blue print of research work is known as

Single

Multiple

Field

experiment

Ethnography

Multiple lab

Simulation

experiment

Validity

Math

frontier

Single lab

proofs

experiment

Internal validity

Figure 5.1. Internal and external validity

Some researchers claim that there is a tradeoff between internal and external validity: higher external validity can come only at the cost of internal validity and vice-versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs is ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organizational learning are difficult to define, much less measure. For instance, construct validity must assure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure is valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical test, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Blueprints for Academic Research Projects

Today's post is written by Dr. Ben Ellway, the founder of www.academic-toolkit.com . Ben completed his Ph.D. at The University of Cambridge and created the  Research Design Canvas , a multipurpose tool for learning about academic research and designing a research project. 

Based on requests from students for examples of completed research canvases, Ben created the  Research Model Builder Canvas . 

This canvas modifies the original questions in the nine building blocks to enable students to search for key information in a journal article and then reassemble it on the canvas to form a research model — a single-page visual summary of the journal article which captures how the research was designed and conducted. 

Ben’s second book,  Building Research Models,  explains how to use the Research Model Builder Canvas to become a more confident and competent reader of academic journal articles, while simultaneously building research models to use as blueprints to guide the design of your own project .  

Ben has created a template for Stormboard based on this tool and this is his brief guide on how to begin using it.

Starting with a blank page can be daunting

The Research Design Canvas brings together the key building blocks of academic research on a single page and provides targeted questions to help you design your own project. However, starting with a blank page can be a daunting prospect! 

Academic research is complex as it involves multiple components, so designing and conducting your own project can be overwhelming, especially if you lack confidence in making decisions or are confused about how the components of a project fit together. It is much easier to start a complex task and long process such as designing a research project when you have an existing research model or ‘blueprint’ to work from. 

Starting with a ‘blueprint’ — tailored to your topic area — is much easier

Using the Research Model Builder Canvas, you can transform a journal article in your topic into a research model or blueprint — a single-page visualization of how a project was designed and conducted. 

The research model — and equally importantly the process of building it — will improve your understanding of academic research, and will also provide you with a personalized learning resource for your Thesis. You can use the research model as a blueprint to refer to specific decisions and their justification, and how components of research fit together, to help you begin to build your own project. 

Obviously, each project is unique so you’ll be using the blueprint as a guide rather than as a ‘cookie cutter’ solution. Seeing the components of a completed research project together on a single page (which  you  produced from a ten or twenty-page journal article) — is a very powerful learning resource to have on your academic research journey.

Build research models on Stormboard 

If you prefer to work digitally rather than with paper and pen, you can use the Research Model Builder Canvas Template in Stormboard. 

By using the Stormboard template, you’ll be able to identify key content and points from the journal article and then quickly summarize these on digital sticky notes. You can easily edit the sticky notes to rearrange, delete, or expand upon the ideas and points. You can then refer back to the permanent visual research model you created, share it with fellow students, or discuss it with your supervisors.

What are the building blocks of the research model?

The template has nine building blocks. 

The original questions in the building blocks of the research design canvas are modified in the research model builder canvas. They are designed to help you locate the most important points, decisions, and details in a journal article.  

A brief introduction to the purpose of each building block is provided below to help you familiarize yourself with the research model you will build.

Phenomenon / Problem

What does the research focus on? What were the main ‘things’ investigated and discussed in the journal article? Did the research involve a real-world problem?

What area (or areas) of past literature are identified and introduced? Which sources are especially important?

Observations & Arguments 

What are the most crucial points made by the authors in their analysis of past research? What evidence, issues, and themes are the focus of the literature review? Is a gap in past research identified? 

Research Questions / Hypotheses 

What are the research questions and/or hypotheses? How are they justified? If none are stated, what line of investigation is pursued?  

Theory & Concepts 

Does the research involve a theoretical or conceptual component? If so, what are the key concepts / theory? What role do they play in the research?  

Methodology / Design / Methods  

What methods and data were used? How are the decisions justified? 

Sample / Context 

What sampling method is used? Is the research context important?

Contributions

What contribution(s) do the authors claim that their research makes? Is the value-add more academically or practically-oriented? Are real-world stakeholders and the implications for them mentioned? 

Philosophical Assumptions / Research Paradigm 

These are not usually mentioned or discussed in journal articles. Indeed, this building block can be confusing if you are not familiar with research philosophy or are confused by its seemingly abstract focus. If you understand these ideas, can you identify any implicit assumptions or a research paradigm in the article?

Compare two research models to appreciate the diversity of research

The easiest way to increase your appreciation of the different types and ways of conducting academic research is to build  multiple  research models. 

Start by building two models. Compare and contrast them. Which decisions and aspects are similar and which are different? What can you learn from each research model and how can this help you when designing your own research and Thesis? 

Building research models will help you to appreciate the diversity in the different types of research conducted in your topic area.

Transforming a ten or twenty-page journal article into a single-page visual summary is a powerful way to learn about how academic research is designed and conducted — and also what a completed research project looks like. 

The Stormboard template makes the process of building research models easy, and the ability to save, edit, and share them ensures that you’ll be able to refer back to these blueprints at various stages throughout your research journey and Thesis writing process. 

When you get confused, become stuck, or feel overwhelmed by the complexity of academic research, you can fall back on the research models you created to guide you and get you back on track. Good luck!

Are you interested in trying the Research Model Builder Canvas? Sign up for a free trial now!

Keep reading.

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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!

