails are most efficient).
• Develop a list of questions.
• Choose a facilitator.
• Choose a location in which to hold the focus group and the method by which it will be recorded.
• Conduct the focus group. If the focus group is not conducted electronically, include name tags for the participants, pens and notepads, any materials the participants need to see, and refreshments. Record participants’ responses.
• Summarize the notes from the focus group and write a report for management.

A case study looks at how another company solved the problem that’s being researched. Sometimes multiple cases, or companies, are used in a study. Case studies nonetheless have a mixed reputation. Some researchers believe it’s hard to generalize, or apply, the results of a case study to other companies. Nonetheless, collecting information about companies that encountered the same problems your firm is facing can give you a certain amount of insight about what direction you should take. In fact, one way to begin a research project is to carefully study a successful product or service.

Two other types of qualitative data used for exploratory research are ethnographies and projective techniques. In an ethnography , researchers interview, observe, and often videotape people while they work, live, shop, and play. The Walt Disney Company has recently begun using ethnographers to uncover the likes and dislikes of boys aged six to fourteen, a financially attractive market segment for Disney, but one in which the company has been losing market share. The ethnographers visit the homes of boys, observe the things they have in their rooms to get a sense of their hobbies, and accompany them and their mothers when they shop to see where they go, what the boys are interested in, and what they ultimately buy. (The children get seventy-five dollars out of the deal, incidentally.) (Barnes, 2009)

Projective techniques are used to reveal information research respondents might not reveal by being asked directly. Asking a person to complete sentences such as the following is one technique:

People who buy Coach handbags __________.

(Will he or she reply with “are cool,” “are affluent,” or “are pretentious,” for example?)

KFC’s grilled chicken is ______.

Or the person might be asked to finish a story that presents a certain scenario. Word associations are also used to discern people’s underlying attitudes toward goods and services. Using a word-association technique, a market researcher asks a person to say or write the first word that comes to his or her mind in response to another word. If the initial word is “fast food,” what word does the person associate it with or respond with? Is it “McDonald’s”? If many people reply that way, and you’re conducting research for Burger King, that could indicate Burger King has a problem. However, if the research is being conducted for Wendy’s, which recently began running an advertising campaign to the effect that Wendy’s offerings are “better than fast food,” it could indicate that the campaign is working.

Completing cartoons is yet another type of projective technique. It’s similar to finishing a sentence or story, only with the pictures. People are asked to look at a cartoon such as the one shown in Figure 10.8 “Example of a Cartoon-Completion Projective Technique” . One of the characters in the picture will have made a statement, and the person is asked to fill in the empty cartoon “bubble” with how they think the second character will respond.

Figure 10.8 Example of a Cartoon-Completion Projective Technique

In some cases, your research might end with exploratory research. Perhaps you have discovered your organization lacks the resources needed to produce the product. In other cases, you might decide you need more in-depth, quantitative research such as descriptive research or causal research, which are discussed next. Most marketing research professionals advise using both types of research, if it’s feasible. On the one hand, the qualitative-type research used in exploratory research is often considered too “lightweight.” Remember earlier in the chapter when we discussed telephone answering machines and the hit TV sitcom Seinfeld ? Both product ideas were initially rejected by focus groups. On the other hand, relying solely on quantitative information often results in market research that lacks ideas.

The Stone Wheel—What One Focus Group Said

Watch the video to see a funny spoof on the usefulness—or lack of usefulness—of focus groups.

Descriptive Research

Anything that can be observed and counted falls into the category of descriptive research design. A study using a descriptive research design involves gathering hard numbers, often via surveys, to describe or measure a phenomenon so as to answer the questions of who , what , where , when , and how . “On a scale of 1–5, how satisfied were you with your service?” is a question that illustrates the information a descriptive research design is supposed to capture.

Physiological measurements also fall into the category of descriptive design. Physiological measurements measure people’s involuntary physical responses to marketing stimuli, such as an advertisement. Elsewhere, we explained that researchers have gone so far as to scan the brains of consumers to see what they really think about products versus what they say about them. Eye tracking is another cutting-edge type of physiological measurement. It involves recording the movements of a person’s eyes when they look at some sort of stimulus, such as a banner ad or a Web page. The Walt Disney Company has a research facility in Austin, Texas, that it uses to take physical measurements of viewers when they see Disney programs and advertisements. The facility measures three types of responses: people’s heart rates, skin changes, and eye movements (eye tracking) (Spangler, 2009).

Figure 10.9

A woman shows off her headgear for an eye-tracking study. The gear’s not exactly a fashion statement but . . .

lawrencegs – Google Glass – CC BY 2.0.

A strictly descriptive research design instrument—a survey, for example—can tell you how satisfied your customers are. It can’t, however, tell you why. Nor can an eye-tracking study tell you why people’s eyes tend to dwell on certain types of banner ads—only that they do. To answer “why” questions an exploratory research design or causal research design is needed (Wagner, 2007).

Causal Research

Causal research design examines cause-and-effect relationships. Using a causal research design allows researchers to answer “what if” types of questions. In other words, if a firm changes X (say, a product’s price, design, placement, or advertising), what will happen to Y (say, sales or customer loyalty)? To conduct causal research, the researcher designs an experiment that “controls,” or holds constant, all of a product’s marketing elements except one (or using advanced techniques of research, a few elements can be studied at the same time). The one variable is changed, and the effect is then measured. Sometimes the experiments are conducted in a laboratory using a simulated setting designed to replicate the conditions buyers would experience. Or the experiments may be conducted in a virtual computer setting.

You might think setting up an experiment in a virtual world such as the online game Second Life would be a viable way to conduct controlled marketing research. Some companies have tried to use Second Life for this purpose, but the results have been somewhat mixed as to whether or not it is a good medium for marketing research. The German marketing research firm Komjuniti was one of the first “real-world” companies to set up an “island” in Second Life upon which it could conduct marketing research. However, with so many other attractive fantasy islands in which to play, the company found it difficult to get Second Life residents, or players, to voluntarily visit the island and stay long enough so meaningful research could be conducted. (Plus, the “residents,” or players, in Second Life have been known to protest corporations invading their world. When the German firm Komjuniti created an island in Second Life to conduct marketing research, the residents showed up waving signs and threatening to boycott the island.) (Wagner, 2007)

Why is being able to control the setting so important? Let’s say you are an American flag manufacturer and you are working with Walmart to conduct an experiment to see where in its stores American flags should be placed so as to increase their sales. Then the terrorist attacks of 9/11 occur. In the days afterward, sales skyrocketed—people bought flags no matter where they were displayed. Obviously, the terrorist attacks in the United States would have skewed the experiment’s data.

An experiment conducted in a natural setting such as a store is referred to as a field experiment . Companies sometimes do field experiments either because it is more convenient or because they want to see if buyers will behave the same way in the “real world” as in a laboratory or on a computer. The place the experiment is conducted or the demographic group of people the experiment is administered to is considered the test market . Before a large company rolls out a product to the entire marketplace, it will often place the offering in a test market to see how well it will be received. For example, to compete with MillerCoors’ sixty-four-calorie beer MGD 64, Anheuser-Busch recently began testing its Select 55 beer in certain cities around the country (McWilliams, 2009).

Figure 10.10

Select 55 beer: Coming soon to a test market near you? (If you’re on a diet, you have to hope so!)

Martine – Le champagne – CC BY-NC 2.0.

Many companies use experiments to test all of their marketing communications. For example, the online discount retailer O.co (formerly called Overstock.com) carefully tests all of its marketing offers and tracks the results of each one. One study the company conducted combined twenty-six different variables related to offers e-mailed to several thousand customers. The study resulted in a decision to send a group of e-mails to different segments. The company then tracked the results of the sales generated to see if they were in line with the earlier experiment it had conducted that led it to make the offer.

Step 3: Design the Data-Collection Forms

If the behavior of buyers is being formally observed, and a number of different researchers are conducting observations, the data obviously need to be recorded on a standardized data-collection form that’s either paper or electronic. Otherwise, the data collected will not be comparable. The items on the form could include a shopper’s sex; his or her approximate age; whether the person seemed hurried, moderately hurried, or unhurried; and whether or not he or she read the label on products, used coupons, and so forth.

The same is true when it comes to surveying people with questionnaires. Surveying people is one of the most commonly used techniques to collect quantitative data. Surveys are popular because they can be easily administered to large numbers of people fairly quickly. However, to produce the best results, the questionnaire for the survey needs to be carefully designed.

Questionnaire Design

Most questionnaires follow a similar format: They begin with an introduction describing what the study is for, followed by instructions for completing the questionnaire and, if necessary, returning it to the market researcher. The first few questions that appear on the questionnaire are usually basic, warm-up type of questions the respondent can readily answer, such as the respondent’s age, level of education, place of residence, and so forth. The warm-up questions are then followed by a logical progression of more detailed, in-depth questions that get to the heart of the question being researched. Lastly, the questionnaire wraps up with a statement that thanks the respondent for participating in the survey and information and explains when and how they will be paid for participating. To see some examples of questionnaires and how they are laid out, click on the following link: http://cas.uah.edu/wrenb/mkt343/Project/Sample%20Questionnaires.htm .

How the questions themselves are worded is extremely important. It’s human nature for respondents to want to provide the “correct” answers to the person administering the survey, so as to seem agreeable. Therefore, there is always a hazard that people will try to tell you what you want to hear on a survey. Consequently, care needs to be taken that the survey questions are written in an unbiased, neutral way. In other words, they shouldn’t lead a person taking the questionnaire to answer a question one way or another by virtue of the way you have worded it. The following is an example of a leading question.

Don’t you agree that teachers should be paid more ?

The questions also need to be clear and unambiguous. Consider the following question:

Which brand of toothpaste do you use ?

The question sounds clear enough, but is it really? What if the respondent recently switched brands? What if she uses Crest at home, but while away from home or traveling, she uses Colgate’s Wisp portable toothpaste-and-brush product? How will the respondent answer the question? Rewording the question as follows so it’s more specific will help make the question clearer:

Which brand of toothpaste have you used at home in the past six months? If you have used more than one brand, please list each of them 1 .

Sensitive questions have to be asked carefully. For example, asking a respondent, “Do you consider yourself a light, moderate, or heavy drinker?” can be tricky. Few people want to admit to being heavy drinkers. You can “soften” the question by including a range of answers, as the following example shows:

How many alcoholic beverages do you consume in a week ?

• __0–5 alcoholic beverages
• __5–10 alcoholic beverages
• __10–15 alcoholic beverages

Many people don’t like to answer questions about their income levels. Asking them to specify income ranges rather than divulge their actual incomes can help.

Other research question “don’ts” include using jargon and acronyms that could confuse people. “How often do you IM?” is an example. Also, don’t muddy the waters by asking two questions in the same question, something researchers refer to as a double-barreled question . “Do you think parents should spend more time with their children and/or their teachers?” is an example of a double-barreled question.

Open-ended questions , or questions that ask respondents to elaborate, can be included. However, they are harder to tabulate than closed-ended questions , or questions that limit a respondent’s answers. Multiple-choice and yes-and-no questions are examples of closed-ended questions.

Testing the Questionnaire

You have probably heard the phrase “garbage in, garbage out.” If the questions are bad, the information gathered will be bad, too. One way to make sure you don’t end up with garbage is to test the questionnaire before sending it out to find out if there are any problems with it. Is there enough space for people to elaborate on open-ended questions? Is the font readable? To test the questionnaire, marketing research professionals first administer it to a number of respondents face to face. This gives the respondents the chance to ask the researcher about questions or instructions that are unclear or don’t make sense to them. The researcher then administers the questionnaire to a small subset of respondents in the actual way the survey is going to be disseminated, whether it’s delivered via phone, in person, by mail, or online.

Getting people to participate and complete questionnaires can be difficult. If the questionnaire is too long or hard to read, many people won’t complete it. So, by all means, eliminate any questions that aren’t necessary. Of course, including some sort of monetary incentive for completing the survey can increase the number of completed questionnaires a market researcher will receive.

Step 4: Specify the Sample

Once you have created your questionnaire or other marketing study, how do you figure out who should participate in it? Obviously, you can’t survey or observe all potential buyers in the marketplace. Instead, you must choose a sample. A sample is a subset of potential buyers that are representative of your entire target market, or population being studied. Sometimes market researchers refer to the population as the universe to reflect the fact that it includes the entire target market, whether it consists of a million people, a hundred thousand, a few hundred, or a dozen. “All unmarried people over the age of eighteen who purchased Dirt Devil steam cleaners in the United States during 2011” is an example of a population that has been defined.

Obviously, the population has to be defined correctly. Otherwise, you will be studying the wrong group of people. Not defining the population correctly can result in flawed research, or sampling error. A sampling error is any type of marketing research mistake that results because a sample was utilized. One criticism of Internet surveys is that the people who take these surveys don’t really represent the overall population. On average, Internet survey takers tend to be more educated and tech savvy. Consequently, if they solely constitute your population, even if you screen them for certain criteria, the data you collect could end up being skewed.

The next step is to put together the sampling frame , which is the list from which the sample is drawn. The sampling frame can be put together using a directory, customer list, or membership roster (Wrenn et. al., 2007). Keep in mind that the sampling frame won’t perfectly match the population. Some people will be included on the list who shouldn’t be. Other people who should be included will be inadvertently omitted. It’s no different than if you were to conduct a survey of, say, 25 percent of your friends, using friends’ names you have in your cell phone. Most of your friends’ names are likely to be programmed into your phone, but not all of them. As a result, a certain degree of sampling error always occurs.

There are two main categories of samples in terms of how they are drawn: probability samples and nonprobability samples. A probability sample is one in which each would-be participant has a known and equal chance of being selected. The chance is known because the total number of people in the sampling frame is known. For example, if every other person from the sampling frame were chosen, each person would have a 50 percent chance of being selected.

A nonprobability sample is any type of sample that’s not drawn in a systematic way. So the chances of each would-be participant being selected can’t be known. A convenience sample is one type of nonprobability sample. It is a sample a researcher draws because it’s readily available and convenient to do so. Surveying people on the street as they pass by is an example of a convenience sample. The question is, are these people representative of the target market?

