research model meaning

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

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

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199 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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Strategies and Models

The choice of qualitative or quantitative approach to research has been traditionally guided by the subject discipline. However, this is changing, with many “applied” researchers taking a more holistic and integrated approach that combines the two traditions. This methodology reflects the multi-disciplinary nature of many contemporary research problems.

In fact, it is possible to define many different types of research strategy. The following list ( Business research methods / Alan Bryman & Emma Bell. 4th ed. Oxford : Oxford University Press, 2015 ) is neither exclusive nor exhaustive.

• Clarifies the nature of the problem to be solved
• Can be used to suggest or generate hypotheses
• Includes the use of pilot studies
• Used widely in market research
• Provides general frequency data about populations or samples
• Does not manipulate variables (e.g. as in an experiment)
• Describes only the “who, what, when, where and how”
• Cannot establish a causal relationship between variables
• Associated with descriptive statistics
• Breaks down factors or variables involved in a concept, problem or issue
• Often uses (or generates) models as analytical tools
• Often uses micro/macro distinctions in analysis
• Focuses on the analysis of bias, inconsistencies, gaps or contradictions in accounts, theories, studies or models
• Often takes a specific theoretical perspective, (e.g. feminism; labour process theory)
• Mainly quantitative
• Identifies measurable variables
• Often manipulates variables to produce measurable effects
• Uses specific, predictive or null hypotheses
• Dependent on accurate sampling
• Uses statistical testing to establish causal relationships, variance between samples or predictive trends
• Associated with organisation development initiatives and interventions
• Practitioner based, works with practitioners to help them solve their problems
• Involves data collection, evaluation and reflection
• Often used to review interventions and plan new ones
• Focuses on recognised needs, solving practical problems or answering specific questions
• Often has specific commercial objectives (e.g. product development )

Approaches to research

For many, perhaps most, researchers, the choice of approach is straightforward. Research into reaction mechanisms for an organic chemical reaction will take a quantitative approach, whereas qualitative research will have a better fit in the social work field that focuses on families and individuals. While some research benefits from one of the two approaches, other research yields more understanding from a combined approach.

In fact, qualitative and quantitative approaches to research have some important shared aspects. Each type of research generally follows the steps of scientific method, specifically:

In general, each approach begins with qualitative reasoning or a hypothesis based on a value judgement. These judgements can be applied, or transferred to quantitative terms with both inductive and deductive reasoning abilities. Both can be very detailed, although qualitative research has more flexibility with its amount of detail.

Selecting an appropriate design for a study involves following a logical thought process; it is important to explore all possible consequences of using a particular design in a study. As well as carrying out a scoping study, a researchers should familiarise themselves with both qualitative and quantitative approaches to research in order to make the best decision. Some researchers may quickly select a qualitative approach out of fear of statistics but it may be a better idea to challenge oneself. The researcher should also be prepared to defend the paradigm and chosen research method; this is even more important if your proposal or grant is for money, or other resources.

Ultimately, clear goals and objectives and a fit-for-purpose research design is more helpful and important than old-fashioned arguments about which approach to research is “best”. Indeed, there is probably no such thing as a single “correct” design – hypotheses can be studied by different methods using different research designs. A research design is probably best thought of as a series of signposts to keep the research headed in the right direction and should not be regarded as a highly specific plan to be followed without deviation.

Research models

There is no common agreement on the classification of research models but, for the purpose of illustration, five categories of research models and their variants are outlined below.

A physical model is a physical object shaped to look like the represented phenomenon, usually built to scale e.g. atoms, molecules, skeletons, organs, animals, insects, sculptures, small-scale vehicles or buildings, life-size prototype products. They can also include 3-dimensional alternatives for two-dimensional representations e.g. a physical model of a picture or photograph.

In this case, the term model is used loosely to refer to any theory phrased in formal, speculative or symbolic styles. They generally consist of a set of assumptions about some concept or system; are often formulated, developed and named on the basis of an analogy between the object, or system that it describes and some other object or different system; and they are considered an approximation that is useful for certain purposes. Theoretical models are often used in biology, chemistry, physics and psychology.

