research designs in dissertation

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…

Need a helping hand?

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 .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

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 .

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• Cookies & Privacy
• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Research design

The quantitative research design that you set in your dissertation should reflect the type of research questions/hypotheses that you have set. When we talk about quantitative research designs, we are typically referring to research following either a descriptive , experimental , quasi-experimental and relationship-based research design, which we will return to shortly. However, there are also specific goals that you may want to achieve within these research designs. You may want to: (Goal A) explore whether there is a relationship between different variables; (Goal B) predict a score or a membership of a group; or (Goal C) find out the differences between groups you are interested in or treatment conditions that you want to investigate:

GOAL A Exploring the relationship between variables

Are you trying to determine if there is a relationship between two or more variables, and what this relationship is? This kind of design is used to answer questions such as: Is there a relationship between height and basketball performance? Are males more likely to be smokers than females? Does you level of anxiety reduce your exam ability?

GOAL B Predicting a score or a membership of a group

Are you trying to examine whether one variable's value (i.e., the dependent or outcome variable) can be predicted based on another's (i.e., the independent variable). These designs answer questions such as: Can I predict 10km run time based on an individual's aerobic capacity? Can I predict exam anxiety based on knowing the number of hours spent revising? Can I predict whether someone is classified as computer literate based on their performance in different computer tasks? Can I predict an individual's preferred transport (car/motorcycle) based on their response to a risk questionnaire?

GOAL C Testing for differences between groups or treatment conditions

Are you trying to test for differences between groups (e.g., exam performance of males and females) or treatment conditions (e.g., employee turnover among employees (a) given a bonus and (b) not given a bonus)? This type of design aims to answer questions such as: What is the difference in jump height between males and females? Can an exercise-training programme lead to a reduction in blood sugar levels? Do stressed males and females respond differently to different stress-reduction therapies? In each of these cases, we have different groups that we are comparing (e.g., males versus females), and we may also have different treatments (e.g., the example of multiple stress-reduction therapies).

Goals A and B reflect the use of relationship-based research questions/hypotheses, whilst goal C reflects the use of comparative research questions/hypotheses. Just remember that in addition to relating and comparing (i.e., relationship-based and comparative research questions/hypotheses), quantitative research can also be used to describe the phenomena we are interested in (i.e., descriptive research questions). These three basic approaches (i.e., describing , relating and comparing ) can be seen in the following example:

Let's imagine we are interested in examining Facebook usage amongst university students in the United States .

We could describe factors relating to the make-up of these Facebook users, quantifying how many (or what proportion) of these university students were male or female, or what their average age was. We could describe factors relating to their behaviour, such as how frequently they used Facebook each week or the reasons why they joined Facebook in the first place (e.g., to connect with friends, to store all their photos in one place, etc.).

We could compare some of these factors (i.e., those factors that we had just described). For example, we could compare how frequently the students used Facebook each week, looking for differences between male and female students.

We could relate one or more of these factors (e.g., age) to other factors we had examined (e.g., how frequently students used Facebook each week) to find out if there were any associations or relationships between them. For example, we could relate age to how frequently the students used Facebook each week. This could help us discover if there was an association or relationship between these variables (i.e., age and weekly Facebook usage), and if so, tell us something about this association or relationship (e.g., its strength, direction, and/or statistical significance).

These three approaches to examining the constructs you are interested in (i.e., describing , comparing and relating ) are addressed by setting descriptive research questions, and/or comparative or relationship-based research questions/hypotheses. By this stage, you should be very clear about the type of research questions/hypotheses you are addressing, but if you are unsure, refer back to the Research Questions & Hypotheses section of the Fundamentals part of Lærd Dissertation now.

If you are exploring the relationship between variables (i.e., Goal A ), you are likely to be following a relationship-based research design (i.e., a type of non-experimental research design). However, if you are predicting the score or a membership of a group (i.e., Goal B ) or testing for differences between groups or treatment conditions (i.e., Goal C ), you are likely to be following either an experimental or quasi-experimental research design. Unless you already understand the differences between experimental, quasi-experimental and relationship-based research designs, you should read about these different research designs in the Research Designs section of the Fundamentals part of Lærd Dissertation now. You need to do this for two main reasons:

You will have to state which type of research design you are using in your dissertation when writing up the Research Design section of your Chapter Three: Research Strategy .

The research design that you use has a significant influence on your choice of research methods , the research quality of your findings, and even aspects of research ethics that you will have to think about.

Once you are familiar with the four types of research design (i.e., descriptive, experimental, quasi-experimental and relationship-based), you need to think about the route that you are adopting, and the approach within that route in order to set the research design in your dissertation:

• ROUTE A: Duplication
• ROUTE B: Generalisation
• ROUTE C: Extension

Route A: Duplication

If you are taking on Route A: Duplication , you would typically not be expected to make any changes to the research design used in the main journal article when setting the research design for your dissertation. After all, the purpose of the dissertation is duplication , where you are, in effect, re-testing the study in the main journal article to see if the same (or similar) findings are found. An important aspect of such re-testing is typically the use of the same research strategy applied in the main journal article. As such, if an experimental research design was used in the main journal article, with 3 groups (e.g., two treatment groups and one control group), your dissertation would also use an experimental design with the same group characteristics (i.e., 3 groups, with two treatment groups and one control group). The research design you used would also have the same goals as those in the main journal article (e.g., the goal of relating two constructs, perhaps study time and exam performance, in order to answer a relationship-based research question/hypothesis).