For example, suppose a grocery store needed to quickly conduct some research on shoppers to get ready for an upcoming promotion. Now suppose that the researcher assigned to the project showed up between the hours of 10 a.m. and 12 p.m. on a weekday and surveyed as many shoppers as possible. The problem is that the shoppers wouldn’t be representative of the store’s entire target market. What about commuters who stop at the store before and after work? Their views wouldn’t be represented. Neither would people who work the night shift or shop at odd hours. As a result, there would be a lot of room for sampling error in this study. For this reason, studies that use nonprobability samples aren’t considered as accurate as studies that use probability samples. Nonprobability samples are more often used in exploratory research.

Lastly, the size of the sample has an effect on the amount of sampling error. Larger samples generally produce more accurate results. The larger your sample is, the more data you will have, which will give you a more complete picture of what you’re studying. However, the more people surveyed or studied, the more costly the research becomes.

Statistics can be used to determine a sample’s optimal size. If you take a marketing research or statistics class, you will learn more about how to determine the optimal size.

Of course, if you hire a marketing research company, much of this work will be taken care of for you. Many marketing research companies, like ResearchNow, maintain panels of prescreened people they draw upon for samples. In addition, the marketing research firm will be responsible for collecting the data or contracting with a company that specializes in data collection. Data collection is discussed next.

Step 5: Collect the Data

As we have explained, primary marketing research data can be gathered in a number of ways. Surveys, taking physical measurements, and observing people are just three of the ways we discussed. If you’re observing customers as part of gathering the data, keep in mind that if shoppers are aware of the fact, it can have an effect on their behavior. For example, if a customer shopping for feminine hygiene products in a supermarket aisle realizes she is being watched, she could become embarrassed and leave the aisle, which would adversely affect your data. To get around problems such as these, some companies set up cameras or two-way mirrors to observe customers. Organizations also hire mystery shoppers to work around the problem. A mystery shopper is someone who is paid to shop at a firm’s establishment or one of its competitors to observe the level of service, cleanliness of the facility, and so forth, and report his or her findings to the firm.

Make Extra Money as a Mystery Shopper

Watch the YouTube video to get an idea of how mystery shopping works.

Survey data can be collected in many different ways and combinations of ways. The following are the basic methods used:

• Face-to-face (can be computer aided)
• Telephone (can be computer aided or completely automated)
• Mail and hand delivery
• E-mail and the Web

A face-to-face survey is, of course, administered by a person. The surveys are conducted in public places such as in shopping malls, on the street, or in people’s homes if they have agreed to it. In years past, it was common for researchers in the United States to knock on people’s doors to gather survey data. However, randomly collected door-to-door interviews are less common today, partly because people are afraid of crime and are reluctant to give information to strangers (McDaniel & Gates, 1998).

Nonetheless, “beating the streets” is still a legitimate way questionnaire data is collected. When the U.S. Census Bureau collects data on the nation’s population, it hand delivers questionnaires to rural households that do not have street-name and house-number addresses. And Census Bureau workers personally survey the homeless to collect information about their numbers. Face-to-face surveys are also commonly used in third world countries to collect information from people who cannot read or lack phones and computers.

A plus of face-to-face surveys is that they allow researchers to ask lengthier, more complex questions because the people being surveyed can see and read the questionnaires. The same is true when a computer is utilized. For example, the researcher might ask the respondent to look at a list of ten retail stores and rank the stores from best to worst. The same question wouldn’t work so well over the telephone because the person couldn’t see the list. The question would have to be rewritten. Another drawback with telephone surveys is that even though federal and state “do not call” laws generally don’t prohibit companies from gathering survey information over the phone, people often screen such calls using answering machines and caller ID.

Probably the biggest drawback of both surveys conducted face-to-face and administered over the phone by a person is that they are labor intensive and therefore costly. Mailing out questionnaires is costly, too, and the response rates can be rather low. Think about why that might be so: if you receive a questionnaire in the mail, it is easy to throw it in the trash; it’s harder to tell a market researcher who approaches you on the street that you don’t want to be interviewed.

By contrast, gathering survey data collected by a computer, either over the telephone or on the Internet, can be very cost-effective and in some cases free. SurveyMonkey and Zoomerang are two Web sites that will allow you to create online questionnaires, e-mail them to up to one hundred people for free, and view the responses in real time as they come in. For larger surveys, you have to pay a subscription price of a few hundred dollars. But that still can be extremely cost-effective. The two Web sites also have a host of other features such as online-survey templates you can use to create your questionnaire, a way to set up automatic reminders sent to people who haven’t yet completed their surveys, and tools you can use to create graphics to put in your final research report. To see how easy it is to put together a survey in SurveyMonkey, click on the following link: http://help.surveymonkey.com/app/tutorials/detail/a_id/423 .

Like a face-to-face survey, an Internet survey can enable you to show buyers different visuals such as ads, pictures, and videos of products and their packaging. Web surveys are also fast, which is a major plus. Whereas face-to-face and mailed surveys often take weeks to collect, you can conduct a Web survey in a matter of days or even hours. And, of course, because the information is electronically gathered it can be automatically tabulated. You can also potentially reach a broader geographic group than you could if you had to personally interview people. The Zoomerang Web site allows you to create surveys in forty different languages.

Another plus for Web and computer surveys (and electronic phone surveys) is that there is less room for human error because the surveys are administered electronically. For instance, there’s no risk that the interviewer will ask a question wrong or use a tone of voice that could mislead the respondents. Respondents are also likely to feel more comfortable inputting the information into a computer if a question is sensitive than they would divulging the information to another person face-to-face or over the phone. Given all of these advantages, it’s not surprising that the Internet is quickly becoming the top way to collect primary data. However, like mail surveys, surveys sent to people over the Internet are easy to ignore.

Lastly, before the data collection process begins, the surveyors and observers need to be trained to look for the same things, ask questions the same way, and so forth. If they are using rankings or rating scales, they need to be “on the same page,” so to speak, as to what constitutes a high ranking or a low ranking. As an analogy, you have probably had some teachers grade your college papers harder than others. The goal of training is to avoid a wide disparity between how different observers and interviewers record the data.

Figure 10.11

Training people so they know what constitutes different ratings when they are collecting data will improve the quality of the information gathered in a marketing research study.

Ricardo Rodriquez – Satisfaction survey – CC BY-NC-ND 2.0.

For example, if an observation form asks the observers to describe whether a shopper’s behavior is hurried, moderately hurried, or unhurried, they should be given an idea of what defines each rating. Does it depend on how much time the person spends in the store or in the individual aisles? How fast they walk? In other words, the criteria and ratings need to be spelled out.

Collecting International Marketing Research Data

Gathering marketing research data in foreign countries poses special challenges. However, that doesn’t stop firms from doing so. Marketing research companies are located all across the globe, in fact. Eight of the ten largest marketing research companies in the world are headquartered in the United States. However, five of these eight firms earn more of their revenues abroad than they do in the United States. There’s a reason for this: many U.S. markets were saturated, or tapped out, long ago in terms of the amount that they can grow. Coke is an example. As you learned earlier in the book, most of the Coca-Cola Company’s revenues are earned in markets abroad. To be sure, the United States is still a huge market when it comes to the revenues marketing research firms generate by conducting research in the country: in terms of their spending, American consumers fuel the world’s economic engine. Still, emerging countries with growing middle classes, such as China, India, and Brazil, are hot new markets companies want to tap.

What kind of challenges do firms face when trying to conduct marketing research abroad? As we explained, face-to-face surveys are commonly used in third world countries to collect information from people who cannot read or lack phones and computers. However, face-to-face surveys are also common in Europe, despite the fact that phones and computers are readily available. In-home surveys are also common in parts of Europe. By contrast, in some countries, including many Asian countries, it’s considered taboo or rude to try to gather information from strangers either face-to-face or over the phone. In many Muslim countries, women are forbidden to talk to strangers.

And how do you figure out whom to research in foreign countries? That in itself is a problem. In the United States, researchers often ask if they can talk to the heads of households to conduct marketing research. But in countries in which domestic servants or employees are common, the heads of households aren’t necessarily the principal shoppers; their domestic employees are (Malhotra).

Translating surveys is also an issue. Have you ever watched the TV comedians Jay Leno and David Letterman make fun of the English translations found on ethnic menus and products? Research tools such as surveys can suffer from the same problem. Hiring someone who is bilingual to translate a survey into another language can be a disaster if the person isn’t a native speaker of the language to which the survey is being translated.

One way companies try to deal with translation problems is by using back translation. When back translation is used, a native speaker translates the survey into the foreign language and then translates it back again to the original language to determine if there were gaps in meaning—that is, if anything was lost in translation. And it’s not just the language that’s an issue. If the research involves any visual images, they, too, could be a point of confusion. Certain colors, shapes, and symbols can have negative connotations in other countries. For example, the color white represents purity in many Western cultures, but in China, it is the color of death and mourning (Zouhali-Worrall, 2008). Also, look back at the cartoon-completion exercise in Figure 10.8 “Example of a Cartoon-Completion Projective Technique” . What would women in Muslim countries who aren’t allowed to converse with male sellers think of it? Chances are, the cartoon wouldn’t provide you with the information you’re seeking if Muslim women in some countries were asked to complete it.

One way marketing research companies are dealing with the complexities of global research is by merging with or acquiring marketing research companies abroad. The Nielsen Company is the largest marketing research company in the world. The firm operates in more than a hundred countries and employs more than forty thousand people. Many of its expansions have been the result of acquisitions and mergers.

Step 6: Analyze the Data

Step 6 involves analyzing the data to ensure it’s as accurate as possible. If the research is collected by hand using a pen and pencil, it’s entered into a computer. Or respondents might have already entered the information directly into a computer. For example, when Toyota goes to an event such as a car show, the automaker’s marketing personnel ask would-be buyers to complete questionnaires directly on computers. Companies are also beginning to experiment with software that can be used to collect data using mobile phones.

Once all the data is collected, the researchers begin the data cleaning , which is the process of removing data that have accidentally been duplicated (entered twice into the computer) or correcting data that have obviously been recorded wrong. A program such as Microsoft Excel or a statistical program such as Predictive Analytics Software (PASW, which was formerly known as SPSS) is then used to tabulate, or calculate, the basic results of the research, such as the total number of participants and how collectively they answered various questions. The programs can also be used to calculate averages, such as the average age of respondents, their average satisfaction, and so forth. The same can done for percentages, and other values you learned about, or will learn about, in a statistics course, such as the standard deviation, mean, and median for each question.

The information generated by the programs can be used to draw conclusions, such as what all customers might like or not like about an offering based on what the sample group liked or did not like. The information can also be used to spot differences among groups of people. For example, the research might show that people in one area of the country like the product better than people in another area. Trends to predict what might happen in the future can also be spotted.

If there are any open-ended questions respondents have elaborated upon—for example, “Explain why you like the current brand you use better than any other brand”—the answers to each are pasted together, one on top of another, so researchers can compare and summarize the information. As we have explained, qualitative information such as this can give you a fuller picture of the results of the research.

Part of analyzing the data is to see if it seems sound. Does the way in which the research was conducted seem sound? Was the sample size large enough? Are the conclusions that become apparent from it reasonable?

The two most commonly used criteria used to test the soundness of a study are (1) validity and (2) reliability. A study is valid if it actually tested what it was designed to test. For example, did the experiment you ran in Second Life test what it was designed to test? Did it reflect what could really happen in the real world? If not, the research isn’t valid. If you were to repeat the study, and get the same results (or nearly the same results), the research is said to be reliable . If you get a drastically different result if you repeat the study, it’s not reliable. The data collected, or at least some it, can also be compared to, or reconciled with, similar data from other sources either gathered by your firm or by another organization to see if the information seems on target.

Stage 7: Write the Research Report and Present Its Findings

If you end up becoming a marketing professional and conducting a research study after you graduate, hopefully you will do a great job putting the study together. You will have defined the problem correctly, chosen the right sample, collected the data accurately, analyzed it, and your findings will be sound. At that point, you will be required to write the research report and perhaps present it to an audience of decision makers. You will do so via a written report and, in some cases, a slide or PowerPoint presentation based on your written report.

The six basic elements of a research report are as follows.

• Title Page . The title page explains what the report is about, when it was conducted and by whom, and who requested it.
• Table of Contents . The table of contents outlines the major parts of the report, as well as any graphs and charts, and the page numbers on which they can be found.
• Executive Summary . The executive summary summarizes all the details in the report in a very quick way. Many people who receive the report—both executives and nonexecutives—won’t have time to read the entire report. Instead, they will rely on the executive summary to quickly get an idea of the study’s results and what to do about those results.

Methodology and Limitations . The methodology section of the report explains the technical details of how the research was designed and conducted. The section explains, for example, how the data was collected and by whom, the size of the sample, how it was chosen, and whom or what it consisted of (e.g., the number of women versus men or children versus adults). It also includes information about the statistical techniques used to analyze the data.

Every study has errors—sampling errors, interviewer errors, and so forth. The methodology section should explain these details, so decision makers can consider their overall impact. The margin of error is the overall tendency of the study to be off kilter—that is, how far it could have gone wrong in either direction. Remember how newscasters present the presidential polls before an election? They always say, “This candidate is ahead 48 to 44 percent, plus or minus 2 percent.” That “plus or minus” is the margin of error. The larger the margin of error is, the less likely the results of the study are accurate. The margin of error needs to be included in the methodology section.

• Findings . The findings section is a longer, fleshed-out version of the executive summary that goes into more detail about the statistics uncovered by the research that bolster the study’s findings. If you have related research or secondary data on hand that back up the findings, it can be included to help show the study did what it was designed to do.
• Recommendations . The recommendations section should outline the course of action you think should be taken based on the findings of the research and the purpose of the project. For example, if you conducted a global market research study to identify new locations for stores, make a recommendation for the locations (Mersdorf, 2009).

As we have said, these are the basic sections of a marketing research report. However, additional sections can be added as needed. For example, you might need to add a section on the competition and each firm’s market share. If you’re trying to decide on different supply chain options, you will need to include a section on that topic.

As you write the research report, keep your audience in mind. Don’t use technical jargon decision makers and other people reading the report won’t understand. If technical terms must be used, explain them. Also, proofread the document to ferret out any grammatical errors and typos, and ask a couple of other people to proofread behind you to catch any mistakes you might have missed. If your research report is riddled with errors, its credibility will be undermined, even if the findings and recommendations you make are extremely accurate.

Many research reports are presented via PowerPoint. If you’re asked to create a slideshow presentation from the report, don’t try to include every detail in the report on the slides. The information will be too long and tedious for people attending the presentation to read through. And if they do go to the trouble of reading all the information, they probably won’t be listening to the speaker who is making the presentation.