A mathematical model refers to the use of mathematical equations to depict relationships between variables, or the behaviour of persons, groups, communities, cultural groups, nations, etc.

It is an abstract model that uses mathematical language to describe the behaviour of a system. They are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science). Types of mathematical models include trend (time series), stochastic, causal and path models. Examples include models of population and economic growth, weather forecasting and the characterisation of large social networks.

Mechanical (or computer) models tend to use concepts from the natural sciences, particularly physics, to provide analogues for social behaviour. They are often an extension of mathematical models. Many computer-simulation models have shown how a research problem can be investigated through sequences of experiments e.g. game models; microanalytic simulation models (used to examine the effects of various kinds of policy on e.g. the demographic structure of a population); models for predicting storm frequency, or tracking a hurricane.

These models are used to untangle meanings that individuals give to symbols that they use or encounter. They are generally simulation models, i.e. they are based on artificial (contrived) situations, or structured concepts that correspond to real situations. They are characterised by symbols, change, interaction and empiricism and are often used to examine human interaction in social settings.

The advantages and disadvantages of modelling

Take a look at the advantages and disadvantages below. It might help you think about what type of model you may use.

• The determination of factors or variables that most influence the behaviour of phenomena
• The ability to predict, or forecast the long term behaviour of phenomena
• The ability to predict the behaviour of the phenomenon when changes are made to the factors influencing it
• They allow researchers a view on difficult to study processes (e.g. old, complex or single-occurrence processes)
• They allow the study of mathematically intractable problems (e.g. complex non-linear systems such as language)
• They can be explicit, detailed, consistent, and clear (but that can also be a weakness)
• They allow the exploration of different parameter settings (i.e. evolutionary, environmental, individual and social factors can be easily varied)
• Models validated for a category of systems can be used in many different scenarios e.g. they can be reused in the design, analysis, simulation, diagnosis and prediction of a technical system
• Models enable researchers to generate unrealistic scenarios as well as realistic ones
• Difficulties in validating models
• Difficulties in assessing the accuracy of models
• Models can be very complex and difficult to explain
• Models do not “provide proof”

The next section describes the processes and design of research.

Modeling in Scientific Research: Simplifying a system to make predictions

by Anne E. Egger, Ph.D., Anthony Carpi, Ph.D.

Listen to this reading

Did you know that scientific models can help us peer inside the tiniest atom or examine the entire universe in a single glance? Models allow scientists to study things too small to see, and begin to understand things too complex to imagine.

Modeling involves developing physical, conceptual, or computer-based representations of systems.

Scientists build models to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses.

Computer modeling is a relatively new scientific research method, but it is based on the same principles as physical and conceptual modeling.

LEGO ® bricks have been a staple of the toy world since they were first manufactured in Denmark in 1953. The interlocking plastic bricks can be assembled into an endless variety of objects (see Figure 1). Some kids (and even many adults) are interested in building the perfect model – finding the bricks of the right color, shape, and size, and assembling them into a replica of a familiar object in the real world, like a castle, the space shuttle , or London Bridge. Others focus on using the object they build – moving LEGO knights in and out of the castle shown in Figure 1, for example, or enacting a space shuttle mission to Mars. Still others may have no particular end product in mind when they start snapping bricks together and just want to see what they can do with the pieces they have.

Figure 1 : On the left, individual LEGO® bricks. On the right, a model of a NASA space center built with LEGO bricks.

On the most basic level, scientists use models in much the same way that people play with LEGO bricks. Scientific models may or may not be physical entities, but scientists build them for the same variety of reasons: to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses .

