a research 2

Research Methods

Chapter 2 introduction.

Maybe you have already gained some experience in doing research, for example in your bachelor studies, or as part of your work.

The challenge in conducting academic research at masters level, is that it is multi-faceted.

The types of activities are:

• Finding and reviewing literature on your research topic;
• Designing a research project that will answer your research questions;
• Collecting relevant data from one or more sources;
• Analyzing the data, statistically or otherwise, and
• Writing up and presenting your findings.

Some researchers are strong on some parts but weak on others.

We do not require perfection. But we do require high quality.

Going through all stages of the research project, with the guidance of your supervisor, is a learning process.

The journey is hard at times, but in the end your thesis is considered an academic publication, and we want you to be proud of what you have achieved!

Probably the biggest challenge is, where to begin?

• What will be your topic?
• And once you have selected a topic, what are the questions that you want to answer, and how?

In the first chapter of the book, you will find several views on the nature and scope of business research.

Since a study in business administration derives its relevance from its application to real-life situations, an MBA typically falls in the grey area between applied research and basic research.

The focus of applied research is on finding solutions to problems, and on improving (y)our understanding of existing theories of management.

Applied research that makes use of existing theories, often leads to amendments or refinements of these theories. That is, the applied research feeds back to basic research.

In the early stages of your research, you will feel like you are running around in circles.

You start with an idea for a research topic. Then, after reading literature on the topic, you will revise or refine your idea. And start reading again with a clearer focus ...

A thesis research/project typically consists of two main stages.

The first stage is the research proposal .

Once the research proposal has been approved, you can start with the data collection, analysis and write-up (including conclusions and recommendations).

Stage 1, the research proposal consists of he first three chapters of the commonly used five-chapter structure :

• Chapter 1: Introduction
• An introduction to the topic.
• The research questions that you want to answer (and/or hypotheses that you want to test).
• A note on why the research is of academic and/or professional relevance.
• Chapter 2: Literature
• A review of relevant literature on the topic.
• Chapter 3: Methodology

The methodology is at the core of your research. Here, you define how you are going to do the research. What data will be collected, and how?

Your data should allow you to answer your research questions. In the research proposal, you will also provide answers to the questions when and how much . Is it feasible to conduct the research within the given time-frame (say, 3-6 months for a typical master thesis)? And do you have the resources to collect and analyze the data?

In stage 2 you collect and analyze the data, and write the conclusions.

• Chapter 4: Data Analysis and Findings
• Chapter 5: Summary, Conclusions and Recommendations

This video gives a nice overview of the elements of writing a thesis.

Chapter 2. Research Design

Getting started.

When I teach undergraduates qualitative research methods, the final product of the course is a “research proposal” that incorporates all they have learned and enlists the knowledge they have learned about qualitative research methods in an original design that addresses a particular research question. I highly recommend you think about designing your own research study as you progress through this textbook. Even if you don’t have a study in mind yet, it can be a helpful exercise as you progress through the course. But how to start? How can one design a research study before they even know what research looks like? This chapter will serve as a brief overview of the research design process to orient you to what will be coming in later chapters. Think of it as a “skeleton” of what you will read in more detail in later chapters. Ideally, you will read this chapter both now (in sequence) and later during your reading of the remainder of the text. Do not worry if you have questions the first time you read this chapter. Many things will become clearer as the text advances and as you gain a deeper understanding of all the components of good qualitative research. This is just a preliminary map to get you on the right road.

Research Design Steps

Before you even get started, you will need to have a broad topic of interest in mind. [1] . In my experience, students can confuse this broad topic with the actual research question, so it is important to clearly distinguish the two. And the place to start is the broad topic. It might be, as was the case with me, working-class college students. But what about working-class college students? What’s it like to be one? Why are there so few compared to others? How do colleges assist (or fail to assist) them? What interested me was something I could barely articulate at first and went something like this: “Why was it so difficult and lonely to be me?” And by extension, “Did others share this experience?”

Once you have a general topic, reflect on why this is important to you. Sometimes we connect with a topic and we don’t really know why. Even if you are not willing to share the real underlying reason you are interested in a topic, it is important that you know the deeper reasons that motivate you. Otherwise, it is quite possible that at some point during the research, you will find yourself turned around facing the wrong direction. I have seen it happen many times. The reason is that the research question is not the same thing as the general topic of interest, and if you don’t know the reasons for your interest, you are likely to design a study answering a research question that is beside the point—to you, at least. And this means you will be much less motivated to carry your research to completion.

Researcher Note

Why do you employ qualitative research methods in your area of study? What are the advantages of qualitative research methods for studying mentorship?

Qualitative research methods are a huge opportunity to increase access, equity, inclusion, and social justice. Qualitative research allows us to engage and examine the uniquenesses/nuances within minoritized and dominant identities and our experiences with these identities. Qualitative research allows us to explore a specific topic, and through that exploration, we can link history to experiences and look for patterns or offer up a unique phenomenon. There’s such beauty in being able to tell a particular story, and qualitative research is a great mode for that! For our work, we examined the relationships we typically use the term mentorship for but didn’t feel that was quite the right word. Qualitative research allowed us to pick apart what we did and how we engaged in our relationships, which then allowed us to more accurately describe what was unique about our mentorship relationships, which we ultimately named liberationships ( McAloney and Long 2021) . Qualitative research gave us the means to explore, process, and name our experiences; what a powerful tool!

How do you come up with ideas for what to study (and how to study it)? Where did you get the idea for studying mentorship?

Coming up with ideas for research, for me, is kind of like Googling a question I have, not finding enough information, and then deciding to dig a little deeper to get the answer. The idea to study mentorship actually came up in conversation with my mentorship triad. We were talking in one of our meetings about our relationship—kind of meta, huh? We discussed how we felt that mentorship was not quite the right term for the relationships we had built. One of us asked what was different about our relationships and mentorship. This all happened when I was taking an ethnography course. During the next session of class, we were discussing auto- and duoethnography, and it hit me—let’s explore our version of mentorship, which we later went on to name liberationships ( McAloney and Long 2021 ). The idea and questions came out of being curious and wanting to find an answer. As I continue to research, I see opportunities in questions I have about my work or during conversations that, in our search for answers, end up exposing gaps in the literature. If I can’t find the answer already out there, I can study it.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

When you have a better idea of why you are interested in what it is that interests you, you may be surprised to learn that the obvious approaches to the topic are not the only ones. For example, let’s say you think you are interested in preserving coastal wildlife. And as a social scientist, you are interested in policies and practices that affect the long-term viability of coastal wildlife, especially around fishing communities. It would be natural then to consider designing a research study around fishing communities and how they manage their ecosystems. But when you really think about it, you realize that what interests you the most is how people whose livelihoods depend on a particular resource act in ways that deplete that resource. Or, even deeper, you contemplate the puzzle, “How do people justify actions that damage their surroundings?” Now, there are many ways to design a study that gets at that broader question, and not all of them are about fishing communities, although that is certainly one way to go. Maybe you could design an interview-based study that includes and compares loggers, fishers, and desert golfers (those who golf in arid lands that require a great deal of wasteful irrigation). Or design a case study around one particular example where resources were completely used up by a community. Without knowing what it is you are really interested in, what motivates your interest in a surface phenomenon, you are unlikely to come up with the appropriate research design.

These first stages of research design are often the most difficult, but have patience . Taking the time to consider why you are going to go through a lot of trouble to get answers will prevent a lot of wasted energy in the future.

There are distinct reasons for pursuing particular research questions, and it is helpful to distinguish between them.  First, you may be personally motivated.  This is probably the most important and the most often overlooked.   What is it about the social world that sparks your curiosity? What bothers you? What answers do you need in order to keep living? For me, I knew I needed to get a handle on what higher education was for before I kept going at it. I needed to understand why I felt so different from my peers and whether this whole “higher education” thing was “for the likes of me” before I could complete my degree. That is the personal motivation question. Your personal motivation might also be political in nature, in that you want to change the world in a particular way. It’s all right to acknowledge this. In fact, it is better to acknowledge it than to hide it.

There are also academic and professional motivations for a particular study.  If you are an absolute beginner, these may be difficult to find. We’ll talk more about this when we discuss reviewing the literature. Simply put, you are probably not the only person in the world to have thought about this question or issue and those related to it. So how does your interest area fit into what others have studied? Perhaps there is a good study out there of fishing communities, but no one has quite asked the “justification” question. You are motivated to address this to “fill the gap” in our collective knowledge. And maybe you are really not at all sure of what interests you, but you do know that [insert your topic] interests a lot of people, so you would like to work in this area too. You want to be involved in the academic conversation. That is a professional motivation and a very important one to articulate.