Instead of including all the information from the study in the slides, boil each section of the report down to key points and add some “talking points” only the presenter will see. After or during the presentation, you can give the attendees the longer, paper version of the report so they can read the details at a convenient time, if they choose to.

Key Takeaway

Step 1 in the marketing research process is to define the problem. Businesses take a look at what they believe are symptoms and try to drill down to the potential causes so as to precisely define the problem. The next task for the researcher is to put into writing the research objective, or goal, the research is supposed to accomplish. Step 2 in the process is to design the research. The research design is the “plan of attack.” It outlines what data you are going to gather, from whom, how, and when, and how you’re going to analyze it once it has been obtained. Step 3 is to design the data-collection forms, which need to be standardized so the information gathered on each is comparable. Surveys are a popular way to gather data because they can be easily administered to large numbers of people fairly quickly. However, to produce the best results, survey questionnaires need to be carefully designed and pretested before they are used. Step 4 is drawing the sample, or a subset of potential buyers who are representative of your entire target market. If the sample is not correctly selected, the research will be flawed. Step 5 is to actually collect the data, whether it’s collected by a person face-to-face, over the phone, or with the help of computers or the Internet. The data-collection process is often different in foreign countries. Step 6 is to analyze the data collected for any obvious errors, tabulate the data, and then draw conclusions from it based on the results. The last step in the process, Step 7, is writing the research report and presenting the findings to decision makers.

Review Questions

• Explain why it’s important to carefully define the problem or opportunity a marketing research study is designed to investigate.
• Describe the different types of problems that can occur when marketing research professionals develop questions for surveys.
• How does a probability sample differ from a nonprobability sample?
• What makes a marketing research study valid? What makes a marketing research study reliable?
• What sections should be included in a marketing research report? What is each section designed to do?

1 “Questionnaire Design,” QuickMBA , http://www.quickmba.com/marketing/research/qdesign (accessed December 14, 2009).

Barnes, B., “Disney Expert Uses Science to Draw Boy Viewers,” New York Times , April 15, 2009, http://www.nytimes.com/2009/04/14/arts/television/14boys.html?pagewanted=1&_r=1 (accessed December 14, 2009).

Burns A. and Ronald Bush, Marketing Research , 6th ed. (Upper Saddle River, NJ: Prentice Hall, 2010), 85.

Malhotra, N., Marketing Research: An Applied Approach , 6th ed. (Upper Saddle River, NJ: Prentice Hall), 764.

McDaniel, C. D. and Roger H. Gates, Marketing Research Essentials , 2nd ed. (Cincinnati: South-Western College Publishing, 1998), 61.

McWilliams, J., “A-B Puts Super-Low-Calorie Beer in Ring with Miller,” St. Louis Post-Dispatch , August 16, 2009, http://www.stltoday.com/business/next-matchup-light-weights-a-b-puts-super-low-calorie/article_47511bfe-18ca-5979-bdb9-0526c97d4edf.html (accessed April 13, 2012).

Mersdorf, S., “How to Organize Your Next Survey Report,” Cvent , August 24, 2009, http://survey.cvent.com/blog/cvent-survey/0/0/how-to-organize-your-next-survey-report (accessed December 14, 2009).

Rappeport A. and David Gelles, “Facebook to Form Alliance with Nielsen,” Financial Times , September 23, 2009, 16.

Spangler, T., “Disney Lab Tracks Feelings,” Multichannel News 30, no. 30 (August 3, 2009): 26.

Wagner, J., “Marketing in Second Life Doesn’t Work…Here Is Why!” GigaOM , April 4, 2007, http://gigaom.com/2007/04/04/3-reasons-why-marketing-in-second-life-doesnt-work (accessed December 14, 2009).

Wrenn, B., Robert E. Stevens, and David L. Loudon, Marketing Research: Text and Cases , 2nd ed. (Binghamton, NY: Haworth Press, 2007), 180.

Zouhali-Worrall, M., “Found in Translation: Avoiding Multilingual Gaffes,” CNNMoney.com , July 14, 2008, http://money.cnn.com/2008/07/07/smallbusiness/language_translation.fsb/index.htm (accessed December 14, 2009).

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6 Types of Research Design

Learning Objectives

By the end of this chapter, students must be able to:

• Explain the three types of research design used by marketers
• Understand the application of these designs

Research Design

This chapter looks at the types of research designs that are utilized by marketers. A research design is an overall plan or structure for a research project. A research design will use different combinations of primary, secondary, qualitative, and quantitative data. Depending on the overall research questions, research designs in marketing may fall into one of the following three categories:

• Exploratory research design
• Descriptive research design
• Causal research design (experiments)

An   exploratory research design   is more informal and unstructured than the other two types of designs. Exploratory design is used to explore a situation, especially when the researcher is in unfamiliar territory. Most academic projects begin with exploratory research when researchers undertake desk research. Similarly, when marketers plan to venture into new markets, such as India, it is advisable to employ an exploratory research design. This could mean going through case studies, accessing published reports (i.e., secondary data) on the market, undertaking experience interviews with experts, and conducting focus groups – if needed. Exploratory design is useful in gaining background information and deciding about future research approaches. It may generate more questions, which need to be tackled with other research designs.

As the name suggests, descriptive research design is employed to describe the market or respondents’ characteristics. This type of research design generates quantitative information. Therefore, such a design would often involve surveys. Surveys are useful to measure the descriptive numbers relating to respondents’ age groups, income levels, expenditure patterns, and even attitudes. This can be done by employing relevant scales, such as, “On a scale of 1–5, how satisfied were you with this organisation’s service?”.

Physiological measurements, such as people’s involuntary responses (such as heart rate, skin changes, and eye movement) to marketing stimuli, such as an advertisement may also be categorised in descriptive research. While recording such information requires special instruments, it is nevertheless a type of descriptive information that can be useful for marketers.

Source: Meanthat and authentic data science [1]

Causal research design (also known as experimental research design) examines cause-and-effect relationships. A well-designed experiment is the best way of understanding how one variable (e.g., advertisement) may influence another variable (e.g., sale of a product). An experiment involves one or more independent variables (for example, price level, and product features) which are manipulated to determine how they may impact one or more dependent variables (such as customer preference or customer satisfaction). Independent variables can be ‘manipulated’ by the researcher while the dependent variables are variables that get influenced due to changes in the independent variables.

Experiments can be categorised as field experiments or lab experiments .  When an experiment is conducted in a natural setting – such as in a retail store – it is referred to as a field experiment . On the other hand, experiments conducted in a researcher’s office or a university classroom are lab experiments. A lab experiment may be undertaken to measure the impact of an ad on the subject’s attitude.

A field experiment is often seen as providing researchers with reliable results as it is undertaken in the actual environment. However, it is not easy to implement a field experiment. It is difficult to control other factors – in the real environment – which may also impact the final results. Moreover, running an experiment requires expertise in the design of the experiment. It may also require financial and time resources. As an example, Mcdonald’s may wish to see if a drop in the price of its cheeseburger results in greater sales. The fast-food restaurant may drop the price in one locality, such as Fairfield. It may also tediously measure the sales around the time of a price reduction. While interpreting the results, it would need to be assured that any changes in sales figures can be attributed to the price change – and not to other environmental factors such as an increase in prices at Hungry Jacks or an overall increase in customers due to a local football match. Thus, controlling such extraneous variables – that is, all those variables besides the identified independent variables which may also affect the dependent variable.

One of the popular experimental research designs is the ‘Before-After’ Testing design . As the name suggests, it measures the dependent variable, before and after a change in the independent variable. An example will help clarify the concept:

Experimental Group       (R)                 O 1             X           O 2

Control Group                                          O 3                           O 4

Experimental Effect (E) =                     (O 2   – O 1  ) – (O 4  – O 3 )

R = random allocation of subjects to experimental or control group

O = observation

X = treatment or manipulation of the independent variable, such as a change in price

Explanation of the above example:

• Subjects or research participants are randomly allocated to one of the groups
• The experimental group is the one in which the independent variable (e.g., price) is manipulated (e.g., reduced)
• The control group is not exposed to any changes in the independent variable
• Measurements (i.e., O) for the dependent variable (e.g., sales) – for both groups – are taken before the change in price (X) – PRE-TEST
• Measurements (i..e., O)  for the dependent variable are taken after the change in price – POST-TEST
• The difference between O 2  and O 1  demonstrates the change in the dependent variable due to the change in X (independent variable)
• The difference between O 4  and O 3  demonstrate any change in the dependent variable due to factors other than X
• Therefore, the difference between the two groups  (O 2   – O 1  ) – (O 4  – O 3 ) demonstrates the true effect of the independent variable as it removes any influence of extraneous variables.
• Meanthat and authentic data science 2016, 1.3 Exploratory, descriptive and explanatory nature of research , 17 March, online video, viewed 3 March 2022, <https://www.youtube.com/watch?v=FlBFdEgrTBM>. ↵

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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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What is Research Design? Elements, Types, Examples

Appinio Research · 06.09.2023 · 23min read

Ever wondered what lays the foundation for successful research studies? It all starts with a well-crafted research design. In the world of inquiry, research design is the guiding compass that shapes the entire process, helping you navigate complexities and unlock the doors to meaningful insights. Whether you're embarking on your first research journey or seeking to refine your skills, understanding the art and science of research design is the key to unlocking the true potential of your investigations.

What is Research Design?

Research design is like the roadmap for your research journey. Imagine planning a cross-country trip: you wouldn't hit the road without a clear route, right? Similarly, research design provides the structure and strategy you need to navigate your way through the complexities of a study.

It's the blueprint that outlines the steps you'll take, the methods you'll use, and the goals you aim to achieve.

At its core, research design is all about making smart decisions. It's about choosing the best tools to answer your questions and gather information. Whether you're exploring the effects of a new drug, understanding the habits of a specific demographic, or investigating the behaviors of animals, a well-designed research plan sets the stage for success.

In a nutshell, research design is your guide, helping you collect data, draw conclusions, and make meaningful contributions to your field.

Why is Research Design Important in the Research Process?

Research design plays a crucial role in ensuring the success of your research study. A well-designed research plan:

• Provides structure and direction to your study.
• Helps in clearly defining research objectives and questions.
• Guides the choice of appropriate methodologies and data collection methods .
• Ensures that ethical considerations are addressed.
• Enhances the validity and reliability of your findings.

How Research Design Affects Study Outcomes

Your research design has a direct impact on the outcomes of your study. A well-crafted research plan:

• Increases the likelihood of obtaining accurate and reliable results.
• Enables you to draw valid conclusions and make meaningful interpretations.
• Enhances the credibility and generalizability of your findings.
• Guides the implementation of research procedures in a consistent and organized manner.

Key Elements of a Research Study

A well-designed research study is like a puzzle where every piece fits perfectly to reveal a clear picture. These fundamental elements ensure that your research is structured, meaningful, and capable of generating credible insights.

Clear Research Objectives

Think of research objectives as your guiding stars. They define what you aim to achieve with your study. Clear goals keep you on track, guiding your research questions, methods, and analysis.

Precise Research Questions and Hypotheses

Research questions and hypotheses are the compass that points you in the right direction. They provide focus by outlining what you want to explore and predict. Well-crafted questions and hypotheses make your study purposeful and relevant.

Appropriate Methodology Selection

Choosing a suitable methodology is like selecting the best tool for the job. Quantitative methods are your go-to for measurable data, while qualitative methods help you dive deep into complex human experiences. Mixed methods offer the best of both worlds.

Thoughtful Participant Selection

Selecting the right participants is like assembling a diverse team for a project. Your sample should represent the population you're studying. Choose appropriate sampling techniques and determine the sample size that strikes the right balance between accuracy and feasibility.

Effective Data Collection Strategies

Data collection is like gathering puzzle pieces. Choose methods that align with your research goals. Surveys, interviews, observations, and experiments are just a few of the tools at your disposal.

Reliable Research Instrument Development

Research instruments are your tools for collecting data. Whether it's a questionnaire or an interview guide, they need to be well-constructed, unbiased, and capable of capturing the information you need.

Thoughtful Research Procedure Design

Your research procedure is the timeline that ensures everything happens in the proper order. From recruiting participants to data analysis, a well-structured procedure keeps your study organized and efficient.

Rigorous Data Analysis and Interpretation

Data analysis is where you piece the puzzle together. Applying the right techniques to your data—whether quantitative or qualitative —reveals patterns, relationships, and insights that answer your research questions.

Validity and Reliability Considerations

Validity and reliability are the quality checks of your study. Validity ensures that your measurements are accurate, while reliability guarantees consistency. Addressing these ensures your findings hold true and can be trusted.

Ethical Considerations

Ethical considerations are the foundation of responsible research. Protect participants' rights, ensure their consent, and follow ethical guidelines to conduct your study with integrity.

A well-designed research study brings all these elements together harmoniously, resulting in a comprehensive, credible, and impactful exploration of your chosen research topic.

Types of Research Design

Research design comes in various flavors, each tailored to answer different types of questions and explore diverse aspects of your research topic. Let's dive into the main types of research designs to help you choose the one that aligns with your objectives.

Quantitative Research Designs

Quantitative research is all about numbers and measurements. If you're interested in uncovering patterns, relationships, and trends through numerical data, these designs are your go-to options:

• Experimental Design: This design allows you to manipulate variables to establish cause-and-effect relationships. Think of it as a controlled experiment where you change one thing to see how it impacts another.
• Survey Research: Surveys are your ticket to collecting a lot of data from a wide range of people. Structured questionnaires help gather standardized responses, making it easy to analyze patterns.
• Longitudinal Studies : Imagine tracking a group of people over years to see how they change. Longitudinal studies dive deep into understanding development, behaviors, or changes within a specific group.

Qualitative Research Designs

Qualitative research focuses on understanding the complexities of human experiences, behaviors, and contexts. If you're intrigued by narratives and in-depth insights, consider these designs:

• Case Study: Dive deep into a single subject, exploring it from every angle. It's like zooming in on a single puzzle piece to understand its intricate details.
• Ethnographic Study: If you want to immerse yourself in a culture or community, ethnography is your tool. Live among the people you're studying to grasp their worldviews and practices.
• Grounded Theory: This design is all about building theories from scratch based on the data you collect. It's like letting the information guide you toward new insights and concepts.

