Four types: single holistic, single embedded, multiple holistic, multiple embedded
The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.
Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.
A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6
Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.
Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.
Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.
Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).
Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36
Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5 , 37 By seeking patterns within and across data sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39
Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.
Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.
Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.
An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46
Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.
Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55
Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37
Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.
The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7
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The power of combining real and synthetic respondents in market research.
Abigail Stuart, with 20+ years in brand and market research, drives innovation and champions AI. Connect on LinkedIn .
In the 2013 science fiction film Her , set in the near future, a lonely writer named Theodore develops an unexpected and profound relationship with an artificially intelligent virtual assistant designed to meet his every need. Theodore is captivated by her ability to learn, adapt and exhibit human-like psychological growth. This portrayal of human-AI interaction is no longer confined to science fiction. The rise of artificial intelligence and machine learning has led to the creation and use of synthetic humans —digital beings engineered through artificial intelligence to resemble and behave like humans in appearance, personality and intelligence.
This evolution in AI technology extends beyond personal relationships and into various industries, including market research. Just as synthetic humans can mimic real people in terms of interaction and behavior, synthetic respondents have the potential to revolutionize the field of market research. These virtual beings can simulate the opinions, preferences and responses of real people, providing new opportunities and insights for researchers.
However, synthetic respondents are not without controversy and are being hotly debated within the market research community. Examining the comments left on LinkedIn posts about these innovations reveals a clear divide in opinions. Proponents are enthusiastic about the potential for this innovation to deliver cost-effective and efficient ways of gathering customer feedback. Critics argue about the authenticity and reliability of data derived from synthetic respondents, and there is a growing concern among market research professionals that synthetic respondents might overshadow or even replace traditional methodologies. A recent article published by Raconteur summarizes some of the key points.
These perspectives both miss the broader point: Synthetic respondents should be seen as complementary tools that enhance and augment real respondents, not as replacements. Synthetic data is generated based on existing patterns and trends, meaning it cannot capture novel behaviors or emerging trends that have not been previously recorded. Furthermore, synthetic data, no matter how well-crafted, lacks the nuanced insights that come from engaging with real people.
Today’s nyt mini crossword clues and answers for thursday, august 15, the backlash against blake lively, explained, the reality: complementary strengths.
To fully harness the power of synthetic respondents, they should always be integrated with traditional market research. Here’s why:
One of the significant advantages of synthetic respondents is their ability to expedite the research process through the creation of learning loops. When used alongside real respondents, synthetic respondents help accelerate the research timeline by providing initial insights that can be rapidly tested and refined. This iterative process, combining synthetic and real data, allows researchers to learn and adapt quickly, ensuring that the depth and reliability of insights are not compromised but enhanced, ultimately leading to faster and more robust conclusions.
Much of today's market research is conducted online with samples drawn from panels of market research respondents, often leading to a natural bias in the sample. Synthetic respondents offer the opportunity to reach niche audiences and uncover opinions from more diverse populations. By simulating a wide range of demographic and psychographic profiles, synthetic respondents help mitigate sample bias and provide insights from a broader spectrum of perspectives, enriching the overall quality and inclusiveness of your research.
Of course, synthetic respondents are no substitute for engaging with real people in niche audiences and diverse populations. Synthetic data can replicate existing opinions and behaviors, but it is less effective at predicting new behaviors. Therefore, it is essential to validate synthetic responses with the views and opinions of real individuals. However, this can often be accomplished with a smaller sample size than what is typically required in traditional market research.
Synthetic respondents have limitless capacity to answer your questions. Unlike real respondents, synthetic respondents never experience fatigue or boredom, allowing you to explore a wider array of queries and test a multitude of ideas. This opens up the opportunity to gather feedback on hundreds of concepts without worrying about respondent fatigue.
To illustrate the power of combining synthetic and real respondents, consider a recent project we conducted for a pharmaceutical client. Gathering insights from patients suffering from rare diseases can be particularly challenging, often requiring weeks or months and considerable effort and cost to speak to a handful of patients. When one of our pharma clients tasked us with conducting patient journey research in acute myeloid leukemia (a rare cancer affecting a small percentage of the U.S. population), we needed to be creative.
We initiated the research using ChatGPT, creating patient personas that mirrored the characteristics of real patients. Through in-depth simulated interviews, we explored their experiences and key milestones along their journey. These simulations allowed our client to quickly gain a broad understanding of patient needs, pain points and desires.
Next, we focused on the most relevant areas identified in the simulations and conducted interviews with real patients to validate the insights and enrich the findings with their unique experiences and detailed stories. This approach provided a comprehensive and nuanced view of the patient journey, blending the efficiency of synthetic data with the depth of real patient insights.
Let’s stop pitting synthetic respondents against insights gleaned from real people. In reality, they can be incredibly powerful when used together. AI simulations are not replacements for the continuous discovery of insights from real individuals; rather, they are tools to enhance creativity and guide human research.