Practical and strategic motivations are a third kind. Perhaps you want to encourage people to take better care of the natural resources around them. If this is also part of your motivation, you will want to design your research project in a way that might have an impact on how people behave in the future. There are many ways to do this, one of which is using qualitative research methods rather than quantitative research methods, as the findings of qualitative research are often easier to communicate to a broader audience than the results of quantitative research. You might even be able to engage the community you are studying in the collecting and analyzing of data, something taboo in quantitative research but actively embraced and encouraged by qualitative researchers. But there are other practical reasons, such as getting “done” with your research in a certain amount of time or having access (or no access) to certain information. There is nothing wrong with considering constraints and opportunities when designing your study. Or maybe one of the practical or strategic goals is about learning competence in this area so that you can demonstrate the ability to conduct interviews and focus groups with future employers. Keeping that in mind will help shape your study and prevent you from getting sidetracked using a technique that you are less invested in learning about.

STOP HERE for a moment

I recommend you write a paragraph (at least) explaining your aims and goals. Include a sentence about each of the following: personal/political goals, practical or professional/academic goals, and practical/strategic goals. Think through how all of the goals are related and can be achieved by this particular research study . If they can’t, have a rethink. Perhaps this is not the best way to go about it.

You will also want to be clear about the purpose of your study. “Wait, didn’t we just do this?” you might ask. No! Your goals are not the same as the purpose of the study, although they are related. You can think about purpose lying on a continuum from “ theory ” to “action” (figure 2.1). Sometimes you are doing research to discover new knowledge about the world, while other times you are doing a study because you want to measure an impact or make a difference in the world.

Basic research involves research that is done for the sake of “pure” knowledge—that is, knowledge that, at least at this moment in time, may not have any apparent use or application. Often, and this is very important, knowledge of this kind is later found to be extremely helpful in solving problems. So one way of thinking about basic research is that it is knowledge for which no use is yet known but will probably one day prove to be extremely useful. If you are doing basic research, you do not need to argue its usefulness, as the whole point is that we just don’t know yet what this might be.

Researchers engaged in basic research want to understand how the world operates. They are interested in investigating a phenomenon to get at the nature of reality with regard to that phenomenon. The basic researcher’s purpose is to understand and explain ( Patton 2002:215 ).

Basic research is interested in generating and testing hypotheses about how the world works. Grounded Theory is one approach to qualitative research methods that exemplifies basic research (see chapter 4). Most academic journal articles publish basic research findings. If you are working in academia (e.g., writing your dissertation), the default expectation is that you are conducting basic research.

Applied research in the social sciences is research that addresses human and social problems. Unlike basic research, the researcher has expectations that the research will help contribute to resolving a problem, if only by identifying its contours, history, or context. From my experience, most students have this as their baseline assumption about research. Why do a study if not to make things better? But this is a common mistake. Students and their committee members are often working with default assumptions here—the former thinking about applied research as their purpose, the latter thinking about basic research: “The purpose of applied research is to contribute knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment. While in basic research the source of questions is the tradition within a scholarly discipline, in applied research the source of questions is in the problems and concerns experienced by people and by policymakers” ( Patton 2002:217 ).

Applied research is less geared toward theory in two ways. First, its questions do not derive from previous literature. For this reason, applied research studies have much more limited literature reviews than those found in basic research (although they make up for this by having much more “background” about the problem). Second, it does not generate theory in the same way as basic research does. The findings of an applied research project may not be generalizable beyond the boundaries of this particular problem or context. The findings are more limited. They are useful now but may be less useful later. This is why basic research remains the default “gold standard” of academic research.

Evaluation research is research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems. We already know the problems, and someone has already come up with solutions. There might be a program, say, for first-generation college students on your campus. Does this program work? Are first-generation students who participate in the program more likely to graduate than those who do not? These are the types of questions addressed by evaluation research. There are two types of research within this broader frame; however, one more action-oriented than the next. In summative evaluation , an overall judgment about the effectiveness of a program or policy is made. Should we continue our first-gen program? Is it a good model for other campuses? Because the purpose of such summative evaluation is to measure success and to determine whether this success is scalable (capable of being generalized beyond the specific case), quantitative data is more often used than qualitative data. In our example, we might have “outcomes” data for thousands of students, and we might run various tests to determine if the better outcomes of those in the program are statistically significant so that we can generalize the findings and recommend similar programs elsewhere. Qualitative data in the form of focus groups or interviews can then be used for illustrative purposes, providing more depth to the quantitative analyses. In contrast, formative evaluation attempts to improve a program or policy (to help “form” or shape its effectiveness). Formative evaluations rely more heavily on qualitative data—case studies, interviews, focus groups. The findings are meant not to generalize beyond the particular but to improve this program. If you are a student seeking to improve your qualitative research skills and you do not care about generating basic research, formative evaluation studies might be an attractive option for you to pursue, as there are always local programs that need evaluation and suggestions for improvement. Again, be very clear about your purpose when talking through your research proposal with your committee.

Action research takes a further step beyond evaluation, even formative evaluation, to being part of the solution itself. This is about as far from basic research as one could get and definitely falls beyond the scope of “science,” as conventionally defined. The distinction between action and research is blurry, the research methods are often in constant flux, and the only “findings” are specific to the problem or case at hand and often are findings about the process of intervention itself. Rather than evaluate a program as a whole, action research often seeks to change and improve some particular aspect that may not be working—maybe there is not enough diversity in an organization or maybe women’s voices are muted during meetings and the organization wonders why and would like to change this. In a further step, participatory action research , those women would become part of the research team, attempting to amplify their voices in the organization through participation in the action research. As action research employs methods that involve people in the process, focus groups are quite common.

If you are working on a thesis or dissertation, chances are your committee will expect you to be contributing to fundamental knowledge and theory ( basic research ). If your interests lie more toward the action end of the continuum, however, it is helpful to talk to your committee about this before you get started. Knowing your purpose in advance will help avoid misunderstandings during the later stages of the research process!

The Research Question

Once you have written your paragraph and clarified your purpose and truly know that this study is the best study for you to be doing right now , you are ready to write and refine your actual research question. Know that research questions are often moving targets in qualitative research, that they can be refined up to the very end of data collection and analysis. But you do have to have a working research question at all stages. This is your “anchor” when you get lost in the data. What are you addressing? What are you looking at and why? Your research question guides you through the thicket. It is common to have a whole host of questions about a phenomenon or case, both at the outset and throughout the study, but you should be able to pare it down to no more than two or three sentences when asked. These sentences should both clarify the intent of the research and explain why this is an important question to answer. More on refining your research question can be found in chapter 4.

Chances are, you will have already done some prior reading before coming up with your interest and your questions, but you may not have conducted a systematic literature review. This is the next crucial stage to be completed before venturing further. You don’t want to start collecting data and then realize that someone has already beaten you to the punch. A review of the literature that is already out there will let you know (1) if others have already done the study you are envisioning; (2) if others have done similar studies, which can help you out; and (3) what ideas or concepts are out there that can help you frame your study and make sense of your findings. More on literature reviews can be found in chapter 9.

In addition to reviewing the literature for similar studies to what you are proposing, it can be extremely helpful to find a study that inspires you. This may have absolutely nothing to do with the topic you are interested in but is written so beautifully or organized so interestingly or otherwise speaks to you in such a way that you want to post it somewhere to remind you of what you want to be doing. You might not understand this in the early stages—why would you find a study that has nothing to do with the one you are doing helpful? But trust me, when you are deep into analysis and writing, having an inspirational model in view can help you push through. If you are motivated to do something that might change the world, you probably have read something somewhere that inspired you. Go back to that original inspiration and read it carefully and see how they managed to convey the passion that you so appreciate.

At this stage, you are still just getting started. There are a lot of things to do before setting forth to collect data! You’ll want to consider and choose a research tradition and a set of data-collection techniques that both help you answer your research question and match all your aims and goals. For example, if you really want to help migrant workers speak for themselves, you might draw on feminist theory and participatory action research models. Chapters 3 and 4 will provide you with more information on epistemologies and approaches.

Next, you have to clarify your “units of analysis.” What is the level at which you are focusing your study? Often, the unit in qualitative research methods is individual people, or “human subjects.” But your units of analysis could just as well be organizations (colleges, hospitals) or programs or even whole nations. Think about what it is you want to be saying at the end of your study—are the insights you are hoping to make about people or about organizations or about something else entirely? A unit of analysis can even be a historical period! Every unit of analysis will call for a different kind of data collection and analysis and will produce different kinds of “findings” at the conclusion of your study. [2]

Regardless of what unit of analysis you select, you will probably have to consider the “human subjects” involved in your research. [3] Who are they? What interactions will you have with them—that is, what kind of data will you be collecting? Before answering these questions, define your population of interest and your research setting. Use your research question to help guide you.