Mixed Methods Research

Sometimes, one approach just isn't enough. Mixed methods research combines both quantitative and qualitative methods to give you a comprehensive view of your research topic. It's like using wide-angle and macro lenses together to capture the big picture and the tiny details.

Each research design has its strengths and shines in different situations. The type you choose will depend on your research questions, goals, and the kind of insights you aim to uncover.

How to Define Research Objectives and Questions?

At the heart of every research study are clear and focused objectives, along with well-crafted research questions or hypotheses. We'll dive into the process of formulating these crucial components, ensuring that your study remains on track and purposeful.

1. Formulate Clear Research Objectives

Research objectives outline the specific goals you aim to achieve through your study. Clear and concise (SMART) objectives provide direction and purpose to your research. Here's how to formulate well-crafted research objectives:

• Be Specific: Clearly state what you intend to accomplish.
• Be Measurable: Define outcomes that can be quantified or observed.
• Be Achievable: Set realistic goals within the scope of your study.
• Be Relevant: Ensure that your objectives align with the research problem.
• Be Time-Bound: Specify a timeframe for achieving your objectives.

2. Develop Research Questions and Hypotheses

Research questions and hypotheses guide your study and direct your research efforts. They should be focused, relevant, and provide a clear framework for investigation.

• Research Questions: These are open-ended queries that help you explore a particular topic. They often start with words like "what," "how," or "why." For example: "What are the factors that influence consumer purchasing decisions?"
• Hypotheses: Hypotheses are statements that propose a specific relationship between variables. They are testable predictions about the outcomes of your study. For example: "Increasing the price of a product will result in decreased sales."

3. Ensure Alignment Between Objectives and Questions

It's essential to ensure that your research objectives and questions are well-aligned. Your research questions should directly address your objectives, helping you fulfill the purpose of your study.

By formulating clear research objectives and crafting well-structured questions or hypotheses, you'll establish a strong foundation for your research study.

How to Select Research Participants?

The participants in your research study form the foundation upon which your findings rest. Proper participant selection is crucial for obtaining relevant and reliable data.

Sampling Techniques

Sampling involves selecting a subset of individuals from a larger pool to represent the whole. The choice of sampling technique depends on the research goals and the nature of the population.

• Probability Sampling: Probability sampling ensures that each member of the population has an equal chance of being selected. Common methods include simple random sampling, stratified sampling, and cluster sampling .
• Non-Probability Sampling: Non-probability sampling methods do not guarantee equal representation. These methods include convenience sampling, purposive sampling, and snowball sampling.

Sample Size Determination

Determining the appropriate sample size  is essential to ensure the reliability of your findings. An inadequate sample size might lead to biased results, while an excessively large sample might be wasteful.

Ethical Considerations in Participant Selection

Respecting the rights and well-being of your participants is paramount. Ethical considerations include obtaining informed consent, ensuring participant confidentiality, and minimizing potential harm.

By selecting the right participants and adhering to ethical guidelines, you'll lay the groundwork for collecting meaningful and trustworthy data.

Research Data Collection Strategies

Collecting data is a fundamental step in the research process. The strategies you choose for data collection directly influence the quality and validity of your findings.

Quantitative Data Collection

Quantitative data collection involves gathering numerical information that can be analyzed statistically. Here are some common strategies:

• Surveys and Questionnaires: Surveys and questionnaires allow you to collect standardized responses from a large number of participants. They are useful for obtaining quantitative data on attitudes, preferences, and behaviors.
• Experiments: Experimental design involves manipulating variables to observe their effects. Controlled experiments provide insights into causal relationships, and random assignment helps minimize bias.
• Observations and Secondary Data Analysis: Direct observations of subjects or behaviors can provide valuable data. Additionally, analyzing existing datasets (secondary data) can save time and resources.

Qualitative Data Collection

Qualitative data collection focuses on capturing rich, context-specific information. Here are some effective methods:

• Interviews: Interviews involve direct interaction with participants to gather in-depth insights. Types include structured, semi-structured, and unstructured interviews, each offering a different level of flexibility.
• Focus Groups : Focus groups bring together a small group of participants to discuss a specific topic. This method encourages open discussions and the exploration of diverse perspectives.
• Participant Observation: Participant observation involves immersing yourself in the research setting to understand behaviors, interactions, and dynamics. It's particularly beneficial in ethnographic studies.

Data Validity and Reliability Across Methods

Ensuring the validity and reliability of collected data is crucial for drawing accurate conclusions. Validity refers to the accuracy of measurements, while reliability is the consistency of results. Across quantitative and qualitative methods, these principles apply:

• Quantitative: Ensure survey questions are straightforward, and measures are accurate and consistent.
• Qualitative: Maintain consistency in data collection procedures, and use techniques like member checking and triangulation to enhance validity.

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How to Develop Research Instruments?

Research instruments, such as surveys, interview protocols, and observation guides, are tools that help you collect data from participants. Developing effective instruments requires careful planning and attention to detail.

How to Construct Survey Instruments?

Surveys are a standard method for collecting data from many participants. To construct an effective survey instrument:

• Define Your Variables: Clearly define the variables you're measuring and ensure they align with your research questions.
• Use Clear Language: Write clear and concise questions using simple language to avoid confusion.
• Avoid Bias: Avoid leading or biased questions that could influence participant responses.
• Include Validity Checks: Incorporate validation questions to ensure respondents are providing accurate information.

How to Create Interview Protocols?

Interviews offer an opportunity to gather in-depth insights directly from participants. To create effective interview protocols:

• Structure Questions: Organize questions logically and flow from general to specific topics.
• Open-Ended Questions: Include open-ended questions encouraging participants to share their thoughts and experiences.
• Probing Questions: Develop probing questions to dig deeper into participant responses and gain deeper insights.

Pre-testing and Piloting Research Instruments

Before launching your research, pre-test or pilot your instruments with a small group of participants. This helps identify issues with clarity, wording, or question order and allows you to refine the instruments for maximum effectiveness.

By investing time in constructing well-designed research instruments, you'll collect accurate and relevant data that contribute to the success of your study.

How to Design the Research Procedure?

The research procedure outlines the step-by-step plan for conducting your study. A well-designed procedure ensures consistency, reliability, and efficiency in data collection.

To design an effective research procedure:

1. Sequence Research Activities

Sequencing research activities involves arranging the order in which different tasks will be carried out. Consider the following when creating your sequence:

• Logical Flow: Ensure that activities are organized in a logical order, from participant recruitment to data analysis.
• Dependencies: Identify tasks that depend on the completion of others and plan accordingly.
• Flexibility: Allow for some flexibility to accommodate unexpected challenges or opportunities.

2. Establish a Data Collection Timeline

Creating a timeline for your research helps you stay on track and manage your resources efficiently. Consider the following when establishing your timeline:

• Breakdown of Tasks: Divide the research process into manageable tasks and allocate time for each.
• Realistic Deadlines: Set realistic deadlines that consider the complexity of each task and potential delays.
• Buffer Periods: Include buffer periods to account for unforeseen delays or revisions.

3. Ensure Consistency in Data Collection Procedures

Consistency is crucial in obtaining reliable and valid data. Establish standardized procedures for data collection:

• Training: Train researchers involved in data collection to follow consistent procedures and protocols.
• Detailed Instructions: Provide clear and detailed instructions for each data collection method.
• Monitoring: Regularly monitor data collection to ensure adherence to procedures and address any issues.

By designing a well-structured research procedure, you'll ensure that your study progresses smoothly, data is collected consistently, and timelines are met. The next step is moving on to the crucial phase of data analysis and interpretation.

Research Data Analysis and Interpretation

Data analysis is the process of transforming raw data into meaningful insights. It's where you draw conclusions and make sense of the information you collected.

Quantitative Data Analysis Techniques

Quantitative data analysis involves processing numerical data to identify patterns and relationships. Here are some common techniques:

• Descriptive Statistics: Descriptive statistics, such as mean, median, and standard deviation, summarize and describe the main features of a dataset.
• Inferential Statistics: Inferential statistics help you draw conclusions about a population based on a sample. Techniques include t-tests, ANOVA, and regression analysis.
• Regression Analysis: Regression analysis helps you understand the relationships between variables and predict outcomes. Linear and logistic regressions are widely used.

Qualitative Data Analysis Approaches

Qualitative data analysis involves interpreting non-numerical data to uncover themes and patterns. Here are some common approaches:

• Thematic Analysis : Thematic analysis involves identifying recurring themes or patterns in qualitative data. It helps you discover meaningful insights and concepts.
• Content Analysis: Content analysis is used to systematically analyze textual or visual content to identify specific patterns, themes, or trends.
• Constant Comparative Method: The constant comparative method involves comparing data points throughout the analysis to uncover patterns and relationships.

Validity and Reliability in Data Analysis

Ensuring the validity and reliability of your data analysis is essential for producing accurate findings:

• Triangulation: Use multiple data sources, methods, or analysts to validate your findings.
• Member Checking: Share your findings with participants to confirm that your interpretations align with their experiences.

By carefully analyzing and interpreting your data, you'll uncover insights that address your research questions and contribute to the overall understanding of your topic.

Validity and Reliability in Research Design

Validity and reliability are essential concepts in research design that ensure the credibility and trustworthiness of your study. In this section, we'll delve into these concepts and explore how they impact the quality of your research.

Internal Validity: Controlling for Confounding Variables

Internal validity refers to the degree to which your study accurately measures the cause-and-effect relationship you intend to study without interference from extraneous variables. To enhance internal validity:

• Control Groups : Use control groups in experimental designs to compare the effects of variables.
• Randomization: Randomly assign participants to groups to ensure unbiased distribution of characteristics.
• Eliminate Confounding Variables: Identify and control for factors that could influence your results but are not part of your research question.

External Validity: Generalizability of Findings

External validity refers to the extent to which your findings can be generalized to a broader population or real-world settings. To enhance external validity:

• Random Sampling: Use random sampling to ensure that your sample is representative of the larger population.
• Ecological Validity: Design your study to mirror real-world situations as closely as possible.
• Replication: Replicate your study with different populations or settings to validate your findings.

How to Ensure Research Reliability and Reproducibility?

Reliability refers to the consistency and stability of your measurements over time and across different conditions. To ensure research reliability:

• Consistent Procedures: Use standardized procedures for data collection and analysis.
• Inter-Rater Reliability: Have multiple researchers analyze data independently to assess agreement.
• Test-Retest Reliability: Repeat measurements on the same subjects to evaluate consistency.

Ethical Considerations in Research Design

Ethical guidelines are a fundamental aspect of research design. Respecting the rights and well-being of participants is paramount. These include:

• Informed Consent: Obtain informed consent from participants, ensuring they understand the study's purpose, procedures, and risks.
• Confidentiality: Protect participant privacy by safeguarding their personal information.
• Institutional Review Board (IRB): Obtain ethical approval from an IRB before conducting research involving human participants.
• Minimizing Harm: Ensure participants are not subjected to unnecessary physical, emotional, or psychological harm.

By addressing these validity, reliability, and ethical considerations, you'll ensure that your research study is rigorous, credible, and contributes meaningfully to the field.

As you progress, it's crucial to communicate your findings effectively. Let's explore how to do that next.

How to Report and Present Research Findings?

Effectively reporting and presenting your research findings is essential for sharing your insights with the academic community and beyond.

1. Structure the Research Report

A well-structured research report communicates your study clearly and concisely. The typical structure includes:

• Title: A clear and informative title that captures the essence of your study.
• Abstract: A brief summary of your research question, methods, findings, and conclusions.
• Introduction: Introduce the research problem, objectives, and significance of the study.
• Literature Review: Review existing research and theories relevant to your topic.
• Methodology: Describe your research design, participants, data collection, and analysis methods.
• Results: Present your findings using tables, charts, and statistical analysis .
• Discussion: Interpret your results, relate them to existing literature, and address implications.
• Conclusion: Summarize your study, restate findings, and suggest future research directions.
• References: Cite sources you've referenced throughout the report.

2. Create Visual Representations of Data

Visual representations, such as graphs, charts, and tables, help convey complex information more easily. Use appropriate visuals to illustrate trends, patterns, and relationships in your data.

3. Write Clear and Compelling Research Summaries

In addition to your full research report, consider creating concise and engaging summaries that capture the essence of your study. These summaries help share findings with a broader audience, such as policymakers or the general public.

By effectively reporting and presenting your research findings, you contribute to disseminating knowledge and ensuring that your study's insights are accessible and impactful.

In conclusion, research design is like the blueprint of your investigation. It's the plan that makes sure everything fits together just right. By choosing the proper methods, asking the right questions, and following ethical guidelines, you're setting yourself up for success. Remember, research design isn't just for the experts—it's a powerful tool anyone can use to uncover knowledge and make informed decisions. So, whether you're analyzing economic trends or trying to understand your customers' preferences, a solid research design will guide you on your path to discovery.

How to Design Your Research in Minutes?

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• Speedy Insights: Say goodbye to waiting days or weeks for results. With Appinio, you can transform your questions into actionable insights in just minutes, allowing you to make swift and informed decisions for your business.
• Streamlined Process: Appinio takes care of all the heavy lifting in research and technology, allowing you to focus on what truly matters—your business goals. We've seamlessly integrated real-time consumer insights into everyday decision-making, making it an essential tool for your research arsenal.
• Market Research Reinvented: Gone are the days of market research being seen as boring, intimidating, and overpriced. Appinio breaks down those barriers, presenting a platform that's exciting, intuitive, and accessible to all. We're on a mission to change the perception of research and show that it can be a dynamic asset for your business growth.

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Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

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The Complete Guide to Market Research

Research design.

A research design is a blueprint describing how to conduct a research project. It is a plan describing which estimates are to be computed, how they are to be computed and how models are to be tested and refined. A good research design is one that identifies all the things that need to be estimated and works out the best way to measure them.

Information Needs

The starting point for creating a research design should be an understanding of which decisions need to be made. Examples of the type of decisions that research is used to address are:

• Should a pack of chewing gum change from 10 to 12 pieces?
• Should a vegetable juice replace its glass bottles with plastic bottles?
• Should an airline replace its economy seats with stand-up beds?
• Who is the best Republican candidate for President?
• How many phone plans should a company offer?
• Which of 10 new product ideas should be further investigated?
• Should a company replace its American call centers with call centers in India?

It is rare that research can actually provide a definitive answer to such questions. Rather, it provides data for a business case. For example, research can provide an estimate of the impact of sales that occurs when a pack of gum is increased by two pieces, and this is combined with the company’s cost data to work to create a  business case  for the change in pack size.

