Growing pressure for positive results.
Science is a competitive field. Scientists have intense pressure to produce meaningful results. As a result, fewer and fewer papers are being published that show “negative results”—i.e. that their hypothesis was false. In 1990, “negative results” accounted for 30% of published papers—that number has fallen to a mere 14%.
Another problem is that scientists are under pressure to publish new, groundbreaking research, rather than performing studies to replicate results from previous research. Journals are exclusive and want to publish striking results that present a “major advance.” Replicating studies, however, is extremely important. It’s a key part of confirming findings and eliminating scientific fraud.
Labs can often be messy and chaotic. In far too many cases, samples and chemicals are mislabeled and even forgotten. The Wall Street Journal took a hard look at this issue after a cancer researcher had his work on head-and-neck cancer retracted from the journal Oral Oncology due to the fact that the cells he was studying were actually cervical cancer cells. The WSJ highlights the extent of the problem: “Cancer experts seeking to solve the problem have found that a fifth to a third or more of cancer cell lines tested were mistakenly identified—with researchers unwittingly studying the wrong cancers, slowing progress toward new treatments and wasting precious time and money.”
The problem is incredibly widespread: “Cell repositories in the U.S., U.K., Germany and Japan have estimated that 18% to 36% of cancer cell lines are incorrectly identified.”
While the National Institutes of Health and the scientific community are slowly trying to weed out these problems by increasing scrutiny on papers submitted using cell lines and setting up a central repository of cell lines, cell contamination remains a major problem in scientific research.
Sloppy lab conditions can also lead to another major problem: mycoplasma infestations. Mycoplasma is a bacteria that can spread rapidly throughout lab cultures, compromising scientists’ potential findings. The problem is also widespread. A recent article in Nature covered the problem and interviewed researchers who “found that more than one-tenth of gene-expression studies, many published in leading journals, show evidence of Mycoplasma contamination.”
Alarmingly, the pressure to produce prestigious research has led a number of scientists to simply fake results or plagiarize from other researchers. In the last year, articles have been retracted from prestigious journals in which authors:
Unfortunately, this is just a small sample of the many instances of fraud every year. A recent study found that fraud is the reason for 43% of all journal retractions.
Scientific fraud can have huge implications. Remember the study that linked vaccinations and autism? Even though it was retracted after researchers said it was based on doctored information about children’s medical records, the myth of the vaccine/autism link is pervasive and continues to be repeated.
A number of frequently cited studies, particularly studies of nutrition, rely on the information that study participants self-report. This makes it difficult to fully trust a study’s findings–self-reported data is notoriously unreliable.
Just how reliable is self-reported data? Consider that consumers consistently give the food on Southwest Airlines high marks…despite the fact that the airline doesn’t serve meals.
During a recent session hosted by the American Society for Nutrition, Dr. David Allison took a highly critical look at self-reported data, highlighting a recent paper “that looked at energy intake of respondents in NHANES from 1971-2012, finding that 67.3% of women and 58.7% of men were not physiologically plausible – i.e. the number of calories is ‘incompatible with life.'”
That certainly doesn’t stop researchers from using this method or stop the media from reporting on these studies. Here are just a few examples of major media outlets reporting on studies that rely on self-reported data without explaining the limitations of such research:
Run a free plagiarism check in 10 minutes, automatically generate references for free.
Published on 8 November 2022 by Shona McCombes and Tegan George.
A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.
Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.
This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .
Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, frequently asked questions about research problems.
Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.
You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.
Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.
As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.
If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organisation. You might look for:
Voter turnout in New England has been decreasing, in contrast to the rest of the country.
The HR department of a local chain of restaurants has a high staff turnover rate.
A non-profit organisation faces a funding gap that means some of its programs will have to be cut.
If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:
The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.
The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.
Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.
Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.
A local non-profit organisation focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organisation requires research into more effective fundraising strategies.
Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .
Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.
Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.
I will compare …
The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .
A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis – a prediction that will be confirmed or disproved by your research.
Research objectives describe what you intend your research project to accomplish.