Let’s use an example from a real study. In Geographies of Campus Inequality , Benson and Lee ( 2020 ) list three related research questions: “(1) What are the different ways that first-generation students organize their social, extracurricular, and academic activities at selective and highly selective colleges? (2) how do first-generation students sort themselves and get sorted into these different types of campus lives; and (3) how do these different patterns of campus engagement prepare first-generation students for their post-college lives?” (3).

Note that we are jumping into this a bit late, after Benson and Lee have described previous studies (the literature review) and what is known about first-generation college students and what is not known. They want to know about differences within this group, and they are interested in ones attending certain kinds of colleges because those colleges will be sites where academic and extracurricular pressures compete. That is the context for their three related research questions. What is the population of interest here? First-generation college students . What is the research setting? Selective and highly selective colleges . But a host of questions remain. Which students in the real world, which colleges? What about gender, race, and other identity markers? Will the students be asked questions? Are the students still in college, or will they be asked about what college was like for them? Will they be observed? Will they be shadowed? Will they be surveyed? Will they be asked to keep diaries of their time in college? How many students? How many colleges? For how long will they be observed?

Recommendation

Take a moment and write down suggestions for Benson and Lee before continuing on to what they actually did.

Have you written down your own suggestions? Good. Now let’s compare those with what they actually did. Benson and Lee drew on two sources of data: in-depth interviews with sixty-four first-generation students and survey data from a preexisting national survey of students at twenty-eight selective colleges. Let’s ignore the survey for our purposes here and focus on those interviews. The interviews were conducted between 2014 and 2016 at a single selective college, “Hilltop” (a pseudonym ). They employed a “purposive” sampling strategy to ensure an equal number of male-identifying and female-identifying students as well as equal numbers of White, Black, and Latinx students. Each student was interviewed once. Hilltop is a selective liberal arts college in the northeast that enrolls about three thousand students.

How did your suggestions match up to those actually used by the researchers in this study? It is possible your suggestions were too ambitious? Beginning qualitative researchers can often make that mistake. You want a research design that is both effective (it matches your question and goals) and doable. You will never be able to collect data from your entire population of interest (unless your research question is really so narrow to be relevant to very few people!), so you will need to come up with a good sample. Define the criteria for this sample, as Benson and Lee did when deciding to interview an equal number of students by gender and race categories. Define the criteria for your sample setting too. Hilltop is typical for selective colleges. That was a research choice made by Benson and Lee. For more on sampling and sampling choices, see chapter 5.

Benson and Lee chose to employ interviews. If you also would like to include interviews, you have to think about what will be asked in them. Most interview-based research involves an interview guide, a set of questions or question areas that will be asked of each participant. The research question helps you create a relevant interview guide. You want to ask questions whose answers will provide insight into your research question. Again, your research question is the anchor you will continually come back to as you plan for and conduct your study. It may be that once you begin interviewing, you find that people are telling you something totally unexpected, and this makes you rethink your research question. That is fine. Then you have a new anchor. But you always have an anchor. More on interviewing can be found in chapter 11.

Let’s imagine Benson and Lee also observed college students as they went about doing the things college students do, both in the classroom and in the clubs and social activities in which they participate. They would have needed a plan for this. Would they sit in on classes? Which ones and how many? Would they attend club meetings and sports events? Which ones and how many? Would they participate themselves? How would they record their observations? More on observation techniques can be found in both chapters 13 and 14.

At this point, the design is almost complete. You know why you are doing this study, you have a clear research question to guide you, you have identified your population of interest and research setting, and you have a reasonable sample of each. You also have put together a plan for data collection, which might include drafting an interview guide or making plans for observations. And so you know exactly what you will be doing for the next several months (or years!). To put the project into action, there are a few more things necessary before actually going into the field.

First, you will need to make sure you have any necessary supplies, including recording technology. These days, many researchers use their phones to record interviews. Second, you will need to draft a few documents for your participants. These include informed consent forms and recruiting materials, such as posters or email texts, that explain what this study is in clear language. Third, you will draft a research protocol to submit to your institutional review board (IRB) ; this research protocol will include the interview guide (if you are using one), the consent form template, and all examples of recruiting material. Depending on your institution and the details of your study design, it may take weeks or even, in some unfortunate cases, months before you secure IRB approval. Make sure you plan on this time in your project timeline. While you wait, you can continue to review the literature and possibly begin drafting a section on the literature review for your eventual presentation/publication. More on IRB procedures can be found in chapter 8 and more general ethical considerations in chapter 7.

Once you have approval, you can begin!

Research Design Checklist

Before data collection begins, do the following:

• Write a paragraph explaining your aims and goals (personal/political, practical/strategic, professional/academic).
• Define your research question; write two to three sentences that clarify the intent of the research and why this is an important question to answer.
• Review the literature for similar studies that address your research question or similar research questions; think laterally about some literature that might be helpful or illuminating but is not exactly about the same topic.
• Find a written study that inspires you—it may or may not be on the research question you have chosen.
• Consider and choose a research tradition and set of data-collection techniques that (1) help answer your research question and (2) match your aims and goals.
• Define your population of interest and your research setting.
• Define the criteria for your sample (How many? Why these? How will you find them, gain access, and acquire consent?).
• If you are conducting interviews, draft an interview guide.
•  If you are making observations, create a plan for observations (sites, times, recording, access).
• Acquire any necessary technology (recording devices/software).
• Draft consent forms that clearly identify the research focus and selection process.
• Create recruiting materials (posters, email, texts).
• Apply for IRB approval (proposal plus consent form plus recruiting materials).
• Block out time for collecting data.
• At the end of the chapter, you will find a " Research Design Checklist " that summarizes the main recommendations made here ↵
• For example, if your focus is society and culture , you might collect data through observation or a case study. If your focus is individual lived experience , you are probably going to be interviewing some people. And if your focus is language and communication , you will probably be analyzing text (written or visual). ( Marshall and Rossman 2016:16 ). ↵
• You may not have any "live" human subjects. There are qualitative research methods that do not require interactions with live human beings - see chapter 16 , "Archival and Historical Sources." But for the most part, you are probably reading this textbook because you are interested in doing research with people. The rest of the chapter will assume this is the case. ↵

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A methodological tradition of inquiry and research design that focuses on an individual case (e.g., setting, institution, or sometimes an individual) in order to explore its complexity, history, and interactive parts.  As an approach, it is particularly useful for obtaining a deep appreciation of an issue, event, or phenomenon of interest in its particular context.

The controlling force in research; can be understood as lying on a continuum from basic research (knowledge production) to action research (effecting change).

In its most basic sense, a theory is a story we tell about how the world works that can be tested with empirical evidence.  In qualitative research, we use the term in a variety of ways, many of which are different from how they are used by quantitative researchers.  Although some qualitative research can be described as “testing theory,” it is more common to “build theory” from the data using inductive reasoning , as done in Grounded Theory .  There are so-called “grand theories” that seek to integrate a whole series of findings and stories into an overarching paradigm about how the world works, and much smaller theories or concepts about particular processes and relationships.  Theory can even be used to explain particular methodological perspectives or approaches, as in Institutional Ethnography , which is both a way of doing research and a theory about how the world works.

Research that is interested in generating and testing hypotheses about how the world works.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

Research that contributes knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment.

Research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems.  There are two kinds: summative and formative .

Research in which an overall judgment about the effectiveness of a program or policy is made, often for the purpose of generalizing to other cases or programs.  Generally uses qualitative research as a supplement to primary quantitative data analyses.  Contrast formative evaluation research .

Research designed to improve a program or policy (to help “form” or shape its effectiveness); relies heavily on qualitative research methods.  Contrast summative evaluation research

Research carried out at a particular organizational or community site with the intention of affecting change; often involves research subjects as participants of the study.  See also participatory action research .

Research in which both researchers and participants work together to understand a problematic situation and change it for the better.

The level of the focus of analysis (e.g., individual people, organizations, programs, neighborhoods).

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A fictional name assigned to give anonymity to a person, group, or place.  Pseudonyms are important ways of protecting the identity of research participants while still providing a “human element” in the presentation of qualitative data.  There are ethical considerations to be made in selecting pseudonyms; some researchers allow research participants to choose their own.

A requirement for research involving human participants; the documentation of informed consent.  In some cases, oral consent or assent may be sufficient, but the default standard is a single-page easy-to-understand form that both the researcher and the participant sign and date.   Under federal guidelines, all researchers "shall seek such consent only under circumstances that provide the prospective subject or the representative sufficient opportunity to consider whether or not to participate and that minimize the possibility of coercion or undue influence. The information that is given to the subject or the representative shall be in language understandable to the subject or the representative.  No informed consent, whether oral or written, may include any exculpatory language through which the subject or the representative is made to waive or appear to waive any of the subject's rights or releases or appears to release the investigator, the sponsor, the institution, or its agents from liability for negligence" (21 CFR 50.20).  Your IRB office will be able to provide a template for use in your study .