A key thing to keep in mind when working out which business decisions need to be addressed by research is that the more precisely a firm can identify the key decisions, the more precise the resulting estimates will be. A firm that is able to specify that it wants to understand the impact on sales of changing its pack of gum from 10 to 12 pieces has a good chance of conducting research that is sufficiently accurate to help make this decision. However, if the same firm says that it wants to “understand the consumer value equation for gum, in terms of how consumers trade off price, pack size, multi-packs, flavors and health benefits” it will end up with a much less precise estimate of the impact of a change in the number of pieces and thus risks making worse business decisions. [note 1]

Models of Behavior Change

Research is often desired when a problem has been identified but there is no clear idea of how to solve the problem. For example, a company may wish to identify opportunities for increasing sales. Thus, the behavior to be changed is brand choice, and a model is required which explains what causes this behavior. Although it is possible to conduct  qualitative research  to try and create such a model from scratch, often models which already exist can be used. For example, when trying to understand how to increase market share, it is common to use a model such as this: [1]

Whatever model is used needs to be able to accommodate the theories and hypotheses that are held by the users of the research. And, it should take into account which outcomes the client is trying to affect (e.g., if the the business objective is to try and reduce the level of defection of its customers, then the model should mention defection as an outcome and have arrows pointing to this outcome).

Experienced researchers often do not write their model down, instead creating it in their heads. Inexperienced researchers often do not know they need to create a model, and instead design their research by repeating aspects of projects they have seen or previously worked upon. Nevertheless, their research is still structured around models. As mentioned, it is impossible to do research without a model. When you do not explicitly create a model, you risk implicitly using an inappropriate model as the basis of your research design.

Decompositions

A decomposition involves breaking something apart. It is a useful way to estimate many things. Let us start with a simple estimation problem. How many Japanese people are there in Australia with dentures (fake teeth that can be taken in and out)? And yes, this was a real-world consulting project.

The simplest and laziest approach to this problem is to do a survey. For example, you might email 1,000 people in Australia and ask how many people in each household are Japanese and have dentures. The result of this would be an estimate of the proportion of Australians that say they are Japanese and have dentures. To get to our required result we would multiply this by the number of Australians. Thus, we have used the following model:

Number Proportion Japanese = Japanese × Number dentures dentures Australians

This formula is a decomposition. We have decomposed the thing we are trying to estimate into two separate estimates – the proportion and the population size.

As research designs go, this is a poor one. The proportion we are trying to estimate is likely to be a very small one (e.g., less than 0.1%). Thus, we would expect that we might need to interview well over 1,000 people before we identified a single one of them and perhaps 50,000 or 100,000 people before we obtained a sufficiently precise estimate, for a cost of millions dollars. People researching bizarre topics are usually short of funds, so we can feel confident that this research design is inappropriate. Furthermore, we probably would not get a very precise answer anyway, as Japanese speakers are relatively less likely to participate. So, how can we resolve this? There are lots of other possible decompositions. For example:

Number Number Japanese = people with × Proporton Dentures dentures Japanese

This decomposition requires two completely different inputs: the number of people with dentures and the proportion of people that are Japanese. Both of these numbers may be available from publicly available sources (e.g., trade associations, government statistics), so we might be able to do this very cheaply. Of course, the result may also be quite inaccurate, because this decomposition implicitly assumes that people of Japanese origin are neither more nor less likely to have dentures than the rest of the population.

So, we can use different decompositions to solve the same problem. The trick is to trade-off which will be cheapest with which will be most precise.

Now, let’s solve a more traditional problem. Consider the problem of trying to predict sales of a new brand of laundry detergent. A standard decomposition for this is:

Sales = Market Share × Market Size

There are lots and lots of ways to estimate market share. One of them is to present people with a screen showing a picture of a supermarket shelf, including the new brand, and ask people to choose one; the proportion of people who choose the new brand is then an estimate of the market share. The market size can usually be obtained from historic sales data.

An alternative decomposition of sales of a new product is:

Intention Sales = to × Purchase × Population purchase frequency size

Intention to purchase is estimated by showing somebody a picture of the proposed new product, and asking them whether they will buy it (this is called  Concept Testing ). Purchase frequency can be estimated by asking people how many times they will buy it (although looking at historical purchase rates of similar products will often be more valuable). The population size can be obtained from government statistical agencies.

When designing research we need to find the most cost-effective way of producing sufficiently precise estimates. Consider the decomposition of:

Let us say we are trying to forecast laundry detergent and we are trying to produce a forecast for next year. To produce our sales estimate we need to estimate market share and market size. It is inevitable that the market size will be broadly similar to the sales from the previous year, with a little growth. So, if the market size last year was $13 billion, the market size next year will probably be between $13 billion and $14 billion.

Now think about the market share estimate. If the new product is a ‘dog’, it will get 0% market share. If it is wonderful, perhaps it can get 20% of the market. So, if we multiply the lower bound estimates of market size and market share we compute a lower bound estimate sales of 0% of $13 billion = $0 and an upper bound of 20% of $14 billion = $2.8 billion. Our range of estimates for market size make comparatively little difference to our forecast. If we assume that the market size is $13 billion, this drops the upper bound from $2.8 billion to $2.6 billion. By contrast, changing the estimated market share from 20% to 0% drops the upper bound all the way down to $0. Thus, the estimate of sales is most sensitive to the estimated market share analysis. This process, of working out which bit of the research design will most impact upon the precision of an estimate, is known as sensitivity analysis. It follows, from this sensitivity analysis, that if we are decomposing sales into market share and market size, our focus in producing an estimate of sales should be on estimating the market share.

Measurement Models

If you have ever watched  House  or used alternative media, you will be familiar with the idea that people do not always tell us the truth. Expressed in a slightly more academic way: what people say is a mixture of the truth and error:

Observed = Truth + Error

This model is known as a  measurement model . And depending on context, it is often described with slightly different terms (e.g., using the terms ‘Measured’, ‘Claimed’, ‘Manifest’ or ‘Estimate’ in place of ‘Observed’). Measurement models are most commonly used in more academic studies. More detail on measurements models is in  Measuring Abstract Concepts.

Data Collection Plan

A data collection plan is a document that illustrates the who, what, when, where, why, and how of data collection. This is covered in more detail in the data collection section of this guide.

Analysis Plan

An analysis plan is a list of all the intended analyses that the study will need to address. For example, if the purpose of the study is to work out whether a new product will be successful, then the analysis plan will indicate which analyses will be conducted to answer this question.

Different types of analysis are discussed in the Basic Data Analysis and Advanced Data Analysis sections.

Common things that are usually listed in an analysis plan

• Any particular steps required for  data preparation , such as new variables to create, NETs, and weighting.
• The key sub-groups that should be used when exploring the answers to all the questions (e.g., age, gender, heavy vs light buyers).
• Specific analyses that are required (e.g., specific hypotheses to be tested).

The following analysis plan is for a simple  Concept Test :

• Create a  top two box   NET  on the purchase intent question.
• Compare the top two box score with top two box scores from similar studies.
• Conduct a  Segmentation  using the attitude questions.
• Compare top two box scores by segment.
• Code  the score’s reasons for liking and disliking the product using the same code frame.
• Age: Under 30; 30 to 49; 50+
• Gender: Male, Female
• Income: Under $100,000; $100,000 and above

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How Important Is Research Design To Your Marketing Strategy?

Estimated Reading Time : 5 mins

A company CEO believes strongly that his reinsurance company can successfully enter the auto market by promising dealers a 30 percent increase in gross profits for zero investment. He wants to develop the branding around this theme — a 90-degree change from the business’s current position. Unfortunately, the board sees the shift as too risky. Currently, there’s a stalemate disrupting progress, and the only way to break the logjam is to find out whether the CEO’s instincts are on the money. The immediate questions that validate a “success-conclusion” are:

• Is the promise realistic? Even if so, it’s only half the conversation.
• Is it significantly better than what competitors can offer?

This mini case study shows us that theorizing is one thing, while proving something is quite another. To get the rest of the team behind the idea, the CEO must conduct a systematic and objective research effort. Indeed, everything he does from that point depends on the research design to rubber-stamp investment in the new initiative.

What is research design?

Research design aims to methodically resolve a research problem by structuring a roadmap to collect, measure, and analyze relevant data. A research problem is anything in the market that’s puzzling the stakeholders in a business who are trying to advance a company’s competitive positioning. Companies rely on research design to reveal obstructions, opportunities, or both. Alternatively, management can prove a theory they think is valid with verified data and assessment, as demonstrated by the reinsurance proposition above.

Research routes can go in a quantitative or a qualitative direction. There is, of course, a middle road approach — a hybrid option, if you will — integrating the two types of research design to get the most out of collected data. Nonetheless, the research mechanics are substantially different, so let’s get into the nitty-gritty of research design.

Quantitative research design

Quantitative research design relies on substantial sample sizes, focusing on the response volume which is then broken down numerically and filtered through statistical analysis. Thus, it examines how respondents react to the same stimuli, not why . In other words, it doesn’t get into the emotional and cognitive drivers behind the responses. The process keeps things standardized and on a level playing field by asking respondents to answer the same questions. As a result, research uniformity eliminates biases, but there are exceptions. Variations may arise from the way the company decides to conduct interviews, the most common being:

• Face-to-face
• Online surveys

There is a place for creativity in quantitative design. When relevant, particularly in surveys, respondents may reach a point where they can choose between A or B for a specific question. The following question may be: “If you answered A to the question above, please answer the following.” Another option would be to allow “Other” responses within a quantitative research project, along with a box that asks the respondent to explain his or her answer.

Mostly, however, researchers don’t encourage open-ended responses. Why? Because they dilute the researchers’ ability to quantify results. The more open-ended it becomes, the more it moves to the qualitative side of the research design spectrum. Conversely, closed-ended questions create numerical efficiency, although it sacrifices the ability to dig below the surface into motivations. In addition, coding quantitative answers takes less time, eliminating the burden of categorizing opinions that can differ widely.

Qualitative research design

Qualitative research design focuses on emotional drivers and thoughts that underlie customers’ market behavior. It aligns closely with behavioral and psychographic segmentation. Moreover, it frequently requires the services of trained interviewers with a psychology or sociology background to draw conclusions from observing respondents via video, audio, group discussions, and text in emails and mobile SMS. Relative to quantitative research, it’s a substantially more expensive format — restricted only to a handful of respondents (versus quantitative’s collection of hundreds, even thousands, of responses in much less time.)

The open-ended questionnaire is fundamental to this type of design research, probing to uncover the “how” and “why” of market behavior. For example, “Why did you switch brands after ten years of loyalty?” or “What are the most compelling reasons motivating you to buy more?”

Bias in this type of research

Bias in qualitative research boils down to interviewees assuming the role of expert or opinion leader while being interviewed in an unnatural setting. In other words, what people say in one situation is quite different from how they feel or think about the same thing when an actual buying decision occurs. For this reason, specialized training and expertise are essential to draw accurate conclusions.

Analyzing the data

Qualitative data analysis breaks down into these five categories:

• Content analysis : Systematically compartmentalizing verbal and observed activity data into a format that anyone can understand. It involves skills of classifying, summarizing, and tabulating.
• Narrative assessment : Reformulating respondents’ often diverse stories to extract the primary qualitative data related to the company’s marketing performance.
• Discourse analysis : Interpreting text or recordings of natural conversations in different situations.
• Framework analysis : A process of taking the data through stages that begin with familiarization to identify a thematic framework, followed by coding, charting, and mapping out the data, and finally, interpreting what it all means.
• Grounded analysis : Often connected to thematic theory, begins with analyzing one observed situation to develop a thesis, and looking at a broader landscape to see if other cases support the original proposition.

Qualitative research methods

Researchers apply techniques like in-depth unstructured interviews , focus groups , case study research , and ethnography to get to the crux of how respondents think or feel about things. It can spell the difference between marketing success or failure. Interviews, simple “sit-back-and-observe” formats, cultural and sociological records, and personal intuition all come into play with qualitative research. The researcher coaxes the emergence of valuable information by creatively providing direction and confirming theory validity.

Hybrid research design: the best of both worlds

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What is design research methodology and why is it important?

What is design research.

Design research is the process of gathering, analyzing and interpreting data and insights to inspire, guide and provide context for designs. It’s a research discipline that applies both quantitative and qualitative research methods to help make well-informed design decisions.

Not to be confused with user experience research – focused on the usability of primarily digital products and experiences – design research is a broader discipline that informs the entire design process across various design fields. Beyond focusing solely on researching with users, design research can also explore aesthetics, cultural trends, historical context and more.

Design research has become more important in business, as brands place greater emphasis on building high-quality customer experiences as a point of differentiation.

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Design research vs. market research

The two may seem like the same thing at face value, but really they use different methods, serve different purposes and produce different insights.

Design research focuses on understanding user needs, behaviors and experiences to inform and improve product or service design.  Market research , on the other hand, is more concerned with the broader market dynamics, identifying opportunities, and maximizing sales and profitability.

Both are essential for the success of a product or service, but cater to different aspects of its lifecycle.

Design research in action: A mini mock case study

A popular furniture brand, known for its sleek and simple designs, faced an unexpected challenge: dropping sales in some overseas markets. To address this, they turned to design research – using quantitative and qualitative methods – to build a holistic view of the issue.

Company researchers visited homes in these areas to interview members of their target audience and understand local living spaces and preferences. Through these visits, they realized that while the local customers appreciated quality, their choices in furniture were heavily influenced by traditions and regional aesthetics, which the company's portfolio wasn’t addressing.

To further their understanding, the company rolled out surveys, asking people about their favorite materials, colors and furniture functionalities. They discovered a consistent desire for versatile furniture pieces that could serve multiple purposes. Additionally, the preference leaned towards certain regional colors and patterns that echoed local culture.

Armed with these insights, the company took to the drawing board. They worked on combining their minimalist style with the elements people in those markets valued. The result was a refreshed furniture line that seamlessly blended the brand's signature simplicity with local tastes. As this new line hit the market, it resonated deeply with customers in the markets, leading to a notable recovery in sales and even attracting new buyers.

When to use design research

Like most forms of research, design research should be used whenever there are gaps in your understanding of your audience’s needs, behaviors or preferences. It’s most valuable when used throughout the product development and design process.

When differing opinions within a team can derail a design process, design research provides concrete data and evidence-based insights, preventing decisions based on assumptions.