• Types of models: Physical, conceptual, mathematical

At the St. Anthony Falls Laboratory at the University of Minnesota, a group of engineers and geologists have built a room-sized physical replica of a river delta to model a real one like the Mississippi River delta in the Gulf of Mexico (Paola et al., 2001). These researchers have successfully incorporated into their model the key processes that control river deltas (like the variability of water flow, the deposition of sediments transported by the river, and the compaction and subsidence of the coastline under the pressure of constant sediment additions) in order to better understand how those processes interact. With their physical model, they can mimic the general setting of the Mississippi River delta and then do things they can't do in the real world, like take a slice through the resulting sedimentary deposits to analyze the layers within the sediments. Or they can experiment with changing parameters like sea level and sedimentary input to see how those changes affect deposition of sediments within the delta, the same way you might "experiment" with the placement of the knights in your LEGO castle.

Figure 2 : A photograph of the St. Anthony Falls lab river delta model, showing the experimental setup with pink-tinted water flowing over sediments. Image courtesy the National Center for Earth-Surface Dynamics Data Repository http://www.nced.umn.edu [accessed September, 2008]

Not all models used in scientific research are physical models. Some are conceptual, and involve assembling all of the known components of a system into a coherent whole. This is a little like building an abstract sculpture out of LEGO bricks rather than building a castle. For example, over the past several hundred years, scientists have developed a series of models for the structure of an atom . The earliest known model of the atom compared it to a billiard ball, reflecting what scientists knew at the time – they were the smallest piece of an element that maintained the properties of that element. Despite the fact that this was a purely conceptual model, it could be used to predict some of the behavior that atoms exhibit. However, it did not explain all of the properties of atoms accurately. With the discovery of subatomic particles like the proton and electron , the physicist Ernest Rutherford proposed a "solar system" model of the atom, in which electrons orbited around a nucleus that included protons (see our Atomic Theory I: The Early Days module for more information). While the Rutherford model is useful for understanding basic properties of atoms, it eventually proved insufficient to explain all of the behavior of atoms. The current quantum model of the atom depicts electrons not as pure particles, but as having the properties of both particles and waves , and these electrons are located in specific probability density clouds around the atom's nucleus.

Both physical and conceptual models continue to be important components of scientific research . In addition, many scientists now build models mathematically through computer programming. These computer-based models serve many of the same purposes as physical models, but are determined entirely by mathematical relationships between variables that are defined numerically. The mathematical relationships are kind of like individual LEGO bricks: They are basic building blocks that can be assembled in many different ways. In this case, the building blocks are fundamental concepts and theories like the mathematical description of turbulent flow in a liquid , the law of conservation of energy, or the laws of thermodynamics, which can be assembled into a wide variety of models for, say, the flow of contaminants released into a groundwater reservoir or for global climate change.

Comprehension Checkpoint

• Modeling as a scientific research method

Whether developing a conceptual model like the atomic model, a physical model like a miniature river delta , or a computer model like a global climate model, the first step is to define the system that is to be modeled and the goals for the model. "System" is a generic term that can apply to something very small (like a single atom), something very large (like the Earth's atmosphere), or something in between, like the distribution of nutrients in a local stream. So defining the system generally involves drawing the boundaries (literally or figuratively) around what you want to model, and then determining the key variables and the relationships between those variables.

Though this initial step may seem straightforward, it can be quite complicated. Inevitably, there are many more variables within a system than can be realistically included in a model , so scientists need to simplify. To do this, they make assumptions about which variables are most important. In building a physical model of a river delta , for example, the scientists made the assumption that biological processes like burrowing clams were not important to the large-scale structure of the delta, even though they are clearly a component of the real system.

Determining where simplification is appropriate takes a detailed understanding of the real system – and in fact, sometimes models are used to help determine exactly which aspects of the system can be simplified. For example, the scientists who built the model of the river delta did not incorporate burrowing clams into their model because they knew from experience that they would not affect the overall layering of sediments within the delta. On the other hand, they were aware that vegetation strongly affects the shape of the river channel (and thus the distribution of sediments), and therefore conducted an experiment to determine the nature of the relationship between vegetation density and river channel shape (Gran & Paola, 2001).

