what is the research design of this study yamamoto

Yamamoto et al. (2012)

Aim To learn more about chimpanzees’ helping behaviour. To find out: • whether they can understand the needs of conspecifics. • whether they respond to those needs with targeted helping.

Original Study

Study Summary Sheet

Topical Past Paper

Background Humans offer targeted helping, and animals help after a direct request from the conspecific. The ability to offer targeted help is linked to the theory of mind. Some say only humans can offer targeted helping however, recent studies have shown primates to offer altruistic helping. Targeted helping is help given based on the cognitive understanding of the situation of others. There is some evidence that chimpanzees engage in targeted helping.

Research Method, Design and Variables Research Method : A lab experiment. Research Design : Repeated measures design was implemented. Independent Variable : the ability of the chimpanzee to give targeted help in 2 situations: • Can see the tool use task of the recipient chimpanzee. • Cannot see. Dependent Variable : targeted helping behaviour (stick or straw)

Sample Socially housed chimpanzees at the Primate Research Institute, Kyoto University. Previously taken part in perceptual/cognitive studies including helping behaviour similar to the present study’s settings. 5 chimpanzees: Ai, Ayumu, Pan, Pal, Cleo. They were familiar with the tool use task, tested and cared for in accordance with the Animal Care Committee. The opportunity sampling technique was used. • Ai (mom) with Ayumu (child) • Pan (mom) with Pal (child) • Chloe (mom) with Cleo (child)

Procedure The helper chimpanzee had to offer the recipient chimpanzee the correct tool. 1 task required a stick and the other straw. The recipient chimpanzee obtained a reward: a juice box. There were 7 objects in the helping chimpanzee’s tray: stick, straw, belt, chain, brush, hose, string. First, the chimpanzee did condition 1 (can see). Next, the chimpanzee did condition 2 (cannot see). Then, they repeated condition 1 (can see) to check for order effects. There were 48 trials carried out in each condition. 24-stick use and 24 straw-use trials were randomly ordered. There were 2 – 4 trials per day. A trial starts when the tray was presented to the helper chimpanzee. A trial ends when the recipient succeeded in obtaining the juice box or when 5 minutes had passed without receiving an object.

Data Recording ‘Offers’ were counted when chimpanzees held out objects regardless of if whether the recipients took them. Only the first offer was counted. Participant’s behaviours were recorded using 3 cameras. Behaviours: 1. Upon request offer: a tool is offered when the recipient requests. A request was when the recipient poked an arm through the hole. 2. Voluntary offer: help is actively offered without the recipient’s explicit request. 3. No offer: the tools are taken away without an offer.

Results Object offer is when chimpanzees offer any object which may be right or wrong. 'Tool' is the correct object given. 'Non-tools' are any other objects given. Can See Condition 1. Object offer = 90% of trials. In the familiarisation phase, object offer was 5%. 'Upon request offer' accounted for 90% of all offers. 2. Except for Pan, sticks and straws were significantly more frequently offered the non-tools (78% - 97.4%). Pan most frequently offered non-tool, brush, which may be due to past experience. 3. Chimpanzees demonstrated flexible targeted helping depending on their partner's tool-use situation. Cannot See Condition 1. Object offer = 90% of trials. Upon request offer accounted for 71.7% of all offers. 2. Cleo showed a significant increase in offering help in the 'cannot see condition' and this may be due to a carryover effect. This increased voluntary offer as the helper learned that they are expected to offer an object to their partner. 3. Stick/straw was not offered more than the non-tools. Except Ayumu did as he kept peeking through the hole. This shows that chimpanzees understood their partner’s goals only when they could see. 2nd Can See Condition : 3 chimpanzees who had shown a significant decrease in tool selection in the first condition and a non-significant decrease in the cannot see condition were used. 1. Object offer observed for 98% of trials. Upon request offer for 79% of all offers. 2. Significant decrease in the offer of stick/straw depending on the partner’s situation. This confirms that flexible targeted helping with an understanding of the tool need to complete the task was possible when chimpanzees could see the task for themselves.

Conclusion 1. Chimpanzees will help conspecifics in most cases, but usually as a response to a direct request rather than voluntarily. 2. Chimpanzees rely on visual confirmation of conspecific needs in order to offer targeted helping.

• High level of controls and standardised procedures increased reliability. Objects on the tray were the same for all trials, and chimpanzees sat at the same booth.
• Repeated measures design reduced the risk of individual differences affecting the study and increased validity.
• Lack of ecological validity as they were in artificial settings. Chimpanzees were also given tasks/ tools they would not normally use in the natural environment. However, as they were socially housed, they probably showed their natural behaviour so, we can say that the study is valid.
• We cannot conclude that the correct tool offered is an intentional cognitive decision as it could be an automatic assumption from previous experience. Pan had repeatedly offered the brush because her experience with previous similar tasks created a bias in her response.
• There were only 5 chimpanzees, and they were from the same Research Institute thus, we can see that the sample was very small and low in generalizability. Also, captive chimpanzees are not identically representative of the wild chimpanzees.
• Chimpanzee Ayumu had shown demand characteristics by peeking through the hole to understand the task.

Application It helps us understand more about chimpanzee societies, and we now know that chimpanzees have the capacity to help conspecifics.

Individual vs Situational Explanation to Behaviour Most chimpanzees showed similar patterns of behaviour. However, the influence of individual personalities was seen when Pan showed a preference for the brush and Ayumu peeked through the hole.

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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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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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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• 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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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

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Organizing Your Social Sciences Research Paper: Types of Research Designs

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

• Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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Research Design

• First Online: 13 April 2022

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• Yanmei Li 3 &
• Sumei Zhang 4  

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This chapter introduces methods to design the research. Research design is the blueprint of how to conduct research from conception to completion. It requires careful crafts to ensure success. The initial step of research design is to theorize key concepts of the research questions, operationalize the variables used to measure the key concepts, and carefully identify the levels of measurements for all the key variables. After theorization of the key concepts, a thorough literature search and synthetization is imperative to explore extant studies related to the research questions. The purpose of literature review is to retrieve ideas, replicate studies, or fill the gap for issues and theories that extant research has (or has not) investigated.

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Borrego, M., Douglas, E. P., & Amelink, C. T. (2009). Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education, 98 (1), 53–66.

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Creswell, J. W., Plano Clark, V. L., & Garrett, A. L. (2008). Methodological issues in conducting mixed methods research design. In M. M. Bergman (Ed.), Advances in mixed methods research: Theories and application (pp. 66–83). Sage.

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Li, Y., & Walter, R. (2013). Single-family housing market segmentation, post-foreclosure resale duration, and neighborhood attributes. Housing Policy Debate, 23 (4), 643–665. https://doi.org/10.1080/10511482.2013.835331

Opoku, A., Ahmed, V., & Akotia, J. (2016). Choosing an appropriate research methodology and method. In V. Ahmed, A. Opoku, & Z. Aziz (Eds.), Research methodology in the built environment: A selection of case studies . Routledge.

Pickering, C., Johnson, M., & Byrne, J. (2021). Using systematic quantitative literature reviews for urban analysis. In S. Baum (Ed.). Methods in Urban Analysis (Cities Research Series) (pp. 29–49) . Singapore: Springer.

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Li, Y., Zhang, S. (2022). Research Design. In: Applied Research Methods in Urban and Regional Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-93574-0_3

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Lessons in Surprise

July-August 2010

Admiral Isoroku Yamamoto , who led the Japanese attack on Pearl Harbor in December 1941, was a special student at Harvard from 1919 to 1921. Seymour Morris Jr. ’68, M.B.A. ’72, of New York City, advances a theory that lessons Yamamoto learned at the University emboldened him to launch the attack, and that if the United States military had known their enemy as well as he knew them, they might not have been caught flatfooted, betting that he would first attack the Philippines.

In American History Revised: 200 Startling Facts That Never Made It Into the Textbooks (Broadway Books), Morris argues that if Washington had done some serious background checking into Yamamoto’s student days, they would have uncovered useful clues to his psychological makeup. “Classmates would have remembered Yamamoto well: a hard worker but not a grind, exceptionally curious and imaginative,” Morris writes. “When they introduced him to the game of poker, he became a fanatical poker player who would stay up all night, winning hand after hand. And what did he do with his poker winnings--lead the good life? No, not at all: he hitchhiked around the country during the summer, exploring America.” Years later, as a naval attaché at the Japanese embassy in Washington, D.C., and still a compulsive poker player, Yamamoto gambled with members of the United States military. “Spurred on by his victories,” Morris writes, “he developed contempt for the mental agility of his American naval opponents at the poker table.”

