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Social Media Programs for Outreach and Recruitment Supporting Aging and Alzheimer Disease and Related Dementias Research: Longitudinal Descriptive Study

This study describes the Johns Hopkins Alzheimer’s Disease Research Center’s social media activities for community engagement related to aging, memory loss, and Alzheimer’s/related dementias. The data demonstrate that social media activities can provide meaningful community educational opportunities and have a measurable impact on the recruitment of participants into research studies. The article provides an example of how tracking outreach program outcomes can help ensure targeted promotional activities.

Teano AL, et al. Social media programs for outreach and recruitment supporting aging and Alzheimer disease and related dementias research: Longitudinal descriptive study . JMIR Aging . 2024. Epub July 9. doi: 10.2196/51520.

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Title: can llms generate novel research ideas a large-scale human study with 100+ nlp researchers.

Abstract: Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.

Comments:	main paper is 20 pages
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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FDA to investigate metals found in tampons after study; study's co-author 'excited' over decision

The U.S. Food and Drug Administration announced it would investigate heavy metals in tampons after a 2024 study found that tampons contained arsenic and lead.

"The FDA is aware of concerns about tampon safety after a 2024 study found metals in tampons during laboratory testing," stated the FDA's announcement, which was published last week. "We want the public to know that before tampons can be legally sold in the U.S., they must meet FDA requirements for safety and effectiveness."

The FDA will test if the metals are being released from the tampon and absorbed into the vaginal lining and infiltrating the bloodstream while it is being used.

"Our study only showed what's present in tampons, but not whether or not those metals are impacting our health from tampon use," Jenni Shearston, a co-author of the original study and a post-doctoral scholar at the University of California Berkley, told USA TODAY.

Shearston, who has her PhD in Environmental Health Sciences, is excited to see the FDA testing if the metals would leach into people's bodies.

"I'm really glad that they're doing that and looking more into potential health complications that might come about from the presence of these metals in tampons," she said.

The lab Shearston works in is also conducting research on if the body absorbs the metals. It is "currently wrapping up" the experiments and will be sending the results to be peer-reviewed.

Single boomers are having 'S-E-X.' They're also seeing a surge in STIs

What did the study find?

The study, which was published by Environment International on July 3, analyzed 30 tampons from 14 tampon brands and found they had measurable concentrations of the following metals:

Between 50% to 80% of people who menstruate use tampons, but the metals listed above "could be absorbed by the vagina’s highly absorptive tissue, resulting in systemic exposure," according to the study.

Some metals aren't a concern

Metals like zinc, which was the most prevalent, and calcium aren't a concern for Shearston because they're both essential elements , which is "any chemical element required by an organism for healthy growth," according to Dictionary.com.

What concerned her most, however, is the fact that the study detected lead in the menstrual product because "there's no safe level of exposure to that," she said.

“Although toxic metals are ubiquitous and we are exposed to low levels at any given time, our study clearly shows that metals are also present in menstrual products and that women might be at higher risk for exposure using these products,” said study co-author  Kathrin Schilling .

Pads vs. Tampons: Potential for leaching

Shearston has not done a similar study for pads and said she and the other researchers at her lab are more focused on researching tampons.

They suspect the metal is more likely to leach out into the body while using tampons than it is while using a pad because tampons are inserted vaginally.

"The vagina has a lot of blood vessels," she said. "It's permeable so substances can cross over it, and so we think it's more of a concerning exposure route."

However, a person's vulva, the outside of the vagina which touches the pad, is also permeable and does have the potential to absorb any metals or chemicals found in the sanitary product.

"I think it's definitely something that should be studied as well," said Shearston. "But the main concern right now is tampons because they're inserted into your body, and it's such a sensitive area."

Julia is a trending reporter for USA TODAY. You can connect with her on  LinkedIn , follow her on  X, formerly Twitter ,  Instagram  and  TikTok : @juliamariegz, or email her at [email protected]

NOAA: Doppler radar indicates we may be underestimating tornado wind speeds

A research team from noaa studied 105 tornadoes that passed very close to national weather service doppler radar sites in various terrains and determined that estimating wind speeds from damage may be selling tornadic wind speeds short..