Design research brings value to any product development and design process, but it’s especially important in larger, resource intensive projects to minimize risk and create better outcomes for all.

The benefits of design research

Design research may be perceived as time-consuming, but in reality it’s often a time – and money – saver that can. easily prove to be the difference between strong product-market fit and a product with no real audience.

Deeper customer knowledge

Understanding your audience on a granular level is paramount – without tapping into the nuances of their desires, preferences and pain points, you run the risk of misalignment.

Design research dives deep into these intricacies, ensuring that products and services don't just meet surface level demands. Instead, they can resonate and foster a bond between the user and the brand, building foundations for lasting loyalty .

Efficiency and cost savings

More often than not, designing products or services based on assumptions or gut feelings leads to costly revisions, underwhelming market reception and wasted resources.

Design research offers a safeguard against these pitfalls by grounding decisions in real, tangible insights directly from the target market – streamlining the development process and ensuring that every dollar spent yields maximum value.

New opportunities

Design research often brings to light overlooked customer needs and emerging trends. The insights generated can shift the trajectory of product development, open doors to new and novel solutions, and carve out fresh market niches.

Sometimes it's not just about avoiding mistakes – it can be about illuminating new paths of innovation.

Enhanced competitive edge

In today’s world, one of the most powerful ways to stand out as a business is to be relentlessly user focused. By ensuring that products and services are continuously refined based on user feedback, businesses can maintain a step ahead of competitors.

Whether it’s addressing pain points competitors might overlook, or creating user experiences that are not just satisfactory but delightful, design research can be the foundations for a sharpened competitive edge.

Design research methods

The broad scope of design research means it demands a variety of research tools, with both numbers-driven and people-driven methods coming into play. There are many methods to choose from, so we’ve outlined those that are most common and can have the biggest impact.

This stage is about gathering initial insights to set a clear direction.

Literature review

Simply put, this research method involves investigating existing secondary research, like studies and articles, in your design area. It's a foundational method that helps you understand current knowledge and identify any gaps – think of it like surveying the landscape before navigating through it.

Field observations

By observing people's interactions in real-world settings, we gather genuine insights. Field observations are about connecting the dots between observed behaviors and your design's intended purpose. This method proves invaluable as it can reveal how design choices can impact everyday experiences.

Stakeholder interviews

Talking to those invested in the design's outcome, be it users or experts, is key. These discussions provide first-hand feedback that can clarify user expectations and illuminate the path towards a design that resonates with its audience.

This stage is about delving deeper and starting to shape your design concepts based on what you’ve already discovered.

Design review

This is a closer look at existing designs in the market or other related areas. Design reviews are very valuable because they can provide an understanding of current design trends and standards – helping you see where there's room for innovation or improvement.

Without a design review, you could be at risk of reinventing the wheel.

Persona building

This involves creating detailed profiles representing different groups in your target audience using real data and insights.

Personas help bring to life potential users, ensuring your designs address actual needs and scenarios. By having these "stand-in" users, you can make more informed design choices tailored to specific user experiences.

Putting your evolving design ideas to the test and gauging their effectiveness in the real world.

Usability testing

This is about seeing how real users interact with a design.

In usability testing you observe this process, note where they face difficulties and moments of satisfaction. It's a hands-on way to ensure that the design is intuitive and meets user needs.

Benchmark testing

Benchmark testing is about comparing your design's performance against set standards or competitor products.

Doing this gives a clearer idea of where your design stands in the broader context and highlights areas for improvement or differentiation. With these insights you can make informed decisions to either meet or exceed those benchmarks.

This final stage is about gathering feedback once your design is out in the world, ensuring it stays relevant and effective.

Feedback surveys

After users have interacted with the design for some time, use feedback surveys to gather their thoughts. The results of these surveys will help to ensure that you have your finger on the pulse of user sentiment – enabling iterative improvements.

Remember, simple questions can reveal a lot about what's working and where improvements might be needed.

Focus groups

These are structured, moderator-led discussions with a small group of users . The aim is for the conversation to dive deep into their experiences with the design and extract rich insights – not only capturing what users think but also why.

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• Quick insights: Get to the heart of the matter faster and make informed decisions swiftly
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With insights from Qualtrics, organizations can identify and resolve the greatest friction points in their business, retain and engage top talent, and bring the right products and services to market. Nearly 20,000 organizations around the world use Qualtrics’ advanced AI to listen, understand, and take action. Qualtrics uses its vast universe of experience data to form the largest database of human sentiment in the world. Qualtrics is co-headquartered in Provo, Utah and Seattle.

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Research Design: The Key to Successful Marketing Research

by Prince Kumar

Last updated: 24 July 2023

Table of Contents

The success of marketing research depends on the research design. Research design is a crucial component of the research process that outlines the methodology and procedures for conducting the research. In this blog, we will explore the concept of research design, its importance, and the various types of research design that businesses can use to conduct effective marketing research.

What is Research Design?

Research design is the framework that outlines the research methodology, procedures, and techniques for conducting research. It helps to ensure that the data collected is reliable, valid, and relevant to the research problem. The research design should be based on the research objectives, research questions, and hypotheses, and should consider various factors such as the target population, data collection method, and sampling technique.

Importance of Research Design in Marketing Research

A well-designed research plan is crucial for successful marketing research. Here are some of the reasons why research design is essential in marketing research:

• Ensures the data collected is reliable and valid: Research design helps to ensure that the data collected is reliable and valid. It outlines the methodology and procedures for data collection, and it should be based on the research objectives and research questions.
• Provides a clear structure for conducting research: Research design provides a clear structure for conducting research. It outlines the steps involved in the research process, such as data collection, analysis, and reporting, and ensures that the research is conducted in a systematic and organized manner.
• Helps to avoid bias and errors: A well-designed research plan helps to avoid bias and errors in the research. It considers various factors such as the target population, sampling technique, and data collection method, to ensure that the data collected is representative of the population and free from bias.

Types of Research Design

There are several types of research design that businesses can use to conduct marketing research. Some of the most common types are:

• Descriptive research design: This type of research design is used to describe a particular phenomenon or situation. It is often used to identify customer needs, preferences, and behavior patterns.
• Exploratory research design: This type of research design is used to explore a particular problem or situation. It is often used to generate ideas and insights that can be used to develop research hypotheses.
• Causal research design: This type of research design is used to establish a cause-and-effect relationship between variables. It is often used to test hypotheses and to identify the factors that influence customer behavior.
• Experimental research design: This type of research design is used to test a hypothesis under controlled conditions. It is often used to measure the effect of an intervention or treatment on a particular outcome.

Research design is a critical component of marketing research. It helps to ensure that the data collected is reliable, valid, and relevant to the research problem. A well-designed research plan provides a clear structure for conducting research and helps to avoid bias and errors. By understanding the different types of research design, businesses can choose the right approach for their research needs and conduct effective marketing research that leads to informed business decisions.

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Syllabus – Marketing Research

1. Marketing Research Concepts and Design

• Marketing Research Meaning and Importance, Research Process
• Organisation of Marketing Research in India
• Research Design

2. Data Collection

• Data Collection
• Questionnaire Design and Development
• Attitude Measurement and Scaling

3. Data Processing and Analysis

• Qualitative Research – Meaning, Scope and Methodology
• Data Processing – Coding, Tabulation Data Presentation
• Description and inference from Sample Data
• Analysis of Association

4. Multivariate Analysis

• Regression Analysis, Discriminant Analysis and Factor Analysis
• Conjoint Analysis
• Cluster Analysis and Multi-dimensional Scaling
• Applications of Marketing Research in India – Some Case Studies

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• → Market research: What it is, how to u...

Market research: What it is, how to use it, + examples

Market research allows you to categorize your target audience to better understand your consumers. Learn more about how to do market research here.

Latest posts on Tips

Typeform    |    05.2024

Typeform    |    04.2024

So, you’ve got the next billion-dollar idea that’ll blow the top off your profit margins. You just know you’re onto a winner! Time to throw a huge budget (or your life savings) at this idea, right? 

Not so fast! You're not likely to get very far in the marketplace if you only rely on your gut instincts.

How can you know if your idea even has a chance of surviving in the cutthroat marketplace? 

The answer: market research. A realistic prediction, based on data , of your chances of success. Basically, it’s a way to find out the market viability of your idea.

If you’re new to market research, don’t be intimidated. This guide will take you from basic concepts through to advanced techniques. Plus, our in-house experts will walk you through real-life examples of how we do it here at Typeform.

What is market research and why does it matter?

Market research is the process of collecting information about your target market and customers so you can:

Learn who your customers are

Find out what they want and/or need

Gauge potential market size

Discover trends in your industry

Get wise about what your competitors are up to

Determine how you can stand out

This way, you’ll better understand how to serve your customers, prioritize, and get higher returns on your own marketing and product development efforts. Market research is an essential part of any business’s strategy, whatever the size of your company.

There are many ways to approach market research, and at Typeform, we’ve developed our own spin on it, thanks to continuous testing and the insights we get from being a market research tool ourselves ( forms and surveys).

Uncertainty is an inevitable part of business—however, it’s still possible to reduce some of the uncertainty.

This is where market research is your best ally. Nothing is guaranteed, but making an informed decision based on comprehensive research beats a stab in the dark. Market research helps reduce the thickness of that fog to see what your options are and which direction you might want to take.

Convinced you shouldn’t be sleeping on market research? Great—let’s dive deeper.

Types of market research

Finding what works best for you is a must for useful and actionable market research. We don’t believe in a cut-and-paste approach for all businesses and markets, nor in one definitive “right” way to do things. However, there are some basic principles that apply across the board. Here are a few types of market research.

Secondary and primary research 

Secondary market research delves into information that you don’t create yourself. It’s data that’s already out there, which you can buy or access for free, and is great for benchmarking. 

Examples of secondary research:

Industry reports

Census data

Research paper

Articles in journals or newspapers

Primary market research involves collecting information yourself—this may be more expensive and time-consuming than secondary research, but it’s a better investment in the long run. Focus on your own target audience and gather information directly relevant to your goals. 

Examples of primary research:

Interviews (face-to-face or over the phone)

Focus groups

User testing

Quantitative and Qualitative

Ahh, the classic quantitative vs. qualitative dichotomy.

Quantitative market research gathers data that's numerical, descriptive, and structured. You can draw statistics from quantitative research. It involves more of the “what” questions and can be done at scale.

This type of market research is usually carried out through surveys and questionnaires and can be internal or external. Internal quantitative research examines your current customers, while external can help you identify new customers and see the actual distribution of the whole market. External is more likely to be objective, as your own customers already know you and will have formed opinions.

Examples of quantitative questions:

“Where do you live?”

“How much do you spend on electricity per month, on average?”

“Do you use this product?”

“How often do you go to the gym?”

“On a scale of 1–10, how satisfied are you with our service?”

Qualitative market research involves more of the “how” and “why” questions. It’s done at a much smaller scale, is less structured and more exploratory, aiming for insight rather than certainty. It helps you find out how customers feel about your product, their opinions and preferences—in other words, things that can't be quantified.

Examples of qualitative questions:

“Why did you choose product A over product B?”

“How does this image make you feel?”

“What do you feel is missing from this service?

“Describe the last time you purchased something online.”

“What are your favorite brands for dog grooming products?”

Usually, this type of market research is done through surveys with open-ended questions or interviews. A small number of interviews are conducted, which are then projected to apply to a larger population. 

Quantitative and qualitative research don’t need to be seen as opposite or distinct techniques. It can be an “and” instead of an “either-or.”

Market research for product development and marketing efforts

Market research tends to inform two main areas in a business: product development and marketing efforts. Whether it’s creating a new product or a new set of features, at Typeform, we always start from the end. 

Who’s going to use this? 

Who will buy it? 

How do I justify engineers spending time on this? 

Market research is one of the most important tools to answer these questions. Nobody wants to invest time, money, and effort into making something that no one wants or needs. Market research allows you to assess the market size, its opportunities, and your competitors. This is also where user research and market research inform one another.

Segmenting the market is one of the main activities in market research, as it gives you your target audience(s). How else will you know who is buying from you already, who to market to, and which marketing messages work best?

Competitor analysis , another cornerstone of market research, helps you craft your positioning. In simple terms: How you're different from your competitors and why should buyers pick you?

How to conduct market research

So you can probably see by now how varied market research is. The way we do our own market research here at Typeform has evolved over the years through testing and experimentation. After much trial and error, we finally landed on the approach that works best for us.

Set your goals

Before we even think about launching market research of any scale, we make sure to have a clear objective in mind. 

Are you trying to enhance a particular metric (such as customer numbers or customer satisfaction level), gauge potential market size, or something else?

Define your objective(s) first, then move on to the next step.

Define your audience 

Whatever your approach, the next thing you should always have at the front of your mind is your customer.

Still, focusing on the customer can mean different things to different people.

Focus on jobs, not personas

Brace yourself, because we’re about to say something controversial: don’t focus on buyer personas.

This flies in the face of what most other market research guides will tell you: Research your audience to create buyer personas and frame your offering around them.

Not that buyer personas aren't important—they are. And at Typeform, we definitely use them, but we also follow the “Jobs To Be Done (JTBD)” model. This is the backbone for how we conceptualize everything, from our marketing messaging to our product development. It informs how we see our customers and how we segment them.

How many people in your business speak directly to customers? The bigger your organization, the smaller this number is likely to be, and the further removed the customer becomes from the decision-making. The job creates a consistent framework for everyone to work with and remains close to the customer’s needs.

As you identify needs that intersect, you can begin to find unique differentiators for your product. 

At the end of the day, your customers don’t care about you or your product or its features. They care about the job or jobs they are trying to get done, and if you provide the best solution, they'll pay you for it. If you don’t, they'll move on to your competition faster than you can say, “job to be done.”

 So how does this all relate to market research?

Rather than framing your market research efforts on creating buyer personas and targeting them, frame them around jobs your customers are trying to get done. There'll be some natural overlap with personas, but you need not be wed to them.

Market segmentation

Market segmentation is the act of dividing a target market into groups (or segments). This lets you tailor your efforts to each segment, whether that be your marketing strategy or deciding on features for your product.

The four most common methods: 

Demographics: age, gender, ethnicity, income, industry, job

Psychographic: lifestyle, values, personality traits, interests

Geographic: country, region, city, town

Behavioral: spending habits, internet browsing habits

Depending on your situation, any of these might be useful focus points, and all of them no doubt provide valuable insight.