We've found that combining AI-powered simulations with focused human discovery leads to sharper insights and quicker iterations. Synthetic respondents can accelerate the initial stages of research, provide broad and diverse perspectives, and help formulate hypotheses. When these synthetic insights are validated and enriched with real-world data from actual respondents, the results are both comprehensive and nuanced.
The greatest successes will come to those who can thoughtfully integrate human and machine intelligence. It's not about picking one or the other but harnessing the strengths of both. By leveraging the power of synthetic respondents alongside traditional research methods, we can achieve a deeper, more holistic understanding of our subjects, ultimately driving more effective and impactful strategies in market research.
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The skills you need to succeed in the era of large language models
Today artificial intelligence can be harnessed by nearly anyone, using commands in everyday language instead of code. Soon it will transform more than 40% of all work activity, according to the authors’ research. In this new era of collaboration between humans and machines, the ability to leverage AI effectively will be critical to your professional success.
This article describes the three kinds of “fusion skills” you need to get the best results from gen AI. Intelligent interrogation involves instructing large language models to perform in ways that generate better outcomes—by, say, breaking processes down into steps or visualizing multiple potential paths to a solution. Judgment integration is about incorporating expert and ethical human discernment to make AI’s output more trustworthy, reliable, and accurate. It entails augmenting a model’s training sources with authoritative knowledge bases when necessary, keeping biases out of prompts, ensuring the privacy of any data used by the models, and scrutinizing suspect output. With reciprocal apprenticing, you tailor gen AI to your company’s specific business context by including rich organizational data and know-how into the commands you give it. As you become better at doing that, you yourself learn how to train the AI to tackle more-sophisticated challenges.
The AI revolution is already here. Learning these three skills will prepare you to thrive in it.
Generative artificial intelligence is expected to radically transform all kinds of jobs over the next few years. No longer the exclusive purview of technologists, AI can now be put to work by nearly anyone, using commands in everyday language instead of code. According to our research, most business functions and more than 40% of all U.S. work activity can be augmented, automated, or reinvented with gen AI. The changes are expected to have the largest impact on the legal, banking, insurance, and capital-market sectors—followed by retail, travel, health, and energy.
Featured Article
Thanks to unitedhealth, snowflake and at&t (twice).
We’re over halfway through 2024, and already this year we have seen some of the biggest, most damaging data breaches in recent history. And just when you think that some of these hacks can’t get any worse, they do.
From huge stores of customers’ personal information getting scraped, stolen and posted online, to reams of medical data covering most people in the United States getting stolen, the worst data breaches of 2024 to date have already surpassed at least 1 billion stolen records and rising. These breaches not only affect the individuals whose data was irretrievably exposed, but also embolden the criminals who profit from their malicious cyberattacks.
Travel with us to the not-so-distant past to look at how some of the biggest security incidents of 2024 went down, their impact and. in some cases, how they could have been stopped.
For AT&T, 2024 has been a very bad year for data security. The telecoms giant confirmed not one, but two separate data breaches just months apart.
In July, AT&T said cybercriminals had stolen a cache of data that contained phone numbers and call records of “nearly all” of its customers, or around 110 million people , over a six-month period in 2022 and in some cases longer. The data wasn’t stolen directly from AT&T’s systems, but from an account it had with data giant Snowflake (more on that later).
Although the stolen AT&T data isn’t public (and one report suggests AT&T paid a ransom for the hackers to delete the stolen data ) and the data itself does not contain the contents of calls or text messages, the “metadata” still reveals who called who and when, and in some cases the data can be used to infer approximate locations. Worse, the data includes phone numbers of non-customers who were called by AT&T customers during that time. That data becoming public could be dangerous for higher-risk individuals , such as domestic abuse survivors.
That was AT&T’s second data breach this year. Earlier in March, a data breach broker dumped online a full cache of 73 million customer records to a known cybercrime forum for anyone to see, some three years after a much smaller sample was teased online.
The published data included customers’ personal information, including names, phone numbers and postal addresses, with some customers confirming their data was accurate .
But it wasn’t until a security researcher discovered that the exposed data contained encrypted passcodes used for accessing a customer’s AT&T account that the telecoms giant took action. The security researcher told TechCrunch at the time that the encrypted passcodes could be easily unscrambled, putting some 7.6 million existing AT&T customer accounts at risk of hijacks. AT&T force-reset its customers’ account passcodes after TechCrunch alerted the company to the researcher’s findings.
One big mystery remains: AT&T still doesn’t know how the data leaked or where it came from .