Figure 3: Dalton's ball and hook model for the atom.

Once a model is built (either in concept, physical space, or in a computer), it can be tested using a given set of conditions. The results of these tests can then be compared against reality in order to validate the model. In other words, how well does the model do at matching what we see in the real world? In the physical model of delta sediments , the scientists who built the model looked for features like the layering of sand that they have seen in the real world. If the model shows something really different than what the scientists expect, the relationships between variables may need to be redefined or the scientists may have oversimplified the system . Then the model is revised, improved, tested again, and compared to observations again in an ongoing, iterative process . For example, the conceptual "billiard ball" model of the atom used in the early 1800s worked for some aspects of the behavior of gases, but when that hypothesis was tested for chemical reactions , it didn't do a good job of explaining how they occur – billiard balls do not normally interact with one another. John Dalton envisioned a revision of the model in which he added "hooks" to the billiard ball model to account for the fact that atoms could join together in reactions , as conceptualized in Figure 3.

While conceptual and physical models have long been a component of all scientific disciplines, computer-based modeling is a more recent development, and one that is frequently misunderstood. Computer models are based on exactly the same principles as conceptual and physical models, however, and they take advantage of relatively recent advances in computing power to mimic real systems .

• The beginning of computer modeling: Numerical weather prediction

In the late 19 th century, Vilhelm Bjerknes , a Norwegian mathematician and physicist, became interested in deriving equations that govern the large-scale motion of air in the atmosphere . Importantly, he recognized that circulation was the result not just of thermodynamic properties (like the tendency of hot air to rise), but of hydrodynamic properties as well, which describe the behavior of fluid flow. Through his work, he developed an equation that described the physical processes involved in atmospheric circulation, which he published in 1897. The complexity of the equation reflected the complexity of the atmosphere, and Bjerknes was able to use it to describe why weather fronts develop and move.

• Using calculations predictively

Bjerknes had another vision for his mathematical work, however: He wanted to predict the weather. The goal of weather prediction, he realized, is not to know the paths of individual air molecules over time, but to provide the public with "average values over large areas and long periods of time." Because his equation was based on physical principles , he saw that by entering the present values of atmospheric variables like air pressure and temperature, he could solve it to predict the air pressure and temperature at some time in the future. In 1904, Bjerknes published a short paper describing what he called "the principle of predictive meteorology", (Bjerknes, 1904) (see the Research links for the entire paper). In it, he says:

Based upon the observations that have been made, the initial state of the atmosphere is represented by a number of charts which give the distribution of seven variables from level to level in the atmosphere. With these charts as the starting point, new charts of a similar kind are to be drawn, which represent the new state from hour to hour.

In other words, Bjerknes envisioned drawing a series of weather charts for the future based on using known quantities and physical principles . He proposed that solving the complex equation could be made more manageable by breaking it down into a series of smaller, sequential calculations, where the results of one calculation are used as input for the next. As a simple example, imagine predicting traffic patterns in your neighborhood. You start by drawing a map of your neighborhood showing the location, speed, and direction of every car within a square mile. Using these parameters , you then calculate where all of those cars are one minute later. Then again after a second minute. Your calculations will likely look pretty good after the first minute. After the second, third, and fourth minutes, however, they begin to become less accurate. Other factors you had not included in your calculations begin to exert an influence, like where the person driving the car wants to go, the right- or left-hand turns that they make, delays at traffic lights and stop signs, and how many new drivers have entered the roads.

Trying to include all of this information simultaneously would be mathematically difficult, so, as proposed by Bjerknes, the problem can be solved with sequential calculations. To do this, you would take the first step as described above: Use location, speed, and direction to calculate where all the cars are after one minute. Next, you would use the information on right- and left-hand turn frequency to calculate changes in direction, and then you would use information on traffic light delays and new traffic to calculate changes in speed. After these three steps are done, you would solve your first equation again for the second minute time sequence, using location, speed, and direction to calculate where the cars are after the second minute. Though it would certainly be rather tiresome to do by hand, this series of sequential calculations would provide a manageable way to estimate traffic patterns over time.