However, there are some instances where, from a practical standpoint, you may find that it is not possible to use the same research design, perhaps because an experimental research design was used, but you are unable to randomly selected people from the population you can get access to, forcing you to use a quasi-experimenta l research design. But the goal will be to use the same research design in your dissertation as the one applied in the main journal article. Again, you can learn about the differences between experimental and quasi-experimental designs in the Research Designs section of the Fundamentals part of Lærd Dissertation.

Research Design 101: Research Methods for Students

Which of the many different types of research design is best for you?

One thing that you will want to consider early in your dissertation process is the design of your research study. By the time you start your dissertation or thesis, you have probably taken graduate and undergraduate courses about research methods; however, it has probably been a while since you have taken these courses, and you may need help sorting through all the different types of research design. Below is a brief refresher on different research designs and methodologies.

General Types of Research Designs

Descriptive: Researchers use descriptive research designs to describe particular phenomena or relationships within a single group sample. Descriptive designs are typically used as either pilot or preliminary studies and generally have rather basic statistical procedures. By nature, descriptive studies do not and cannot be used to explain causation.

Descriptive research designs usually provide researchers with information about a group or phenomenon about which there has been little research (e.g., mating patterns of Martians). However, descriptive studies lack randomization and control and cannot be used to determine causation and other implications; in other words, descriptive research designs can only be used to determine “who” and “what,” not “why.”

Quasi-Experimental: Researchers use quasi-experimental research designs to identify differences between two or more groups in an attempt to explain causation. What keeps these types of experiments from being true experiments is lack of randomization. For example, researchers cannot randomly assign gender to participants; therefore, any study in which researchers are investigating differences between genders is inherently quasi-experimental.

Quasi-experimental designs allow researchers more control to make assumptions about causation and implications of findings. Quasi-experimental designs are also useful when researchers want to study particular groups in which group members cannot be randomly assigned (e.g., persons with depression, single mothers, people from different races or ethnic groups, etc.). A major drawback to using quasi-experimental designs is that quasi-experimental research designs typically have less internal validity than do true experimental designs.

Experimental: Experimental research designs have the most control, and, thus, allow researchers to explain differences between groups. One of the key features of an experimental design is that participants are randomly assigned to groups. Experimental designs can be used to test differences between groups (e.g., treatment a group, treatment b group, and control group) or factorial differences within multiple levels of each group (e.g., a drug group [Xanax or Valium] and a psychotherapy group [Cognitive Behavioral Therapy or Rational Emotive Behavioral Therapy]).

True experimental research designs are understood to be the gold standard of research because experimental research designs are the best designs for researchers to predict causation. However, true experimental designs often require more resources than do other research designs and will not work with all research questions.

Specific Types of Research Designs

Single-Sample Repeated Measures: A design method in which the same group is tested at multiple points in time. Giving students an assessment of knowledge the first day of class and giving the same assessment on the last day of class is an example of a research design based on a single-sample repeated measures.

ABA: A specific single-sample repeated measures design in which participants are measured at baseline (A), after an intervention (B), and again after the intervention has been removed (A).

Between Groups: A design in which researchers compare the scores of two or more groups. Between-group designs can be used as either a single or repeated measure.

Matched Sample: A specific between-groups design in which researchers match participants across groups based on criteria determined by the researchers (e.g., age, IQ, gender, etc.). After matching participants based on the predetermined criteria, researchers examine differences between matched pairs (not between group means).

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• How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative vs. quantitative data.

Also, see; Research methods, design, and analysis .

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• Hire an expert from ResearchProspect today!
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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

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Research Design

Same as research approach, different textbooks place different meanings on research design. Some authors consider research design as the choice between qualitative and quantitative research methods. Others argue that research design refers to the choice of specific methods of data collection and analysis. Research design is also placed as a master plan for conducting a research project and this appears to be the most authentic explanation of the term.

 In your dissertation you can define research design as a general plan about what you will do to answer the research question. [1] It is a framework for choosing specific methods of data collection and data analysis.

Research design can be divided into two groups:  exploratory  and  conclusive . Exploratory research, according to its name merely aims to explore specific aspects of the research area. Exploratory research does not aim to provide final and conclusive answers to research questions. The researcher may even change the direction of the study to a certain extent, however not fundamentally, according to new evidences gained during the research process.

Conclusive research, on the contrary, generate findings that can be practically useful for decision-making. The following Table 1 illustrates the main differences between exploratory and conclusive research in relation to important components of a dissertation.

Table 1 Major differences between exploratory and conclusive research design [2]

The following can be mentioned as examples with exploratory design:

• A critical analysis of argument of mandatory CSR for UK private sector organisations
• A study into contradictions between CSR program and initiatives and business practices: a case study of Philip Morris USA
• An investigation into the ways of customer relationship management in mobile marketing environment

Studies listed above do not aim to generate final and conclusive evidences to research questions. These studies merely aim to explore their respective research areas.

Conclusive research  can be divided into two categories:  descriptive  and  causal . Descriptive research design, as the name suggests, describes specific elements, causes, or phenomena in the research area.

Table 2 Examples for descriptive research design

Causal research design , on the other hand, is conducted to study cause-and-effect relationships.  Table 3 below illustrates some examples for studies with causal research design.

Table 3 Examples for studies with causal design

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research designs. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

[2] Source: Pride and Ferrell (2007)

Research Design in Business and Management pp 1–17 Cite as

Introducing Research Designs

• Stefan Hunziker 3 &
• Michael Blankenagel 3  
• First Online: 10 November 2021

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We define research design as a combination of decisions within a research process. These decisions enable us to make a specific type of argument by answering the research question. It is the implementation plan for the research study that allows reaching the desired (type of) conclusion. Different research designs make it possible to draw different conclusions. These conclusions produce various kinds of intellectual contributions. As all kinds of intellectual contributions are necessary to increase the body of knowledge, no research design is inherently better than another, only more appropriate to answer a specific question.