Yamamoto strongly opposed Japan’s entry into the war; he feared American might. But when ordered, he would do his best. As commander of the Combined Fleet, he calculated that to beat the United States, it was necessary to strike first. “Yamamoto wasn’t a great poker player for nothing,” writes Morris. He resolved, as in poker, to “blow the best player out of the game, good and early....The shame of the Joint Chiefs was their lack of imagination in trying to figure out their opponent. They thought of him as a traditional Japanese who would do everything ‘by the book’ (just as they did). They failed to consider that maybe, just maybe, Isoroku Yamamoto was more American than they were.”

Green surprises. Gardens can surprise: often agreeably, as when a puckered, rock-hard seed generates a nasturtium; sometimes otherwise, as when hornworms appear among the tomatoes. To city-dwellers used to getting their vegetables at the supermarket, time spent in a garden can teach many lessons, among them that that unknown bunch of foliage over there has a radish at its root.

The new Harvard Community Garden (below) was built this spring in raised beds of different heights in a 560-square-foot growing space between the front door of Lowell House and Mount Auburn Street. Its mission is “to provide experiential education in sustainable, urban agriculture, and to provide food for students, faculty, and the local community.” It was planted--with arugula, mizuna, Swiss chard, Toscano kale, onions, snow peas, peppers, eggplant, and much more--and will be maintained by undergraduates, with advice from various quarters, including the Center for Health and the Global Environment at the Medical School.

Coming along are Sun Gold tomatoes, “my favorite cherry tomato,” says Louisa C. Denison ’11, of Dudley House and Cambridge, one of the prime movers of the project. “We are excited,” she adds, “to be the first generation of Harvard students to grow food on campus.

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What is the research design of the Yamamoto (Chimpanzee) study?

Independent groups

Repeated measures

Laboratory experiment

Field experiment

In the Yamamoto study, trial begins when the helper chimp had a box of 7 items, trial recorded as ended when

The helper chimp chose an item

The juice was obtained or when 5 minutes passed without success

When the helper chimp handed over the first tool; or 10 minutes had passed

When the helper chimp handed over any object

In the Yamamoto (chimpanzee) study, what was the AIM of the study?

Will conspecifics help when asked?

Understand helping behavior

Whether chimpanzees understand the needs of conspecifics and whether they will respond with targeted helping

Can chimpanzees use targeted helping when asked for help?

What is Theory of Mind?

Reading the mind of another person

Understanding the goals of another person without asking

Helping without being asked to do so

A cognitive concept found in chimpanzees in this study

Choose the overall weakness of the study. - Yamamoto

Low ecological validity

All answers are correct

Too much standardization in the procedure

No answers are correct

Which is the dependent variable? - Yamamoto

How many chimpanzees offered the correct tool.

How long it took each chimp to offer the correct tool.

How many trials ended with the helper looking through window

All answers are correct.

Which of the following dimensions are correct for the Hole in the booth. - Yamamoto

12.5 x 35 cm and 1 metre above the floor

20.5 x 45 and 2 metres above the floor

13.5 x 25 cm and 3 metres above the floor

30.5 x 15 and 1/2 metre above the floor

What were the names of the chimpanzees?

Marsha, Jan, Peter, Cindy, Greg, Bobby

Ai, Pal, Chloe, Cleo, Pan, Ayumu.

Buffy, Spike, Willow, Xander, Angel, Cordelia

Betty, Veronica, Archie, Jughead, Cheryl, Josie

What was the object that Pan offered the most? - Yamamoto

What is the biggest difference between the results of the two conditions? - Yamamoto

How often a tool was offered.

If the correct tool was offered.

Request was needed all the time.

How long it took to help.

Explore all questions with a free account

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Riken Yamamoto: Ideology and Philosophy

Riken Yamamoto, born in Beijing , China is a Japanese architect . He obtained his bachelor’s degree in architecture from Nihon University in 1968 and his master’s degree from the University of Arts, Tokyo in 1971. He founded the Yamamoto & Field Shop Co., Ltd in 1973. He pursued a teaching career as a Professor at the Kogakuin University Department of Architecture from 2002 to 2007. He attended as a Visiting Professor at the Graduate School of Architecture of the Yokohama National University and at the Nihon University, respectively. He has also been serving as the President of the Nagoya Zokei University of Art & Design since 2018.

Works | Riken Yamamoto

Riken Yamamoto’s earliest projects greatly influenced his subsequent works. Yamakawa Villa (1977) was his first project, designed to have a large terrace space and small windows. A gable roof was used as that was the only form Yamamoto could think of.

Studio Steps (1978) used to be an atelier, a private workshop of an artist and a sculptor. The space was reconstructed to allow for the structure to have a private, invisible living space below ground and a public concert space above ground. Riken Yamamoto quotes in his book, “A house always has a place that is open to the outside world.” 

Both of the above buildings follow Yamamoto’s principle about how a building should allow its occupants to engage with the external world.

Riken Yamamoto’s own house, Gazebo (1986), allows him to experience everyday local community life. Rotunda (1987) and Hamlet (1988) explore different roof forms. The Hamlet is an “archetypal house for how people who choose to live together might dwell”. It creates shared spaces for a multi-generational family while also granting them privacy.

Hotakubo Housing (1991) explores the concept of creating a community between 100 different families through architectural spaces. It does so by creating a central courtyard that can only be accessed through the building units, creating a private gathering space for the community.

The Saitama Prefectural University (1999) treats that entire building space as a society. The university specialised in nursing and welfare and aimed to create mutual spaces of cooperation. This was made possible by creating a single volume and a framework of open spaces.

The Future University, Hakodate (2000) has two departments where the students and researchers work together. There is a glass partition between the collaborative working space and the laboratories that allows transparency. 

The Future University Research Building (2005) is an extension designed by the same architect. The main characteristic of the research building is a ‘lattice wall,’ which is a truss frame of flat bars set with cast glass or steel panels. The lattice wall is both the structure that supports the whole building and a partition wall system whose proportion of open area can be freely arranged. 

The Yokohama Public Housing (2000) is public housing for senior citizens. The project required low-cost, low-rise, and high-density building units. The project does so with its clustered organisation. There is ample open space and terraces and services are easily available.

The Hiroshima Fire Station (2000) was created with a transparent louvred glass facade to expose the inner workings of the department. All interior spaces have been designed around a central atrium and they have been divided internally with glass partitions as well.

The Tokyo Weld Technical Centre (2001) is a research and development centre. The first floor has been allocated for loading and laboratory work while the second floor acts as the research area. The third floor juts out from the first two to allow trucks to park underneath it.

The Yokosuka Museum of Art (2007) is an outstanding example of how Riken Yamamoto has achieved a balance between the interior and exterior. The museum faces the sea to the north and is surrounded by mountains on other sides. Most of the museum is underground to let the structure sit in harmony with the landscape . The double skin of the roof and wall, consisting of glass plate outside and iron board inside, covers the area for exhibition and collection and is a system to control sunlight.

The Fussa City Hall (2008) consists of twin towers that conform to the natural topography of the area. The pillars and beams of the outer skin structure become thinner on the upper parts of the tower, helping the building to look light and soft towards the sky. The factory-made, precast concrete is used for the slab and outer skin structure. Pre-cast concrete is a high-performance material, it is earthquake resistant and suits speedy constructions. The greenery on the roof also makes the structure energy efficient.

The Namics Techno Core (2008) seems to be floating due to its lack of outer pillars as the structure has been supported on inverted cone-like columns. There is a green roof over some of the rooms which help decrease the air conditioning load. 

Pangyo Housing (2010) is a creative and environmentally-friendly low-rise multi-generational housing project. The building units have again been formed into clusters with each cluster having a communal deck to inspire a sense of community.

The Tianjin Library (2012) is a grand library housing 5 million books. It is a five-storied building that has mezzanine floors on each level, giving it 10 sub-floors. The entrance hall extends the entire north-south length. The entire structure has been supported on wall beams in a grid organisation, with bookshelves being incorporated into the walls themselves. Every reading space has been made as diverse as possible to allow the users to experience a wide variety as they move through the library.