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

10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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Research Design

• First Online: 01 January 2013

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• Pradip Kumar Sahu 2  

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A research design is the blueprint of the different steps to be undertaken starting with the formulation of the hypothesis to drawing inference during a research process. The research design clearly explains the different steps to be taken during a research program to reach the objective of a particular research. This is nothing but advance planning of the methods to be adopted at various steps in the research, keeping in view the objective of the research, the availability of the resources, time, etc. As such, the research design raises various questions before it is meticulously formed. The following questions need to be clarified before the formulation of the research design:

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Sahu, P.K. (2013). Research Design. In: Research Methodology: A Guide for Researchers In Agricultural Science, Social Science and Other Related Fields. Springer, India. https://doi.org/10.1007/978-81-322-1020-7_4

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A guide to 'big team science' creates a blueprint for research collaboration on a large scale

by Concordia University

Scientific research depends on collaboration between researchers and institutions. But over the past decade, there has been a surge of large-scale research projects involving extraordinarily large numbers of researchers, from dozens to hundreds, all working on a common project.

Examples of this trend include ManyBabies , centered on infant cognition and development, and ManyManys , focused on comparative cognition and behavior across animal taxa. These kinds of projects, known as big team science (BTS), benefit from pooled human and material resources and draw on diverse data sets to gird studies with a robustness not found in smaller ones.

However, as exciting as these projects can be, they can also be monsters to manage. Communication, team building, governance, authorship and credit are just some of the issues BTS team leaders must negotiate, along with the logistical difficulties involved in working across languages, cultures and time zones.

Fortunately, a group of BTS veterans, including two Concordia researchers, has published a how-to guide to help their fellow academics build their own projects. The article, published in the Royal Society Open Science journal, is based on expertise gained over multiple BTS projects, and it provides a road map for best practices and overcoming challenges.

"BTS is a new way of conducting research where many researchers come together to answer a common question that is crucial to their field," says Nicolás Alessandroni, a postdoctoral fellow at Concordia's Infant Research Lab. Alessandroni works under the direction of Krista Byers-Heinlein, a professor of psychology and the Concordia University Research Chair in Bilingualism and Open Science. The two co-authored the article with researchers at Stanford University, the University of British Columbia and the University of Manitoba.

"This is important because research has traditionally been conducted in a siloed manner, where individual teams from one institution work with small, limited samples."

Building up step by step

The authors acknowledge that no two projects are alike, and there is no one model to creating a BTS study. But success can be achieved by applying a common approach.

They suggest starting by identifying whether a research community believes the project is necessary. Consensus buy-in gets things rolling.

Once a project is a go, the authors outline how team leaders can work together to share data and collaborate on writing. The guide is designed to provide a path forward for researchers and to smooth over differences that are almost inevitable when the number of collaborators reaches three or even four digits. It touches on topics ranging from governance and codes of ethics to designated writing teams and authorship protocols.

"The beauty of big team science is that anyone can join, from undergraduates to faculty," says Alessandroni. "There are all these different experiences that coalesce around BTS, and this provides an opportunity to integrate many perspectives into a project . You can have some people who are very seasoned researchers and others who are young students willing to collaborate and embrace this new way of doing science."

Open to all

He admits that BTS projects are not easy to manage, but they do have clear strengths and benefits.

"Its very definition relates to important values in science: transparency, collaboration, accessibility, equity, diversity and inclusion—it touches on many important topics that have been disregarded in the practice of science traditionally. In many ways, it overlaps with the concept of open science , where data is shared openly and publications are available in open-access journals and repositories, making knowledge available without charging readers."

Alessandroni notes that universities will have to adapt to accommodate changes in the way research is supported.

"Institutions worldwide can help foster BTS collaborations by devising new workflows, policies and incentive structures," he says. "Naturally, this would involve important changes to the academic ecosystem, so there is much to discuss."

Journal information: Royal Society Open Science

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The Research Proposal – The Blueprint Of Your Dissertation

As a student pursuing your Masters/PhD you have to undergo rigorous research. Well, what is research? It is a scientific and organized evaluation to gather new knowledge. You will be getting the relevant solution to your problem through a streamlined research. By research there is vast expansion in the domain of knowledge. But there is one important step to perform before conducting a research i.e. the research proposal . Writing a research proposal is a pivotal step in the dissertation process .

The Significance Of A Research Proposal

All proposals show that the student has found out a research topic and has gone through information such that he/she knows what other researchers have researched about the topic. There is the formation of the research question, which is the heart of the proposal. A pertinent methodology is formed to answer the research question .

• The dissertation proposal is the key that opens the door to the research journey .
• Whatever is important for the dissertation, the research proposal should encompass it.
• The importance of getting engaged in the research work is highlighted in the research proposal.
• Planning is done diligently in the research proposal.
• The proposal should provide justification for approval. It can either contribute to available knowledge or adds to the existing knowledge.
• A well-formed research proposal helps the researcher not to think about various other alternatives once the research commences.
• The proposal gives information on how the data will be gathered, handled, and interpreted.
• The structure of a research proposal.

A Typical Research Proposal Has The Following Format

• Introduction
• Literature Review
• Research Methodology

Common Mistakes In Dissertation Proposal

• Not giving the appropriate context to develop the research question.
• Not citing important studies.
• Not staying on track of the research question.
• Not forming a comprehensible and compelling argument for the proposed research.
• Beating about the bush and emphasizing on minor concerns.
• Not maintaining a strong sense of direction.

What Is The Student’s Contribution To The Research Proposal?

• The student has to prove that he/she is contributing something new to the domain.
• The student should choose a viable topic with regard to data, financing, materials and supervisors.
• The student proposes that he/she will complete the research within the expected time.
• The student takes into consideration all ethical issues.
• The student gets approval from all pertinent bodies.

Will the student be able to carry out independent research? This is the question that the research proposal answers . The research proposal brings forth a problem, and examines pertinent research endeavors. What are the steps required to solve the problem? This is also the question that is answered by the research proposal. The research proposal also goes a step beyond in collecting and evaluating the data.

Overall, the questions what, why, where, whom and when are provided answers by the research proposal. The dissertation proposal assists you in concentrating on your research aims, get a clear idea about the significance and the needs, elucidate on the methods, and forecast issues and results. Eventually it plans alternative solutions and assistance, if needed.

Links, References, Related Posts

– What are the elements of a research proposal?

– What is a Research Proposal?