Although this method made calculations tedious, Bjerknes imagined "no intractable mathematical difficulties" with predicting the weather. The method he proposed (but never used himself) became known as numerical weather prediction, and it represents one of the first approaches towards numerical modeling of a complex, dynamic system .

• Advancing weather calculations

Bjerknes' challenge for numerical weather prediction was taken up sixteen years later in 1922 by the English scientist Lewis Fry Richardson . Richardson related seven differential equations that built on Bjerknes' atmospheric circulation equation to include additional atmospheric processes. One of Richardson's great contributions to mathematical modeling was to solve the equations for boxes within a grid; he divided the atmosphere over Germany into 25 squares that corresponded with available weather station data (see Figure 4) and then divided the atmosphere into five layers, creating a three-dimensional grid of 125 boxes. This was the first use of a technique that is now standard in many types of modeling. For each box, he calculated each of nine variables in seven equations for a single time step of three hours. This was not a simple sequential calculation, however, since the values in each box depended on the values in the adjacent boxes, in part because the air in each box does not simply stay there – it moves from box to box.

Figure 4: Data for Richardson's forecast included measurements of winds, barometric pressure and temperature. Initial data were recorded in 25 squares, each 200 kilometers on a side, but conditions were forecast only for the two central squares outlined in red.

Richardson's attempt to make a six-hour forecast took him nearly six weeks of work with pencil and paper and was considered an utter failure, as it resulted in calculated barometric pressures that exceeded any historically measured value (Dalmedico, 2001). Probably influenced by Bjerknes, Richardson attributed the failure to inaccurate input data , whose errors were magnified through successive calculations (see more about error propagation in our Uncertainty, Error, and Confidence module).

Figure 5: Norwegian stamp bearing an image of Vilhelm Bjerknes

In addition to his concerns about inaccurate input parameters , Richardson realized that weather prediction was limited in large part by the speed at which individuals could calculate by hand. He thus envisioned a "forecast factory," in which thousands of people would each complete one small part of the necessary calculations for rapid weather forecasting.

• First computer for weather prediction

Richardson's vision became reality in a sense with the birth of the computer, which was able to do calculations far faster and with fewer errors than humans. The computer used for the first one-day weather prediction in 1950, nicknamed ENIAC (Electronic Numerical Integrator and Computer), was 8 feet tall, 3 feet wide, and 100 feet long – a behemoth by modern standards, but it was so much faster than Richardson's hand calculations that by 1955, meteorologists were using it to make forecasts twice a day (Weart, 2003). Over time, the accuracy of the forecasts increased as better data became available over the entire globe through radar technology and, eventually, satellites.

The process of numerical weather prediction developed by Bjerknes and Richardson laid the foundation not only for modern meteorology , but for computer-based mathematical modeling as we know it today. In fact, after Bjerknes died in 1951, the Norwegian government recognized the importance of his contributions to the science of meteorology by issuing a stamp bearing his portrait in 1962 (Figure 5).

• Modeling in practice: The development of global climate models

The desire to model Earth's climate on a long-term, global scale grew naturally out of numerical weather prediction. The goal was to use equations to describe atmospheric circulation in order to understand not just tomorrow's weather, but large-scale patterns in global climate, including dynamic features like the jet stream and major climatic shifts over time like ice ages. Initially, scientists were hindered in the development of valid models by three things: a lack of data from the more inaccessible components of the system like the upper atmosphere , the sheer complexity of a system that involved so many interacting components, and limited computing powers. Unexpectedly, World War II helped solve one problem as the newly-developed technology of high altitude aircraft offered a window into the upper atmosphere (see our Technology module for more information on the development of aircraft). The jet stream, now a familiar feature of the weather broadcast on the news, was in fact first documented by American bombers flying westward to Japan.