The Seoul Gangnam Housing (2014) is a housing initiative for low-income people. It is a housing prototype that allows for individual privacy as much as community interaction. It takes into consideration the declining birth rate of Japan and predicts the housing demands of the future.

The Circle in Zurich (2020) is an airport complex building – a business centre, hotel, shopping mall, and entertainment centre, all in one. It was a competition win that is the most recent and acclaimed project by Riken Yamamoto. The structure fulfils the design brief and incorporates “Swissness, Surprise, and Connections to the World” in its design.

Riken Yamamoto also bagged the win for the design of the Taoyuan Museum of Art (2023). The museum consists of two buildings connected by a corridor. These buildings have an inclined green roof to create continuity with the environment. The museum has permanent collections in the “cubes”, the “hill” acts as a connection space, providing a place for activities and the pavilions on the hill allow for an outdoor display of artwork.

Ideology and Philosophy

Riken Yamamoto believes in the concept of transparency, in need for the space to reflect its functionality. The principle of integrating buildings into the landscape of the surroundings and attaining harmony with the environment is also emphasised upon. He claims that the distribution of spaces determines the character of the building, which in turn reveal the relation of the building with the exterior. Yamamoto’s notion is that buildings should enhance their social contexts and work to facilitate community.

References: Riken Yamamoto

• Yamamoto, R. (2012). Riken Yamamoto. Japan: Toto.
• MCH. Riken Yamamoto. [online]. Available at: https://www.mchmaster.com/faculty/riken-yamamoto/
• Riken Yamamoto. Riken Yamamoto Official Web. [online]. Available at: http://riken-yamamoto.co.jp/index.html?lng=_Eng
• Wikipedia. Riken Yamamoto. [online]. Available at: https://en.wikipedia.org/wiki/Riken_Yamamoto
• Misfits Architecture. Architecture Misfit #40: Riken Yamamoto. [online]. Available at: https://misfitsarchitecture.com/2021/02/07/architecture-misfit-40-riken-yamamoto/
• Design Boom (2018). riken yamamoto & field shop: sloped rooftop park for taoyuan museum of art in taiwan. [online]. Available at: https://www.designboom.com/tag/riken-yamamoto-and-field-shop/
• T-ADS. (2021). Riken Yamamoto 1: T-ADS/Four Facets of Contemporary Japanese Architecture/City. [Youtube video]. Available at: https://www.youtube.com/watch?v=bcvmEQlrnhk
• wocomoCULTURE. (2020). Tokyo: Architecture and Landscape|Visions for Megacities. [Youtube video]. Available at: https://www.youtube.com/watch?v=KRexyLK9RYs

Kanak Holani is a first year architecture student who can usually be found reading novels and trying her hand at various crafts. She is passionate about history, culture and climate and how it all ties in with architecture.

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Yoko Yamamoto

Website information, visiting associate professor of education.

Yoko Yamamoto has examined education, families, and children in diverse sociocultural contexts. Integrating theories and research in psychology, sociology, and education, she has focused on understanding cultural beliefs related to learning and home-school partnerships in relation to ethnicity, immigrant context, and socioeconomic resources. She has conducted longitudinal research examining strengths and challenges in diverse families' socialization and their children's development in Japan and the U.S. 

Dr. Yamamoto was an Abe Fellow (2012) and a recipient of the Erin Phelps Award for her journal article (2017). She has also been an invited summer scholar at Osaka University, Japan (2013-present). She has co-founded and co-chaired the Diversity Science Initiative Committee for the Society for the Study of Human Development (SSHD). 

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Yoko Yamamoto has examined families and schooling in diverse sociocultural contexts. She explores cultural beliefs and practices as well as parental support for young children's learning in relation to socioeconomic status, culture, and immigrant context. She also strives to understand empowering and culturally responsive home-school relations that foster and enhance students' socioemotional and academic development. She is currently involved in several research projects that investigate parenting, socialization processes, and family-school partnerships in Japan and the United States.

Funded Research

Principal Investigator. "Social stratification and early educational processes: Student beliefs about learning in Japan and the U.S." Social Science Research Council, The American Council of Learned Societies, The Japan Foundation.

Principal Investigator. "Meanings, values, and development of 'ganbari' in socialization and child development in Japan." Mayekawa Foundation. 

Education and Training

Honors and awards.

• 2017-2019   Research Grant, Mayekawa Foundation 
• 2018  Published Interview. Stephen J. Dubner & Stephanie Tam, “ How to Train Your Dragon Child ,” Freakonomics, New York Public Radio . 
• 2017    SSHD Erin Phelps Award   (Best publication in Research in Human Development)   
• 2016    APA-USNC/Psychology International Conference Selected Mentor (National Science Foundation)
• 2013-2015     Abe Fellowship, Social Science Research Council and Japan Foundation
• 2009    NIH/NICHD Summer Institute Fellow (Applied Research in Child and Adolescent Development)

Affiliations Visualize it 

Collaborators, affiliations.

• Society for the Study of Human Development (SSHD Steering Committee)
• Society for Research in Child Development (SRCD)
• American Educational Research Association (AERA)
• International Network of Scholars on School, Family and Community Partnerships (INET)
• International Society for the Study of Behavioural Development (ISSBD)
• Invited Summer Scholar, Cultural Studies of Education, Graduate School of Human Sciences, Osaka University, Japan (Summer, 2013-current)

Credentials/Licenses

This paper is in the following e-collection/theme issue:

Published on 30.5.2024 in Vol 26 (2024)

Influence of Physical Attractiveness and Gender on Patient Preferences in Digital Doctor Consultations: Experimental Study

Authors of this article:

Original Paper

• Xia Wei 1 , BA, MBA, PhD   ; 
• Shubin Yu 2 , BA, MA, MSc, PhD   ; 
• Changxu (Victor) Li 3 , BA, MA, MSc  

1 College of Management, Shenzhen University, Shenzhen, China

2 Department of Communication and Culture, BI Norwegian Business School, Oslo, Norway

3 Department of Marketing, KU Leuven, Leuven, Belgium

Corresponding Author:

Shubin Yu, BA, MA, MSc, PhD

Department of Communication and Culture

BI Norwegian Business School

Nydalsveien 37

Phone: 47 41228055

Email: [email protected]

Background: The rise of digital health services, particularly digital doctor consultations, has created a new paradigm in health care choice. While patients traditionally rely on digital reviews or referrals to select health care providers, the digital context often lacks such information, leading to reliance on visual cues such as profile pictures. Previous research has explored the impact of physical attractiveness in general service settings but is scant in the context of digital health care.

Objective: This study aims to fill the research gap by investigating how a health care provider’s physical attractiveness influences patient preferences in a digital consultation setting. We also examine the moderating effects of disease severity and the availability of information on health care providers’ qualifications. The study uses signal theory and the sexual attribution bias framework to understand these dynamics.

Methods: Three experimental studies were conducted to examine the influence of health care providers’ physical attractiveness and gender on patient preferences in digital consultations. Study 1 (n=282) used a 2×2 between-subjects factorial design, manipulating doctor attractiveness and gender. Study 2 (n=158) focused on women doctors and manipulated disease severity and participant gender. Study 3 (n=150) replicated study 2 but added information about the providers’ abilities.

Results: This research found that patients tend to choose attractive doctors of the opposite gender but are less likely to choose attractive doctors of the same gender. In addition, our studies revealed that such an effect is more prominent when the disease severity is high. Furthermore, the influence of gender stereotypes is mitigated in both the high and low disease severity conditions when service providers’ qualification information is present.

Conclusions: This research contributes to the literature on medical information systems research and sheds light on what information should be displayed on digital doctor consultation platforms. To counteract stereotype-based attractiveness biases, health care platforms should consider providing comprehensive qualification information alongside profile pictures.