– The Comprehensive Checklist for Research Proposal Writing

– Top Tips For Writing PhD Research Proposal In Finance

– A Research Proposal: An Initial Success of Your Research

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NIH Blueprint Overview

The blueprint mission .

The NIH Blueprint for Neuroscience Research aims to accelerate transformative discoveries in brain function in health, aging, and disease. Blueprint is a collaborative framework that includes the NIH Office of the Director together with NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, Blueprint identifies cross-cutting areas of research and confronts challenges too large for any single Institute or Center. Since its inception in 2004, Blueprint has supported the development of new research tools, training opportunities, and resources to assist neuroscientists. 

In addition to supporting cross-cutting neuroscience activities like research training , workforce diversity , and  therapeutic development , Blueprint also funds research initiatives. Topics have ranged from transforming our understanding of dynamic neuroimmune interactions to enhancing our fundamental knowledge of interoception, supporting the development of innovative tools and technologies to monitor and manipulate biomolecular condensates, and more. To learn about both current and past areas of research, visit the Blueprint Research Initiatives page . 

Blueprint Grand Challenges

In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders.

The Human Connectome Project (HCP) is an ambitious effort to map all the connections within the human brain. Beginning in 2010, Blueprint awarded $40 million to two major research consortia which took complementary approaches to deciphering the brain’s complex wiring diagram. In five years, this highly coordinated effort mapped the connections of 1,200 healthy adults paired with behavioral assessments and GWAS results, resulting in the publication of over 100 papers. The MRI scanner system developed by  HCP scientists was 4-8 times more powerful than conventional systems, providing ten-fold faster imaging times and better spatial resolution than ever before. Building on the success of the Connectome Project, in 2014 Blueprint authorized funds to expand the age range of normal subjects to include both young people and older adults. The Connectome Coordination Facility, funded by Blueprint in 2015, maintains a central data repository for HCP data and offers advice to the research community regarding data collection strategies and harmonization. 

The Grand Challenge on Chronic Neuropathic Pain supported research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative has supported multi-investigator projects partnering researchers in the pain field with researchers in the neuroplasticity field. Starting in 2010, Blueprint funded 10 R01 grants investigating various models, mechanisms, and plasticity in the transition to chronic pain, resulting in more than 80 publications related to pain and neural plasticity.

The  Blueprint Neurotherapeutics Network (BPN)  helps small labs develop new drugs for nervous system disorders. BPN provides research funding, plus access to millions of dollars’ worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Since 2010, project teams across the U.S. have received funding to pursue drugs for conditions from vision loss toneurodegenerative disease to depression.  A hallmark of the program is  the research institution retains the intellectual property rights. Now in its eighth year, BPN has awarded 22 grants resulting in 1 Phase 1 clinical trial, 5 licensed programs, and several successful partnerships with industry. 

The BRAIN Initiative ®

April 2013 marked the beginning of the  Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative , a coordinated effort among public and private institutions and agencies aimed at revolutionizing our understanding of the human brain. NIH has a large role in this effort. Blueprint was one of the inaugural sponsors of the BRAIN Initiative by investing $10 million in 2014 on initial high priority research areas and continues to partner with NIH BRAIN and invest in BRAIN research.  

Historic Blueprint Resources 

Since 2004, Blueprint has supported the development of new resources , tools, and opportunities for neuroscientists. From fiscal years 2007 to 2009, Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources that continue to be available to researchers and the public. Some of these resources include:

• The  Gene Expression Nervous System Atlas (GENSAT)  and the  Cre Driver Network  are projects that have developed, characterized and continue to distribute transgenic mouse lines (GFP reporters and Cre drivers) to serve as tools for research on the central nervous system. Over 100 lines are available from the Cre driver network and over 1400 (GFP and Cre) lines are available from GENSAT.
• The  Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC)  triad of services include a resources registry, data commons, and cloud-based virtual machine with popular neuroimaging software pre-installed. These services help researchers save time, meet data sharing requirements, and leverage cloud-based computing on increasingly larger data sets. 
• The  Neuroscience Information Framework (NIF)  is an online portal to neuroscience information that includes a customized search engine, a curated registry of resources and direct access to more than 100 databases.
• The  NIH Toolbox for Assessment of Neurological and Behavioral Function  is a set of integrated tools for measuring neurologic and behavioral function, and for generating data that can be used and compared across diverse clinical studies.
• The  NIH Blueprint Enhancing Neuroscience Diversity through Undergraduate Research Experiences (ENDURE)  supports undergraduates from underrepresented groups in a two-year neuroscience research program and encourages matriculation into PhD programs.

Download the NIH Blueprint Overview Flyer (pdf, 1215 KB) .

Blueprint of a Proposal

Trying to make sense of proposal preparation, review and submission at the UW?

This course introduces participants to the UW processes, concepts and terminology that will help get you started in the right direction.

Through discussion, hands-on exercises, annotated online resources and in class handouts, we will cover:

• Proposal Process Policies and Procedures
• Roles & Responsibilities
• Where to find critical information needed for proposal preparation
• Proposal best practices

Anyone involved in the preparation or review of sponsored programs proposals to external sponsors, especially those new to the process.

Research Administration Certificate

Last updated, course materials.

Class Slides , Reading the FOA (online exercise)

Related Learning

SAGE: Creating and Submitting eGC1s

Introduction to Sponsored Project Budgets

Workshop: Preparing Sponsored Project Budgets

SAGE:  Budget

SAGE:  Creating NIH Proposals in Grant Runner

CORE [email protected] 206.616.0804

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What is a blueprint of a research paper?

A blueprint of a research paper is kind of an outline except less formal and with more information.

Here are some links that I found very helpful....

http://www.teachervision.fen.com/research-papers/writing/2123.html?detoured=1

http://www.suite101.com/content/writing-a-research-paper-a191693

!!! i hope that you found this information helpful!!!

Add your answer:

How do you keep a research paper from being biased?

You can keep a research paper from being biased by presenting the facts. You can also research both sides and present them in your paper.