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Orr, K., & Bennett, M. (2009). Reflexivity in the co-production of academic-practitioner research. Qual Research in Orgs & Mgmt, 4, 85–102.

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Yin, R. K. (2014). Case study research. Design and methods (5th ed.). SAGE.

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• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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What are the steps to research design for your dissertation .

In this blog, Explore thesis research design, emphasizing its importance, essential elements, and practical guidance for success.

1. Understanding the Research Design

Fundamentally, a research design dissertation delineates the structured strategy employed by a researcher to explore a particular problem or research question . Such a dissertation in design-based research provides comprehensive guidance throughout the research process.

Research design includes key components: research question, objectives, methodology, data collection, analysis, and addressing limitations, ensuring a logical, credible, and reliable study.

2. Crafting the Research Question 

A well-defined research question lies at the heart of a research design dissertation. It sets the stage for the entire study, acting as a compass that directs the researcher's efforts. A good research design thesis examples should be clear, concise, and focused, allowing for in-depth exploration.

Explore More What constitutes a well-structured research question

It aligns with the existing literature and contributes to the broader academic discourse. While formulating the research question, it is essential to consider its feasibility, significance, and potential impact on the field.

3. Choosing the Methodology 

Selecting an appropriate methodology is crucial to the success of a research design dissertation. The chosen methodology should align with the research question and enable the researcher to obtain relevant and reliable data.

Common methodologies include qualitative, quantitative, and mixed methods approaches.

Find out Research Design Example Quantitative Thesis Here

In this quantitative methodology thesis, we'll gather numerical data using surveys and experiments to uncover patterns and trends. Statistical analysis will help us interpret the data and draw meaningful conclusions.

The research design includes clear timelines, a systematic approach, and rigorous methodologies to enhance validity.

Our findings will contribute to existing knowledge and provide valuable insights.

Explore Research Design Qualitative Example Thesis Here

The qualitative methodology thesis goes with in-depth interviews to understand the research topic holistically. Also, analyze themes, meanings, and interpretations to generate deep insights. The research design ensures a systematic approach and allows for a complete exploration of the research problem.

Learn about Research Design Mixed Method Thesis Here

Understanding the research topic holistically by integrating quantitative and qualitative approaches. Combining surveys, interviews, and statistical analysis will uncover numerical trends and rich narratives. The research design incorporates rigorous data validation techniques and ethical considerations.

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4. Data Collection and Analysis 

Efficient data collection and analysis play a crucial role in a research design dissertation, encompassing methods such as surveys, interviews, observations, and the examination of existing data sources.

Data analysis techniques, such as statistical or thematic analysis , should be employed to extract meaningful insights from the collected data. The results should be presented in a clear and logical manner, allowing readers to comprehend and interpret the findings accurately.

5. Potential Limitations and Ethical Considerations

Acknowledge research limitations, stemming from factors like sample size, data collection, and resource constraints. Prioritize ethical considerations, including informed consent, confidentiality, and institutional guidelines, to safeguard participants' rights and well-being.

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Writing a dissertation is the most challenging for the student and researchers. This task requires meticulous planning, critical thinking, and perseverance. Developing clear research can unlock the potential for academic excellence. 

Remember, a well-executed research design dissertation not only contributes to the existing knowledge but also paves the way for future research in the field. So, embrace this opportunity, and let your intellectual curiosity soar as you embark on this remarkable journey of academic exploration.

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Prize-Winning Thesis and Dissertation Examples

Published on September 9, 2022 by Tegan George . Revised on July 18, 2023.

It can be difficult to know where to start when writing your thesis or dissertation . One way to come up with some ideas or maybe even combat writer’s block is to check out previous work done by other students on a similar thesis or dissertation topic to yours.

This article collects a list of undergraduate, master’s, and PhD theses and dissertations that have won prizes for their high-quality research.

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Award-winning undergraduate theses, award-winning master’s theses, award-winning ph.d. dissertations, other interesting articles.

University : University of Pennsylvania Faculty : History Author : Suchait Kahlon Award : 2021 Hilary Conroy Prize for Best Honors Thesis in World History Title : “Abolition, Africans, and Abstraction: the Influence of the “Noble Savage” on British and French Antislavery Thought, 1787-1807”

University : Columbia University Faculty : History Author : Julien Saint Reiman Award : 2018 Charles A. Beard Senior Thesis Prize Title : “A Starving Man Helping Another Starving Man”: UNRRA, India, and the Genesis of Global Relief, 1943-1947

University: University College London Faculty: Geography Author: Anna Knowles-Smith Award:  2017 Royal Geographical Society Undergraduate Dissertation Prize Title:  Refugees and theatre: an exploration of the basis of self-representation

University: University of Washington Faculty:  Computer Science & Engineering Author: Nick J. Martindell Award: 2014 Best Senior Thesis Award Title:  DCDN: Distributed content delivery for the modern web

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University:  University of Edinburgh Faculty:  Informatics Author:  Christopher Sipola Award:  2018 Social Responsibility & Sustainability Dissertation Prize Title:  Summarizing electricity usage with a neural network

University:  University of Ottawa Faculty:  Education Author:  Matthew Brillinger Award:  2017 Commission on Graduate Studies in the Humanities Prize Title:  Educational Park Planning in Berkeley, California, 1965-1968