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

2.2 Approaches to Research

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research
• Compare and contrast correlation and causation

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

Clinical or Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

Watch this CBC video about Krista's and Tatiana's lives to learn more.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

Over time, it has become clear that while Krista and Tatiana share some sensory experiences and motor control, they remain two distinct individuals, which provides invaluable insight for researchers interested in the mind and the brain (Egnor, 2017).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a precious amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this chapter: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway ( Figure 2.7 ).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa ( Figure 2.8 ). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity , or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally ( Figure 2.9 ). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: People don't always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure 2.10 ).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research. In cross-sectional research , a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of studying a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals that make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) ( Figure 2.11 ).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increase over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

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The Research Process

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Research is a process that requires not only time but considerable effort. Research is intended to answer a specific question that is pertinent to a field of study. The research question or study purpose determines the type of research approach taken. Prior to conducting research, it is important to determine if the research must be approved by an institutional review board to ensure that it is being conducted in an ethically sound manner. After the study implementation, the researcher has the obligation to write about the research process. This assists other researchers by providing additional knowledge to the literature surrounding the research topic.

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Bem D. Writing the empirical journal article. In: Darley JM, Zanna MP, Roediger III HL, editors. The compleat academic: a practical guide for the beginning social scientist. 2nd ed. Washington, DC: American Psychological Association (APA); 2004.

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Course: biology library   >   unit 1, the scientific method.

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Introduction

• Make an observation.
• Ask a question.
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• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
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What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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• Indian J Anaesth
• v.60(9); 2016 Sep

Methodology for research II

S bala bhaskar.

Department of Anaesthesiology, Vijayanagar Institute Medical Sciences, Bellary, Karnataka, India

M Manjuladevi

1 Department of Anaesthesiology, St. John's Medical College, Bengaluru, Karnataka, India

Research is a systematic process, which uses scientific methods to generate new knowledge that can be used to solve a query or improve on the existing system. Any research on human subjects is associated with varying degree of risk to the participating individual and it is important to safeguard the welfare and rights of the participants. This review focuses on various steps involved in methodology (in continuation with the previous section) before the data are submitted for publication.

INTRODUCTION

Research uses a systematic approach to generate new knowledge to answer questions based on needs of patient health and practice. The investigator identifies research question, examines the ethical implications, describes the research design and collects appropriate data[ 1 , 2 , 3 ] which is evaluated by statistical tests before it can be published.[ 4 ] Before putting this to use in clinical practice, the relevant data are critically appraised for validity and reliability.[ 1 ] This review covers these aspects of the research methodology, in continuation with the first part by Garg et al . published in this issue of Indian Journal of Anaesthesia (IJA).[ 5 ]

REGULATORY AND ETHICAL CONSIDERATIONS

The Indian Council of Medical Research (ICMR) is the apex body in India responsible for the formulation, coordination and promotion of biomedical research. The International Committee of Medical Journal Editors (ICMJE) makes it mandatory for clinical trials to be included in a clinical trials registry for acceptance for publication. Clinical Trials.gov, run by the United States National Library of Medicine, was the first online registry established in 2005 and is widely used today. All trials to be conducted in India should have mandatory prospective registration with the Clinical Trial Registry of India (CTRI- www.ctri.in ). Good clinical practice (GCP) guidelines is a set of guidelines for biomedical studies which encompasses the design, conduct, termination, audit, analysis, reporting and documentation of the studies involving human subjects. It protects rights of human subjects and the authenticity of biomedical data. ( www.cdsco.nic.in/html/GCP1.html ). Table 1 lists the type of the research involved and their regulatory bodies.[ 6 ]

Research involved and their regulatory bodies

The International Standard Randomised Controlled Trial Number (ISRCTN) registry is a primary clinical trial registry recognised by the World Health Organization. The ICMJE provides content validation of all submitted studies (proposed, ongoing or completed). The study is assigned a unique identification number, and records of the study in the database can be easily accessed ( www.isrctn.com ).

To conduct a clinical trial in India, Institution Ethics Committee (IEC) approval is mandatory, and it must be registered with CTRI- www.ctri.nic.in .[ 2 , 6 ] When ‘off-label’ use of a drug (drug being used for a new indication/new dose/formulation/route) is tested for purely academic purposes and not for commercial use, currently there is no requirement of regulator approval.[ 2 , 6 ] However, the IEC has to consider the risks-benefits and ethical basis for approval of the research.

Drugs Controller General of India (DCGI) in India insists on registration and approval of clinical trials through CTRI and ensures scientific and safe conduct of the study. Most of the academic medical centres have Institutional Review Board (IRB) or IEC. They (‘internal’ Ethics Committees) can assess research proposals first and approve before submitting to national bodies. The approval may also go in parallel with DCGI approval. It is responsible for the supervision and protection of rights, safety and welfare of human subjects. During the progress of the trial, the IEC reviews safety reports, any significant violation/deviations in the protocol and for any amendments in the study protocol or informed consent.[ 2 , 7 ]

If IEC is not available in the institution, proposals can be sent independent ethics committee outside the institution (‘external’ Ethics Committees).[ 2 ] The ICMR suggests the establishment of registered Independent Ethics Committees (I nd EC) without institutional affiliation, functioning as per national guidelines. Proposals can also be sent to another institution, following established protocol, including providing a ‘no objection certificate’ and allow the external IEC necessary access.[ 2 ] When there is a large load of research, multiple ECs can function in the same institution as also subcommittees (e.g., subcommittees on adverse event, data safety monitoring, expedited review, etc.,).

The IRB consists of 7–15 members and at least five members are required to form the quorum to make a decision on the research [ Table 2 ].[ 2 ]

Composition of Institution Ethics Committee

All the research involving human participants should follow four basic ethical principles;[ 2 ] (a) Respect for persons autonomy, (b) beneficence (balance the risks against benefits bearing in mind the welfare of the research participant[s]), (c) nonmaleficence (no harm or reduce exposure to greater harm) and (d) Justice (distribution of research subjects equitably in all groups, for example, social, economic demographic, etc).

Informed consent is a process by which a subject confirms his/her willingness to participate in a clinical study.[ 4 ] It protects the individual's freedom of choice and respect for individual's autonomy. It ensures proper regulations in clinical trials and assures patient safety by dealing with both legal and ethical basis.[ 7 ] The process of informed consent consists of providing relevant information, its comprehension and voluntariness.[ 2 ] The details of the clinical study are explained to the subject in a simple and easily understandable language. The ‘subject/participant information sheet’ should include research aspect of the study, sponsor of the study, purpose and procedure, side effects, risks and discomforts, benefits, compensation for any study-related injury, alternatives to participation, right to withdraw, confidentiality of records and contact information of the investigators and IRB.[ 2 , 6 ] The informed and written consent form is duly signed by the subject in a document called ‘informed consent form’.[ 1 , 2 , 3 ] The documents consisting of patient/participant information sheet and informed consent form should be reviewed and approved by the IEC before enrolment of the participants.

A legal authorised representative (LAR) should be involved in the decision-making of vulnerable subjects who lack the ability to consent. The consent is taken from parent/LAR (in kids <7 years) and consent of parent/LAR along with assent form (oral/written) in children aged 7–18 years.[ 2 ] Audio/audio-visual recording of the informed consent process may be required in case of certain regulatory, clinical trials.[ 2 ] After the completion/termination of the study, all records within the IEC must be archived for at least 3 years; those related to regulatory, clinical trials must be archived for 5 years as per CDSCO regulation. Longer preservation may be needed as required by the sponsors/regulatory bodies.

Many finer aspects of the legal and ethical issues in research are discussed by Yip et al in this issue of IJA.[ 8 ]

The ethical duty of confidentiality refers to the obligation of an individual or organisation to safeguard entrusted information of the research data. It is essential for the integrity of the research project and protects information from unauthorised access, use, disclosure, modification, loss or theft.[ 6 , 7 ]

Data related to any of the studies of individual participant can be disclosed only under the following circumstances:

(a) Threat to a person's life, (b) Communication with drug registration authority in cases of severe adverse reaction, (c) Communication to health authority whenever there is risk to public health, (d) In a court of law under the orders of the presiding judge and (e) As a requirement for government agencies or regulatory authorities.[ 2 ]

DATA COLLECTION

‘Data’ includes the information that is systematically collected by the investigator during the study. The primary data are those which are originally done for the first time. The secondary data are a compilation of information done by someone else and have already been passed through the statistical process. A Data Monitoring Committee or Data and Safety Monitoring Board may be appointed, independent of IEC for interim analysis; their report forms the basis for early termination of planned study when there is compelling evidence of beneficial effectiveness or harmful side effects or for major flaws in the study.