Is a background paper the same as a research paper?

A "background" paper refers to a person's background and includes the past actions or past dealings. A research paper refers to facts about something that has been chosen as the topic of research.

Is a survey research paper the same as a persuasive research paper?

definitely not. while a survey research paper discusses what the overall people think, that is the research you would be doing, a persuasive research paper is researching something & then telling the reader what they ought to do based on this research you have just presented. for example, if you researched going green, in a survey research paper you might say how most people in x place do x thing. but in a persuasive research paper, you would say people in x place should do z thing instead.

Should you use contractions in a research paper?

Hey, never use contractions in a research paper. It was meant for words.

Are you supposed to use information from outline to do research paper?

Yes, you should use the information from the outline to do the research paper.

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Perceptions of the Use of Blueprinting in a Formative Theory Assessment in Pharmacology Education

This study aimed to assess perceptions of the use of a blueprint in a pharmacology formative theory assessment.

This study took place from October 2015 to February 2016 at a medical college in Gujurat, India. Faculty from the Department of Pharmacology used an internal syllabus to prepare an assessment blueprint. A total of 12 faculty members prepared learning objectives and categorised cognitive domain levels by consensus. Learning objectives were scored according to clinical importance and marks were distributed according to proportional weighting. A three-dimensional test specification table of syllabus content, assessment tools and cognitive domains was prepared. Based on this table, a theory paper was created and administered to 126 pharmacology students. Feedback was then collected from the faculty members and students using a 5-point Likert scale.

The majority of faculty members agreed that using a blueprint ensured proper weighting of marks for important topics (90.00%), aligned questions with learning objectives (80.00%), distributed questions according to clinical importance (100.00%) and minimised inter-examiner variations in selecting questions (90.00%). Few faculty members believed that use of the blueprint created too many easy questions (10.00%) or too many difficult questions (10.00%). Most students felt that the paper had a uniform distribution of questions from the syllabus (90.24%), that important topics were appropriately weighted (77.23%), was well organised (79.67%) and tested indepth subject knowledge (74.80%).

These findings indicate that blueprinting should be an integral part of written assessments in pharmacology education.

Advances in Knowledge

• - This study prepared a blueprint for a formative theory assessment in pharmacology. A blueprint aligns an assessment with learning objectives and distributes questions according to weighting based on clinical importance and the core learning objectives of the syllabus.
• - The findings of this study indicated that the majority of the faculty members and students had positive perceptions of the use of blueprinting in the formation of the assessment.

Application to Patient Care

• - A blueprint is an important approach to stimulating deep learning among medical students, thus indirectly influencing future patient care and clinical practice.
• - Positive attitudes towards scientific research among undergraduate medical students is likely to enhance the quality of future patient care.

Written examinations are the most commonly employed method to assess knowledge in medical education and are used to test recall abilities as well as higher-order cognitive functions, such as the interpretation of data and problem-solving skills. 1 , 2 Valid assessment methods are necessary to determine whether students have learned the required information. 3 Content validity gauges the extent to which an assessment covers a representative sample of the material which should be assessed; for example, if examination questions cover the learning objectives of the syllabus, the examination is considered to have content validity. 4 In contrast, construct validity covers all aspects of subject knowledge such as application, data gathering and interpretation as a collection of interrelated components, which together allow for the assessment to make sense. 5

Construct under-representation and construct irrelevance variance are two important challenges to construct validity. If an examiner creates a test with overly easy or difficult questions, asks well-known questions or uses unnecessarily complex language, it would mean that there is construct irrelevance variance, which can lead to the inflation or deflation of test scores. 6 , 7 Content under-representation in a paper can occur due to the inadequate weighting of marks for clinically relevant topics, unequal distribution of course content across the assessment or examiner bias, such as a tendency to focus on popular topics. 7 Moreover, the teachers who deliver lectures are usually not the ones who create the assessments; this can reduce the content and construct validity of an assessment, which in turn can lead to a mistrust in the assessment system on the part of the students. 7 , 8 Content imbalance may also result in students focusing less on key areas of learning during revision. 7 For an assessment paper to be valid, it should match course content, have proportional weighting of content according to clinical importance, consist of questions which are neither overly difficult nor easy and have multiple tools to determine various types of information. 7

Blueprinting can be defined as the creation of a template to determine the content of a test; it lists the number and type of questions across the course content, with learning objectives and relative weighting given to each topic. 7 , 8 A blueprint provides a systematic multi-step approach to an assessment, defining the purpose (e.g. formative/summative and written/practical) and scope (e.g. for undergraduate or postgraduate students) of the test in order to subsequently determine content and method of assessment. Based on the content, learning objectives and their domains are identified and different assessment tools are chosen, such as short answer questions (SAQs), essay questions (EQs) or multiple choice questions. 7 , 8 The content of the assessment is then proportionally weighted as per clinical importance, learning domains and methods of assessment. The total weighting of the number of items to be included in the assessment is decided and a three-dimensional (3D) table of test specifications is prepared to align content, learning domains and assessment tools, as well as prepare individual questions. 7 , 8

Blueprinting is increasingly used in the field of medical education worldwide. 9 , 10 In the UK, assessments created using a blueprint are considered essential to enable future doctors to meet mandatory standards; the assessments are prepared in such a way that students who have not met important learning outcomes are not able to graduate. 10 Previous research indicates that blueprinting optimises student assessment, making a positive impact and helping them to focus on key areas in an examination, thereby improving performance. 11 – 13 This approach can reduce inter-individual variability by providing guidance to examiners; moreover, set question papers are usually more valid and reliable than those created without a blueprint. 7 However, studies conducted on the use of blueprinting in India are scarce.