In 2022, the U.S. Justice Department sued health insurance giant UnitedHealth Group to block its attempted acquisition of health tech giant Change Healthcare, fearing that the deal would give the healthcare conglomerate broad access to about “half of all Americans’ health insurance claims” each year. The bid to block the deal ultimately failed. Then, two years later, something far worse happened: Change Healthcare was hacked by a prolific ransomware gang; its almighty banks of sensitive health data were stolen because one of the company’s critical systems was not protected with multi-factor authentication .
The lengthy downtime caused by the cyberattack dragged on for weeks, causing widespread outages at hospitals, pharmacies and healthcare practices across the United States. But the aftermath of the data breach has yet to be fully realized, though the consequences for those affected are likely to be irreversible. UnitedHealth says the stolen data — which it paid the hackers to obtain a copy — includes the personal, medical and billing information on a “substantial proportion” of people in the United States.
UnitedHealth has yet to attach a number to how many individuals were affected by the breach. The health giant’s chief executive, Andrew Witty, told lawmakers that the breach may affect around one-third of Americans , and potentially more. For now, it’s a question of just how many hundreds of millions of people in the U.S. are affected.
A June cyberattack on U.K. pathology lab Synnovis — a blood and tissue testing lab for hospitals and health services across the U.K. capital — caused ongoing widespread disruption to patient services for weeks. The local National Health Service trusts that rely on the lab postponed thousands of operations and procedures following the hack, prompting the declaration of a critical incident across the U.K. health sector.
A Russia-based ransomware gang was blamed for the cyberattack, which saw the theft of data related to some 300 million patient interactions dating back a “significant number” of years. Much like the data breach at Change Healthcare, the ramifications for those affected are likely to be significant and life-lasting.
Some of the data was already published online in an effort to extort the lab into paying a ransom. Synnovis reportedly refused to pay the hackers’ $50 million ransom , preventing the gang from profiting from the hack but leaving the U.K. government scrambling for a plan in case the hackers posted millions of health records online.
One of the NHS trusts that runs five hospitals across London affected by the outages reportedly failed to meet the data security standards as required by the U.K. health service in the years that ran up to the June cyberattack on Synnovis.
A series of data thefts from cloud data giant Snowflake quickly snowballed into one of the biggest breaches of the year, thanks to the vast amounts of data stolen from its corporate customers.
Cybercriminals swiped hundreds of millions of customer data from some of the world’s biggest companies — including an alleged 560 million records from Ticketmaster , 79 million records from Advance Auto Parts and some 30 million records from TEG — by using stolen credentials of data engineers with access to their employer’s Snowflake environments. For its part, Snowflake does not require (or enforce) its customers to use the security feature, which protects against intrusions that rely on stolen or reused passwords.
Incident response firm Mandiant said around 165 Snowflake customers had data stolen from their accounts, in some cases a “significant volume of customer data.” Only a handful of the 165 companies have so far confirmed their environments were compromised, which also includes tens of thousands of employee records from Neiman Marcus and Santander Bank , and millions of records of students at Los Angeles Unified School District . Expect many Snowflake customers to come forward.
Cencora notifies over a million and counting that it lost their data:
U.S. pharma giant Cencora disclosed a February data breach involving the compromise of patients’ health data, information that Cencora obtained through its partnerships with drug makers. Cencora has steadfastly refused to say how many people are affected, but a count by TechCrunch shows well over a million people have been notified so far. Cencora says it’s served more than 18 million patients to date.
MediSecure data breach affects half of Australia:
Close to 13 million people in Australia — roughly half of the country’s population — had personal and health data stolen in a ransomware attack on prescriptions provider MediSecure in April. MediSecure, which distributed prescriptions for most Australians until late 2023, declared insolvency soon after the mass theft of customer data.
Kaiser shared health data on millions of patients with advertisers:
U.S. health insurance giant Kaiser disclosed a data breach in April after inadvertently sharing the private health information of 13.4 million patients, specifically website search terms about diagnoses and medications, with tech companies and advertisers. Kaiser said it used their tracking code for website analytics. The health insurance provider disclosed the incident in the wake of several other telehealth startups, like Cerebral , Monument and Tempest , admitting they too shared data with advertisers.
USPS shared postal address with tech giants, too:
And then it was the turn of the U.S. Postal Service caught sharing postal addresses of logged-in users with advertisers like Meta, LinkedIn and Snap, using a similar tracking code provided by the companies. USPS removed the tracking code from its website after TechCrunch notified the postal service in July of the improper data sharing, but the agency wouldn’t say how many individuals had data collected. USPS has over 62 million Informed Delivery users as of March 2024.
Evolve Bank data breach affected fintech and startup customers:
A ransomware attack targeting Evolve Bank saw the personal information of more than 7.6 million people stolen by cybercriminals in July. Evolve is a banking-as-a-service giant serving mostly fintech companies and startups , like Affirm and Mercury. As a result, many of the individuals notified of the data breach had never heard of Evolve Bank, let alone have a relationship with the firm, prior to its cyberattack.