As a result, global atmospheric models began to feel more within reach. In the early 1950s, Norman Phillips, a meteorologist at Princeton University, built a mathematical model of the atmosphere based on fundamental thermodynamic equations (Phillips, 1956). He defined 26 variables related through 47 equations, which described things like evaporation from Earth's surface , the rotation of the Earth, and the change in air pressure with temperature. In the model, each of the 26 variables was calculated in each square of a 16 x 17 grid that represented a piece of the northern hemisphere. The grid represented an extremely simple landscape – it had no continents or oceans, no mountain ranges or topography at all. This was not because Phillips thought it was an accurate representation of reality, but because it simplified the calculations. He started his model with the atmosphere "at rest," with no predetermined air movement, and with yearly averages of input parameters like air temperature.

Phillips ran the model through 26 simulated day-night cycles by using the same kind of sequential calculations Bjerknes proposed. Within only one "day," a pattern in atmospheric pressure developed that strongly resembled the typical weather systems of the portion of the northern hemisphere he was modeling (see Figure 6). In other words, despite the simplicity of the model, Phillips was able to reproduce key features of atmospheric circulation , showing that the topography of the Earth was not of primary importance in atmospheric circulation. His work laid the foundation for an entire subdiscipline within climate science: development and refinement of General Circulation Models (GCMs).

Figure 6: A model result from Phillips' 1956 paper. The box in the lower right shows the size of a grid cell. The solid lines represent the elevation of the 1000 millibar pressure, so the H and L represent areas of high and low pressure, respectively. The dashed lines represent lines of constant temperature, indicating a decreasing temperature at higher latitudes. This is the 23 rd simulated day.

By the 1980s, computing power had increased to the point where modelers could incorporate the distribution of oceans and continents into their models . In 1991, the eruption of Mt. Pinatubo in the Philippines provided a natural experiment: How would the addition of a significant volume of sulfuric acid , carbon dioxide, and volcanic ash affect global climate? In the aftermath of the eruption, descriptive methods (see our Description in Scientific Research module) were used to document its effect on global climate: Worldwide measurements of sulfuric acid and other components were taken, along with the usual air temperature measurements. Scientists could see that the large eruption had affected climate , and they quantified the extent to which it had done so. This provided a perfect test for the GCMs . Given the inputs from the eruption, could they accurately reproduce the effects that descriptive research had shown? Within a few years, scientists had demonstrated that GCMs could indeed reproduce the climatic effects induced by the eruption, and confidence in the abilities of GCMs to provide reasonable scenarios for future climate change grew. The validity of these models has been further substantiated by their ability to simulate past events, like ice ages, and the agreement of many different models on the range of possibilities for warming in the future, one of which is shown in Figure 7.

Figure 7: Projected change in annual mean surface air temperature from the late 20th century (1971-2000 average) to the middle 21st century (2051-2060 average). Image courtesy NOAA Geophysical Fluid Dynamics Laboratory.

• Limitations and misconceptions of models

The widespread use of modeling has also led to widespread misconceptions about models , particularly with respect to their ability to predict. Some models are widely used for prediction, such as weather and streamflow forecasts, yet we know that weather forecasts are often wrong. Modeling still cannot predict exactly what will happen to the Earth's climate , but it can help us see the range of possibilities with a given set of changes. For example, many scientists have modeled what might happen to average global temperatures if the concentration of carbon dioxide (CO 2 ) in the atmosphere is doubled from pre-industrial levels (pre-1950); though individual models differ in exact output, they all fall in the range of an increase of 2-6° C (IPCC, 2007).

All models are also limited by the availability of data from the real system . As the amount of data from a system increases, so will the accuracy of the model. For climate modeling, that is why scientists continue to gather data about climate in the geologic past and monitor things like ocean temperatures with satellites – all those data help define parameters within the model. The same is true of physical and conceptual models, too, well-illustrated by the evolution of our model of the atom as our knowledge about subatomic particles increased.