Introduction

Digital health services have become increasingly important in health care, particularly since the COVID-19 pandemic necessitated the development of new digital consultation systems [ 1 - 4 ]. Previous research indicates that patients often use digital reviews to select health care providers [ 5 ]. However, there is limited understanding of how decisions are made when such reviews are not available. In traditional health care settings (ie, offline health care), patients typically choose or are assigned a general practitioner based on their geographical location. In contrast, digital health care settings remove these geographical restrictions, presenting patients with a vast array of choices and different information. This abundance of choices and inconsistent information can make it challenging for patients to compare and select health care providers [ 6 ]. For instance, the availability of digital information can lead patients to select doctors based on inherent biases, which can arise from their personal preferences or the way the information is presented on the platform. Given these limitations, patients may resort to evaluating health care providers based on available photographs. This situation raises questions about the influence of a health care provider’s appearance on patient choices and whether gender differences affect these decisions. Previous research indicates that attractive service providers can positively influence consumer attitudes and purchase intentions [ 7 ]. High physical attractiveness is often associated with greater credibility and professional ability, affecting consumer attitudes accordingly [ 8 - 10 ]. However, high attractiveness can also have negative effects, such as difficulty in forming same-gender friendships due to competition or eliciting envy and perceived threats among members of the same gender [ 11 - 14 ].

Much of the existing research focuses on general services such as fitness clubs and educational consulting [ 15 ]. These findings may not be directly transferable to specialized digital services, such as digital doctor consultations. Such services are categorized as credence services, which are difficult for patients to evaluate due to their complex and specialized nature [ 16 , 17 ]. The challenge of evaluation is further exacerbated in a digital setting where information about the provider’s abilities may be limited.

There is a research gap in understanding how physical appearance affects patient preferences in digital health care settings. This study aims to fill this gap by examining preferences for doctors of varying levels of physical attractiveness in digital consultations. We also consider the moderating effects of disease severity and the availability of information on health care providers’ qualifications to offer both theoretical and practical insights.

Research on doctor selection uses signal theory to explore how patients navigate information asymmetry [ 18 , 19 ]. Text-mining research identified key service features, such as overall service experience and personality traits, that affects patients’ trust and, consequently, consultation volumes [ 20 ]. Digital doctor consultation platforms typically display limited information about doctors, such as their names, profile pictures, and titles. The appearance of the doctor may influence patients’ preferences. Prior studies have shown that the attire of physicians influenced patients’ perceptions [ 21 - 23 ]. For example, physicians in white coats were viewed as more experienced and professional than those in casual jackets [ 24 ]. Despite this, there is still scant research on how such visual and textual information affect patients’ decision-making.

In non–health care service settings, the impact of physical attractiveness on performance evaluation has shown mixed results. While some studies indicate that higher levels of attractiveness positively influence performance evaluations [ 25 , 26 ], others suggest a negative effect [ 27 ]. These mixed outcomes may be attributed to various contextual factors such as gender [ 28 ] and service quality conditions [ 29 ]. In digital settings, service providers’ physical appearance has been shown to influence customer choice, preference, and purchase intent [ 30 ]. For example, in the sharing economy, such as Airbnb, facial characteristics contribute to reputation mechanisms [ 31 ]. Studies have found that the perceived trustworthiness and attractiveness of a host’s profile photograph significantly affect Airbnb prices [ 32 , 33 ]. Physical attractiveness also influences digital consumer shopping, with more attractive avatars correlating with higher sales when product involvement is moderate [ 34 ].

Previous research indicates that gender differences significantly influence how patients perceive doctors [ 24 , 35 , 36 ]. The sexual attribution bias (SAB) offers an explanatory framework for these gender effects [ 37 ]. SAB leads individuals to attribute the success of same-gender individuals with high attractiveness to luck rather than ability, whereas for opposite-gender individuals, high attractiveness is attributed to ability [ 38 ]. This bias manifests in 2 ways: demeaning attractive individuals of the same gender and praising attractive individuals of the opposite gender. Studies have shown that negative vigilance against attractive same-gender individuals is strong and automatic due to intragender competition [ 39 - 42 ].

Unlike general services where attractiveness universally enhances provider popularity [ 43 , 44 ], we posit that in digital doctor platforms, gender plays a significant role in shaping preferences for providers with varying levels of attractiveness. Influenced by same-gender competition, consumers may perceive same-gender providers with high attractiveness as less qualified. Conversely, influenced by mating motivation, consumers may prefer highly attractive providers of the opposite gender. Given the importance of competence in selecting credence service providers [ 45 ], SAB suggests that individuals may make derogatory attributions about the competence of same-gender providers with high physical attractiveness.

• Hypothesis 1 : In digital doctor consultations, people are more likely to perceive a more attractive doctor of the opposite (vs same) gender as more (less) competent, thereby influencing their likelihood of selecting that doctor.

We anticipate that disease severity will modulate the effects of gender and attractiveness on provider selection. Previous research has established a relationship between disease severity and behavior in various health care contexts [ 46 - 49 ]. The Elaboration Likelihood Model posits 2 routes of information processing: central and peripheral, determined by the individual’s level of involvement [ 50 ]. In low-involvement situations, attitudes are influenced by simple cues, whereas high involvement leads to a deeper consideration of complex information. Applying this model and stereotype theory, we suggest that in high-involvement scenarios, consumers will scrutinize providers’ abilities more closely, potentially leading to greater influence of SAB on their choices. For example, women may question the competence of attractive women providers, suspecting that their success is due to their appearance rather than merit [ 51 ]. This aligns with research showing that physical attractiveness elicits more jealousy among women than men [ 14 ]. Conversely, in low-involvement scenarios, the impact of gender on preferences for providers’ physical attractiveness is expected to be less pronounced.

• Hypothesis 2 : The effect of gender on individual preferences for the doctor’s physical attractiveness is moderated by disease severity. Such an effect is more prominent when disease severity is high (vs low).

Furthermore, a foundational assumption for the effects discussed earlier is that consumers lack additional information about the service providers’ abilities. This is often the case in credence services, where consumers typically have less expertise and access to information compared with noncredence services, making them more reliant on extra information supplied by the provider [ 52 ]. Research has shown that the provision of such information can mitigate consumer-perceived risk or uncertainty [ 53 ].

• Hypothesis 3 : The effect of gender on individual preferences for the service provider’s physical attractiveness disappears when information about the doctor’s abilities is present.

Ethical Considerations

This study has received ethical approval (20180420) from the College of Management, Shenzhen University. Informed consent was obtained from all participants. The consent form provided detailed information about the research’s purpose, the involved institutions, the nature of their participation, and the use of their data. Participants were informed of their right to withdraw at any time and the procedures for data removal. The consent process was designed to comply with data protection legislation, ensuring that participants were aware of their rights and the protections in place. Privacy and confidentiality were protected by anonymizing study data. The data were stored securely and processed. For study 1, we paid CN ¥3 (a currency exchange rate of CN ¥7.21=US $1 is applicable.) for each response we collected. For study 2 and study 3, participants received a gift of approximately CN ¥5 for their participation.

Study 1 used a 2 (attractiveness: high vs moderate)×2 (doctor gender: man vs woman) between-subjects factorial experiment. We created a digital doctor consultation scenario where participants were asked to imagine that they had abdominal pain while traveling. As most clinics are closed at night, they decided to consult a digital doctor via a platform called “ Doctor Online .”

An interface was designed for the platform. In the interface, the doctor’s picture was shown on the left, and their name, title, and department were displayed on the right. There was also a button for digital consultation. To manipulate the physical attractiveness of the doctor, we used an artificial intelligence face generator to generate different faces for male and female doctors ( Figure 1 ). We slightly adapted the physical features of the faces to make them less or more attractive.

We conducted a pretest to check whether the manipulation was successful. We recruited 111 participants (mean age 30.0, SD 7.86 years; women: n=70, 63.1%) to test the male doctor and 105 participants (mean age 30.5, SD 9.61 years; women: n=74, 70.5%) to test the female doctor via a digital panel service WJX. The results showed that for the male doctor, participants perceived the face in the high attractiveness condition to be more attractive than that in the moderate attractiveness condition (mean high 5.53, SD 0.98 vs mean moderate 4.32, SD 1.09; F 1,109 =38.3; P <.001; ƞ 2 =0.26). For the female doctor, the face in the high attractiveness condition was perceived as more attractive than that in the moderate attractiveness condition (mean high 5.98, SD 0.85 vs mean moderate 4.94, SD 0.95; F 1,103 =34.7; P <.001; ƞ 2 =0.25).