At the Gujarat Medical Education & Research Society (GMERS) Medical College in Gotri, Vadodara, Gujurat, India, a five-year undergraduate medical education programme results in a Bachelor of Medicine and Bachelor of Surgery (MBBS). Pharmacology education is mainly taught to second-year MBBS students for a total of three semesters of six months’ duration each. The assessment system consists of one summative and two formative examinations, with two written papers in the summative examination and one written paper in one of the formative examinations. Each paper is scored out of 58 marks, although students now receive up to 40 marks due to optional questions. The papers include constructed-response open-ended questions including EQs (4 marks each), short EQs (3 marks each) and SAQs (2 marks each); the maximum number of marks for EQs, short EQs and SAQs are 20, 24 and 14, respectively. In order to pass, a student must get at least 50% on the examination. Traditionally, the assessment content is determined by a paper setter who selects the questions according to the syllabus and question format. This study aimed to prepare a blueprint for a written theory paper and to analyse subsequent feedback on its use in a formative examination by pharmacology faculty members and students.

This study was conducted between October 2015 and February 2016 at the Department of Pharmacology of GMERS Medical College. In order to familiarise departmental faculty members with the concept of preparing a theory paper via blueprinting, a pilot test consisting of 16 SAQs worth two marks each was administered to 12 members of the faculty. A half-day interactive session was then conducted in three sessions: the first focused on assessment methods and tools, the second on validity and reliability in assessments and the third on the purpose and implementation of blueprinting, including the weighting of topics and assessment methods and how to prepare tables of test specifications. Faculty members were then re-tested using the same SAQs in order to determine the learning outcomes of the interactive session.

The syllabus of the first formative theory examination for second-year MBBS students was used to prepare a blueprint for the written paper, consisting of seven topics: general pharmacology; the autonomic nervous system; the peripheral nervous system; the respiratory system; the gastrointestinal tract; autacoid-related drugs; and drugs affecting blood and blood formation. A literature review of the undergraduate regulations, vision documents, essential drug lists and national health programmes of the Medical Council of India as well as pharmacology textbooks and previous test papers was undertaken to determine learning objectives for each topic. 14 – 20 Each learning objective was categorised into either recall or reasoning domain categories as per Miller’s pyramid of competence. 21 Learning objectives and cognitive domain categorisations were then discussed by the same 12 faculty members who had participated in the pilot test. Each faculty member scored learning objectives individually according to clinical importance, with a score of 3 indicating high importance, a score of 2 indicating moderate importance and a score of 1 reflecting little/no clinical importance. 7

Mean scores for each learning objective were calculated using a Microsoft Excel spreadsheet, Version 2010 (Microsoft Corp., Redmond, Washington, USA). Differences were resolved via consensus. Based on the learning objective scores, total scores were calculated for each topic as well as the overall syllabus. Proportional weighting was calculated for each topic by dividing the topic score with the total syllabus score. 3 , 7 Table 1 shows the learning objectives, total scores, proportional weighting and mark distribution calculations. Based on the literature review and faculty consensus, a total of 292 learning objectives were identified. The total syllabus score was 663 and the maximum number of marks on the theory test paper was 58, with marks in each topic weighted proportionally.

Learning objectives, mean total scores, proportional weighting and distribution of marks per syllabus topic

In the next phase, a 3D table of test specifications for the distribution of marks was prepared by aligning content, assessment tools and cognitive domain categories. Table 2 shows the 3D table of test specifications, representing the mark distribution for each topic according to assessment tool (e.g. EQ, short EQ or SAQ) and cognitive domain category (i.e. recall or reasoning). In terms of assessment tools, EQs were used only to assess reasoning abilities (20 marks; 100.00%). Short EQs were more often used to assess reasoning (15 marks; 62.50%) rather than recall (9 marks; 37.50%). In comparison, SAQs were more frequently used to test recall (8 marks; 57.14%) rather than reasoning (6 marks; 42.86%). Overall, the distribution of recall (17 marks; 29.31%) to reasoning (41 marks; 70.69%) marks was approximately 30:70.

Table of test specifications showing the distribution of marks for each topic according to assessment tool and cognitive domain category

EQs = essay questions; SAQs = short answer questions.

Based on the blueprint, a paper setter prepared individual test questions following good practices. 7 The questions were framed using directive verbs from the revised Bloom’s taxonomy of learning domains to assess the appropriate cognitive domain according to the blueprint and to give students clear directions for their responses. 22 The verb “remember” was used to frame recall questions, while the verbs “understand”, “apply” and “analyse” were used for reasoning questions. 22 The verbs “evaluate” and “create” were not used. The paper setter selected questions of high, moderate and no/little clinical importance in a ratio of 60:30:10. The paper was then administered to a total of 126 second year pharmacology students.

The 12 faculty members were requested to provide feedback regarding their perceptions of the quality and use of the theory assessment paper designed using blueprinting. They were provided with two previous assessment papers for comparison. Feedback was collected using closed- and open-ended questionnaires. For the former, participants were given 14 statements and asked to respond on a 5-point Likert scale using the following responses: strongly disagree, disagree, neutral, agree and strongly agree. In the open-ended questionnaire, they were asked to describe benefits and difficulties in the preparation and implementation of the blueprint as well as the feasibility of this approach for future implementation in formative assessments. The perceptions of the students regarding the paper were determined one week after the assessment. A questionnaire consisting of nine statements was distributed and the students were asked to respond on the previously described 5-point Likert scale.

For the pilot test, the SAQ scores of the faculty members were presented as means and standard deviations and compared using a paired t-test. Results from the feedback questionnaires were presented as percentages. Strongly disagree and disagree responses were merged, as were the agree and strongly agree responses. Statistical analysis was performed using GraphPad Prism, Version 6.0 (GraphPad Software Inc., La Jolla, California, USA). A P value of <0.050 was considered statistically significant.

This study received ethical approval from the Institutional Human Ethics Committee of GMERS Medical College, Gotri (IHEC #101/2015/Pharmacology-16). Informed consent was obtained from the faculty and undergraduate medical students before data collection.