University:  University of Ottawa Faculty: Social Sciences Author:  Heather Martin Award:  2015 Joseph De Koninck Prize Title:  An Analysis of Sexual Assault Support Services for Women who have a Developmental Disability

University : University of Ottawa Faculty : Physics Author : Guillaume Thekkadath Award : 2017 Commission on Graduate Studies in the Sciences Prize Title : Joint measurements of complementary properties of quantum systems

University:  London School of Economics Faculty: International Development Author: Lajos Kossuth Award:  2016 Winner of the Prize for Best Overall Performance Title:  Shiny Happy People: A study of the effects income relative to a reference group exerts on life satisfaction

University : Stanford University Faculty : English Author : Nathan Wainstein Award : 2021 Alden Prize Title : “Unformed Art: Bad Writing in the Modernist Novel”

University : University of Massachusetts at Amherst Faculty : Molecular and Cellular Biology Author : Nils Pilotte Award : 2021 Byron Prize for Best Ph.D. Dissertation Title : “Improved Molecular Diagnostics for Soil-Transmitted Molecular Diagnostics for Soil-Transmitted Helminths”

University:  Utrecht University Faculty:  Linguistics Author:  Hans Rutger Bosker Award: 2014 AVT/Anéla Dissertation Prize Title:  The processing and evaluation of fluency in native and non-native speech

University: California Institute of Technology Faculty: Physics Author: Michael P. Mendenhall Award: 2015 Dissertation Award in Nuclear Physics Title: Measurement of the neutron beta decay asymmetry using ultracold neutrons

University:  Stanford University Faculty: Management Science and Engineering Author:  Shayan O. Gharan Award:  Doctoral Dissertation Award 2013 Title:   New Rounding Techniques for the Design and Analysis of Approximation Algorithms

University: University of Minnesota Faculty: Chemical Engineering Author: Eric A. Vandre Award:  2014 Andreas Acrivos Dissertation Award in Fluid Dynamics Title: Onset of Dynamics Wetting Failure: The Mechanics of High-speed Fluid Displacement

University: Erasmus University Rotterdam Faculty: Marketing Author: Ezgi Akpinar Award: McKinsey Marketing Dissertation Award 2014 Title: Consumer Information Sharing: Understanding Psychological Drivers of Social Transmission

University: University of Washington Faculty: Computer Science & Engineering Author: Keith N. Snavely Award:  2009 Doctoral Dissertation Award Title: Scene Reconstruction and Visualization from Internet Photo Collections

University:  University of Ottawa Faculty:  Social Work Author:  Susannah Taylor Award: 2018 Joseph De Koninck Prize Title:  Effacing and Obscuring Autonomy: the Effects of Structural Violence on the Transition to Adulthood of Street Involved Youth

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• Qualitative Descriptive Design

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A descriptive design is a flexible, exploratory approach to qualitative research. Descriptive design is referred to in the literature by other labels including generic, general, basic, traditional, interpretive, and pragmatic. Descriptive design as an acceptable research design for dissertation and other robust scholarly research has received varying degrees of acceptance within the academic community. However, descriptive design has been gaining momentum since the early 2000’s as a suitable design for studies that do not fall into the more mainstream genres of qualitative research (ie. Case study, phenomenology, ethnography, narrative inquiry and grounded theory). In contrast to other qualitative designs, descriptive design is not aligned to specific methods (for example, bracketing in phenomenology, bounded systems in case study, or constant comparative analysis in grounded theory). Rather, descriptive design “borrows” methods appropriate to the proposed study from other designs. 

Arguments supporting the flexible nature of descriptive designs describe it as being preferable to forcing a research approach into a design that is not quite appropriate for the nature of the intended study. However, descriptive design has also been criticized for this mixing of methods as well as for the limited literature describing it. The descriptive design can be the foundation for a rigorous study within the DSE program. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose methods best suited to a practice-based research purpose.   

The following video provides additional insight into descriptive design and qualitative research:

Sources of Data in Descriptive Design

Because of the exploratory nature of descriptive design, the triangulation of multiple sources of data are often used for additional insight into the phenomenon. Sources of data that can be used in descriptive studies are similar to those that may be used in other qualitative designs and include interviews, focus groups, documents, artifacts, and observations.

The following video provides additional considerations for triangulation in qualitative designs including descriptive design:

• << Previous: Developing the Qualitative Research Design
• Next: Qualitative Narrative Inquiry Research >>
• Last Updated: Nov 2, 2023 10:17 AM
• URL: https://resources.nu.edu/c.php?g=1007179

How to find resources by format

Why use a dissertation or a thesis.

A dissertation is the final large research paper, based on original research, for many disciplines to be able to complete a PhD degree. The thesis is the same idea but for a masters degree.

They are often considered scholarly sources since they are closely supervised by a committee, are directed at an academic audience, are extensively researched, follow research methodology, and are cited in other scholarly work. Often the research is newer or answering questions that are more recent, and can help push scholarship in new directions. 

Search for dissertations and theses

Locating dissertations and theses.

The Proquest Dissertations and Theses Global database includes doctoral dissertations and selected masters theses from major universities worldwide.

• Searchable by subject, author, advisor, title, school, date, etc.
• More information about full text access and requesting through Interlibrary Loan

NDLTD – Networked Digital Library of Theses and Dissertations provides free online access to a over a million theses and dissertations from all over the world.

WorldCat Dissertations and Theses searches library catalogs from across the U.S. and worldwide.

Locating University of Minnesota Dissertations and Theses

Use  Libraries search  and search by title or author and add the word "thesis" in the search box. Write down the library and call number and find it on the shelf. They can be checked out.