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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation. It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied.
In qualitative research, case study is one of the frequently used methodologies (Yazan, 2015). ... The authors interpreted the raw data for case studies with the help of a four-step interpretation process (PESI). Raw empirical material, in the form of texts from interviews, field notes of meetings, and observation and project reports, was ...
The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case. It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed ...
Revised on November 20, 2023. A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are ...
Case study research is typically extensive; it draws on multiple methods of data collection and involves multiple data sources. The researcher begins by identifying a specific case or set of cases to be studied. Each case is an entity that is described within certain parameters, such as a specific time frame, place, event, and process.
A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...
Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data. Analysis of qualitative data from case study research can contribute to knowledge development.
1. Select a case. Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research. 2.
Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data. Example: Mixed methods case study. For a case study of a wind farm development in a ...
Definition, Methods, and Examples. Case study methodology offers researchers an exciting opportunity to explore intricate phenomena within specific contexts using a wide range of data sources and collection methods. It is highly pertinent in health and social sciences, environmental studies, social work, education, and business studies.
Data collected from a case study or an ethnography can undergo the same types of analyses since the data analysis requires researchers to triangulate the diversity of data. This triangulation strengthens the research findings because "various strands of data are braided together to promote a greater understanding of the case" ( Baxter ...
According to the book Understanding Case Study Research, case studies are "small scale research with meaning" that generally involve the following: The study of a particular case, or a number of cases. That the case will be complex and bounded. That it will be studied in its context. That the analysis undertaken will seek to be holistic.
A Case study is: An in-depth research design that primarily uses a qualitative methodology but sometimes includes quantitative methodology. Used to examine an identifiable problem confirmed through research. Used to investigate an individual, group of people, organization, or event. Used to mostly answer "how" and "why" questions.
ve as a brief refresher to the case study method. As a refresher, the chapter does not fully cover all the options or nuances that you might encounter when customizing your own case study (refer to Yin, 2009a, to obtain a full rendition of the entire method).Besides discussing case study design, data collection, and analysis, the refresher addr.
A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the ...
Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising.
Case research. Case research—also called case study—is a method of intensively studying a phenomenon over time within its natural setting in one or a few sites. Multiple methods of data collection, such as interviews, observations, pre-recorded documents, and secondary data, may be employed and inferences about the phenomenon of interest ...
The case study is a data collection method in which in-depth descriptive information. about specific entities, or cases, is collected, organized, interpreted, and presented in a. narrative format ...
Case study research is defined as a qualitative approach in which the investigator explores a real-life, contemporary bounded system (a case) or multiple bound systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information, and reports a case description and case themes.
Introduction. The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the ...
A data science framework has emerged and is presented in the remainder of this article along with a case study to illustrate the steps. This data science framework warrants refining scientific practices around data ethics and data acumen (literacy). A short discussion of these topics concludes the article. 2.
Guiding CWRU researchers on responsible research data management throughout the research lifecycle. As a coordinated and collaborative service to the CWRU research community, this site provides resources with guidelines on research data management best practices, data storage options, funding guidelines, and training & support opportunities.
Case Study: Exploring The Patient Journey Of Those With A Rare Disease To illustrate the power of combining synthetic and real respondents, consider a recent project we conducted for a ...
Summary. Today artificial intelligence can be harnessed by nearly anyone, using commands in everyday language instead of code. Soon it will transform more than 40% of all work activity, according ...
Whole-Genome Research Core Laboratory of Human Diseases, Chang Gung Memorial Hospital, Keelung, Taiwan Correspondence Chun-Bing Chen, Department of Dermatology, Drug Hypersensitivity Clinical and Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan, Linkou, 5, Fuxing St, Guishan Dist, Taoyuan 33305, Taiwan.
The Phone Case Store Related Research & Analysis. Explore institutional-grade private market research from our team of analysts. Verticals. E-Commerce; E-Commerce Report. June 20, 2024. ... Our data operations team has logged over 3.5 million hours researching, organizing, and integrating the information you need most. ...
A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. [1] [2] For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a ...
The Securities and Exchange Commission today announced charges against Cynthia and Eddy Petion, along with their company, NovaTech Ltd., for operating a fraudulent scheme that raised more than $650 million in crypto assets from more than 200,000 investors worldwide, including many in the Haitian-American community.
vis-à-vis the US. However, a bull case for AI data centers— which assumes a slightly higher data center market share of 25% for Europe and no efficiency gains on future server deliveries—could see cumulative electricity consumption growth of around 50% over the next decade. But even in our base case, the incremental power consumption we expect
Change Healthcare hackers stole medical data on "substantial proportion" of people in America. In 2022, the U.S. Justice Department sued health insurance giant UnitedHealth Group to block its ...