The benefits of segmentation include:

A better experience for customers: A better understanding of your customers can only really be a win-win. You’ll be able to tailor each part of your customer experience, from marketing message to product experience, based on their segment.

More targeted marketing: In other words, this means better use of your marketing resources. Rather than casting the net wide and crossing your fingers that you haven’t just thrown a lot of time and money away, your segments let you focus your efforts where they’re likely to have the most return.

Improved product development: Knowing the real demands of your target audience will allow for product development that they'll actually appreciate (read: pay for).

Developing a market research strategy

Now that you’re convinced of the importance of market research and how it can help your business, you’re probably pumped to get started. Having even a basic plan can be the difference between a piece of research that has a real and lasting impact on your business and gathering some interesting insights that are forgotten in two weeks. 

Always start with the question: Why? What’s the purpose of the research? 

Your objective shouldn’t be “to do some research,” nor should you select a method first, whether that be a JTBD-based questionnaire, customer interviews, etc. 

Make sure you’re always starting with a question you want to answer and adapt the method to the question.

Examples of questions to think about:

“How can we increase conversions?”

“Why are people churning after two months?”

“What is the appetite for this product?”

“Which product features are most useful to our customers?”

“In which region(s) should we focus our next marketing campaign?"

Let this always be front and center as you go about planning and executing your research.

Market research tips 

Do preliminary research: Have a basic understanding of the industry and the landscape you’ll be investigating. It doesn't have to be extremely in-depth, but it’s important to have a foundation. This ensures you ask the right questions, know what to assess, and can get a more accurate vision of the market.

Align with potential stakeholders: There may be others in your organization who could benefit from the data you're about to gather. It may be worthwhile checking around to see how you could maximize your research efforts. Even just one extra question on your survey might provide essential data for someone else.

Use the right tools for your market research purposes: Make sure that whichever tools you use are fit for purpose. As technology develops, market research automation becomes more important. Using the right tools won't only save you lots of time and energy; it's also essential for correct and high-quality data.

Market research questions

The questions you ask depend on your objectives. You should write market research questions that are purposeful and will help strengthen your relationship with your customers.

You should also consider running a test first, depending on the scale of your research. Sending your survey to a smaller population and analyzing the first few responses will let you check that you’re getting useful responses that are answering your research questions.

Sometimes, until we start getting results, we’re unaware that a question is ineffective. This may be because the question uses terminology not understood by the target audience. 

For example, you may ask, “What SaaS tools do you currently use?” If you get responses like “iPhone 11” and “desktop computer,” then you know you need to adapt your questions better to your audience! 

Here at Typeform, we sometimes send out test emails to smaller populations (around 10% of the target audience) for this purpose and adjust our surveys if necessary.

How many responses to collect for market research

400 is the magic number.

Well, no, in fact, there is no magic number, sorry.

Generally speaking, 400 is the standard recommended sample size—this just means the number of people who responded to your market research survey. 

But this number can vary greatly depending on your total population (i.e., all the people that this research will apply to) and the way you segment them. 

But there’s a mathematical explanation for the popularity of 400: With 400 responses, your margin of error is 5%. 

For example, say you got 400 customer responses to your market research survey. 80% of your respondents answered “yes” to the question, “Would you buy from us again?” That means there’s a 95% chance that in your total population of customers, around 80% would buy from you again.

Don’t forget that to reach your target sample size, you'll need to reach out to many more people! If sending out surveys by email, open rates tend to hover around 15-25% . The percentage of people who then go on to complete a survey will be even lower. 

To increase your chances of survey opens and completions, offering an incentive is never a bad idea. Prize draws or discounts on your product have worked well for us. And, of course, the experience of answering a market research survey is paramount for completions—make sure your form is user-friendly with a smooth and beautiful interface. 

Try to aim for a sample that'll be a good approximation of your overall population. There’s a risk of bias , depending on the channel through which your research survey is shared. For example, if you share it on social media, you might get a younger average age of respondents, which may not be accurately representative of your total population of customers.

Sample market research template

Below is a sample market research template for planning a piece of primary market data.

A brief summary of why this research was started:

What led to this research being done/requested? 

What needs to be validated or explored?

What's been done prior to this research? E.g., competitive analysis, brainstorming, previous research

What insights will this research generate? 

How will these insights be used?

Business/product objectives

We can't emphasize enough the importance of having a clear goal in mind. What metric(s) are you trying to enhance? E.g., more conversions, less churn. This helps people understand the bigger picture of this research.

State what decisions are going to be made or impacted based on the research. As a general rule, if you’re not prepared to make changes, don’t run the research.

Research objectives 

State the high-level objectives for this research. Try to keep it specific, actionable, and two to three points max. 

Research questions 

Provide a list of market research questions you plan to answer during this research (these questions are not the interview questions). 

Participant criteria 

List the primary characteristics of the people you'll recruit for the research, like:

Job(s) to be done

Also decide on the minimum and maximum number of participants you'll need for your study.

Taking action on market research insights

Remember, data isn't reality—however, market research can give you a pretty decent view of reality.

Data can also be unpredictable. Missing a small detail can skew ‌results significantly, so try to be as methodical and meticulous as you can.

Put our market research survey template to the test with customizable questions and design. Take your questionnaire to the next level with over 1 million photos, videos, and icons, or upload your own. Build your ultimate market research survey today with the help of Typeform.

Useful tools for market research

Demographic survey questionnaire template

User persona survey template

Competitor research tool for the SaaS industry

Margin of error calculator for sample size

Google Sheets

About the author

We're Typeform - a team on a mission to transform data collection by bringing you refreshingly different forms.

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Marketing91

What is Research Design? Type of Research Designs

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

A research design is a step-by-step approach used by a researcher to conduct a scientific study. It includes various methods and techniques to conduct research so that a research problem can be handled efficiently.

A researcher has a series of questions that he needs to find answers by conducting research. Research method provides a logical sequence to conduct experiments so that all questions can be assessed in proper order. An impactful research design makes sure the least bias in the data collected and increases trust in analyzed research information. A research design which leaves the least margin of errors can be considered the best research design.

Let’s learn about the important elements of research design.

• Selection of precise purpose statement of research design.
• Various techniques to be executed to collect details for research.
• Methods opted for the analysis of collected data.
• Types of research methodology opted.
• Possible objections for research.
• Settings required for research.
• Timeline for the research study.
• Techniques and methods to measure analysis.

Table of Contents

4 Key characteristics of Research Design

a) Neutrality

The results collected in research should be free from bias and neutral. Discuss and get evaluated your conclusion with experienced multiple individuals and consider those who agree with your research’s results.

b) Reliability

Research design should be able to ensure the standards results by indicating how research questions can be formed because a researcher will always want the same results every time, he performs an experiment.

c) Validity

The validity of a research design is used to calculate the expected results and to estimate the truthfulness of the result. In most cases, researchers opt for their own definition when it comes to what is considered valid. Therefore, the questionnaire prepared from the research design is considered valid.

d) Generalization

Generalization is one of the most important key characteristics of research design. The results obtained from the research should be applicable to a population and not just to a limited sample.

12 Types of research design

A researcher must have knowledge of various types of research designs to choose which type of research design should be applied for the research. There are different types of research designs which are explained below.

1) Case-study design

A case-study research design is used for the in-depth and detailed study of a subject. This technique is usually used to narrow down a big problem into small discrete easily researchable problems.

The case study research design is useful to test the applicability of specific theory or model on the real-life phenomena. A case-study research design is useful in those scenarios where there is not much information is known or available about the phenomena.

The case-study research design has an important place in various disciplines and professions such as sociology, political science, clinical science social science, administrative science, and psychology.

Advantages of using Case study design

• The case-study research design delivers a thorough description of the explicit and rare case.
• The case-study research design is widely opted by social scientists to test modern real-life situations and provides an extension of the existing concepts.
• The case-study research design can modify what is already known through previous research.
• It gives the freedom to the researchers to apply various methodologies and include any number of resources to investigate the problem.
• The case study research design excels at establishing a relationship between a limited number of events or conditions and also helps us to understand complex issues.

Disadvantages of using Case-study research design

• Sometimes research done on a small sample cannot be applied on a large population. Therefore, a case study research design is difficult to establish reliability and generalize.
• The researcher can be biased about the finding of the case because of the intense exposure to the study.
• Case study research design does not enable the assessment of cause and effect relationship.
• Missing important information can make the case hard to interpret.
• Sometimes the case may not be the representation of the larger case being investigated.
• If research is being done on a specific situation or phenomenon then results might be applicable to that particular case.

2) Action research design :

The action research design follows a characteristics-based path, where initially an exploratory stance has opted and understanding is developed about the problem and made some type of strategies for intervention.

While carrying out the interventions, various forms of relevant observations are collected. The same path followed again with the new interventional strategies and continued until a sufficient understanding of the problem is not realized.

The path followed is cyclic or iterative in nature to provide a deeper understanding of the situation, initializing with hypothesizing and specifying the given problem and moving ahead making numerous interventions and assessments.

Advantages of using Action research design

• It is a research design which can be used in work or community situations, because of its cooperative and adaptive research nature.
• Action research design focuses on practical and solution-driven research rather than testing various theories.
• Action research design increases the chances of learning consciously from their experience, therefore, it is also viewed as a learning cycle.
• The Outcomes of the action research design have obvious relevance to practice.
• No information can be hidden or controlled by the researcher.

Disadvantages of using action research design

• It is difficult to perform conventional studies because it is the responsibility of the researcher to boost change for study.
• Over-involvement of the researcher may bias the test results.
• There is no standard format to write action research, therefore, it is hard to document.
• Because of its cyclic nature action research is difficult and time consuming to conduct.

3) Cohort Research design

A cohort study is generally conducted on a certain population (have some commonality or similarity) over a period of time. A cohort study is usually applied in medical sciences and social sciences. A cohort study makes note of statistical occurrence with a specialized subsection of the population, which is unified by similar characteristics that are relevant with the problem being investigated, instead of studying statistical occurrence with the general population.

A cohort can either be open or closed but not both at the same time. Cohort studies collect data applying the method of observation using a qualitative framework. Open cohort studies involve dynamic population which is separated by the state of being studied in the problem.

The size of a cohort study is not constant because the date of entry and exit is defined by an individual. Rate-based data is gathered in open cohort studies. Closed cohort study involves a specific population, where all the participants enter the study at a specific point and no new participants are allowed to take part in later.

Therefore, the number of participants in a closed cohort study remain constant and in a few rare cases, it can only decrease.

Advantages of using Action research study

• In risk-based studies, using action research study is mandatory, because it is unethical to involve random people.
• Both original and secondary data can be used in cohort research.
• A cohort study is flexible in its nature and can be used to provide insights into effects over time and different types of changes for example, social, political, economic, and cultural.
• Cohort studies can gauge probable cause before the outcome has occurred. It can establish that these causes led to the result. Therefore, avoid the debate determining which is the cause and which is the effect.

Disadvantages of cohort research design

• Because of lack of randomization, the external legitimacy of a cohort study is lower than the other researches which select random participants.
• Cohort studies usually take a long time because the researcher has to wait for certain conditions within the group . Therefore, there are chances that variables may change with time, hence, impacting the credibility of the results.
• In the case of comparison of two cohort groups, the factors which differ between the two groups can’t be controlled.

4) Causal design :

This type of research study is used to analyze the phenomena of conditional statements like “if A, then B”. the purpose of using this type of research is to evaluate the impact of a specific change on the existing standards and conventions.

In most of the social studies, a causal explanation is required to test the hypothesis. Causality can be determined by observing the variation in the variables which are assumed to be causing a change in the other variables.

Causal research is difficult to perform and there is never a certainty that there is no other factor influencing the results, especially when the research is dealing with people’s emotions and attitudes . But there could be other deeper psychological reasons that even the subject is not conscious of.

There is a total of three conditions to determine the causality.

• Empirical association:  An effective deduction is based on finding a correlation between the independent variable and dependent variable.
• Appropriate time order: The independent variable must be tackled before the dependent variables.
• Nonspuriousness: a relationship between two variables which is independent of variation is called a third variable.

Advantages of using a causal research study

• There are high chances of replication in this type of research design.
• This study has internal validity because of systematic subject selection.
• By proving a causal link between variables and eliminating other possibilities, it helps people to understand the world better.

Disadvantages of using causal research study

• All relationships can’t be causal. There are chances that two unrelated events seem to be related.
• It is difficult to determine the conclusions about causal relationships, because of various superfluous and perplexing variables that exist in a social environment. Hence, causality can only be inferred, never proven.
• The cause must come before the effect if two variables are related to each other. It is difficult to tell which variable is the cause and which variable is effect in a causal design.

5) Descriptive design :

This type of research design is used to describe the characteristics of a population or phenomena being researched. This study provides the answer to “what” and does not provide the answers to “how”, “when”, and “why”. Descriptive research does not require an internal validity to describe the characteristics of a population. This type of research is used to calculate frequencies, averages, and statistic of data.

Advantages of using Descriptive research design

• This approach gathers a large amount of data for the study.
• With the help of this study rich data can be yielded for future references.
• A more focused study can be developed by using the limitations of the study as a useful tool.
• The descriptive design gives a general overview of the study which is helpful to determine useful pointers for which variables are worth studying.

Disadvantages of using Descriptive research design

• This study entirely depends on the instrumentation for observation and measurement.
• The outcome of a descriptive design can’t be used to disprove a hypothesis.
• Outcomes of descriptive designs can’t be replicated as outcomes of this design is collected using the observational method.

6) Cross-sectional design :

This type of research design can only calculate among or from a variety of people, phenomena or subjects at the place of change. It has three distinguishing features such as no time dimensions, a dependence on the existing differences, and selection of groups based on differences rather than random selection.

Advantages of using a cross-sectional design

• Cross-sectional research design is inexpensive to perform because this is done using surveys.
• Results are more reliable because it is performed on a population.
• This study provides the characteristics of the result at a point in time.
• Grouping of the population is done based on their difference and are not selected randomly.
• A cross-sectional study can use a large number of subjects, unlike many other research designs.

Disadvantages of using cross-sectional study

• It is difficult to find people, phenomena or subjects of same interest.
• Outcomes are time-bound and do not provide any reliability for historical occurrences.
• This study can’t be used to determine the cause and effect relationship.
• AS outcomes are timebound, therefore, there are chances of getting different outcomes in different time-frame.