Check the  University Digital Conservancy  for online access to dissertations and theses from 2007 to present as well as historic, scanned theses from 1887-1923.

Other Sources for Dissertations and Theses

• Center for Research Libraries
• DART-Europe E-Thesis Portal
• Theses Canada
• Ethos (Great Britain)
• Australasian Digital Theses in Trove
• DiVA (Sweden)
• E-Thesis at the University of Helsinki
• DissOnline (Germany)
• List of libraries worldwide - to search for a thesis when you know the institution and cannot find in the larger collections

University of Minnesota Dissertations and Theses FAQs

What dissertations and theses are available.

With minor exceptions, all doctoral dissertations and all "Plan A" master's theses accepted by the University of Minnesota are available in the University Libraries system. In some cases (see below) only a non-circulating copy in University Archives exists, but for doctoral dissertations from 1940 to date, and for master's theses from 1925 to date, a circulating copy should almost always be available.

"Plan B" papers, accepted in the place of a thesis in many master's degree programs, are not received by the University Libraries and are generally not available. (The only real exceptions are a number of old library school Plan B papers on publishing history, which have been separately cataloged.) In a few cases individual departments may have maintained files of such papers.

In what libraries are U of M dissertations and theses located?

Circulating copies of doctoral dissertations:.

• Use Libraries Search to look for the author or title of the work desired to determine location and call number of a specific dissertation. Circulating copies of U of M doctoral dissertations can be in one of several locations in the library system, depending upon the date and the department for which the dissertation was done. The following are the general rules:
• Dissertations prior to 1940 Circulating copies of U of M dissertations prior to 1940 do not exist (with rare exceptions): for these, only the archival copy (see below) is available. Also, most dissertations prior to 1940 are not cataloged in MNCAT and can only be identified by the departmental listings described below.  
• Dissertations from 1940-1979 Circulating copies of U of M dissertations from 1940 to 1979 will in most cases be held within the Elmer L. Andersen Library, with three major classes of exceptions: dissertations accepted by biological, medical, and related departments are housed in the Health Science Library; science/engineering dissertations from 1970 to date will be located in the Science and Engineering Library (in Walter); and dissertations accepted by agricultural and related departments are available at the Magrath Library or one of the other libraries on the St. Paul campus (the Magrath Library maintains records of locations for such dissertations).  
• Dissertations from 1980-date Circulating copies of U of M dissertations from 1980 to date at present may be located either in Wilson Library (see below) or in storage; consult Libraries Search for location of specific items. Again, exceptions noted above apply here also; dissertations in their respective departments will instead be in Health Science Library or in one of the St. Paul campus libraries.

Circulating copies of master's theses:

• Theses prior to 1925 Circulating copies of U of M master's theses prior to 1925 do not exist (with rare exceptions); for these, only the archival copy (see below) is available.  
• Theses from 1925-1996 Circulating copies of U of M master's theses from 1925 to 1996 may be held in storage; consult Libraries search in specific instances. Once again, there are exceptions and theses in their respective departments will be housed in the Health Science Library or in one of the St. Paul campus libraries.  
• Theses from 1997-date Circulating copies of U of M master's theses from 1997 to date will be located in Wilson Library (see below), except for the same exceptions for Health Science  and St. Paul theses. There is also an exception to the exception: MHA (Masters in Health Administration) theses through 1998 are in the Health Science Library, but those from 1999 on are in Wilson Library.

Archival copies (non-circulating)

Archival (non-circulating) copies of virtually all U of M doctoral dissertations from 1888-1952, and of U of M master's theses from all years up to the present, are maintained by University Archives (located in the Elmer L. Andersen Library). These copies must be consulted on the premises, and it is highly recommended for the present that users make an appointment in advance to ensure that the desired works can be retrieved for them from storage. For dissertations accepted prior to 1940 and for master's theses accepted prior to 1925, University Archives is generally the only option (e.g., there usually will be no circulating copy). Archival copies of U of M doctoral dissertations from 1953 to the present are maintained by Bell and Howell Corporation (formerly University Microfilms Inc.), which produces print or filmed copies from our originals upon request. (There are a very few post-1952 U of M dissertations not available from Bell and Howell; these include such things as music manuscripts and works with color illustrations or extremely large pages that will not photocopy well; in these few cases, our archival copy is retained in University Archives.)

Where is a specific dissertation of thesis located?

To locate a specific dissertation or thesis it is necessary to have its call number. Use Libraries Search for the author or title of the item, just as you would for any other book. Depending on date of acceptance and cataloging, a typical call number for such materials should look something like one of the following:

Dissertations: Plan"A" Theses MnU-D or 378.7M66 MnU-M or 378.7M66 78-342 ODR7617 83-67 OL6156 Libraries Search will also tell the library location (MLAC, Health Science Library, Magrath or another St. Paul campus library, Science and Engineering, Business Reference, Wilson Annex or Wilson Library). Those doctoral dissertations still in Wilson Library (which in all cases should be 1980 or later and will have "MnU-D" numbers) are located in the central section of the third floor. Those master's theses in Wilson (which in all cases will be 1997 or later and will have "MnU-M" numbers) are also located in the central section of the third floor. Both dissertations and theses circulate and can be checked out, like any other books, at the Wilson Circulation desk on the first floor.

How can dissertations and theses accepted by a specific department be located?