The two main types of data are qualitative and quantitative, and most studies will have a combination of both. While quantitative data are easy to analyse and fairly reliable, qualitative data provide more depth in the description of the sample.[ 9 ]

Data collection methods [ Figure 1 ]:

Methods of data collection

• Interview: This method allows face to face contact with respondents, exploring the topic in depth. It allows the interviewer to explain or help to clarify questions increasing the usefulness of a response. It can be of different types-structured, unstructured (informal, conversational approach), semi-structured, focused and standardised.[ 9 , 10 , 11 ] There can be disadvantages-interviewer clarifications can lead to inconsistencies and influence the responses; the subject may distort information through recall error, selective perceptions and in the desire to please the interviewer.[ 10 ] Sometimes, the data may be too voluminous to record or reduce it
• Observation: This method provides direct information about the behaviour of individuals and groups. It allows the investigator to understand the situation and context. It could be ‘Participant’ observation: The observer takes part in the situation he or she observes or ‘Nonparticipant’ observation: The observer watches the situation, openly or concealed, but does not participate[ 9 , 10 , 11 ]
• Questionnaire: It is a simple and inexpensive method not even requiring any research assistants. More honest responses may be available when anonymity is provided. Written questions are presented that are to be answered by the respondents. A written questionnaire can be administered in different ways, such as by sending questionnaires by mail with clear instructions on how to answer the questions and asking for mailed responses; gathering all or part of the respondents in one place at one time, giving oral or written instructions, and letting the respondents fill out the questionnaires; or hand-delivering questionnaires to respondents and collecting them later.[ 10 , 11 ] The disadvantage of this method are observer bias and breach in confidentiality; also, this cannot be used on illiterate subjects. As with other types of outcome measurements, questionnaires and interviews are to be assessed for validity (accuracy) and for reproducibility (precision)-using ‘face validity, content validity and construct validity’
• Documents: It is an inexpensive and unobtrusive method of data collection from locally available records or documents (existing research, hospital records, databases, videotapes, etc.).[ 9 , 10 , 11 ] There is disadvantage of accuracy, authenticity and availability (missing data/omission of needed data). Anaesthesia information management systems used in modern practice have the ability to collect data automatically, in large volumes, which can be converted for specific, focused outcome assessments for research purposes.

Compilation of data includes systematic arrangement of data to facilitate the presentation and analysis.[ 12 ] The data collected are entered in a database where the information about subjects and variables are stored. Simple study database can be maintained in a spreadsheet (MS Excel © ) or statistical software (e.g., Statistical Analysis System (SAS ® ) (NC, USA), IBM SPSS (Statistical Package for the Social Sciences) Statistics ® (IBM Inc., NY, USA). More complex database require integrated database management software (e.g., Access © (Windows) and Filemaker © Pro (Apple Inc.,).[ 13 ] Database ‘queries’ sort and filter the data as well as calculate values based on the raw data fields.[ 12 , 13 ] Queries are used to monitor data entry, report on study progress and format the results for analysis. Data must be stored in ‘secure servers’ so that confidentiality is maintained.[ 13 ] Backup files and off-site storage may be necessary to prevent any data loss. Common methods of summarising and presenting data are tables, pie charts, bar charts, histograms, frequency and cumulative frequency curves, dot plots and x-y scatterplots.[ 13 , 14 , 15 ]

RESEARCH TOOLS: DEVELOPMENT AND VALIDATION

‘Research tool’ is the means of collecting information for the purpose of a study. Observation forms, interview schedules, questionnaires are all classified as research tools. The first practical step in doing a research process is to construct a research tool. Four stage process is involved in developing a research tool.[ 9 , 10 , 11 , 12 ]

• Concept development: The researcher should understand the basic knowledge pertaining to the study
• Specification of concept dimensions: The researcher should be able to build in a dimension based on the concept of the study
• Selection of indicators: Once the concept and its dimensions are developed, each concept element is measured by indicators (respondent's knowledge, opinion, expectation, etc., are measured with scales, devices). More than one indicator increases the score and validity of the study
• Formation of index: Dimension of a concept or different measurements of a dimension are then put into an overall index.

The error may occur at any stage of research, i.e., from selection to interpretation of data to conclusion. Two types of error can occur – random and systematic error. The random error must be reduced as far as possible, and the systemic error should be eliminated. Errors can occur from three sources:[ 16 , 17 , 18 , 19 ]

• Investigator: Due to ignorance, incompetence and bias
• Instrument: Due to variability, calibration, problems and malfunctioning
• Subject: Due to bias, noncompliance and biological variation in response.

Any research can be affected by factors that can invalidate the findings. A good research tool should meet the tests of validity, reliability and practicality.

Validity refers to the extent to which a test measures what we actually wish to measure. Reliability refers to accuracy and precision of a measurement procedure.

The practicality characteristic of a measuring instrument can be judged in terms of economy, convenience and interpretability.

Determining validity can be viewed as constructing an evidence-based argument regarding how well a tool measures what it is supposed to do.

USES OF VALIDITY IN SCIENTIFIC METHODS

External validity refers to generalising the study results to other population groups with similar risk factors, settings, measurement and treatment variables.

Internal validity implies that the differences observed between the treatment groups, apart from random error, are only due to the treatments under investigation.[ 9 ]

Validity assessment can be performed in three ways:

• Content validity is the extent to which a measuring tool provides adequate coverage of all the aspects of the topic under study. (e.g., quality of pain relief to include measurement of analgesia, haemodynamics, sedation, etc.). ‘Face validity’ assesses whether the measurements appear reasonable; a measure of how representative a research project is ‘at face value’, and whether it appears to be a good project
• Construct validity refers to the degree to which a measurement conforms to theoretical constructs. Convergent validity tests whether and how well those ‘constructs’ that are expected to be related are, in fact, related. Discriminant validity or divergent validity tests those ‘constructs’ that should have no relationship do, in fact, not have any relationship
• Criterion validity assesses the degree to which a new measurement correlates with well-accepted existing measures. Predictive validity is a strong variety of criterion validity, representing the ability of the measurement to predict an outcome.

Other Types: Concurrent validity refers to the degree of correlation of two measures of the same concept administered at the same time. Consensual validity is a process by which a panel of experts judge the validity.[ 1 , 16 , 17 , 18 , 19 ]

A measuring instrument is reliable if it provides consistent results.[ 1 , 11 ]

The stability aspect refers to securing consistent results with repeated measurements of the same person and with the same instrument. Determination of the degree of stability by comparing the results of repeated measurements.

The equivalence aspect considers how much error may get introduced by different investigators or different samples of the items being studied.

PRACTICALITY

Measuring instrument practicality is tested in terms of economy, convenience and interpretability.

Economy consideration suggests that some trade-off is needed between the ideal research project and that which the budget can afford.

Convenience test suggests that the measuring instrument should be easy to administer. Interpretability consideration is especially important when persons other than the designers of the test are to interpret the results.

ANALYSIS PLAN: QUALITY AND APPROPRIATENESS OF ANALYSIS

The statistics in research functions as a tool in designing research, analysing its data and drawing conclusions from it.[ 20 , 21 ] Descriptive statistics are the development of certain indices from the raw data, summarised in tables, charts or numerical forms. The inferential analysis is undertaken to apply various tests of significance to test hypotheses of a research question so as to validate conclusions. An essential part of presenting any type of inferential data is by probability ( P value) which reassures the reader that the outcome was secondary to the effect of the studied variable and has not occurred purely by chance.[ 22 ] P < 5% is considered statistically significant. Statistical tests are used for testing the significance. Various parametric tests (variable normally distributed) and nonparametric tests (variables are not normally distributed) are used to meet the objective of the study [ Table 3 ].[ 19 , 20 ] ‘Basic Statistical Tools in Research and Data analysis’ in this issue of IJA by Zulfiqar Ali describe these tests in detail.[ 23 ]

Tests of significance

The ‘methodology’ in a research strategy outlines the steps involved in research process. The research problem is identified, aims and objectives are formulated, sample size is calculated; Ethics Committee approval and informed consent from the subject are taken; data collected are summarised. The research design is planned, and the collected data are then analysed using appropriate statistical tests. The derived evidence is put into clinical practice once the reader is convinced that the clinical study is valid and reliable.

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Research how 2.

Welcome to Office of Research’s integrated and functional portal designed to support University of Cincinnati’s faculty, staff and students. We believe connecting “how to’s” to the “why’s” is important, and key to establishing connections that build and extend knowledge with simple intuitive actions. With this in mind, we have introduced three key features in our newly upgraded website.