They summarise the approach and purpose of the project and help to focus your research.
Your objectives should appear in the introduction of your research paper , at the end of your problem statement .
If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.
McCombes, S. & George, T. (2022, November 08). How to Define a Research Problem | Ideas & Examples. Scribbr. Retrieved 11 June 2024, from https://www.scribbr.co.uk/the-research-process/define-research-problem/
Other students also liked, dissertation & thesis outline | example & free templates, example theoretical framework of a dissertation or thesis, how to write a strong hypothesis | guide & examples.
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.
Mental health is an important part of children’s overall health and well-being. Mental health includes children’s mental, emotional, and behavioral well-being. It affects how children think, feel, and act. It also plays a role in how children handle stress, relate to others, and make healthy choices.
Mental disorders among children are described as serious changes in the way children typically learn, behave, or handle their emotions, causing distress and problems getting through the day. 1 Among the more common mental disorders that can be diagnosed in childhood are attention-deficit/hyperactivity disorder (ADHD), anxiety, and behavior disorders.
There are different ways to assess mental health and mental disorders in children. CDC uses surveys, like the National Survey of Children’s Health, to describe the presence of positive indicators of children’s mental health and to understand the number of children with diagnosed mental disorders and whether they received treatment. In this type of survey, parents report on indicators of positive mental health for their child and report any diagnoses their child has received from a healthcare provider. The information on this page provides data about indicators of positive mental health in children and mental health disorders that are most common in children.
National data on positive mental health indicators that describe mental, emotional, and behavioral well-being for children are limited. Based on the data we do have:
Learn more about high-risk substance use among youth . Learn more about suicide .
Note : The estimates reported on this page are based on parent report, using nationally representative surveys. This method has several limitations. It is not known to what extent children receive these diagnoses accurately. Estimates based on parent-reported diagnoses may match those based on medical records, 7 but some children may also have mental disorders that have not been diagnosed, or receive diagnoses that may not be the best fit for their symptoms. Limited information on measuring children’s mental health nationally is available 2 .
Read more about children’s mental health from a community study .
Early diagnosis and appropriate services for children and their families can make a difference in the lives of children with mental disorders. 7 Access to providers who can offer services, including screening, referrals, and treatment, varies by location. CDC is working to learn more about access to behavioral health services and supports for children and their families.
View information by state describing the rates of different types of providers who can offer behavioral health services providers by county.
Read a recent report describing shortages of services, barriers to treatment, and how integration of behavioral health care with pediatric primary care could address the issues.
Read a policy brief on potential ways to increase access to mental health services for children in rural areas
What is It and Why is It Important?
There are many different datasets which include information on children’s mental health and related conditions for children living in the United States.
Healthy People 2030 Healthy People 2030 sets data-driven national objectives to improve health and well-being over the next decade, including children’s mental health and well-being.
National Survey of Family Growth (NSFG) NSFG gathers information on family life, marriage and divorce, pregnancy, infertility, use of contraception, and general and reproductive health.
National Health and Nutrition Examination Survey (NHANES) NHANES assesses health and nutritional status through interviews and physical examinations, and includes conditions, symptoms, and concerns associated with mental health and substance abuse, as well as the use and need for mental health services.
National Health Interview Survey (NHIS) NHIS collects data on children’s mental health, mental disorders, such as ADHD, autism spectrum disorder, depression and anxiety problems, and use and need for mental health services.
National Survey of Children’s Health (NSCH) NSCH examines the health of children, with emphasis on well-being, including medical homes, family interactions, the health of parents, school and after-school experiences, and safe neighborhoods. This survey was redesigned in 2016.
For previous versions of this survey, see also: National Survey of Children’s Health (NSCH 2003, 2007, 2011-12) National Survey of Children with Special Healthcare Needs (NS-CSHCN 2001, 2005-6, 2009-10)
National Survey of the Diagnosis and Treatment of ADHD and Tourette Syndrome (NS-DATA) NS-DATA collects information about children, 2-15 years old in 2011-2012, who had ever been diagnosed with ADHD and/or Tourette syndrome (TS), with the goal of better understanding diagnostic practices, level of impairment, and treatments for this group of children.