We recruited 282 respondents via a digital panel service WJX in China. The participants were not necessarily actual patients in this experiment. The mean age of the respondents was 27.8 (SD 9.76) years. In all, 156 (55.3%) of the respondents were women. Based on the respondents’ gender, we created a new variable “gender match.” If the gender of the participant is the same as that of the doctor, we assigned the value 1 (same gender). Otherwise, we coded the variable as 0 (opposite gender). In the end, there were 147 participants in the same gender condition, and 135 participants in the opposite gender condition. The majority of respondents had a bachelor’s degree (n=104, 36.9%), followed closely by those with a graduate degree or higher (n=89, 31.6%). Less than a quarter (n=61, 21.6%) of the respondents had an education level of high school or below. The majority of respondents were students (n=132, 46.8%), followed by those working in industrial or manufacturing enterprises (n=42, 14.9%). Less than 5% (n=17) were either retired or unemployed. The data revealed that a slight majority of respondents (n=153, 54.3%) had experience with digital consultations, while 45.7% (n=129) had not engaged in such services. Tables 1 and 2 show detailed information on educational level and occupational types.

Manipulation Checks

For the manipulation check, we asked the participants to rate the physical attractiveness of the doctor using a single item: What do you think of the doctor’s physical appearance? (1—not attractive at all; 7—very attractive). A single item was used to measure the doctor’s perceived competence: Do you think that this doctor is competent in this job ? (1—no; 7—yes). To assess the participants’ intention to select the doctor, we asked the participants how likely he or she would be to select this doctor for a digital consultation (1—very unlikely; 7—very likely). To rule out alternative explanations, we also measured a number of the doctor’s attributes: perceived seniority ( What do you think about the doctor’s work experience? 1—very limited; 7—very rich), anticipated embarrassment ( How likely are you to feel embarrassed about consulting this doctor? 1—not embarrassed at all; 7—very embarrassed), perceived friendliness ( How friendly is this doctor? 1—not friendly at all; 7—very friendly), and perceived willingness to help ( Do you think this doctor is willing to meet patients’ needs? 1—not at all; 7—very much). Finally, respondents were asked to provide some relevant demographic information about their age, gender, educational background, and occupation.

In study 2, to reduce the complexity of the experimental design, we focused on women doctors. A between-subjects experiment was designed with 2 independent variables, each with 2 levels. The manipulated factors were disease severity (2 levels: high vs low) and gender of the participant (2 levels: man vs woman).

In the experiment, respondents were randomly assigned to 1 of 2 groups of scenarios with different degrees of involvement. They were first asked to read material about a scenario of abdominal pain during travel. The low-involvement group was described as having “slight abdominal pain, the same old symptom, not too worried, decided to consult a doctor.” The high-involvement group heard it described as “severe abdominal pain, lumps when pressing with hands, never encountered such symptoms before, and felt both worried and afraid, decided to consult a doctor immediately.”

To manipulate physical attractiveness in the dependent variable, we referred to the studies by Heilman and Stopeck [ 54 ] and Försterling et al [ 37 ], as well as previous studies on the use of photographs as stimuli of attractiveness in job-seeking scenarios [ 55 ]. In total, 30 respondents were invited to participate in rating different women service providers’ physical attractiveness. Respondents were asked to use a 5-point Likert scale to rate the attractiveness of providers in ten 2.54-cm photographs. All photographs were selected from several medical websites and their photograph styles were unified. Finally, 3 photographs, representing the mean value, higher than 1 SD and lower than 1 SD, were selected as photographs to represent the 3 different levels of physical attractiveness, as shown in Figure 2 .

We also conducted a second pretest with 28 respondents to check whether confounding factors such as perceived friendliness, perceived patience, perceived seniority, perceived willingness to help, and anticipated embarrassment were controlled among 3 service providers. The results suggested that all 3 service providers were perceived as equally friendly (mean high PA 2.94, SD 1.692; mean middle PA 2.94, SD 2.016; mean low PA 3.75, SD 2.053; F 2,25 =0.586; P =.56; ƞ 2 =0.031), patient (mean high PA 3.19, SD 1.682; mean middle PA 2.88, SD 1.857; mean low PA 3.88, SD 2.475; F 2,25 =0.720; P =.50; ƞ 2 =0.037), experienced (mean high PA 2.44, SD 1.711; mean middle PA 2.81, SD 1.905; mean low PA 3.50, SD 2.390; F 2,25 =0.806; P =.46; ƞ 2 =0.042), willing to help (mean high PA 3.44, SD 1.931; mean middle PA 2.56, SD 1.750; mean low PA 3.50, SD 2.268; F 2,25 =1.037; P =.37; ƞ 2 =0.053), and embarrassing to consult with (mean high PA 3.38, SD 1.996; mean middle PA 3.44, SD 2.007; mean low PA 3.25, SD 1.488; F 2,25 =0.025; P =.98; ƞ 2 =0.001).

To observe how gender and degree of involvement influence individual preferences, this experiment recruited 158 citizens in the city center of Shenzhen, China. The participants were not necessarily actual patients during the experiment. Of the 158 respondents, 80 were men and 78 were women. We recruited only those participants who were older than 18 years. The age distribution was weighted toward adults, with 55.1% (n=87) aged 18-25 years, 22.8% (n=36) aged 26-30 years, and 16.5% (n=26) aged 31-40 years for both men and women. The respondents were assigned to either high-involvement (n=75, 48%) or low-involvement (n=83, 53%) groups. The data showed that the majority of respondents had a bachelor’s degree (n=91, 57.6%), and less than 5% (n=7) had an educational level of junior high school or below. The data also indicated that the largest group of respondents were students (n=58, 36.7%), followed by those working in the commercial or service industry (n=31, 19.6%). Less than 1% (n=1) were retired. Tables 3 and 4 show detailed information on educational level and occupational types.

Respondents were asked to examine the names and photographs of 3 doctors and asked to select 1 from whom they would like to receive treatment. Then, they completed a questionnaire. A 7-point Likert scale was used in this experiment to measure the perceived severity of the disease. We used a 7-point Likert scale, adapted from previous studies on positive emotions caused by physical attractiveness [ 56 , 57 ] and research about the evaluation of attraction and ability [ 58 ], to evaluate the physical attractiveness of the physicians. Finally, respondents were asked to provide some relevant demographic information about their age, gender, educational background, and occupation.

All basic conditions and requirements were the same as in study 2. Study 3 examined whether the participants would make a different choice after being provided with extra information. We also sought to further verify that the presence of information about providers’ abilities would reduce the influence of gender on respondents’ preferences.

The only difference from the experimental procedure used in study 2 was the adjustment of stimuli. Study 3 added similar ability information about the provider’s professional background and clinical experience below the photograph provided in study 2. The description read: “...graduated from...medical college, ...has participated in many research projects, ...has been working for 5 years, ...is an expert in diagnosis and treatment of common and frequently-occurring diseases...” To minimize confounding factors, the written description of the academic background, clinical experience, scientific research achievements, and areas of expertise of the service providers in the stimuli were very similar.

Study 3 recruited 150 citizens in the city center of Shenzhen, with equal numbers of respondents by gender (n=75, 50% men and n=75, 50% women). The participants were not necessarily actual patients during the experiment. The age distribution was similar to that of study 2, with a high concentration of young adults: 54% (n=81) aged 18-25 years, 26.7% (n=40) aged 26-30 years, and only 12% (n=18) aged 31-40 years. The high- and low-involvement groups included 74 (49.3%) and 76 (50.7%) respondents, respectively. The data indicated that among the 150 respondents, the majority had a bachelor’s degree (n=82, 54.7%). This was followed by those who had completed high school, vocational school, or technical school (n=26, 17.3%), and then by those with an associate degree (n=25, 16.7%). The data also indicated that among the 150 respondents, the largest group was students, comprising 44% (n=66) of the sample. This was followed by those working in the commercial or service industry, who made up 26.7% (n=40) of the respondents. Those in industrial or manufacturing roles accounted for 7.3% (n=11), and self-employed individuals made up 4.7% (n=7). Tables 5 and 6 show detailed information on educational level and occupational types.