NOAA: Tornado wind speeds may be higher than estimates

Dr. Tony Lyza with NOAA's National Severe Storms Lab joins FOX Weather to discuss new research that indicates tornado speeds may be greater than typically estimated using damage reports as a guide. 

NORMAN, Okla. — When a tornado tears through a community, a National Weather Service storm survey team will sort through the damage left behind to estimate the twister’s peak wind and assign the twister a rating on the Enhanced Fujita scale.

But what if those measurements are underestimating the tornado’s strength?

A recent study by researchers at NOAA’s National Severe Storms Laboratory suggests the current methods of estimating winds inside a tornado may be doing just that.

"We were interested in studying exactly how strong tornadoes are, because right now we estimate this off of damage, which is an imperfect way to do so," said NSSL’s Dr. Tony Lyza, one of the lead researchers on the study. "Because how strong the damage is, is a function of not only how strong the winds are, but how strong buildings are constructed or whatever is being hit by the tornadoes."

DAMAGED WIND TURBINES HAVE BECOME IMPORTANT CLUE IN SOLVING A TORNADO'S STRENGTH

A tornado spins near Mountain View, Missouri, on May 26, 2024.

(Vince Waelti / FOX Weather)

Lyza's team went back and studied 105 tornadoes that passed very close to National Weather Service Doppler radar sites in various terrains.

"(We) found that the radars were often finding wind speeds that were higher near the ground than the damage-based estimates were indicating from the Enhanced Fujita Scale," he told FOX Weather.

Lyza said this study was unique in tracking tornadoes that didn’t just roll across flat terrain.

"A lot of the studies that have looked at radar data and how high the wind speeds are based on radar have had that data collected on the Great Plains where you have flat, wide open areas and very few structures to compare those wind speeds to," he said. "So our study looks at not only tornadoes that occurred on the Great Plains but anywhere in the country where a tornado passed close to a Weather Service radar."

HOW ARE TORNADOES RATED? THE ENHANCED FUJITA SCALE EXPLAINED

Enhanced Fujita Scale

(FOX Weather)

He said that allows their research to compare speeds of damage in forested areas, hilly areas, and more populated areas versus what the radar was measuring.

"And what we ended up finding was that consistently, no matter where the tornadoes occurred or how built up those areas were, the wind speeds tended to be higher based on estimates from radar," Lyza said. "Even if there was a more populated area with more structures to hit and estimate wind speeds from."

Lyza hopes the research will give better ideas of the kind of wind speeds buildings and structures need to withstand during a tornado strike.

"And so this work… steers towards a better idea of what the actual distribution of tornado intensities is so that we can build more resilient communities across the United States," he said.

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Half of Pastors Support Trump, but Many Are Hesitant to Share Preference

Pastor Views | Lifeway Research | Sep 17, 2024

Almost all pastors say they plan to vote in the 2024 presidential election, but a quarter refused to say who they’ll cast their ballot for.

By Aaron Earls

Like other Americans, pastors are deciding who they’ll vote for in the November election. Compared to previous elections, however, they’re much more hesitant to share their preference.

Almost all U.S. Protestant pastors (97%) plan to vote in the 2024 presidential election, according to a Lifeway Research study conducted Aug. 8-Sept. 3, 2024. But almost a quarter (23%) refused to answer the question of whom they’ll cast their ballot for. Few felt the same hesitancy in 2020 (4%) or 2016 (3%).

Still, among those who plan to vote and shared their preference, 50% say former President Donald Trump is their choice, while a quarter (24%) back Vice President Kamala Harris and 23% are undecided. No third-party candidate garnered more than 1% support.

“We ask pastors about many things going on in the culture today and they are willing to provide their opinion. However, the growing number of pastors unwilling to respond with their voting intentions shows how sensitive or divisive politics has become in some churches,” said Scott McConnell, executive director of Lifeway Research.