7) Exploratory design :

This type of research design is used for the researches on which no research is done before and have no studies to refer to. The focus of exploratory design is to get understandings and knowledge for later investigations. This study determines if a future study is possible or not and later techniques can be developed for more research.

Advantages of sing Exploratory research design

• It helps to determine research priority.
• It is useful to gather background data for a particular topic.
• This research answers all questions like “what”, “why”, “how”.

Disadvantages of using exploratory design

• Findings of the exploratory group are not generalized on the whole population.
• Outcomes of this study are tentative, because of its unstructured style of research.

8) Experimental design

This type of research design is often used when there is a priority of time such as cause will always precede effect and when there is steadiness in a causal relationship such as a particular cause will always lead to the same effect and the degree of association is great.

Experimental design is the blueprint of the procedure that permits researchers to control all factors of the experiment. Experimental designs use more groups and more measurements for a longer period of time.

Advantages of using experimental design

• It delivers a high level of evidence for a single study.
• This study determines what is the cause of something to take place.
• It helps researchers to determine placebo effects from treatment effects.

Disadvantages of using experimental research design

• Experimental research is not real and it might not fit into the real world.
• The settings of the experiment may change the behavior of the subjects.
• The experimental researches are sometimes costly, because of the use of special equipment and facilities.
• There are a few types of problems which can’t be experimented because of ethical or technical reasons.

9) Longitudinal design :

Longitudinal research design makes repetitive experiments and makes multiple observations. In this type of research design, the same group of people is interviewed at regular intervals. In this way, the researcher tracks their behavior and identify variables that have caused the change in their behaviors. This research study is a type of observational study and is also known as a panel study.

Advantages of using longitudinal research study

• Observation can be made during a particular phenomenon.
• Future outcomes can be predicted on the basis of earlier factors.
• Let the research to establish a causal relationship between various variables.
• Provide an explanation for the pattern of change.

Disadvantages of using Longitudinal Research Study

• Methods of conducting experiment might change over time.
• Original sample might change over time.
• More than one variable can’t be shown in this type of research.
• In this type of research, the researcher assumes that the present trends will remain the same in future also.
• It takes a long time to conduct this type of research.

10) Historical design

In this type of research data from the past is collected, evaluate and the hypothesis is defended based on the outcomes. To make this type of research a lot of resources like logs, documents, notes, diaries, reports, official records, archives, and no textual data like maps, images, drawings, audios) are used. this research is difficult to conduct because documents should be authentic and authorized.

Advantages of Historical research design

• It is useful for trend analysis.
• It can provide a contextual background to understand a research problem better.
• There are no chances of emotional involvement of the researcher with the subject.
• Historical resources can be used multiple times.

Disadvantages of Historical Research design

• The success of research completely relies on the quality of historical resources.
• External variables can’t be controlled in this type of research; thus, research remains weak.
• Gaps in the study are difficult to acknowledge because of the missing pieces of historical resources.
• Interpretation of historical resources consumes a lot of time.

11) Observational research design :

This type of research design is used to draw results by comparing subjects under research with a controlled group. An observational study can be of two types. In the first type, your subjects know that you are observing them and in the second type, you observe your subjects without letting them know. Observational research design let you get the insights of a particular phenomenon without getting into the trouble of setting up a large project.

Advantages of observational research design

• It is a flexible type of research and doesn’t require to stick to a hypothesis.
• In-depth information can be collected about the phenomenon.
• Results can be generalized to real life events.
• It can act as a pre-research before starting any other experiment.
• It accounts for the complexity of group behavior.

Disadvantages of observational research design

• Subjects under study are not equally credible.
• There are high chances for this research turned out to be biased because the researcher might notice what he wants to notice.
• The outcome of this research is limited to a small group and can’t be generalized.
• Subjects might behave differently because of the presence of the researcher.

12) Sequential research design :

This type of research is designed in a staged approach, where you can move to the next stage only after completing research at the first stage. The results from one stage are used in the next stage and this process continues until enough data is collected to test the hypothesis.

The sample size can vary throughout the research. After analyzing each stage, research can admit the null hypothesis or can choose a different hypothesis or even can choose to perform the experiment again. that means in this type of research design there is no limit on a number of subjects selected by a researcher.

Advantages of using sequential research design

• There is no limit on the size of the sample of research.
• Repetitive nature of the research let you make initial changes.
• A sequential research design is not expensive.
• Fewer efforts from the researcher’s side.
• Because of its sequential nature, results of one sample are analyzed and tested before taking the second sample into the study.

Disadvantages of using Sequential Research design

• It is difficult to maintain consistency in the research from one sample to another.
• Samples aren’t randomized. Hence outcomes can’t be generalized on the whole population.
• Moving the results of one sample to another is difficult work.

Liked this post? Check out the complete series on Market research

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• How to Write Research Proposal? Research Proposal Format
• 7 Key Differences between Research Method and Research Methodology
• Qualitative Research: Meaning, and Features of Qualitative Research
• Research Ethics – Importance and Principles of Ethics in Research
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Design Research

What is design research.

Design research is the practice of gaining insights by observing users and understanding industry and market shifts. For example, in service design it involves designers’ using ethnography—an area of anthropology—to access study participants, to gain the best insights and so be able to start to design popular services.

“We think we listen, but very rarely do we listen with real understanding, true empathy. Yet listening, of this very special kind, is one of the most potent forces for change that I know.” — Carl Rogers, Psychologist and founding father of the humanistic approach & psychotherapy research

Service design expert and Senior Director of User Research at Twitch Kendra Shimmell explains what goes into good design research in this video.

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Get Powerful Insights with Proper Design Research

When you do user research well, you can fuel your design process with rich insights into how your target users interact—or might interact—in contexts to do the things they must do to achieve their goals using whatever they need on the way. That’s why it’s essential to choose the right research methods and execute them properly. Then, you’ll be able to reach those participants who agree to be test users/customers, so they’ll be comfortable enough to give you accurate, truthful insights about their needs, desires, pain points and much more. As service design can involve highly intricate user journeys , things can be far more complex than in “regular” user experience (UX) design . That’s where design research comes in, with its two main ingredients:

Qualitative research – to understand core human behaviors, habits and tasks/goals

Industry and Market research – to understand shifts in technology and in business models and design-relevant signs

An ideal situation—where you have enough resources and input from experts—is to combine the above to obtain the clearest view of the target customers of your proposed—or improved—service and get the most accurate barometer reading of what your market wants and why. In any case, ethnography is essential. It’s your key to decoding this very human economy of habits, motivations, pain points, values and other hard-to-spot factors that influence what people think, feel, say and do on their user journeys. It’s your pathway to creating personas —fictitious distillations that prove you empathize with your target users as customers—and to gain the best insights means you carefully consider how to access these people on their level. When you do ethnographic field studies, you strive for accurate observations of your users/customers in the context of using a service .

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How to Leverage Ethnography to Do Proper Design Research

Whatever your method or combination of methods (e.g., semi-structured interviews and video ethnography), the “golden rules” are:

Build rapport – Your “test users” will only open up in trusting, relaxed, informal, natural settings. Simple courtesies such as thanking them and not pressuring them to answer will go a long way. Remember, human users want a human touch, and as customers they will have the final say on a design’s success.

Hide/Forget your own bias – This is a skill that will show in how you ask questions, which can subtly tell users what you might want to hear. Instead of asking (e.g.) “The last time you used a pay app on your phone, what was your worst security concern?”, try “Can you tell me about the last time you used an app on your phone to pay for something?”. Questions that betray how you might view things can make people distort their answers.

Embrace the not-knowing mindset and a blank-slate approach – to help you find users’ deep motivations and why they’ve created workarounds. Trying to forget—temporarily—everything you’ve learned about one or more things can be challenging. However, it can pay big dividends if you can ignore the assumptions that naturally creep into our understanding of our world.

Accept ambiguity – Try to avoid imposing a rigid binary (black-and-white/“yes”-or-“no”) scientific framework over your users’ human world.

Don’t jump to conclusions – Try to stay objective. The patterns we tend to establish to help us make sense of our world more easily can work against you as an observer if you let them. It’s perfectly human to rely on these patterns so we can think on our feet. But your users/customers already will be doing this with what they encounter. If you add your own subjectivity, you’ll distort things.

Keep an open mind to absorb the users’ world as present it – hence why it’s vital to get some proper grounding in user research. It takes a skilled eye, ear and mouth to zero in on everything there is to observe, without losing sight of anything by catering to your own agendas, etc.

Gentle encouragement helps; Silence is golden – a big part of keeping a naturalistic setting means letting your users stay comfortable at their own pace (within reason). Your “Mm-mmhs” of encouragement and appropriate silent stretches can keep your research safe from users’ suddenly putting politeness ahead of honesty if they feel (or feel that you’re) uncomfortable.

Overall, remember that two people can see the same thing very differently, and it takes an open-minded, inquisitive, informal approach to find truly valuable insights to understand users’ real problems.

Learn More about Design Research

Take our Service Design course, featuring many helpful templates: Service Design: How to Design Integrated Service Experiences

This Smashing Magazine piece nicely explores the human dimensions of design research: How To Get To Know Your Users

Let Invision expand your understanding of design research’s value, here: 4 types of research methods all designers should know .

Literature on Design Research

Here’s the entire UX literature on Design Research by the Interaction Design Foundation, collated in one place:

Learn more about Design Research

Take a deep dive into Design Research with our course Service Design: How to Design Integrated Service Experiences .

Services are everywhere! When you get a new passport, order a pizza or make a reservation on AirBnB, you're engaging with services. How those services are designed is crucial to whether they provide a pleasant experience or an exasperating one. The experience of a service is essential to its success or failure no matter if your goal is to gain and retain customers for your app or to design an efficient waiting system for a doctor’s office.

In a service design process, you use an in-depth understanding of the business and its customers to ensure that all the touchpoints of your service are perfect and, just as importantly, that your organization can deliver a great service experience every time . It’s not just about designing the customer interactions; you also need to design the entire ecosystem surrounding those interactions.

In this course, you’ll learn how to go through a robust service design process and which methods to use at each step along the way. You’ll also learn how to create a service design culture in your organization and set up a service design team . We’ll provide you with lots of case studies to learn from as well as interviews with top designers in the field. For each practical method, you’ll get downloadable templates that guide you on how to use the methods in your own work.

This course contains a series of practical exercises that build on one another to create a complete service design project . The exercises are optional, but you’ll get invaluable hands-on experience with the methods you encounter in this course if you complete them, because they will teach you to take your first steps as a service designer. What’s equally important is that you can use your work as a case study for your portfolio to showcase your abilities to future employers! A portfolio is essential if you want to step into or move ahead in a career in service design.

Your primary instructor in the course is Frank Spillers . Frank is CXO of award-winning design agency Experience Dynamics and a service design expert who has consulted with companies all over the world. Much of the written learning material also comes from John Zimmerman and Jodi Forlizzi , both Professors in Human-Computer Interaction at Carnegie Mellon University and highly influential in establishing design research as we know it today.

You’ll earn a verifiable and industry-trusted Course Certificate once you complete the course. You can highlight it on your resume, CV, LinkedIn profile or on your website.

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Adding quality to your design research with an ssqs checklist.

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Jillian Warren ’24 Has Designs for the Future The graphic design student has built up an impressive portfolio of professional work, including assisting on Chapman University marketing campaigns.

“Chapman has been a part of my life since I was a kid,” said Jillian Warren ’24, a graphic design major and psychology minor who has worked for the university’s office of Strategic Marketing and Communications (SMC) for the last two years. Her parents are both alumni, and her father works in the office of Information Services and Technology.

“Obviously my parents really wanted me to go here,” she said.

Fortunately, she loved the campus and appreciated the warm, small environment, so the decision to attend Chapman made everyone happy.

Warren’s professional association with SMC began while she was still in high school, when she needed to fulfill an internship requirement before graduating.

“Because she was still a high school student, I had to work hard to get her on campus so that she could shadow what I did as a designer,” said Rosalinda Monroy, Chapman’s creative director at the time. “I could tell she was talented and had a special gift.”

Warren said she wasn’t too familiar with the programs, but shadowing Monroy and seeing the kinds of projects done by SMC gave her a good taste.

“Learning how to do it was honestly better than anything I could have imagined.”

Warren returned to SMC as a student worker on the design team at the end of her second year at Chapman. She has created a variety of marketing pieces for the university including brochures, one-sheets, ads, animations, banners, iconography and magazine layouts.

“I’ve gotten to work on some pretty big projects that I didn’t expect to be doing when I started my job,” she said.

One of the most memorable was the creation of Chapman’s new brand campaign that launched in 2023.

“I was able to come up with my own concepts for the new visual look and pitch them to the creative team. It was really nerve wracking, but also a great professional experience for me. Getting to see the development of everyone’s ideas and seeing how all that came together was super interesting and impactful for me,” she said, appreciative of how the professional experience enhanced the education she received in her classes. “Now that I’m graduating, I feel so much more prepared to enter the professional field of design.”

“She is an exceptional employee,” said Julie Kennedy, SMC’s current art director. “Professional, dependable, creative and always looking for ways to grow and help the team.”

While her job in SMC gave her a strong professional foundation, Warren’s coursework helped grow her creative skills in diverse areas that included motion design, typography, editorial work, user experience and user interface.

“You get to dabble in a lot of different fields to decide what you want to pursue in design, because there are so many routes that you can potentially take.”

One of her favorite class projects was a campaign designed to call attention to gender inequity in the professional art world.

“I worked as a research assistant for one of the art professors and did research on local art galleries in Los Angeles … I found that there’s a pretty significant disparity in the number of women who are featured in art galleries,” she said.

For her project, she developed a series of posters, infographics and conceptual merchandise to spotlight the issue.

In her senior year, Warren participated in the National Student Advertising Competition , an annual contest held by the American Advertising Federation that has teams of students operate as ad agencies to develop a multi-channel advertising campaign for a corporate sponsor. As a member of the art and production team, Warren helped develop the look and feel of the campaign, including producing mockups of all the creative executions, for this year’s sponsor/client, Tide.

The Chapman team won first place in this year’s district competition against schools from throughout southern California and Nevada.

After graduation, Warren plans to work at a design studio or agency somewhere in Los Angeles or Orange County.

“I want to get as much experience as I can with a broad range of clients,” she said, expressing a particular interest in branding or editorial work. “But hopefully just finding somewhere that allows me to pursue my passion even more.”

Staci Dumoski

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