“Smart search” makes it easy to look for a specific document related to Office of Research’s work stream. Plug in search key words and the search engine queries all of our research resources to give you fast and relevance based results that support UC’s research excellence.

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A Penn team’s push to make research more inclusive

Penn’s palliative and advanced illness research (pair) center is working to bring more underrepresented racial and ethnic backgrounds into their research, and to train ai models to be free from bias..

Research is a driving force of medical progress—but is it truly inclusive of the voices and experiences of those it seeks to help? 

The way research is conducted can often leave out important voices, like people from underrepresented racial and ethnic backgrounds, those who speak languages other than English, or those with limited literacy. Rachel Kohn , an assistant professor of medicine in the Division of Pulmonary, Allergy and Critical Care and core faculty in Penn’s Palliative and Advanced Illness Research (PAIR) Center , is looking to change that.

Health care research, while indispensable for advancing medical knowledge and improving patient outcomes, has long grappled with a glaring issue: the lack of diversity and inclusivity. “In health care, we aim to leave no one behind. But when certain demographics are excluded or marginalized in research, we're failing to uphold that promise," says Kohn. A study led by Kohn, published in the Journal of General Internal Medicine , addresses the underrepresentation and disparities prevalent in research practices.

Working with a group of colleagues through Penn’s Joint Research Practices , Kohn developed a clear goal: to make academic research more inclusive, equitable, and accessible for everyone. What would follow was years of investigation to discuss findings and refine their focus. Subgroups were formed to delve into specific areas to ensure a well-rounded perspective. What Kohn and colleagues have now developed is a set of guidelines covering everything from how participants are paid to how research findings are communicated.

Kohn raises another concern in the field and its impact on diversity, equity, and inclusion in academic research: artificial intelligence, or AI.

AI models don’t begin full of information—they must be fed source material to interpret. What if they are continually fed data that is biased or misinformed?

“Research findings are more and more frequently being fed into AI models to serve as clinical decision support systems for patient care which can have far-reaching effects,” explains Kohn. But concerns are heightened at the possibility of biased data being fed into AI.

“That biased data could propagate into clinical decision support systems, research questions, trial eligibility, risk adjustment, or hospital and quality improvement assessment. One major concern about this process is that clinicians rarely know the source of the data and assume that they should take the decision support recommendation at face value without pausing to consider the algorithm inputs. ‘Algorithmic bias’ is a hugely burgeoning field trying to address this very issue,” explains Kohn.

This story is by Matt Toal. Read more at Penn Medicine News .

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Pokémon Go ‘Catching Wonders’ Masterwork Master Ball Research guide

All the steps from the ‘Catching Wonders’ research that rewards you with a Master Ball

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Pokémon Go has added its second free Master Ball to the game (and third Master Ball overall), as part of a lengthy Masterwork Research called “ Catching Wonders .” As usual with these Masterwork Research Tasks, this is a marathon, not a sprint, and it’s meant to be completed over a long period of time. There’s no time limit on the Master Ball research.

There’s also a short event that’s running alongside the permanent research, from May 14-19. During this event, you’ll get 10 times the XP for your first catch and PokéStop spin of the day. You’ll also get five times the Stardust for your first catch of the day. The event Field Research tasks also reward Stardust, XP, Poké Balls, Great Balls, and Ultra Balls.

Below we list out all the steps for the “Catching Wonders” Masterwork Research in Pokémon Go , so you know what to expect before you claim the Master Ball.

‘Catching Wonders’ Step 1 of 4

• Catch 250 Pokémon (20 Poké Balls)
• Use 150 berries to help catch Pokémon (15 Razz Berries)
• Make 100 nice curveball throws (2,500 Stardust)

Rewards : 2,500 XP, Sandygast encounter

‘Catching Wonders’ Step 2 of 4

• Catch 300 Pokémon (25 Poké Balls)
• Make 100 great curveball throws (15 Pinap Berries)
• Transfer 50 Pokémon (20 Great Balls)

Rewards : 3,500 XP, Dubwool encuonter

‘Catching Wonders’ Step 3 of 4

• Catch 350 Pokémon (20 Great Balls)
• Defeat 25 Team Go Rocket members (5 Charged TMs)
• Catch 75 different species of Pokémon (15 Revives)

Rewards : 4,500 XP, Hisuian Sneasel encounter

‘Catching Wonders’ Step 4 of 4

• Visit PokéStops on seven different days (2,500 XP)
• Catch a Pokémon on seven different days (2,500 Stardust)
• Hatch 20 eggs (Whiscash encounter)
• Catch 75 Pokémon in a single day (20 Ultra Balls)
• Make 50 excellent throws (20 Max Revives)
• Catch 500 Pokémon (3 Silver Pinap Berries)

Rewards : 5,000 XP, 5,000 Stardust, 1 Master Ball

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Experimental R&D Value Added Statistics for the U.S. and States Now Available

Research and development activity accounted for 2.3 percent of the U.S. economy in 2021, according to new experimental statistics released today by the Bureau of Economic Analysis. R&D as a share of each state’s gross domestic product, or GDP, ranged from 0.3 percent in Louisiana and Wyoming to 6.3 percent in New Mexico, home to federally funded Los Alamos National Laboratory and Sandia National Laboratories.

These statistics are part of a new Research and Development Satellite Account  BEA is developing in partnership with the National Center for Science and Engineering Statistics of the National Science Foundation . The statistics complement BEA’s national data on R&D investment  and provide BEA’s first state-by-state numbers on R&D.

The new statistics, covering 2017 to 2021, provide information on the contribution of R&D to GDP (known as R&D value added), compensation, and employment for the nation, all 50 states, and the District of Columbia. In the state statistics, R&D is attributed to the state where the R&D is performed.

Some highlights from the newly released statistics:

R&D activity is highly concentrated in the United States. The top ten R&D-producing states account for 70 percent of U.S. R&D value added. California alone accounts for almost a third of U.S. R&D. Other top R&D-producing states include Washington, Massachusetts, Texas, and New York.

Treating R&D as a sector allows for comparisons with other industries and sectors of the U.S. economy. For instance, R&D’s share of U.S. value added in 2021 is similar to hospitals (2.4 percent) and food services and drinking places (2.2 percent).

Eighty-five percent of R&D value added is generated by the business sector, followed by government, and nonprofit institutions serving households.

Within the business sector, the professional, scientific, and technical services industry accounts for 40 percent of business R&D value added.    Information (15 percent), chemical manufacturing (12 percent), and computer and electronic product manufacturing (11 percent) also account for sizable shares.

Visit the R&D Satellite Account on BEA’s website for the full set of experimental statistics and accompanying information. To help refine the methodology and presentation of these statistics, BEA is seeking your feedback. Please submit comments to  [email protected] .

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Broad Public Support for Legal Abortion Persists 2 Years After Dobbs

By more than 2 to 1, americans say medication abortion should be legal, table of contents.

• Other abortion attitudes
• Overall attitudes about abortion
• Americans’ views on medication abortion in their states
• How statements about abortion resonate with Americans
• Acknowledgments
• The American Trends Panel survey methodology

Pew Research Center conducted this study to understand Americans’ views on the legality of abortion, as well as their perceptions of abortion access. For this analysis, we surveyed 8,709 adults from April 8 to 14, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report and its methodology .

Nearly two years after the Supreme Court overturned the 1973 Roe v. Wade decision guaranteeing a national right to abortion, a majority of Americans continue to express support for abortion access.

About six-in-ten (63%) say abortion should be legal in all or most cases. This share has grown 4 percentage points since 2021 – the year prior to the 2022 decision in Dobbs v. Jackson Women’s Health Organization that overturned Roe.

The new Pew Research Center survey, conducted April 8-14, 2024, among 8,709 adults, surfaces ongoing – and often partisan – divides over abortion attitudes:

• Democrats and Democratic-leaning independents (85%) overwhelmingly say abortion should be legal in all or most cases, with near unanimous support among liberal Democrats.
• By comparison, Republicans and Republican leaners (41%) are far less likely to say abortion should be legal in all or most cases. However, two-thirds of moderate and liberal Republicans still say it should be.

Since before Roe was overturned, both parties have seen a modest uptick in the share who say abortion should be legal.

As in the past, relatively few Americans (25%) say abortion should be legal in all cases, while even fewer (8%) say it should be illegal in all cases. About two-thirds of Americans do not take an absolutist view: 38% say it should be legal in most cases, and 28% say it should be illegal in most cases.

Related: Americans overwhelmingly say access to IVF is a good thing

Women’s abortion decisions

A narrow majority of Americans (54%) say the statement “the decision about whether to have an abortion should belong solely to the pregnant woman” describes their views extremely or very well. Another 19% say it describes their views somewhat well, and 26% say it does not describe their views well.