The individual item scores within a single scale were computed to yield an average score, serving as a composite measure for that particular scale. The results revealed that participants perceived doctors in the high attractiveness condition to be more attractive than those in the moderate attractiveness condition (mean high 5.62, SD 1.03 vs mean moderate 4.72, SD 1.36; F 1,280 =39.1; P <.001; ƞ 2 =0.12). This suggested that our manipulation was successful.

We conducted an ANOVA to evaluate our hypotheses. The results showed that the physical attractiveness did not have a significant main effect on perceived competence (mean high 5.28, SD 0.98 vs mean moderate 5.10, SD 1.12; F 1,278 =2.32; P =.13; ƞ 2 =0.008). In consistence with our expectation, we observed a significant interaction effect of physical attractiveness and gender match on perceived competence ( F 1,278 =5.95; P =.015; ƞ 2 =0.021). In particular, when the doctor and the participant were of different genders, attractive doctors were perceived to be more competent in the job than less attractive doctors (mean high 5.45, SD 0.93 vs mean moderate 4.95, SD 1.10; F 1,133 =8.08; P =.005; ƞ 2 =0.057). However, when the doctor and the participant were of the same gender, attractiveness did not produce any positive effect on perceived competence (mean high 5.09, SD 1.01 vs mean moderate 5.21, SD 1.13; F 1,145 =0.41; P =.52; ƞ 2 =0.003; see Tables 7 and 8 for detailed information).

Physical attractiveness had a positive effect on intention to select the doctor (mean high 5.49, SD 1.13 vs mean moderate 5.10, SD 1.32; F 1,278 =5.10; P =.025; ƞ 2 =0.018). There was also a significant interaction effect of physical attractiveness and gender match on intention to select the doctor ( F 1,278 =3.96; P =.048; ƞ 2 =0.014). More specifically, participants were more likely to select attractive doctors than less attractive doctors of the opposite gender (mean high 5.49, SD 1.17 vs mean moderate 4.86, SD 1.36; F 1,133 =8.15; P =.005; ƞ 2 =0.058). In contrast, when the participants and the doctor were of the same gender, there was no difference in intention to select between more attractive and less attractive doctors (mean high 5.31, SD 1.07 vs mean moderate 5.27, SD 1.27; F 1,145 =0.40; P =.84; ƞ 2 <0.001).

Next, we performed a moderated mediation analysis using Hayes PROCESS (model 7) with 10,000 bootstrap samples. To rule out alternative explanations, we also examined the mediating effect of perceived seniority, perceived embarrassment, perceived friendliness, and perceived willingness to help. The results showed that the moderation effect of gender match on physical attractiveness on intention to select was mediated only by perceived competence (point estimation=0.34, 95% CI 0.0622-0.6593) but not by perceived seniority (point estimation=0.09, 95% CI –0.0601 to 0.2763), anticipated embarrassment (point estimation=0.01, 95% CI –0.0196 to 0.0663), perceived friendliness (point estimation=–0.01, 95% CI –0.0912 to 0.0477), and perceived willingness to help (point estimation=–0.001, 95% CI –0.1395 to 0.1423). In particular, when participants and doctors were of different genders, physical attractiveness led to higher perceived competence of the doctor, thereby increasing the intention to select the doctor (point estimation=0.27, 95% CI 0.0776-0.4994). However, when there was no difference in gender, such an indirect effect was insignificant (point estimation=–0.06, 95% CI –0.2716 to 0.1233).

We conducted a supplementary analysis in which we controlled for age. The results of this age-adjusted analysis remained consistent with our initial findings. Specifically, after accounting for age differences, the interaction effect of gender match and physical attractiveness on perceived competence ( F 1,277 =4.81; P =.03; ƞ 2 =0.017) and user intention ( F 1,277 =2.32; P =.006; ƞ 2 =0.027) remained stable. Also, the moderated mediation effect was significant (point estimation=0.55, 95% CI 0.1598-0.9762). Therefore, hypothesis 1 was supported.

A manipulation check on the perceived severity of the disease showed that the scores of the low-involvement group (mean 3, SD 1.47) were significantly lower than those of the high-involvement group (mean 4.45, SD 1.60; t 158 =–5.95; P< .001; ƞ 2 =0.185). This indicates that the different degree (high vs low) of involvement was successfully manipulated. A 1-way ANOVA was used for a manipulation check on physical attractiveness. The results showed that physical attractiveness of doctors was successfully manipulated (mean high PA 5.61, SD 1.13; mean middle PA 4.77, SD 0.94; mean low PA 3.88, SD 1.28; F 1,58 =98.24; P <.001; ƞ 2 =0.499).

A chi-square test was used to examine hypothesis 1. The result showed that men and women had a significant difference in selecting the doctors with 3 different physical attractiveness levels ( 2 2 =14.165; P <.001; =0.299). Furthermore, this research tried to identify the level of physical attractiveness upon which this kind of difference exists. Therefore, a post hoc test was conducted. The analysis finds that adjusted standardized residual of gender preference has a significant difference on high-level (N men choose high physical attractiveness =48, N women choose high physical attractiveness =29; z score abs =2.9, z score abs >1.96; P =.004) and middle-level (N men choose middle physical attractiveness =25, N women choose middle physical attractiveness =47; z score abs =3.7, z score abs >1.96; P <.001) service providers, while gender has an insignificant effect on those with low physical attractiveness (N men choose middle physical attractiveness =7, N women choose middle physical attractiveness =2; z score abs =1.7, z score abs <1.96; P =.09). This indicated that in terms of different physical attractiveness in service providers, men (vs women) preferred highly attractive women service providers, whereas women (vs men) preferred moderately attractive providers; however, there was no difference between men and women’s choice for the low-attractiveness provider. Thus, hypothesis 1 is supported.

To further investigate how involvement in a credence service scenario influences men’s (vs women’s) preferences for different physical attractiveness in service providers, a post hoc chi-square test and an analysis of logistic regression were used. The test found that involvement moderated the effect of gender on preferences (β gender×involvement =–18.39; Wald=662.36; P =.003). The results illustrated that in the low-involvement condition, the effect of gender on preference was reduced. In particular, the effect of gender on individual preferences existed only in the high-involvement scenario ( 2 2 =9.78; P =.008). For the low-involvement scenario, there was no gender effect ( 2 2 =4.82; P =.09). Therefore, hypothesis 3 was supported. In the high-involvement condition, compared with women, men preferred the highly attractive service provider (N men choose high physical attractiveness =19, N women choose high physical attractiveness =12; z score abs =2.1, z score abs >1.96; P =.04); compared with men, women were more likely to select a service provider with moderate physical attractiveness (N men choose middle physical attractiveness =13, N women choose middle physical attractiveness =28; z score abs =2.9; z score abs >1.96; P =.004; Table 9 ).

A manipulation check on the perceived severity of the disease showed that the scores of the low-severity group (mean 5.17, SD 1.14) were significantly lower than those of the high-severity group (mean 3.24, SD 1.13; t 148 =–10.40; P <.001; ƞ 2 =0.422). This indicated that the degree (high vs low) of severity was successfully manipulated. A 1-way ANOVA was used for a manipulation check on physical attractiveness. The data showed that there was a significant difference between the 3 levels of physical attractiveness (mean high PA 5.81, SD 0.90; mean middle PA =4.73, SD=0.94; mean low PA 3.82, SD 1.18; F 1,50 =144.50; P <.001; ƞ 2 =0.661). Thus, the physical attractiveness of the 3 doctors was successfully manipulated.

We used a post hoc chi-square test and a logistic regression model to test our hypothesis. The chi-square test results showed that there was no significant difference in the physical attractiveness preferences of men and women (χ 2 2 =1.147; P =.56; P >.05; =0.087) when they were provided with extra information about the service provider’s qualification. A further post hoc chi-square test showed that in both the high-involvement (χ 2 2 =1.730; P =.421; P >.05; =0.151) and low-involvement (χ 2 2 =0.046; P =.98; P >.05; =0.025) scenarios, the effect of gender on preferences disappeared after respondents were given information on the service provider’s abilities; Table 10 ).