National Survey on Drug Use and Health (NSDUH) NSDUH, administered by the Substance Abuse and Mental Health Services Administration (SAMHSA), provides national- and state-level data on the use of tobacco, alcohol, and illicit drugs (including non-medical use of prescription drugs), as well as data on mental health in the United States.
National Vital Statistics System (NVSS) NVSS contains vital statistics from the official records of live births, deaths, causes of death, marriages, divorces, and annulment recorded by states and independent registration areas
National Youth Tobacco Survey (NYTS) NYTS is a nationally representative school-based survey on tobacco use by public school students enrolled in grades 6-12.
School Associated Violent Death Study (SAVD) SAVD plays an important role in monitoring trends related to school-associated violent deaths (including suicide), identifying the factors that increase the risk, and assessing the effects of prevention efforts.
School Health Policies and Programs Study (SHPPS) SHPPS is a national survey assessing school health policies and practices at the state, district, school, and classroom levels. Collected data includes mental health and social service policies.
Web-based Injury Statistics Query and Reporting System (WISQARS) WISQARS is an interactive database system that provides customized reports of injury-related data.
Youth Risk Behavior Surveillance System (YRBSS) The YRBSS monitors health-risk behaviors, including tobacco use, substance abuse, unintentional injuries and violence, sexual behaviors that contribute to unintended pregnancy, and sexually transmitted diseases.
To receive email updates about this topic, enter your email address:
If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
Related articles.
Sherry Tiao | Senior Manager, AI & Analytics, Oracle | March 11, 2024
In This Article
The three “vs” of big data, the value—and truth—of big data, the history of big data, big data use cases, big data challenges, how big data works, big data best practices.
What exactly is big data?
The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.”
Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
Volume | The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as X (formerly Twitter) data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes. |
Velocity | Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action. |
Variety | Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a . With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata. |
Two more Vs have emerged over the past few years: value and veracity . Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?
Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.
Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.
Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.
But how did we get here?
Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.
Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.
The development of open source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.
With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.
While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.
Discover the Insights in Your Data
Click below to access the 17 Use Cases for Graph Databases and Graph Analytics ebook.
Big data can help you address a range of business activities, including customer experience and analytics. Here are just a few.
Product development | Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products. |
Predictive maintenance | Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime. |
Customer experience | The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively. |
Fraud and compliance | When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster. |
Machine learning | Machine learning is a hot topic right now. And data—specifically big data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible. |
Operational efficiency | Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand. |
Drive innovation | Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities. |
Download your free ebook to learn about:
While big data holds a lot of promise, it is not without its challenges.
First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.
But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.
Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
Discover more big data resources:
Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:
1. Integrate Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.
During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.
2. Manage Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.
3. Analyze Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.
To help you on your big data journey, we’ve put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.
Align big data with specific business goals | More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data. |
Ease skills shortage with standards and governance | One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms. |
Optimize knowledge transfer with a center of excellence | Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way. |
Top payoff is aligning unstructured with structured data | It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today. Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture. Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries. |
Plan your discovery lab for performance | Discovering meaning in your data is not always straightforward. Sometimes we don’t even know what we’re looking for. That’s expected. Management and IT needs to support this “lack of direction” or “lack of clear requirement.” At the same time, it’s important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need—and are properly governed. |
Align with the cloud operating model | Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements. |
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Methodology
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
Second, decide how you will analyze the data .
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
Qualitative | to broader populations. . | |
---|---|---|
Quantitative | . |
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | . | methods. |
---|---|---|
Secondary |
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
Descriptive | . . | |
---|---|---|
Experimental |
Professional editors proofread and edit your paper by focusing on:
See an example
Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
The research methods you use depend on the type of data you need to answer your research question .
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
Other students also liked, writing strong research questions | criteria & examples.