Among the 12 faculty members, the mean test scores before and after the interactive session were 7.72 ± 4.19 and 19.29 ± 5.29, respectively, out of a maximum score of 32 (paired t-test t-value: 10.23; degree of freedom: 11; P <0.001). In total, 10 out of 12 faculty members gave their opinions of the blueprint (response rate: 83.33%). The two remaining faculty members were transferred before the formative assessment was conducted. All of the faculty members agreed that the blueprint ensured a uniform distribution of questions across the syllabus topics, helped to maintain a balance between questions in the recall and reasoning domains and assured the distribution of questions according to clinical importance. In addition, the majority agreed that the blueprint resulted in the adequate weighting of important topics (90.00%), aligned questions with learning objectives (80.00%), ensured well-organised theory test papers (70.00%), tested in-depth subject knowledge (60.00%) and minimised inter-examiner variations in selecting questions (90.00%). Few faculty members believed that the blueprint created too many easy questions (10.00%) or too many difficult questions (10.00%) [ Figure 1 ].

Perceptions of faculty members regarding the use of a blueprint in the formation of a pharmacology theory assessment paper (N = 10).

*One faculty member did not respond to these statements.

The majority of faculty members (90.00%) believed that a blueprint should be incorporated into the design of future examinations and all of them agreed that the blueprint should be an integral part of theory paper framing. Moreover, the majority of faculty members thought that blueprints should be prepared for the entire syllabus (90.00%) and that there was a need to change teaching schedules as per the blueprint (90.00%) [ Figure 2 ]. Table 3 summarises faculty responses to open-ended questions about their perceptions of the blueprint and assessment paper.

Perceptions of faculty members regarding the feasibility of future implementation of a blueprint in pharmacology education (N = 10).

Summary of faculty responses to open-ended questions regarding the use of a blueprint in the formation of a pharmacology theory assessment paper (N = 10)

A total of 123 out of 126 students provided feedback regarding the formative assessment paper (response rate: 97.62%). The majority of students believed that the distribution of questions was uniform and covered each topic (90.24%) and allowed for proper weighting of clinically important topics (77.23%). In addition, the paper was generally perceived to be well organised (79.67%). Most of the students believed that all of the questions were from the defined syllabus (90.24%) and that the paper tested in-depth knowledge of the subject (74.80%). Overall, few students believed that there were too many easy questions (12.20%) or too many difficult questions (9.75%); indeed, only a minority of the students felt that the paper was exhausting/lengthy (26.83%) or stressful (17.08%) [ Figure 3 ].

Perceptions of students regarding the use of a blueprint in the formation of a pharmacology theory assessment paper (N = 123).

An assessment drives, directs and influences learning and is a tool for educational improvement. 7 , 23 Students successfully learn when a relationship exists between teaching, assessment and results. In an assessment, every question format (e.g. EQs, short EQs and SAQs) has advantages and disadvantages; using a variety of formats helps to counter the possible bias associated with one individual format. 1 A blueprint helps to ensure the balance between different format questions by specifying the content, learning domains across the syllabus and assessment tools of an examination in a rational manner. 7 , 8 In the current study, a blueprint was used to create a written assessment paper for pharmacology students; learning objectives were identified and syllabus topics were marked using a proportional weighting system based on clinical importance to ensure content validity. Moreover, the paper assessed not only knowledge recall, but also understanding, application and critical analysis abilities using the interrelated cognitive domain of reasoning, thus ensuring construct validity. 5

In the current study, the majority of faculty members and students agreed that the use of a blueprint in the formation of a pharmacology formative assessment paper resulted in a uniform distribution of questions and adequate weighting of clinically important topics and testing of in-depth subject knowledge. Faculty members also agreed that the test paper struck an appropriate balance between questions in the recall and reasoning domains and that the questions were aligned with the learning objectives of the syllabus. These findings suggest that the test paper created through the blueprint was able to avoid construct under-representation. Previous research has also indicated that the use of a blueprint in assessment ensures coverage of all aspects of the curriculum, learning objectives and educational domains. 5 , 24 , 25 Very few faculty members and students in the current study believed that the assessment contained questions which were too easy or too difficult; this suggests that the use of a blueprint resulted in a rational paper and avoided construct irrelevance variance. Moreover, the blueprint seemed to make the assessment more ‘fair’ in the eyes of both faculty members and students. The experience of an authentic assessment seems to be a motivating factor; among medical students, a fair assessment has been reported to stimulate a deep approach to learning when tailored to curricular objectives. 26 This may subsequently affect clinical practice and patient care once the students have graduated.

Earlier reports in the literature have provided evidence that blueprints make it easier for an examiner to select questions, set papers according to accepted norms and standards, test higher-order cognitive domains and create well-organised theory papers. 7 , 24 These findings were also reflected in the current study during faculty responses to open-ended questions concerning the benefits of blueprinting. However, certain challenges to the use of blueprinting were also identified; many faculty members stated that the preparation of a blueprint was time-consuming and that it was occasionally difficult to reach a consensus. Nevertheless, despite these barriers, the majority of faculty members found blueprinting to be a promising approach for preparing assessment papers and indicated that they would use this approach in future examinations and change their teaching patterns accordingly. Overall, the use of a blueprint in a formative assessment paper had a positive impact on faculty perceptions; it is therefore realistic to recommend the inclusion of this approach as a valid tool to frame theory papers for summative assessment. Nevertheless, the data from this study would be more valuable if they were combined with evidence of increased academic achievement due to the blueprint; unfortunately, the study was limited in this regard as the students’ performance could not be compared to students in previous years due to the differences in syllabus.

According to the perceptions of both pharmacology faculty members and students, the use of blueprinting in the formation of a written formative assessment paper was found to result in high content and construct validity. As this approach helps to align the content, cognitive domains and assessment tools of a paper in a rational way, it should be implemented as an integral part of the framing of theory assessments in future.

ACKNOWLEDGEMENTS

The authors wish to thank all of the faculty members and students who participated in this study. In addition, they are grateful to Dr. Neena Doshi of the GMERS Medical College, Gotri, for critically reviewing the manuscript before publication.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

No funding was received for this study.