Study 1 provided evidence to support hypothesis 1. In the context of digital doctor consultation, gender influences people’s preferences for attractive health care providers. People are more likely to select a more (vs less) attractive doctor of the opposite (vs same) gender. This can be explained by the perceived competence of the doctor. In study 1, we adopted a separate evaluation method. On one hand, this method helps to rule out confounding factors and provides strong support for the hypothesis. However, in reality, when patients search for a digital doctor consultation, they may see a list of all available doctors and select the one they prefer. Therefore, in study 2, we adopted a joint evaluation method. Furthermore, we also manipulated the level of disease severity. The results of the second study show that when a service provider’s profile picture was provided to consumers, the consumers’ preferences were greatly influenced by the provider’s gender. The impact of this gender stereotype existed only in the high-involvement condition. If involvement was low, consumers did not have higher requirements for the abilities of medical service providers and thus relied less on their physical attractiveness to infer the ability of the service provider. The results of study 3 illustrated that when extra qualification information was provided, the influence of gender on individual preferences for the service provider’s attractiveness disappeared in both the high- and low-involvement conditions. It is important to note that the context of the research was based in China, a leading country in providing digital consultation services. According to data from the China Internet Network Information Center, by June 2023, the number of users accessing digital medical services in China had surged to 364 million, a rise of 1.62 million since December 2022. The data further reveal that the number of digital hospitals in China has surpassed 3000. These hospitals have offered digital diagnostic and treatment services to more than 25.9 million patients [ 59 ]. Thus, the participants in this study were familiar with digital doctor consultations. With the COVID-19 pandemic, an increasing number of European countries have started developing digital consultation services in health care. For instance, Estonia has already begun offering digital services to patients in remote areas. Therefore, the findings from this study can offer significant insights to other countries.

This research contributes to the literature on medical information systems research. First, this study further enhances our understanding of patients’ behavior in digital doctor consultations. As a result of the information asymmetry, patients are always looking for other signals to aid in their decision-making [ 18 ]. The lack of trust is one of the biggest barriers to digital services [ 60 ]. Besides professional status and service feedback, which are known as important signals for patients’ doctor selection [ 47 ], this study has shown that physical attractiveness is also a crucial signal for decision-making in the digital doctor consultation context. This study also helps to deepen the understanding of SAB in the context of digital health care services. Previous studies on SAB focus on contexts such as job interviews [ 61 ]. More recently, the literature has increasingly focused on general services, such as education services [ 15 ]. In addition, this study sheds light on the boundary condition of SAB. Our findings indicate that the degree of disease severity influences the effect of gender on preferences for attractive service providers. Based on the elaboration likelihood model, consumers with low disease involvement tend to rely on peripheral cues to make choices, which means that they may automatically choose a more attractive service provider. Nevertheless, our research points out that this assumption only holds for both men and women when qualification information is present. When qualification information is absent and information asymmetry exists, men and women react differently to attractive service providers when they are highly involved.

These research findings have some crucial implications for digital health care service management. Providing qualification information of service providers on digital health consultation platforms can mitigate the effect of gender on the individual preference for the attractiveness of a service provider, regardless of the scenario’s level of involvement. In the health care service sector, providing sufficient information to eliminate bias is a useful strategy. In the context of information asymmetry, patients may be influenced by the doctor’s physical attractiveness and make irrational decisions. When marketing telehealth services, hospitals should focus on highlighting doctor expertise, patient satisfaction, and health outcomes instead of relying on doctor attractiveness or demographic characteristics to promote their services. Furthermore, hospitals should develop patient education materials that explain the importance of choosing a doctor based on their qualifications and expertise, rather than their appearance. Also, digital health care platforms could consider implementing features that allow patients to filter and search for doctors based on their qualifications, expertise, and patient satisfaction ratings. This can help patients to make more rational decisions.

This study has some clear limitations, which point, in turn, to avenues for future research. One notable limitation of this study is the restricted age range of both the health care providers depicted in the images and the study participants, who were predominantly younger than 25 years. This lack of age diversity could potentially limit the generalizability of the findings, as perceptions of physical attractiveness and professionalism may vary across different age groups. Although supplementary analyses controlling for age did not significantly alter the results, it remains an open question whether these findings would hold in a more age-diverse sample. Future research should aim to include a broader age range of both health care providers and participants to explore the potential moderating effect of age on the relationships examined in this study.

Another limitation of this study pertains to the clinical scenario presented, which focuses on an acute condition that could potentially be resolved in a single consultation. This may not fully capture the complexities involved when patients are seeking long-term care for chronic conditions. In such cases, the level of disease involvement is likely to be higher, and patients may seek more comprehensive information to assess a doctor’s competence. The dynamics of how gender match and physical attractiveness influence patient choices could thus differ in a chronic care setting. Future research could benefit from exploring these nuances to provide a more holistic understanding.

Another limitation of this study is its focus on China. To enhance the generalizability of our research and generate a more worldwide impact, replication in various countries and across different cultures is necessary. Future research should, for instance, involve recruiting subjects from diverse nations. This is because health care systems vary significantly among countries. Systematic differences in health care structures, policies, and practices can substantially influence how patients evaluate doctors digitally. Consequently, these contextual differences should be carefully controlled for and considered in future studies to ensure the validity and applicability of research findings across different health care systems and cultural backgrounds. Furthermore, the cultural orientation of the patient can also play a role in this situation. Future studies should investigate how varying cultural dimensions, such as collectivism, power distance, and uncertainty avoidance, influence patient preferences and decision-making processes in digital health care settings. For instance, individuals from countries with high uncertainty avoidance may require more concrete information in the doctor’s description to feel comfortable with their choice.

Another important research direction involves examining the impact of other forms of information asymmetry in digital health care settings. For instance, the availability and quality of digital reviews can play a significant role in shaping patient choices. By analyzing the content and credibility of web-based reviews, researchers can better understand how they contribute to patients’ decision-making processes and their perceptions of service providers. In addition, assessing the role of digital reputation management and its influence on patient choices can offer valuable insights into digital health care.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 20.02.23; peer-reviewed by N Farre, D Verran; comments to author 20.07.23; revised version received 25.10.23; accepted 08.04.24; published 30.05.24.

©Xia Wei, Shubin Yu, Changxu (Victor) Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Raw Milk Containing Bird-Flu Virus Can Sicken Mice, Study Finds

The results bolster evidence that virus-laden raw milk may be unsafe for humans.

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By Apoorva Mandavilli

Unpasteurized milk contaminated with H5N1, the bird-flu virus that has turned up in dairy herds in nine states, has been found to rapidly make mice sick, affecting multiple organs, according to a study published on Friday .

The findings are not entirely surprising: At least a half-dozen cats have died after consuming raw milk containing the virus. But the new data add to evidence that virus-laden raw milk may be unsafe for other mammals, including humans.

“Don’t drink raw milk — that’s the message,” said Yoshihiro Kawaoka, a virologist at the University of Wisconsin, Madison, who led the study.

Most commercial milk in the United States is pasteurized. The Food and Drug Administration has found traces of the virus in 20 percent of dairy products sampled from grocery shelves nationwide. Officials have not found signs of infectious virus in those samples and have said that pasteurized milk is safe to consume.

But the findings have global implications, said Dr. Nahid Bhadelia, director of the Boston University Center on Emerging Infectious Diseases, who was not involved in the work.

“If this becomes a more widespread outbreak in cows, there are other places where there isn’t central pasteurization,” she cautioned, “and there are a lot more rural communities that drink milk.”

In the study, Dr. Kawaoka and his colleagues analyzed virus from milk samples from an affected dairy herd in New Mexico. The researchers found that levels of the virus declined slowly in a sample of milk stored at 4 degrees Celsius, suggesting that H5N1 in refrigerated raw milk may remain infectious for several weeks. The findings were published in the New England Journal of Medicine.

Flu viruses survive well at refrigerator temperatures, and the protein in milk also helps to stabilize them, said Richard Webby, an influenza expert at St. Jude Children’s Research Hospital in Memphis, who was not involved in the work.

If people who drink raw milk believe that refrigeration kills the virus, “this clearly shows that’s not the case,” Dr. Webby said.

Mice that were fed the contaminated milk quickly became ill, displaying ruffled fur and lethargy. On the fourth day, the mice were euthanized, and researchers found high levels of the virus in the respiratory system and moderate levels in several other organs. Like infected cows, the mice also harbored the virus in their mammary glands — an unexpected finding.

“These mice are not lactating mice; still the virus can be found in mammary glands,” Dr. Kawaoka said. “It’s very interesting.”