Wilson Library contains a series of bound and loose-leaf notebooks, arranged by department and within each department by date, listing dissertations and theses. Information given for each entry includes name of author, title, and date (but not call number, which must be looked up individually). These notebooks are no longer current, but they do cover listings by department from the nineteenth century up to approximately 1992. Many pre-1940 U of M dissertations and pre-1925 U of M master's theses are not cataloged (and exist only as archival copies). Such dissertations can be identified only with these volumes. The books and notebooks are shelved in the general collection under these call numbers: Wilson Ref LD3337 .A5 and Wilson Ref quarto LD3337 .U9x. Major departments of individual degree candidates are also listed under their names in the GRADUATE SCHOOL COMMENCEMENT programs of the U of M, available in University Archives and (for recent years) also in Wilson stacks (LD3361 .U55x).

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Master of Design in Design for Interactions

Our mdes program supports those with design backgrounds who seek to transform their practice..

The School of Design welcomes students who hold undergraduate degrees in a design-based field and at least one year of professional experience to enroll in our MDes program. If you’re looking to build onto your strong foundation in design by studying the “big picture” aspect of designing for interactions—people, organizations, cultures, contexts, and systems—our MDes program can help you. Throughout the program, you will work with some of the brightest thinkers and most talented practitioners in the field, gaining exposure to approaches, ideas, and methods at the forefront of design. Studies rooted in communication, systems thinking, futuring, speculative design, design technology, ethics, and design research form the basis of the MDes. The curriculum enables us to teach you a rigorous process for documenting, analyzing, and understanding the past and present so that you are well-positioned to propose more desirable systems and interactions for the future.

The diversity of our MDes cohort creates an incredible group of people with whom to learn.

In addition to bringing rich cultural experiences from around the world, our MDes students hold a wide range of professional and academic expertise. Holding undergraduate degrees in design-based disciplines such as communication design, product design, user experience design, architecture, and service design, our MDes students bring their unique perspectives to design coursework, which enriches everyone’s learning. Our requirement for MDes students to have at least one year of professional experience also bolsters our program as students bring valuable lessons learned in practice, such as effective collaboration, to their academic studies.

Our rigorous curriculum balances structure and autonomy.

Spanning four semesters over the course of two years, the MDes program will challenge your thinking of the roles design can and should play in aiding various forms of interactions throughout society. Through individual and team-based projects that focus on the design of services or social innovation concepts, you will learn design principles, approaches, theories, and tools that are essential to designing for interactions. Each semester is comprised of thoughtfully-aligned seminars, studios, and labs that equip you with important knowledge and skills that aid your development as a design leader. Despite designers typically working in service of others and responding to specific prompts, our MDes courses provide you with ample autonomy in directing your individual work. We take this approach because we recognize the importance of your individual interests and strive to support you as you chart your personal path that builds on your unique design background and voice. Given that CMU is a liberal arts research university, students may also appreciate pursuing research opportunities with faculty and taking courses across campus to broaden and deepen their education.

The MDes thesis provides an opportunity for you to conduct rigorous design research.

A unique feature of the MDes program is the design thesis, which is characterized as an independent research and design project that you will conduct under the mentorship of a faculty advisor. The thesis is complemented by a required second-year seminar, elective coursework in the School of Design, and other departments across the Carnegie Mellon campus. In the first year, you'll identify possible thesis topics relative to School of Design faculty expertise, investigate ways of conducting a thesis, construct a researchable question that will frame your project, secure a thesis advisor, and write a proposal for your second year of study. In the second year, you'll conduct intensive research that aligns with an appropriate design process and culminates in a robust design project that addresses your research question. You will also write a document that describes your steps and discoveries. Throughout the process you will participate in public sharing sessions of thesis work, where you will give and receive feedback to further your inquiry and understanding. You can peruse masters theses from students in the School of Design online at KiltHub .

• Beyond Big Beef: Transitions to Food Citizenship Through Community, Ema Karavdic
• Affordances for Multi-device Gestural Interactions in Augmented Reality, Shengzhi Wi
• Amplifying ASL: Designing with Futuring and Inclusion, Mackenzie Cherban
• tac.tic: Tactile design language for indoor-outdoor pedestrian navigation, Chirag Murthy
• Designing for Trust, Meric Dagli
• Building Long-Term Relationships between People and Products through Customization, Ashlesha Dhotey
• Designing for Learning Growth: Encouraging Metacognitive Practice to Support Growth Mindsets in Students, Chen Ni
• Project Care: Empowering Elderly Chronic Disease Patients to Better Understand and Manage Their Treatment Plans Through Enhanced Patient-Centric Services and Systems, Suzanne Choi & Laura Rodriguez-eng

Our MDes equips you with important design skills and knowledge that enable you to realize a lifelong career in design.

The MDes is regarded as a terminal degree in design. As a result, graduates are poised to take on leading design roles in professional practice worldwide. Alumni are also well-positioned to acquire entry-level teaching and research positions at universities. As a graduate, you may also seek to deepen your studies through a design-focused PhD program like ours, or continue your education in areas such as business, human-computer interaction, or public policy. However you chart your path, we are confident that our MDes will provide you with a strong design education that builds on your background and strengthens the positive trajectory for achieving your professional goals.

Master of Design in Design for Interactions (MDes) Curriculum

Fall semester, year 1.

Explore design for interactions, design for services, and design for social innovation and study their potential impact in business and policy. Expand your skills in communication and interaction design.

Investigate the history, current state, and future of interaction design practice and research.

Envision and prototype preferred futures by giving form to the behaviors and interactions of products, services, and systems.

Use design strategies to decode complex information and communicate messages clearly.

Learn to use design tools for physical and digital environments to support your studio projects.