Views on an embryo’s rights

About a third of Americans (35%) say the statement “human life begins at conception, so an embryo is a person with rights” describes their views extremely or very well, while 45% say it does not describe their views well.

But many Americans are cross-pressured in their views: 32% of Americans say both statements about women’s decisions and embryos’ rights describe their views at least somewhat well.

Abortion access

About six-in-ten Americans in both parties say getting an abortion in the area where they live would be at least somewhat easy, compared with four-in-ten or fewer who say it would be difficult.

However, U.S. adults are divided over whether getting an abortion should be easier or harder:

• 31% say it should be easier for someone to get an abortion in their area, while 25% say it should be harder. Four-in-ten say the ease of access should be about what it is now.
• 48% of Democrats say that obtaining an abortion should be easier than it is now, while just 15% of Republicans say this. Instead, 40% of Republicans say it should be harder (just 11% of Democrats say this).

As was the case last year, views about abortion access vary widely between those who live in states where abortion is legal and those who live in states where it is not allowed.

For instance, 20% of adults in states where abortion is legal say it would be difficult to get an abortion where they live, but this share rises to 71% among adults in states where abortion is prohibited.

Medication abortion

Americans say medication abortion should be legal rather than illegal by a margin of more than two-to-one (54% vs. 20%). A quarter say they are not sure.

Like opinions on the legality of abortion overall, partisans differ greatly in their views of medication abortion:

• Republicans are closely split but are slightly more likely to say it should be legal (37%) than illegal (32%). Another 30% aren’t sure.
• Democrats (73%) overwhelmingly say medication abortion should be legal. Just 8% say it should be illegal, while 19% are not sure.

Across most other demographic groups, Americans are generally more supportive than not of medication abortion.

Across demographic groups, support for abortion access has changed little since this time last year.

Today, roughly six-in-ten (63%) say abortion should be legal in all (25%) or most (38%) cases. And 36% say it should be illegal in all (8%) or most (28%) cases.

While differences are only modest by gender, other groups vary more widely in their views.

Race and ethnicity

Support for legal abortion is higher among Black (73%) and Asian (76%) adults compared with White (60%) and Hispanic (59%) adults.

Compared with older Americans, adults under 30 are particularly likely to say abortion should be legal: 76% say this, versus about six-in-ten among other age groups.

Those with higher levels of formal education express greater support for legal abortion than those with lower levels of educational attainment.

About two-thirds of Americans with a bachelor’s degree or more education (68%) say abortion should be legal in all or most cases, compared with six-in-ten among those without a degree.

White evangelical Protestants are about three times as likely to say abortion should be illegal (73%) as they are to say it should be legal (25%).

By contrast, majorities of White nonevangelical Protestants (64%), Black Protestants (71%) and Catholics (59%) say abortion should be legal. And religiously unaffiliated Americans are especially likely to say abortion should be legal (86% say this).

Partisanship and ideology

Democrats (85%) are about twice as likely as Republicans (41%) to say abortion should be legal in all or most cases.

But while more conservative Republicans say abortion should be illegal (76%) than legal (27%), the reverse is true for moderate and liberal Republicans (67% say legal, 31% say illegal).

By comparison, a clear majority of conservative and moderate Democrats (76%) say abortion should be legal, with liberal Democrats (96%) overwhelmingly saying this.

Views of abortion access by state

About six-in-ten Americans (58%) say it would be easy for someone to get an abortion in the area where they live, while 39% say it would be difficult.

This marks a slight shift since last year, when 54% said obtaining an abortion would be easy. But Americans are still less likely than before the Dobbs decision to say obtaining an abortion would be easy.

Still, Americans’ views vary widely depending on whether they live in a state that has banned or restricted abortion.

In states that prohibit abortion, Americans are about three times as likely to say it would be difficult to obtain an abortion where they live as they are to say it would be easy (71% vs. 25%). The share saying it would be difficult has risen 19 points since 2019.

In states where abortion is restricted or subject to legal challenges, 51% say it would be difficult to get an abortion where they live. This is similar to the share who said so last year (55%), but higher than the share who said this before the Dobbs decision (38%).

By comparison, just 20% of adults in states where abortion is legal say it would be difficult to get one. This is little changed over the past five years.

Americans’ attitudes about whether it should be easier or harder to get an abortion in the area where they live also varies by geography.

Overall, a decreasing share of Americans say it should be harder to obtain an abortion: 33% said this in 2019, compared with 25% today.

This is particularly true of those in states where abortion is now prohibited or restricted.

In both types of states, the shares of Americans saying it should be easier to obtain an abortion have risen 12 points since before Roe was overturned, as the shares saying it should be harder have gradually declined.

By comparison, changes in views among those living in states where abortion is legal have been more modest.

While Americans overall are more supportive than not of medication abortion (54% say it should be legal, 20% say illegal), there are modest differences in support across groups:

• Younger Americans are somewhat more likely to say medication abortion should be legal than older Americans. While 59% of adults ages 18 to 49 say it should be legal, 48% of those 50 and older say the same.
• Asian adults (66%) are particularly likely to say medication abortion should be legal compared with White (55%), Black (51%) and Hispanic (47%) adults.
• White evangelical Protestants oppose medication abortion by about two-to-one (45% vs. 23%), with White nonevangelicals, Black Protestants, Catholics and religiously unaffiliated adults all being more likely than not to say medication abortion should be legal.
• Republicans are closely divided over medication abortion: 37% say it should be legal while 32% say it should be illegal. But similar to views on abortion access overall, conservative Republicans are more opposed (43% illegal, 27% legal), while moderate and liberals are more supportive (55% legal, 14% illegal).

Just over half of Americans (54%) say “the decision about whether to have an abortion should belong solely to the pregnant woman” describes their views extremely or very well, compared with 19% who say somewhat well and 26% who say not too or not at all well.

Democrats (76%) overwhelmingly say this statement describes their views extremely or very well, with just 8% saying it does not describe their views well.

Republicans are more divided: 44% say it does not describe their views well while 33% say it describes them extremely or very well. Another 22% say it describes them somewhat well.

Fewer Americans (35%) say the statement “human life begins at conception, so an embryo is a person with rights” describes their views extremely or very well. Another 19% say it describes their views somewhat well while 45% say it describes them not too or not at all well.

(The survey asks separately whether “a fetus is a person with rights.” The results are roughly similar: 37% say that statement describes their views extremely or very well.)

Republicans are about three times as likely as Democrats to say “an embryo is a person with rights” describes their views extremely or very well (53% vs. 18%). In turn, Democrats (66%) are far more likely than Republicans (25%) to say it describes their views not too or not at all well.

Some Americans are cross-pressured about abortion

When results on the two statements are combined, 41% of Americans say the statement about a pregnant woman’s right to choose describes their views at least somewhat well , but not the statement about an embryo being a person with rights. About two-in-ten (21%) say the reverse.

But for nearly a third of U.S. adults (32%), both statements describe their views at least somewhat well.

Just 4% of Americans say neither statement describes their views well.

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Americans overwhelmingly say access to ivf is a good thing, what the data says about abortion in the u.s., support for legal abortion is widespread in many countries, especially in europe, nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, most popular, report materials.

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Falcon 2: UAE releases new AI model surpassing Meta’s Llama 3

U NITED ARAB EMIRATES: A government research institute in the United Arab Emirates (UAE) launched a new open-source Generative AI model on Monday, May 13.

Channel News Asia reported that Abu Dhabi’s Technology Innovation Institute (TII) introduced the Falcon 2 series.

This release includes two models: the text-based Falcon 2 11B and the vision-to-language Falcon 2 11B VLM, which can generate text descriptions from uploaded images.

The Technology Innovation Institute operates under the Advanced Technology Research Council of Abu Dhabi.

The UAE, known for its oil exports and regional influence, is now making significant investments in AI. However, the UAE’s foray into AI has not been without its challenges.

Last year, American officials issued a clear ultimatum to choose between American or Chinese technology. This prompted Emirati AI firm G42 to divest from Chinese companies and remove Chinese hardware from its operations.

The move paved the way for a US$1.5 billion (approx. S$2.03 billion) investment from Microsoft, facilitated in coordination with Washington.

Faisal Al Bannai, Secretary General of the Advanced Technology Research Council (ATRC) and president’s advisor on strategic research and advanced technology, stated that the UAE is proving it can be a significant player in artificial intelligence.

The Falcon 2 series was launched as companies and countries competed to create their own large language models following OpenAI’s release of ChatGPT in 2022.

While some have chosen to keep their AI code private, UAE’s Falcon and Meta’s Llama have made their code open-source for public use.

Al Bannai expressed optimism about Falcon 2’s performance and mentioned they are already working on the “Falcon 3 generation.”