It is unclear whether the presence of virus in mammary glands is a feature of this particular virus or of bird-flu viruses in general, Dr. Webby said: “We’re learning new things every single day.” Mice are common pests in farms, providing yet another potential host for the virus, and cats and birds that feed on infected mice could also become ill.

The cats that died after drinking contaminated milk showed striking neurological symptoms , including stiff body movements, blindness, a tendency to walk in circles and a weak blink response. If the mice had been allowed to live longer, they might have developed similar symptoms, Dr. Webby said.

Also unclear is what the findings mean for the course of infection in people. On Wednesday, federal officials announced that a second dairy worker had tested positive for the H5N1 virus; a nasal swab from that person had tested negative for the virus, but an eye swab tested positive.

Pasteurization kills germs by heating milk to high temperatures. In the new study, when researchers heated the milk at the temperatures and time periods typically used for pasteurization, the virus was either undetectable or greatly diminished, but it was not completely inactivated.

Dr. Kawaoka cautioned that the laboratory conditions were different from those used in commercial pasteurization, so the results did not mean that the milk on grocery shelves contains active virus.

By contrast, the findings that raw milk contains large amounts of virus is “solid,” he said.

Raw milk has become popular in recent years as wellness gurus and right-wing commentators have extolled its alleged virtues , even more so since the bird flu outbreak in dairy cows began. Some argue that it tastes better and is more nutritious than pasteurized milk. Others contend that it boosts immunity.

On the contrary, pasteurization preserves calcium, the key nutrient in milk, and adds vitamin D to help absorb it. Consuming raw milk can lead to serious complications or even death from a variety of pathogens, especially in people with weakened immune systems, according to the Centers for Disease Control and Prevention.

From 1998 to 2018, outbreaks traced to raw milk consumption led to 228 hospitalizations, three deaths and illness in more than 2,600 people.

An earlier photo with this article was published in error.   The brand of milk shown in the photo was not connected with the study.

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Apoorva Mandavilli is a reporter focused on science and global health. She was a part of the team that won the 2021 Pulitzer Prize for Public Service for coverage of the pandemic. More about Apoorva Mandavilli

Public have no difficulty getting to grips with an extra thumb, study finds

Researchers stress the need for inclusive testing to ensure new technologies work for everyone.

Cambridge researchers have shown that members of the public have little trouble in learning very quickly how to use a third thumb -- a controllable, prosthetic extra thumb -- to pick up and manipulate objects.

The team tested the robotic device on a diverse range of participants, which they say is essential for ensuring new technologies are inclusive and can work for everyone.

An emerging area of future technology is motor augmentation -- using motorised wearable devices such as exoskeletons or extra robotic body parts to advance our motor capabilities beyond current biological limitations.

While such devices could improve the quality of life for healthy individuals who want to enhance their productivity, the same technologies can also provide people with disabilities new ways to interact with their environment.

Professor Tamar Makin from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge said: "Technology is changing our very definition of what it means to be human, with machines increasingly becoming a part of our everyday lives, and even our minds and bodies.

"These technologies open up exciting new opportunities that can benefit society, but it's vital that we consider how they can help all people equally, especially marginalised communities who are often excluded from innovation research and development. To ensure everyone will have the opportunity to participate and benefit from these exciting advances, we need to explicitly integrate and measure inclusivity during the earliest possible stages of the research and development process."

Dani Clode, a collaborator within Professor Makin's lab, has developed the Third Thumb, an extra robotic thumb aimed at increasing the wearer's range of movement, enhancing their grasping capability and expanding the carrying capacity of the hand. This allows the user to perform tasks that might be otherwise challenging or impossible to complete with one hand or to perform complex multi-handed tasks without having to coordinate with other people.

The Third Thumb is worn on the opposite side of the palm to the biological thumb and controlled by a pressure sensor placed under each big toe or foot. Pressure from the right toe pulls the Thumb across the hand, while the pressure exerted with the left toe pulls the Thumb up toward the fingers. The extent of the Thumb's movement is proportional to the pressure applied, and releasing pressure moves it back to its original position.

In 2022, the team had the opportunity to test the Third Thumb at the annual Royal Society Summer Science Exhibition, where members of the public of all ages were able to use the device during different tasks. The results are published today in Science Robotics .

Over the course of five days, the team tested 596 participants, ranging in age from three to 96 years old and from a wide range of demographic backgrounds. Of these, only four were unable to use the Third Thumb, either because it did not fit their hand securely, or because they were unable to control it with their feet (the pressure sensors developed specifically for the exhibition were not suitable for very lightweight children).

Participants were given up to a minute to familiarise themselves with the device, during which time the team explained how to perform one of two tasks.

The first task involved picking up pegs from a pegboard one at a time with just the Third Thumb and placing them in a basket. Participants were asked to move as many pegs as possible in 60 seconds. 333 participants completed this task.

The second task involved using the Third Thumb together with the wearer's biological hand to manipulate and move five or six different foam objects. The objects were of various shapes that required different manipulations to be used, increasing the dexterity of the task. Again, participants were asked to move as many objects as they could into the basket within a maximum of 60 seconds. 246 participants completed this task.

Almost everyone was able to use the device straightaway. 98% of participants were able to successfully manipulate objects using the Third Thumb during the first minute of use, with only 13 participants unable to perform the task.

Ability levels between participants were varied, but there were no differences in performance between genders, nor did handedness change performance -- despite the Thumb always being worn on the right hand. There was no definitive evidence that people who might be considered 'good with their hands' -- for example, they were learning to play a musical instrument, or their jobs involved manual dexterity -- were any better at the tasks.

Older and younger adults had a similar level of ability when using the new technology, though further investigation just within the older adults age bracket revealed a decline in performance with increasing age. The researchers say this effect could be due to the general degradation in sensorimotor and cognitive abilities that are associated with ageing and may also reflect a generational relationship to technology.

Performance was generally poorer among younger children. Six out of the 13 participants that could not complete the task were below the age of 10 years old, and of those that did complete the task, the youngest children tended to perform worse compared to older children. But even older children (aged 12-16 years) struggled more than young adults.

Dani said: "Augmentation is about designing a new relationship with technology -- creating something that extends beyond being merely a tool to becoming an extension of the body itself. Given the diversity of bodies, it's crucial that the design stage of wearable technology is as inclusive as possible. It's equally important that these devices are accessible and functional for a wide range of users. Additionally, they should be easy for people to learn and use quickly."

Co-author Lucy Dowdall, also from the MRC Cognition and Brain Science Unit, added: "If motor augmentation -- and even broader human-machine interactions -- are to be successful, they'll need to integrate seamlessly with the user's motor and cognitive abilities. We'll need to factor in different ages, genders, weight, lifestyles, disabilities -- as well as people's cultural, financial backgrounds, and even likes or dislikes of technology. Physical testing of large and diverse groups of individuals is essential to achieve this goal."

There are countless examples of where a lack of inclusive design considerations has led to technological failure:

• Automated speech recognition systems that convert spoken language to text have been shown to perform better listening to white voices over Black voices.
• Some augmented reality technologies have been found to be less effective for users with darker skin tones.
• Women face a higher health risk from car accidents, due to car seats and seatbelts being primarily designed to accommodate 'average' male-sized dummies during crash testing.
• Hazardous power and industrial tools designed for a right-hand dominant use or grip have resulted in more accidents when operated by left-handers forced to use their non-dominant hand.

This research was funded by the European Research Council, Wellcome, the Medical Research Council and Engineering and Physical Sciences Research Council.

• Healthy Aging
• Children's Health
• Medical Devices
• Engineering
• Wearable Technology
• Neural Interfaces
• Educational Technology
• Computational neuroscience
• Solar power
• Artificial heart
• Stem cell treatments
• Calorie restricted diet
• Essential nutrient

Story Source:

Materials provided by University of Cambridge . Original written by Craig Brierley. Note: Content may be edited for style and length.

Related Multimedia :

• YouTube Video: Testing the Third Thumb

Journal Reference :

• Dani Clode, Lucy Dowdall, Edmund da Silva, Klara Selén, Dorothy Cowie, Giulia Dominijanni, Tamar R. Makin. Evaluating initial usability of a hand augmentation device across a large and diverse sample . Science Robotics , 2024; 9 (90) DOI: 10.1126/scirobotics.adk5183

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