Investigate your personal interests, probe existing theses, and study various ways of conducting a thesis.

Learn about faculty research.

Spring Semester, Year 1

Investigate business and policy opportunities in design for services and social innovation through research-based team project work in your studio course. Work with advisors to prepare your thesis proposal.

Choose to study either Transition Design, Social Innovation or Design for Service.

Tackle a client-sponsored team project using an integrated research and design process.

Learn and apply a range of participatory methods for exploratory, generative, and evaluative research and design.

Construct a researchable question to frame your project, secure an advisor, and plan and propose the research and design approach you'll conduct in your second year of study.

Take a design elective or a course outside of design to complement your skills and knowledge. We recommend courses in policy, business, service or social innovation, interaction or communication design, or professional writing.

Fall Semester, Year 2

Through thesis project work and your choice of electives, craft a generalist degree in design for interaction, or develop a concentration in design for services or social innovation.

Build on the foundation of coursework and studios through thesis research with your advisor. Conduct research and develop creative concepts to investigate a significant challenge, engage with stakeholders in the real world to inspire and evaluate your ideas, and review your progress and evolving body of work with peers and your advisor to inform your subsequent steps.

Survey new models and approaches to interaction design and design for service in professional practice.

Learn research strategies and tools to assist you in your literature and artifact reviews, investigate making as a means of exploring and understanding your topic, and explore ways of visualizing your discoveries to aid your learning and share your findings with others.

Spring Semester, Year 2

Bring your thesis project to fruition by synthesizing your discoveries and disseminating valuable insights that have the potential to benefit others. Take advantage of electives to cultivate your expertise in design for interaction, and design for services or social innovation.

Model, test, and refine, your design concepts that have emerged from your year of deep research and design exploration to deepen your understanding of your topic, synthesize your findings and apply what you learned to your project, document, present, and publicly defend your thesis, and showcase your project as a unique feature of your design portfolio to demonstrate your ability to take on a significant research and design project.

Explore ways of encapsulating your study, synthesizing and structuring your discoveries, and writing and designing your thesis for dissemination.

We invite you to connect with us and learn more about the School of Design and our MDes program.

Check out examples of students’ work . Join us for an online visitors session . Review other areas of our site such as Frequently Asked Questions and Application Process . Plan a visit to Carnegie Mellon and coordinate a tour of the School of Design while you’re here. Contact us to schedule a call with our academic advisor to discuss any outstanding questions that arise. We look forward to meeting you!

Victoria Kleiner Successfully Defends Dissertation

Join us in congratulating Victoria Kleiner in the Fearns Lab on the successful defense of her dissertation entitled “RNA Synthesis Initiation and Termination by the Non-segmented Negative-strand RNA Virus Polymerase ” on March 14, 2024. Great job, Victoria!

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Thursday, March 21, 2024

Davis College doctoral student wins second place at national pitch competition

Sindupa De Silva, doctoral student from the Davis College of Agriculture, Natural Resources and Design, showed his savvy research communications skills at the Three-Minute Thesis Regional Competition in Greenville, South Carolina, by winning second place.

As the 2023 WVU 3MT Competition winner, De Silva represented WVU at the annual Conference of Southern Graduate Schools by participating in a two-day competition against 60 contestants who were all 3MT competition winners from East Coast schools.

The internationally renowned 3MT competition, founded by the University of Queensland in Australia, challenges doctoral students to present their dissertation research in just three minutes using a single PowerPoint slide. 

De Silva’s presentation, “Human Land-Use Practices and Their Impacts on Nature’s Kidneys,” focuses on how 30% of the world’s population — 2.2 billion people — do not have access to clean water, and how protecting wetland ecosystems globally can help protect the availability of clean water.

Betty Mei, program director for graduate student success in the Office of Graduate Education and Life , helped coach De Silva to perfect his pitch performance.

“It was my pleasure working with Sindupa ahead of the regional competition,” Mei said. “Competing with 3MT winners from over 60 universities is no easy task. I am so proud of Sindupa for giving his best performance yet to garner a second-place award.”

De Silva plans to continue pitching his research dissertation. Next up, he will compete at the Council of Graduate Schools Annual Conference’s 3MT competition in St. Louis later this fall. 

WVU holds the annual competition during Graduate Student Appreciation Week. This year, 10 finalists will compete for cash prizes beginning at 6 p.m. April 3 in the Mountainlair Ballrooms. The public is invited to attend the event and cast votes for their favorite presentation for the People’s Choice Award. 

For questions, contact Mei at [email protected].

ASU student team’s design selected as finalist for 2024 NASA-sponsored BIG Idea Challenge

Luminosity lab student team created an inflatable, reusable lunar landing pad system that could help future artemis missions.

An artistic rendering of the student-designed AEGIS inflatable lunar landing system, which is capable of autonomously deploying to reduce dust and debris generated by landers. Image courtesy Luminosity Lab/ASU

Arizona State University’s Luminosity Lab student team was recently selected as a finalist in NASA’s Breakthrough, Innovative, and Game-Changing (BIG) Idea Challenge . The group is one of six teams selected by NASA to present at the 2024 BIG Idea Challenge Forum, Nov. 5–7, at NASA Langley Research Center in Hampton, Virginia.  

The theme for the 2024 NASA-sponsored engineering competition, “Inflatable Systems for Lunar Operations,” challenges student teams to research, design and demonstrate novel inflatable systems configured for future lunar operations that could help future Artemis missions and beyond. 