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Making and using blueprint paper

Blueprints use the cyanotype process invented by the astronomer John Herschel in 1842. The paper is coated with a solution of two soluble iron(III) salts. The two iron salts do not react with each other in the dark, but when they are exposed to ultraviolet light the iron(III) ammonium citrate becomes an iron(II) salt. The iron(II) ion reacts with the potassium ferricyanide to form an insoluble blue compound, blue iron(III) ferrocyanide, also known as Prussian blue.

Student Sheet

In this practical I will be:

• Carrying out an experiment to produce Blueprint paper.
• Producing an image or diagram on my Blueprint paper.
• Investigating the process of producing Blueprints and the role UV light plays.

Introduction:

While on a school trip, you saw that some renovation work was being carried out by some builders. On a table were the Blueprints for the building. You realise that the shades of white and blue would be perfect for a piece of art you are currently working on. However, before you can use these shades, you need to understand how they are made. You decide to investigate further…   

• 1 beaker (250 cm 3  )
• 2 beakers (100 cm 3 )
• 1 measuring cylinder (100 cm 3 )
• 1 glass stirring rod
• 1 plastic tray
• 1 wash bottle containing distilled water
• 20 sheets (or access to) plain A4 paper avoid shiny or very absorbent papers
• 2 weighing boats (or gallipots)
• Potassium hexacyanoferrate(III) – labelled “Substance A – Irritant ” (low hazard)
• Ammonium iron(III) citrate – labelled “Substance B” (low hazard)
• 1 drying line with 2 bulldog clips (or string and pegs)
• Digital balance
• Drying line (string and pegs)
• Paper towelling
• Disposable gloves
• Newspaper (to cover the work area)

Making the blueprint paper

Wear gloves and goggles. 

• Get two 100 cm 3 beakers, a measuring cylinder and a stirring rod. Mark one beaker A and the other B.
• Weigh 5 g of Substance A into the beaker marked A.
• Now weigh 9 g of Substance B into the beaker marked B. 

Use the measuring cylinder to measure 50 cm 3 of water and pour the water into beaker A. 

• Stir carefully until all the crystals have dissolved.
• Now measure out another 50 cm 3 of water and pour into beaker B.
• Stir carefully with a clean glass rod until all the crystals have dissolved.
• Do steps 10–12 in a dark part of the lab. 
• Mix the two liquids together, and pour them into a tray. Move the tray gently to get the liquid to cover the base of the tray properly.
• Put a piece of white A4 paper into the liquid just long enough to get it damp - not wet! Place a piece of A4 paper onto the liquid in the tray, then lift the paper out of the tray by the two corners nearest to you. Allow the excess solution to drip into the tray before placing it wet side up onto some newspaper on a desk. 

Your paper will turn greenish blue. Hang it up to dry out in a dark part of the laboratory or store it lying flat in a dark drawer. Hang your paper up using the string line and pegs in a darkened area to dry.

• Why do you think you have to wear gloves and goggles?
• What does dissolve mean?
• Why do you think the mixing has to be carried out in a dark place?
• Why do think the experiment will not work if the paper is wet?

Making the blueprints

Wear disposable plastic gloves

• When dry place your prepared paper under another piece of paper to keep it away from the sun.
• Place the package by the window so the light can fall on it.
• Remove the protecting piece of paper and place an object on the surface.
• Leave it in the light for about 1–5 minutes. Longer exposure leaves a shadow; shorter exposure times produce a sharper image. 
• When you think it has gone blue enough, take the object off the paper. The covered parts will still be green.
• Wash the paper with water to wash away the green chemicals and leave the blue behind.
• Hang your blueprint up to dry out.
• Why does your prepared blueprint paper need to be kept in the dark?
• Does the paper change colour quickly when it is exposed to the light?
• What does the washing do to the paper?
• Why do you have to wash your hands at the end?

Going further:

Try a range of different types of paper to see if the paper type makes a difference to exposure time, depth of exposure, etc.

If you can get some old black and white negatives try using those on the blueprint paper. You will have to experiment with exposure times.

Describe how the blueprint paper is similar and how different it is to photographic developing with a film. Research the chemicals used in photography.

Blueprints use the cyanotype process invented by the astronomer John Herschel in 1842. The paper is coated with a solution of two soluble iron(III) salts - potassium hexacyanoferrate(III) (potassium ferricyanide) and iron(III) ammonium citrate.

The two iron salts do not react with each other in the dark, but when they are exposed to ultraviolet light the iron(III) ammonium citrate becomes an iron(II) salt. The iron(II) ion reacts with the potassium ferricyanide to form an insoluble blue compound, blue iron(III) ferrocyanide, also known as Prussian blue.

A blueprint starts out as a black ink sketch on clear plastic or tracing paper. The ink sketch is laid on top of a sheet of blueprint paper and exposed to ultraviolet light or sunlight. Where the light strikes the paper, it turns blue. The black ink prevents the area under the drawing from turning blue. After exposure to UV light, the water-soluble chemicals are washed off the blueprint, leaving a white (or whatever colour the paper is) drawing on a blue background. The resulting blueprint is light-stable and as permanent as the substrate upon which it is printed.

Teacher and Technician Sheet

In this practical students will:

• Produce Blueprint paper.
• Create an image or diagram on Blueprint paper.
• Investigate the process of producing Blueprints and the role UV light plays.

Introduction: 

(The topic could start with a group discussion during which teachers introduce the following ideas, especially the words in bold.)

A blueprint is an old term used for a reproduction of a technical drawing of an object such as an architectural or engineering design. They were made by a contact process using light-sensitive sheets. It was important because it allowed the rapid and accurate reproduction of design documents. It was called a blueprint because of the light lines on a blue background, forming a negative of the original. 

Paper was frequently used but for more durable prints linen was sometimes used. Sadly, over time the linen prints would shrink slightly, so later imitation vellum and polyester film were used instead. Nowadays drawings are produced on computer, printed, and then photocopied.