Commander-in-chief choices

The 2024 voting preferences are similar to those during the leadup to the 2020 election , when 53% of U.S. Protestant pastors said they planned to vote for Trump, 21% for Joe Biden and 22% were undecided. In 2016 , 40% of pastors were still undecided in September, while 32% supported Trump and 19% planned to vote for Hillary Clinton.

Currently, pastors are less likely to be solidly supportive of either major party candidate than their congregants, according to a Pew Research study . Around 3 in 5 U.S. Protestants (61%) say they would vote for or lean toward voting for Trump if the election were held today, while 37% would choose Harris.

Self-identified evangelical pastors are more likely to vote for Trump (61%), while half of mainline Protestant pastors (50%) say they support Harris. African American pastors are among the most likely to say they plan to vote for Harris (71%) and among the least likely to back Trump (5%). Pastors under 45 are among the least likely to support Trump (41%).

Denominationally, Pentecostal (65%), Baptist (64%), non-denominational (64%), Restorationist movement (55%) and Lutheran pastors (48%) are among the most likely to plan to cast their ballot for Trump, while Methodist (52%) and Presbyterian/Reformed pastors (44%) are among the most likely to choose Harris.

Half of U.S. Protestant pastors (50%) say they are either a registered member or consider themselves to be a part of the Republican party. One in 5 (18%) are Democrats, and 25% say they’re independent.

Evangelical pastors are more likely than mainline pastors to be Republicans (64% v. 30%), while mainline are more likely to be Democrats (35% v. 8%). Specifically, Baptist (67%), Pentecostal (67%), non-denominational (67%) and Restorationist movement pastors (57%) are among the most likely to identify as Republican. Methodist (36%), Presbyterian/Reformed (36%) and Lutheran pastors (25%) are among the most likely to say they’re Democrats.

Among Republican pastors, 78% support Trump. Among Democratic pastors, 85% back Harris.

“Out of all the descriptors of pastors, their own political party preference is the best predictor of how they will vote,” said McConnell. “Denominational groups often lean one way politically, but pastors must minister alongside many clergy who don’t share their political views. The same is true within their own congregations. In a culture that increasingly doesn’t want to tolerate people with different political views, pastors lead churches that strive for unity centered on their faith.”

Important issues

From a list of 11 characteristics, a majority of pastors say 10 are important in deciding how to cast their vote. Around 4 in 5 say they are looking for a candidate with the ability to maintain national security (85%), the ability to protect religious freedom (84%), the position on foreign policy (83%), the ability to improve the economy (83%), the position on immigration (81%), the position on abortion (80%) and personal character (79%). Three in 4 (75%) say likely Supreme Court nominees are important. Around 7 in 10 are looking for the ability to address racial injustice (71%) and the position on the size and role of government (70%). Fewer (38%) say the ability to address climate change is an important factor in how they vote.

When forced to choose the most important factor, 24% say personal character, 18% say the candidate’s position on abortion, 16% say the ability to protect religious freedom and 12% say the ability to improve the economy. Every other issue is the top priority of 4% or fewer pastors.

“Pastors are not single-issue voters. They care deeply about where presidential candidates stand on many issues,” said McConnell. “There are moral dimensions to all of the characteristics that could be selected, and pastors did not all pick the same characteristic as most important.”

Pastors voting for Trump are among the most likely to say an important issue in their voting decision is the ability to protect religious freedom (96%), the ability to maintain national security (95%), the ability to improve the economy (94%), the position on abortion (93%), the position on immigration (92%) and the size and role of government (89%).

Those voting for Harris are among the most likely to say they’re looking for a candidate with personal character (96%), the ability to address racial injustice (92%) and the ability to address climate change (91%).

Evangelical pastors are more likely than their mainline counterparts to say their primary vote-determining issue is the candidate’s position on abortion (22% v. 12%). Mainline pastors are more likely than evangelical ones to say their top issue is the personal character of the candidate (35% v. 17%).