• Modeling in modern practice

The various types of modeling play important roles in virtually every scientific discipline, from ecology to analytical chemistry and from population dynamics to geology. Physical models such as the river delta take advantage of cutting edge technology to integrate multiple large-scale processes. As computer processing speed and power have increased, so has the ability to run models on them. From the room-sized ENIAC in the 1950s to the closet-sized Cray supercomputer in the 1980s to today's laptop, processing speed has increased over a million-fold, allowing scientists to run models on their own computers rather than booking time on one of only a few supercomputers in the world. Our conceptual models continue to evolve, and one of the more recent theories in theoretical physics digs even deeper into the structure of the atom to propose that what we once thought were the most fundamental particles – quarks – are in fact composed of vibrating filaments, or strings. String theory is a complex conceptual model that may help explain gravitational force in a way that has not been done before. Modeling has also moved out of the realm of science into recreation, and many computer games like SimCity® involve both conceptual modeling (answering the question, "What would it be like to run a city?") and computer modeling, using the same kinds of equations that are used model traffic flow patterns in real cities. The accessibility of modeling as a research method allows it to be easily combined with other scientific research methods, and scientists often incorporate modeling into experimental, descriptive, and comparative studies.

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Overview of the Research Process

• First Online: 01 January 2012

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Research is a rigorous problem-solving process whose ultimate goal is the discovery of new knowledge. Research may include the description of a new phenomenon, definition of a new relationship, development of a new model, or application of an existing principle or procedure to a new context. Research is systematic, logical, empirical, reductive, replicable and transmittable, and generalizable. Research can be classified according to a variety of dimensions: basic, applied, or translational; hypothesis generating or hypothesis testing; retrospective or prospective; longitudinal or cross-sectional; observational or experimental; and quantitative or qualitative. The ultimate success of a research project is heavily dependent on adequate planning.

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Supino, P.G. (2012). Overview of the Research Process. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_1

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What is a Model? 5 Essential Components

In the research and statistics context, what does the term model mean? This article defines what is a model, poses guide questions on how to create one, lists steps on how to construct a model and provides simple examples to clarify points arising from those questions.

One of the interesting things that I particularly like in statistics is the prospect of being able to predict an outcome (referred to as the independent variable) from a set of factors (referred to as the independent variables). A multiple regression equation or a model derived from a set of interrelated variables achieves this end.

The usefulness of a model is determined by how well it can predict the behavior of dependent variables from a set of independent variables.

To clarify the concept, I will describe here an example of a research activity that aimed to develop a multiple regression model from both secondary and primary data sources.

What is a Model?

Before going into a detailed discussion on what is a model, it is always good practice to define what we mean here by a model.

A model, in research and statistics, is a representation of reality using variables that somehow relate with each other. I italicize the word “somehow” here being reminded of the possibility of a correlation between variables when in fact there is no logical connection between them.

A Classic Example of Nonsensical Correlation

A classic example given to illustrate nonsensical correlation is the high correlation between length of hair and height. They found out in a study that if a person has short hair, that person is tall and vice versa.

Actually, the conclusion of that study is spurious because there is no real correlation between length of hair and height. It so happened that men usually have short hair while women have long hair. Men are taller than women. The true variable behind what really determines height is the sex or gender of the individual, not the length of hair.

The model is only an approximation of the likely outcome of things because there will always be errors involved in building it. This is the reason scientists adopt a five percent error (p=0.05) as a standard in making conclusions from statistical computations. There is no such thing as absolute certainty in predicting the probability of a phenomenon.

Things Needed to Construct A Model

In developing a multiple regression model which will be fully described here, you will need to have a clear idea of:

• What is your intention or reason in constructing the model?
• What is the time frame and unit of your analysis?
• What has been done so far in line with the model that you intend to construct?
• What variables would you like to include in your model?
• How would you ensure your model has predictive value?

These questions will guide you towards developing a model that will help you achieve your goal. I explain the expected answers to the above questions. I provide examples to further clarify the points.