I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”
INFORMATION FOR
Andrew Taylor, MD, MHS , associate professor of biomedical informatics and data science and of emergency medicine, will work with partners in Scotland on a £1 million study to improve unscheduled end-of-life care.
The University of St Andrews has been awarded up to £1 million each by Scotland's Chief Scientist Office to conduct major research programs into population health issues. The grant, announced by Health and Social Care Secretary Neil Gray on June 4, will support an Applied Health Research Program focused on improving unscheduled care for people across Scotland in their last year of life. Collaborators include NHS Fife, NHS Highland / Highland Hospice, the Fife Community Advisory Council, the University of Edinburgh, and Yale University.
The project arose in the context of unprecedented strain on the country’s unscheduled care services due to workforce shortages, demographic change, and widespread multimorbidity (when a person has two or more long-term health conditions). In 2022, Accident & Emergency waiting times hit record levels and over a quarter-million calls to NHS24 went unanswered. Alongside these services, unscheduled care also includes General Practice Out-of-Hours (GPOOH) and the Scottish Ambulance Service (SAS).
Previous research has identified that one group of people who use such services frequently is those in their last year of life. Although it plays an essential role in the healthcare system, unscheduled care is often not the most appropriate option for this population, being necessarily reliant on a reactive approach to care without the benefit of more nuanced, anticipatory, and coordinated planning. The result can often be more fragmented, expensive and less effective care, causing unintended additional distress to patients in their last year of life and their families.
“We are very aware that use of unscheduled care services increases for a person with a palliative diagnosis in the last year of their lives,” said team member and Clinical Partnership Director for NHS Highland/Highland Hospice, Michael Loynd. “We need to understand if better identification of this population and different supports such as dedicated helplines can enable an alternative route of support.” As part of this process, a key objective of the research program will be to develop a single point of contact and care coordination for this vulnerable group.
This program will use machine learning to analyze existing healthcare data and predict future patterns of unscheduled care use by patients in their last year of life. This will in turn allow for the identification of such patients who may be in need of social care reviews, prescribing interventions, or other anticipatory care measures that would reduce their need for unscheduled care.
“The significant CSO funding awarded to the University of St. Andrews, along with NHS Fife, Yale University, and other key partners, signifies a transformative moment in end-of-life care research. At Yale, we are eager to lend our expertise in emergency medicine and artificial intelligence to this critical initiative," said Taylor. "This collaboration will not only aim to improve the quality of life for patients in their final year but also reduce the burden on unscheduled care services through pioneering anticipatory care models. This project offers a remarkable opportunity for cross-institutional collaboration, set to drive substantial enhancements in healthcare delivery and outcomes worldwide.”
The team’s research will not only benefit patients, first and foremost, but aims to improve NHS sustainability by reducing the unscheduled care workload. “Better identification of this group of people will facilitate improved NHS care, but it will also increase the capacity of emergency care services”, said Colin McCowan, Head of the School of Medicine’s Population and Behavioral Science research division.
With this significant grant from the Scottish Government, the University of St Andrews and its partners are poised to make a profound impact on the healthcare landscape in Scotland. By leveraging advanced machine learning techniques and a deep understanding of the challenges facing the unscheduled care system, this research aims to not only enhance the quality of life for patients in their last year of life but also ensure a more sustainable future for NHS services.
Members of the research team include Colin McCowan, Alex Baldacchino, Peter D. Donnelly, Sarah E. E. Mills, Veronica O'Carroll, Frank Sullivan and Joseph Tay Wee Teck from University of St Andrews; Peter Hall and Elizabeth Lemmon from University of Edinburgh; Michael Loynd from NHS Highland/Highland Hospice; Joanna Bowden, Rishma Maini, Christopher McKenna, Frances Quirk and Rajendra Raman from NHS Fife; and Andrew Taylor from Yale School of Medicine.
Taylor was named a University of St Andrews Global Fellow in 2023.