Pastors planning on voting for Trump are the most likely to place as their top priority the candidate’s position on abortion (29%) and ability to protect religious freedom (25%). Those supporting Harris say their most important issue is personal character (58%).

For more information, view the complete report .

Aaron Earls

@WardrobeDoor

Aaron is the senior writer at Lifeway Research.

Methodology  

The phone survey of 1,003 Protestant pastors was conducted Aug. 8 – Sept. 3, 2024. The calling list was a stratified random sample, drawn from a list of all Protestant churches. Quotas were used for church size. Each interview was conducted with the senior pastor, minister or priest at the church. Responses were weighted by region and church size to more accurately reflect the population. The completed sample is 1,003 surveys. The sample provides 95% confidence that the sampling error does not exceed plus or minus 3.3%. This margin of error accounts for the effect of weighting. Margins of error are higher in sub-groups. 

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Soy milk could reduce risk factors for heart disease, research shows

Although soy milk is classified as 'ultra-processed,' nutritionists claim it is a nutritious alternative to cow's milk.

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Soy milk could have great heart health benefits, according to a new study.

Research from the University of Toronto found that drinking soy milk can help lower blood pressure and blood lipids, which are risk factors for heart disease.

The study, which was published in BMC Medicine and funded by the Soy Nutrition Institute Global (SNI) and the United Soybean Board, analyzed 17 randomized control trials and 19 outcomes to determine these health effects .

SLEEPING LONGER ON WEEKENDS COULD LOWER HEART DISEASE RISK BY 20%, STUDY FINDS

The findings revealed a moderate reduction in non-HDL cholesterol, which includes all the "bad" cholesterol. The soy milk was also linked to reduced blood pressure and slightly reduced inflammation.

Other health-related outcomes, including glycemic control and kidney function, did not vary between soy milk and cow’s milk.

Research found that consuming soy milk can help to reduce cholesterol, blood pressure and inflammation. (iStock)

Most soy milk is classified as "ultra-processed" and has been criticized for containing added sugar, according to an SNI press release.

"But the analysis found that soy milk with added sugar exerted health benefits similar to soy milk without added sugar," the release stated.

BLOOD PRESSURE IS ‘HIGHER THAN NORMAL’ FOR 1 IN 7 KIDS, SAYS AMERICAN HEART ASSOCIATION

This finding was the most surprising to the research team, according to lead study author Madeline Erlich, PhD.

"Results of the analysis show that in adults, consumption of soy milk both sweetened and unsweetened can improve heart health by lowering blood pressure and cholesterol levels, without affecting markers of inflammation," she wrote in an email to Fox News Digital.

The analysis found that soy milk with added sugar had similar health benefits to soy milk without added sugar. (iStock)

The researchers sought to understand whether nutrient-dense , plant protein foods like soy milk can be part of a heart-healthy diet despite being classified as "ultra-processed."

"One in three Americans is now familiar with the term ‘ultra-processed foods,’ even though there is no scientific consensus on the definition," Erlich said.

"Many foods classified as ‘ultra-processed’ are highly rated by other food classification systems used around the world."

CAN RED WINE IN MODERATION BOOST BRAIN AND HEART HEALTH? SOME EXPERTS STILL SAY YES

Registered dietitian and nutritionist Ilana Muhlstein, who is based in Los Angeles, agreed that soy gets a "bad rap."

"In America, we genetically modify it and overly process it into byproducts like soybean oil, in order to mass-market ultra-processed foods that can contribute to rising rates of inflammatory diseases and diabetes ," she said in a conversation with Fox News Digital.

Muhlstein called soy-based foods like edamame, tofu and tempeh "great sources of plant-based protein." (iStock)

Muhlstein added that edamame (made from young soybeans), organic tofu and tempeh (both soy-based foods), and unsweetened soy milk are all "great sources of plant-based protein in an overall healthy diet ."

"I wouldn’t consider any of these foods ultra-processed at all, given that they are all nutrient-dense with protein, fiber, calcium and phytonutrients," she said.