These blueprint papers have absorbed certain chemicals that are changed when visible light or ultraviolet (UV) light falls on them. Hence objects put onto the dried blueprinting paper will block visible or UV light from getting to the chemicals and those areas, untouched by the visible or UV light, stay unchanged.

Where the visible or UV light can get to the paper, an intense blue colour develops. The blue colour will not wash out of the paper, but the greenish colour left under the object will. This leaves a white image of the object on a blue background. It is possible to investigate the effects of differing exposure times , screening with certain materials.

(To make the process easier for the students and safer the two solutions can be made up in the dark and stored in dark bottles.)

(This practical can be done with pupils working as individuals or in groups of two. Groups of two allows for good discussion between the pupils. Teachers can use the questions set as the stimulus for discussion and the answers can be used as a group report, article, presentation, poster or talk.)

Curriculum range:

Suitable for middle school or lower secondary students; it links with:

• ask questions and develop a line of enquiry based on observations of the real world, alongside prior knowledge and experience; 
• use appropriate techniques, apparatus, and materials during fieldwork and laboratory work, paying attention to health and safety; 
• make and record observations and measurements using a range of methods for different investigations; and evaluate the reliability of methods and suggest possible improvements; 
• present observations and data using appropriate methods, including tables and graphs; 
• interpret observations and data, including identifying patterns and using observations, measurements and data to draw conclusions; 
• present reasoned explanations, including explaining data in relation to predictions and hypotheses; 
• the concept of a pure substance; 
• mixtures, including dissolving. 

Hazard warnings: 

Potassium hexacyanoferrate(III) – Skin/eye irritant, (Cat 2)  Respiratory irritant (STOT SE3) Ammonium iron(III) citrate –  Skin/eye irritant, (Cat 2)  

Good practice would require exposure to be kept to a minimum and suitable gloves be used by the students. Students with impaired respiratory function may incur further disability if excessive concentrations of particulate are inhaled so good ventilation is required. 

In addition, contact with strong acids causes the release of highly toxic hydrogen cyanide. This is not likely to be an issue but care should be taken on disposal to ensure that the drain/sink does not have acid already present.

Ammonium iron(III) citrate is slightly hazardous as an irritant through skin or eye contact.

Wear safety glasses. Wear disposable gloves.

For a group of students:

• 1 beaker (250 cm 3 )
• 2 glass stirring rods
• 20 sheets (or access to) plain A4 paper (avoid shiny or very absorbent papers)
• 10 g potassium hexacyanoferrate(III) – labelled “Substance A – Irritant”
• 15 g ammonium iron(III) citrate – labelled “Substance B – Irritant”
• 1 digital balance

Technical notes:

If available use a fume cupboard to hang the string lines up in ready to peg the paper to dry and close any blinds near it.

It is possible to dry the prepared sheets more quickly by using radiators and/or hairdryers if available, but otherwise this practical would have to be carried out over two lessons to allow for drying time.

The amount of pages that can be hung out to dry is limited by the amount of space available.

Laminating sheets can be drawn on and placed onto the prepared sheets before placing in bright light to leave an imprint on the paper.

An alternative is to get pupils to use image editing software to produce a negative of their choice that can then be printed out on transparency film.

The paper may stain yellow and dry yellow, but it will still change colour when exposed to bright light and develop a blueprint when washed with water.

This practical works well in normal daylight with the internal lights switched off. 

The amount of space to dry the papers directs how many sheets can be used in a practical.

Good results can be obtained using ordinary A4 paper and using laminating sheets to draw on.

Any shadows will also be processed on the paper so try to place the paper where it is in direct light and is laid flat.

The amount of chemicals used and solution produced could be halved and still cover about 10 sides of A4.

The hazards are minimal assuming the expected level of behaviour from students.

Making and using blueprint paper: student sheet

Making and using blueprint paper: teacher sheet.

• 11-14 years
• Practical experiments
• Cross-curriculum
• Reactions and synthesis

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5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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A One Health Investigation into H5N1 Avian Influenza Virus Epizootics on Two Dairy Farms

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Background In early April 2024 we studied two Texas dairy farms which had suffered incursions of H5N1 highly pathogenic avian influenza virus (HPAIV) the previous month.

Methods We employed molecular assays, cell and egg culture, Sanger and next generation sequencing to isolate and characterize viruses from multiple farm specimens (cow nasal swab, milk specimens, fecal slurry, and a dead bird).

Results We detected H5N1 HPAIV in 64% (9/14) of milk specimens, 2.6% (1/39) of cattle nasal swab specimens, and none of 17 cattle worker nasopharyngeal swab specimens. We cultured and characterized virus from eight H5N1-positive specimens. Sanger and next-generation sequencing revealed the viruses were closely related into other recent Texas epizootic H5N1 strains of clade 2.3.4.4b. Our isolates had multiple mutations associated with increased spillover potential. Surprisingly, we detected SARS-CoV-2 in a nasal swab from a sick cow. Additionally, 14.3% (2/14) of the farm workers who donated sera were recently symptomatic and had elevated neutralizing antibodies against a related H5N1 strain.

Conclusions While our sampling was limited, these data offer additional insight into the large H5N1 HPAIV epizootic which thus far has impacted at least 96 cattle farms in twelve US states. Due to fears that research might damage dairy businesses, studies like this one have been few. We need to find ways to work with dairy farms in collecting more comprehensive epidemiological data that are necessary for the design of future interventions against H5N1 HPAIV on cattle farms.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This project was supported in part by the Agriculture and Food Research Initiative Competitive Grant from the American Rescue Plan Act, award number 2023-70432-39558, through USDA APHIS and Professor Gregory C. Grays startup funding from the University of Texas Medical Branch. The findings and conclusions in this presentation are those of the authors and should not be construed to represent any official USDA or US Government determination or policy.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This research was approved by the University of Texas Medical Branch IRB, Protocol 23-0085

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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