Numbers, Facts and Trends Shaping Your World
Read our research on:
Full Topic List
Read Our Research On:
New York Gov. Kathy Hochul recently announced that she will introduce legislation to ban smartphones in schools during her state’s 2025 legislative session. She cited the impact that social media and technology can have on youth, including leaving them “cut off from human connection, social interaction and normal classroom activity.”
Hochul’s legislative push comes as K-12 teachers in the United States face challenges around students’ cellphone use, according to a Pew Research Center survey conducted in fall 2023. One-third of public K-12 teachers say students being distracted by cellphones is a major problem in their classroom, and another 20% say it’s a minor problem.
Following news that New York Gov. Kathy Hochul is seeking to ban smartphones in schools, Pew Research Center published this analysis to examine how K-12 teachers and teens in the United States feel about cellphones, including the use of cellphones at school.
This analysis is based on two recent Center surveys, one of public K-12 teachers in the U.S. and the other of U.S. teens ages 13 to 17. More information about these surveys, including their field dates, sample sizes and other methodological details, is available at the links in the text.
High school teachers are especially likely to see cellphones as problematic. About seven-in-ten (72%) say that students being distracted by cellphones is a major problem in their classroom, compared with 33% of middle school teachers and 6% of elementary school teachers.
Many schools and districts have tried to address this challenge by implementing cellphone policies , such as requiring students to turn off their phones during class or give them to administrators during the school day.
Overall, 82% of K-12 teachers in the U.S. say their school or district has a cellphone policy of some kind. Middle school teachers (94%) are especially likely to say this, followed by elementary (84%) and high school (71%) teachers.
However, 30% of teachers whose schools or districts have cellphone policies say they are very or somewhat difficult to enforce. High school teachers are more likely than their peers to report that enforcing these policies is difficult. Six-in-ten high school teachers in places with a cellphone policy say this, compared with 30% of middle school teachers and 12% of elementary school teachers.
Our survey asked teachers about cellphones in general, whereas Hochul’s plan would apply only to smartphones. Even so, nearly all U.S. teenagers ages 13 to 17 – 95% – say they have access to a smartphone , according to a separate Center survey from 2023.
Even as some policymakers and teachers see downsides to smartphones, teens tend to view the devices as a more positive than negative thing in their lives overall.
Seven-in-ten teens ages 13 to 17 say there are generally more benefits than harms to people their age using smartphones , while three-in-ten say the opposite. And 45% of teens say smartphones make it easier for people their age to do well in school, compared with 23% who say they make it harder. Another 30% say smartphones don’t affect teens’ success in school.
Jenn Hatfield is a writer/editor at Pew Research Center .
A quarter of u.s. teachers say ai tools do more harm than good in k-12 education, most americans think u.s. k-12 stem education isn’t above average, but test results paint a mixed picture, about 1 in 4 u.s. teachers say their school went into a gun-related lockdown in the last school year, about half of americans say public k-12 education is going in the wrong direction, most popular.
1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 | Media Inquiries
ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
© 2024 Pew Research Center
IMAGES
VIDEO
COMMENTS
A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation. The statement of a problem orients the reader to the importance of the topic, sets ...
A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge. Some research will do both of these things, but usually the research problem focuses on one or the other.
A research problem has two essential roles in setting your research project on a course for success. 1. They set the scope. The research problem defines what problem or opportunity you're looking at and what your research goals are. It stops you from getting side-tracked or allowing the scope of research to creep off-course.
A research problem is an issue of concern that is the catalyst for your research. It demonstrates why the research problem needs to take place in the first ... possibly using ecological momentary assessment for real-time data collection. 8. Video Games and Cognitive Skills: "How do action video games influence cognitive skills such as ...
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
E ven though Big data is in the mainstream of operations as of 2020, there are still potential issues or challenges the researchers can address. Some of these issues overlap with the data science field. In this article, the top 20 interesting latest research problems in the combination of big data and data science are covered based on my personal experience (with due respect to the ...
Formulation of research problem should depict what is to be determined and scope of the study.It also involves key concept definitions questions to be asked. The objective of the present paper highlights the above stated issues. Booth, W. C., Colomb, G. G., & Williams, J. M. (2016). Craft of Research (4th Edition).