For more Health articles, visit foxnews.com/health

While this latest research focused on soy milk's heart-benefiting features, Muhlstein noted that soy has been shown in several studies to be "cancer-preventative."

Soy milk vs. cow's milk

Mark Messina, PhD, SNI's global director of nutrition science and research, noted that plant-based milks have become increasingly popular in recent years, which makes it important to "fully understand their health effects, since they vary in composition."

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"The current comprehensive analysis shows that soy milk, regardless of whether it contains added sugar, has advantages for cardiometabolic health ," he wrote in a statement sent to Fox News Digital.

"These findings are aligned with the Dietary Guidelines for Americans, which state that soy milk is the only plant milk that can be an appropriate substitute for cow’s milk," he added.

The total sugar content of most soy milks is about 60% less than cow’s milk, according to SNI. (iStock)

Fortified soy milk includes levels of protein, calcium and vitamin D that are comparable to cow’s milk, and it is lower in saturated fat, SNI wrote.

The total sugar content of most soy milks is about 60% less than cow’s milk, according to the institute.

Erlich agreed that soy milk is the "only nutritionally suitable alternative to cow’s milk" that can provide "high-quality protein similar to animal protein." 

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A cup of soy milk contains 7 to 8 grams of soy protein, which is comparable to the amount of protein in cow's milk, she noted.

The Food and Drug Administration (FDA) has stated that 25 grams or more of soy protein per day has been associated with reduced risk of coronary heart disease, when consumed as part of a diet low in saturated fat and cholesterol.

Angelica Stabile is a lifestyle reporter for Fox News Digital.

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on September 5, 2024 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

You might have to write up a research design as a standalone assignment, or it might be part of a larger   research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach	Quantitative approach
	and describe frequencies, averages, and correlations about relationships between variables

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Professional editors proofread and edit your paper by focusing on:

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See an example

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental	relationships effect on a
Quasi-experimental	)
Correlational
Descriptive

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design	Purpose and characteristics
Grounded theory
Phenomenology

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling	Non-probability sampling

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Questionnaires	Interviews
	)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability	Validity
	) )

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

Approach	Characteristics
Thematic analysis
Discourse analysis

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Superbugs predicted to kill 39 million people by 2050, study finds

New research in the lancet underscores our growing antibiotic resistance crisis, by rae hodge.

A new study published Monday in  The Lancet predicts drug-resistant infections could be linked to approximately 169 million deaths by 2050, according to the latest findings of a global research group. And antimicrobial resistant pathogens — AMR, or superbugs — are expected to directly kill more than 39 million people by that year.

The world's first global analysis of such trends, the findings were presented by more than 500 researchers who analyzed data from more than 204 countries over a 30 year period for The Global Research on Antimicrobial Resistance (GRAM) Project. The study's authors are calling for the priority development of new antimicrobial drugs and new prevention methods by global health policymakers.    

"Our analysis of trends in AMR mortality by age suggests that there is a need for interventions to tackle the increasing burden of AMR in older age groups going forward. Findings from this study provide evidentiary support to policy measures that combat AMR and have the potential to save lives, by adopting strategies that decrease risk of infections through new vaccines, improved quality of health care in hospitals and health centers, improved access to antibiotics and promotion of antibiotic stewardship," the scientists wrote. 

The new findings continue The Lancet's 2024 series on antibiotic-resistant illnesses, in which the publication has continued to advocate for several global targets to help toward a larger 10% reduction in superbug mortality by 2030. The latest findings echo those found previously by the World Health Organization, which attributes superbug development to the overuse of antimicrobials to fight viruses, bacteria and fungi .

GRAM project research said that millions of deaths could be prevented through improved healthcare access . As reported by Time , the notable finding arrives just ahead of a United Nations meeting on superbugs to be held Sept. 24 in New York. 

about superbugs

• Climate change could return us to the pre-antibiotic era
• A drug that can kill antibiotic-resistant bacteria could stave off the coming superbug crisis
• Can cannabis help us win the war against antibiotic resistance?

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