The Luminosity Lab’s student group designed an inflatable and reusable lunar landing pad system, Aegis, which is capable of autonomously deploying to reduce dust and debris generated by landers; the system will also provide precision landing assistance to enable a safe landing. Being chosen as finalists means students will spend the next several months designing, building and testing their ideas, supported by as much as $150,000 from NASA.

“We have a phenomenal team of students working on this project, and I’m really proud of what they have accomplished by winning this proposal,” said Tyler Smith , senior director at ASU’s Luminosity Lab. “The inflatable landing pad system they designed is both innovative and solves a real issue for landers on the lunar surface.”

Presenting at the finals this fall provides students the opportunity to work with experts from NASA and the commercial space industry, and to receive guidance from faculty advisors from ASU’s School of Earth and Space Exploration (SESE) and Space Technology and Science ("NewSpace") Initiative , such as Professor Jim Bell and Jim Rice , assistant research scientist, both of whom have been actively involved with several NASA solar system exploration missions.

"The team at Luminosity Lab has done a fantastic job pitching an elegant solution to NASA's call for innovative inflatable technologies on the moon,” said Bell. “A number of us in SESE and ASU/NewSpace are eager to work with these talented students to help them mature their design and test/prototype it first here in Arizona and then later this year at NASA."

The BIG Idea Challenge is an initiative supporting NASA’s Space Technology Mission Directorate’s Game Changing Development program’s efforts to rapidly mature innovative and high-impact capabilities and technologies for infusion in a broad array of future NASA missions. This engineering design competition seeks innovative ideas from the higher education academic community for new topics each year relevant to current NASA Space Technology priorities. 

NASA’s Space Technology Mission Directorate sponsors the BIG Idea Challenge through a unique collaboration between its Game Changing Development program and the agency’s Office of STEM Engagement. It is managed by a partnership between the National Institute of Aerospace and the Johns Hopkins Applied Physics Laboratory.

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Medics design AI tool to predict side-effects in breast cancer patients

Trials in UK, France and the Netherlands indicate tool can predict if patient will experience problems from surgery and radiotherapy

Doctors have developed an artificial intelligence tool that can predict which breast cancer patients are more at risk of side-effects after treatment.

Worldwide, 2 million women are diagnosed every year with the disease, which is the most common cancer in females in most countries.

Greater awareness, earlier detection and a wider range of treatment options have improved survival rates in recent years, but many patients will experience often debilitating side-effects after treatment.

An international team of medics, scientists and researchers have designed an AI tool that can indicate how likely a patient is to experience problems after surgery and radiotherapy. The technology, being trialled in the UK, France and the Netherlands, could help patients access more personalised care.

“Thankfully, long-term survival rates from breast cancer continue to increase, but for some patients, this means having to live with the side-effects of their treatment,” said Dr Tim Rattay, a consultant breast surgeon and associate professor at the University of Leicester. “These include skin changes, scarring, lymphoedema, which is a painful swelling of the arm, and even heart damage from radiation treatment.

“That’s why we are developing an AI tool to inform doctors and patients about the risk of chronic arm swelling after surgery and radiotherapy for breast cancer. We hope this will assist doctors and patients in choosing options for radiation treatment and reduce side-effects for all patients.”

The AI tool was trained to predict lymphoedema up to three years after surgery and radiotherapy using data from 6,361 breast cancer patients. Patients found to be at a higher risk of arm swelling could be offered alternative treatments or additional support during and after treatments.

Dr Guido Bologna, associate professor at the University of Applied Sciences and Arts of Western Switzerland in Geneva and co-investigator on the project, said: “The final, best-performing model makes predictions using 32 different patient and treatment features, including whether or not patients had chemotherapy, whether sentinel lymph node biopsy under the armpit was carried out, and the type of radiotherapy given.”

The AI tool correctly predicted lymphoedema in an average of 81.6% of cases and correctly identified patients who would not develop it in an average of 72.9% of cases. The overall predictive accuracy of the model was 73.4%.

“Patients identified at higher risk of arm swelling could be offered additional supportive measures, such as wearing an arm compression sleeve during treatment, which has been shown to reduce arm swelling in the long term,” said Rattay. “Clinicians may also use this information to discuss options for lymph node irradiation in patients, where its benefit may be fairly borderline.”

Speaking at the European Breast Cancer Conference in Milan, Rattay said the technology was “an explainable AI tool, which means that it shows the reasoning behind its decision-making.

“This makes it easier not only for doctors to make decisions, but also to provide data-backed explanations to their patients,” he added.

The research team hopes to enrol 780 patients as part of a clinical trial called the Pre-Act project, who will be followed up for a period of two years. They are also developing the tool to predict other side-effects, including skin and heart damage.

Dr Simon Vincent, director of research, support and influencing at Breast Cancer Now, said ways to improve treatments were urgently needed. “This exciting project will explore whether using AI could enable people with breast cancer to receive more personalised care and support that helps minimise side-effects, such as chronic arm swelling, after surgery and radiotherapy.

“This research is in its early stages and more evidence is needed before we can consider whether or not the AI tool could be used in medical settings, and we look forward to seeing results from the trial.”

In other developments at the conference, researchers from Italy found that using combined positron emission tomography-magnetic resonance imaging (PET-MRI) scans enabled doctors to spot that a breast cancer patient’s tumour had begun to spread. That meant they could benefit from alternative treatment, such as chemotherapy or a different type of surgery.

Meanwhile, researchers from the Netherlands said young breast cancer patients given a low-dose boost of radiotherapy to where their tumour was removed, in addition to whole breast radiotherapy, remained free of local recurrence after 10 years.

• Medical research
• Cancer research
• Breast cancer
• Artificial intelligence (AI)

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