Al Bannai says, “ We’re very proud that we can still punch way above our weight, really compete with the best players globally.”

According to AFP, Falcon 2 11B has been tested against several leading AI models in its category of pre-trained models.

It surpasses the performance of Meta’s recently launched Llama 3 with 8 billion parameters and is nearly equal to Google’s Gemma 7B, achieving scores of 64.28 and 64.29, respectively.

Hugging Face, a US-based platform known for its objective evaluation tools and global leaderboard for open LLMs independently verified these performance results.

Falcon 2 11B is released under the TII Falcon License 2.0, a permissive licence based on Apache 2.0. This licence includes an acceptable use policy that encourages responsible AI use. To know more about Falcon 2, check here . / TISG

Read also: Apple and OpenAI to join forces to bring ChatGPT to iPhones!

Featured image by Depositphotos

The post Falcon 2: UAE releases new AI model surpassing Meta’s Llama 3 appeared first on The Independent Singapore News - Latest Breaking News

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May 13, 2024

Multifaceted Purdue program focuses on strategic defense technologies and engineering

Online degree and certificates are designed for working professionals in national defense

WEST LAFAYETTE, Ind. — A multifaceted, interdisciplinary defense technology- and engineering-oriented program from Purdue University is designed to serve professionals in the field of national defense whether they are seeking an advanced degree or a quick skill set upgrade. 

The new program and courses are available in noncredit and degree-seeking pathways. The newest program offering, the noncredit certificate in strategic defense technologies , is now available for learners to take courses individually or to earn the certificate as a whole. A related concentration option is available for professionals admitted into Purdue’s online interdisciplinary Master of Science in Engineering . A graduate certificate is set to launch in spring 2025.

All the options combine history; strategy; and social, military, and data sciences with engineering and technology in a new way. They provide learners with knowledge and competencies needed to analyze, understand, design and execute defense and civilian strategies that involve technologies supporting the strategic interests of the United States.

“Purdue is uniquely qualified to train military and civilian learners interested in strategic defense technologies,” said Sorin Adam Matei, professor and the associate dean of research and graduate education in the College of Liberal Arts. “While our program examines and explores technologies and engineering solutions, it does it from a policy and social sciences perspective, both calibrated to support and expand practical work in national defense and strategic military planning.”

The program was created by the experts grouped around the FORCES research incubator, a Purdue College of Liberal Arts initiative that brings social sciences and humanities expertise to bear on the nation’s strategic defense needs.

Core courses titled Grand Challenges in Defense Engineering; Space Strategy; Strategic Foresight; Technology, War and Strategy; and Data and AI Storytelling are embedded across the program.

To earn the Strategic Defense Technologies certificate, learners complete the Technology, War and Strategy course and two of the other four core courses, but all the courses can be taken individually outside of the certificate program. The courses are self-paced and there are no prerequisites. 

“This allows you to pick up the knowledge that you need when you need it,” Matei said. “We want to provide a just-in-time type of education.”

All students in the strategy and defense engineering concentration of Purdue’s 100% online interdisciplinary engineering master’s degree must take Technology, War and Strategy and either Grand Challenges in Defense Engineering or Space Strategy, and choose two courses from among Data and AI Storytelling, Strategic Foresight, and a course titled Ethical Reasoning in Defense Technology. Master’s students can then draw on an extensive collection of electives to fill out the 30-credit hour program and to customize their educational experience to their interests and needs. Admission to the master’s program requires a bachelor’s degree in engineering or in another STEM field. Purdue’s online engineering master’s programs are ranked No. 3 nationally by U.S. News & World Report .

The noncredit certificate and courses, master’s degree, and planned graduate certificate are designed for midcareer military and civilian personnel in the Department of Defense ecosystem, including employees of DOD contractors, staff members for policymakers as well as think tanks and other nongovernmental organizations, and graduate students planning to pursue defense-oriented professions.

The anytime, anywhere online format makes it accessible for working professionals looking to boost their careers. Created by expert Purdue instructional designers, the courses are structured to move learners smoothly through the curriculum as they gain new knowledge and skills.

Faculty who teach on Purdue’s flagship campus, rated as a top 10 public university in the U.S. by QS World University Rankings, along with subject matter experts, developed and teach in the program. The instructors have worked or are working in fields as diverse as diplomacy, learning innovation, nuclear military applications and space policy.

“All our instructors are people with vast practical experience who have an ear to the ground,” Matei said. “Our courses are regularly updated and checked against reality.”

One of the country’s leading educational institutions for defense-related research and teaching, Purdue is committed to helping the United States remain a world leader in defense technologies.

For more information on the interdisciplinary Master of Science in Engineering degree with a concentration in strategy and defense engineering, visit the master’s program website . To learn more about the noncredit certificate in strategic defense technologies, visit the certificate program website . 

Writer: Greg Kline, [email protected]

Media contact: Tim Doty, [email protected]

Sources: Sorin Adam Matei, [email protected]

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Realization of high-fidelity unitary operations on up to 64 frequency bins

Syamsundar de, vahid ansari, jan sperling, sonja barkhofen, benjamin brecht, and christine silberhorn, phys. rev. research 6 , l022040 – published 13 may 2024.

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Supplemental Material

• ACKNOWLEDGMENTS

The ability to apply user-chosen large-scale unitary operations with high fidelity to a quantum state is key to realizing future photonic quantum technologies. Here, we realize the implementation of programmable unitary operations on up to 64 frequency-bin modes. To benchmark the performance of our system, we probe different quantum walk unitary operations, in particular, Grover walks on four-dimensional hypercubes with similarities exceeding 95% and quantum walks with 400 steps on circles and finite lines with similarities of 98%. Our results open a path toward implementing high-quality unitary operations, which can form the basis for applications in complex tasks, such as Gaussian boson sampling.

• Received 24 March 2023
• Accepted 9 April 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.L022040

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

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Authors & Affiliations

• 1 Integrated Quantum Optics Group, Institute for Photonic Quantum Systems (PhoQS), Paderborn University, 33098 Paderborn, Germany
• 2 Advanced Technology Development Centre, IIT Kharagpur, Kharagpur 721302, India
• 3 E. L. Ginzton Laboratory, Stanford University, 348 Via Pueblo Mall, Stanford, California 94305, USA
• 4 Theoretical Quantum Science, Institute for Photonic Quantum Systems (PhoQS), Paderborn University, 33098 Paderborn, Germany
• * [email protected]

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Vol. 6, Iss. 2 — May - July 2024

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Concept of our scheme. (a) Conventional approach: a multimode input state | ψ 〉 evolves under the action of a unitary operation U ̂ and is subject to a POVM Π ̂ . The resulting probabilities p are given by Born's rule, p = 〈 ψ ′ | Π ̂ | ψ ′ 〉 . (b) Our approach: we prepare a multimode input state | ψ 〉 . We then apply the unitary operation U ̂ to Π ̂ and obtain a rotated POVM Π ̂ ′ = U ̂ † Π ̂ U ̂ , which yields probabilities p = 〈 ψ | Π ̂ ′ | ψ 〉 = 〈 ψ ′ | Π ̂ | ψ ′ 〉 . (c) Schematic of our experimental setup. The inset illustrates an example of a projector onto frequency-bin superpositions. For details, see the text; titanium sapphire oscillator (Ti:sapph), optical parametric oscillator (OPO), neutral density filter (NDF), dichroic mirror (DM), delay line (DL), periodically poled lithium niobate (PPLN), silicon avalanche photodiode (SiAPD).

QW on a four-dimensional hypercube. (a) We encode the nodes of the four-dimensional hypercubes as binary numerals, 0 , ... , 2 4 − 1 = 15 . The walker is initialized at position x ⃗ = 0 in a coin-superposition state, with all four coin degrees of freedom uniformly populated. (b) Comparison between experimental data and theory predictions for the position distribution P ( x ⃗ , n ) for different QW steps n . During each step, a four-dimensional Grover coin is applied to all positions. Perfect state transfer occurs within a period of 12 steps.

Bounded QWs over large step numbers. (a) Position distribution of a Hadamard QW on a circle with 21 positions. We measure the evolution up to the 400th step (note skips on the horizontal axis) and see no degradation in similarities. (b) Coin-resolved position distribution of the 400th step of the Hadamard walk. We find a mean similarity of S ¯ ≈ 98 % . Error bars indicate statistical uncertainties and are hardly visible. (c) Same as (a) but for a noninteracting walk with reflecting boundaries. The walk was initialized with a walker in a position superposition. (d) Same as (b) but for the noninteracting walk. Because of the noninteracting nature of the walk, a population of the c = 1 coin state is neither expected nor observed. The mean similarity yields S ¯ ≈ 99 % .

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