A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of ...
Feasibility: A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources. Novelty: A research problem should be novel or original in some way.
Data Collection | Definition, Methods & Examples. Published on June 5, 2020 by Pritha Bhandari.Revised on June 21, 2023. Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem.
A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation.
Research is a procedure based on a sequence and a research problem aids in following and completing the research in a sequence. Repetition of existing literature is something that should be avoided in research. Therefore research problem in a dissertation or an essay needs to be well thought out and presented with a clear purpose.
Parlindungan Pardede Research in ELT (Module 1) 1. Identifyin g and Fo rmulatin g the Researc h Problem. Parlindungan Pardede. [email protected]. English Education Department. Universitas ...
A research problem outlines the precise field of inquiry and knowledge gaps that the research attempts to address, defining the scope and objective of a study. Photo by Scott Graham on Unsplash. Learn the procedure involved in defining research problems, highlighting important considerations and steps researchers should take.
Given persistent problems (e.g., replicability), psychological research is increasingly scrutinised. Arocha (2021) critically analyses epistemological problems of positivism and the common population-level statistics, which follow Galtonian instead of Wundtian nomothetic methodologies and therefore cannot explore individual-level structures and processes.
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...
A research problem refers to an area or issue that requires investigation, analysis, and resolution through a systematic and scientific approach. It is a. ... Data availability for a problem; Formulating a Research Problem. Formulating a research problem is usually done under the first step of research process, i.e., defining the research ...
Common Research Problems Growing Pressure for Positive Results. Science is a competitive field. Scientists have intense pressure to produce meaningful results. ... Dr. David Allison took a highly critical look at self-reported data, highlighting a recent paper "that looked at energy intake of respondents in NHANES from 1971-2012, finding that ...
5. Select and include important variables. A clear and manageable research problem typically includes the variables that are most relevant to the study. A research team summarizes how they plan to consider and use these variables and how they might influence the results of the study. Selecting the most important variables can help the study's ...
Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures. 4. Data Management and Analysis. Implement robust data management practices to maintain the integrity and security of research data. Transparently document data analysis procedures, including software and statistical methods used.
A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge. Some research will do both of these things, but usually the research problem focuses on one or the other.
A data analyst gathers, cleans, and studies data sets to help solve problems. Here's how you can start on a path to become one. A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. They work in many industries, including business, finance, criminal justice, science, medicine, and government.
ADHD, anxiety problems, behavior problems, and depression are the most commonly diagnosed mental disorders in children. Estimates for ever having a diagnosis among children aged 3-17 years, in 2016-19, are given below. ADHD 9.8% (approximately 6.0 million) 2; Anxiety 9.4% (approximately 5.8 million) 2; Behavior problems 8.9% (approximately 5.5 ...
These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. ... About the research. The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 ...
2. Immigration attitudes and the 2024 election. Voters who are backing Joe Biden this fall and those who back Donald Trump express sharply contrasting views about immigration. In part, this reflects long-standing gaps between Republicans and Democrats over how much of a problem illegal immigration is for the country, and recent differences in ...
The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three "Vs.". Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't ...
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Andrew Taylor, MD, MHS, associate professor of biomedical informatics and data science and of emergency medicine, will work with partners in Scotland on a £1 million study to improve unscheduled end-of-life care.. The University of St Andrews has been awarded up to £1 million each by Scotland's Chief Scientist Office to conduct major research programs into population health issues.
1. The biggest problems and greatest strengths of the U.S. political system. The public sees a number of specific problems with American politics. Partisan fighting, the high cost of political campaigns, and the outsize influence of special interests and lobbyists are each seen as characteristic of the U.S. political system by at least 84% of ...
72% of U.S. high school teachers say cellphone distraction is a major problem in the classroom. New York Gov. Kathy Hochul recently announced that she will introduce legislation to ban smartphones in schools during her state's 2025 legislative session. She cited the impact that social media and technology can have on youth, including leaving ...