1. Purpose in Constructing the Model

Why would you like to have a model in the first place? What would you like to get from it? The objectives of your research, therefore, should be clear enough so that you can derive full benefit from it.

Here, I sought to develop a model. The main purpose is to determine the predictors of the number of published papers produced by the faculty in the university. The major question, therefore, is:

“What are the crucial factors that will motivate the faculty members to engage in research and publish research papers?”

Once the research director of the university, I figured out that the best way to increase the number of research publications is to zero in on those variables that really matter. There are so many variables that will influence the turnout of publications, but which ones do really matter?

A certain number of research publications is required each year, so what should the interventions be to reach those targets? There is a need to identify the reasons for the failure of the faculty members to publish research papers to rectify the problem.

2. Time Frame and Unit of Analysis

You should have a specific time frame on which you should base your analysis from.

There are many considerations in selecting the time frame of the analysis but of foremost importance is the availability of data. For established universities with consistent data collection fields, this poses no problem. But for struggling universities without an established database, it will be much more challenging.

Why do I say consistent data collection fields? If you want to see trends, then the same data must be collected in a series through time.

What do I mean by this?

In the particular case I mentioned, i.e., number of publications, one of the suspected predictors is the time spent by the faculty in administrative work. In a 40-hour work week, how much time do they spend in designated posts such as unit head, department head, or dean? This variable which is a unit of analysis , therefore, should be consistently monitored every semester, for many years for correlation with the number of publications.

How many years should these data be collected?

From what I collect, peer-reviewed publications can be produced normally from two to three years. Hence, the study must cover at least three years of data to log the number of publications produced. That is, if no systematic data collection ensued to supply the study’s data needs.

If data was systematically collected, you can backtrack and get data for as long as you want. It is even possible to compare publication performance before and after implementation of a research policy in the university.

3. Literature Review

You might be guilty of “reinventing the wheel” if you did not take time to review published literature on your specific research concern. Reinventing the wheel means you duplicate the work of others. It is possible that other researchers have already satisfactorily studied the area you are trying to clarify issues on. For this reason, an exhaustive review of literature will enhance the quality and predictive value of your model.

For the model I attempted to make on the number of publications made by the faculty, I bumped on a summary of the predictors made by Bland et al . [1] based on a considerable number of published papers. Below is the model they prepared to sum up the findings.

Bland and colleagues found that three major areas determine research productivity namely,

1) the individual’s characteristics,

2) institutional characteristics, and

3) leadership characteristics.

This just means that you cannot just threaten the faculty with the so-called publish and perish policy if the required institutional resources are absent and/or leadership quality is poor.

4. Select the Variables for Study

The model given by Bland and colleagues in the figure above is still too general to allow statistical analysis to take place.

For example, in individual characteristics, how can socialization as a variable be measured? How about motivation ?

This requires you to further delve on literature on how to properly measure socialization and motivation, among other variables you are interested in. The dependent variable I reflected productivity in a recent study I conducted with students is the number of total publications , whether these are peer-reviewed.

5. Ensuring the Predictive Value of the Model

The predictive value of a model depends on influence of a set of predictor variables on the dependent variable. How do you determine influence of these variables?

In Bland’s model, we may include all the variables associated with those concepts identified in analyzing data. But of course, this will be costly and time-consuming as there are a lot of variables to consider. Besides, the greater the number of variables you included in your analysis, the more samples you will need to get a good correlation between the predictor variables and the dependent variable .

Stevens [2] recommends a nominal number of 15 cases for one predictor variable. This means that if you want to study 10 variables, you will need at least 150 cases to make your multiple regression model valid in some sense. But of course, the more samples you have, the greater the certainty in predicting outcomes.

Once you have decided on the number of variables you intend to incorporate in your multiple regression model, you will then be able to input your data on a spreadsheet or a statistical software such as SPSS, Statistica, or related software applications. The software application will automatically produce the results for you.

The next concern is how to interpret the results of a model such as the results of a multiple regression analysis . I will consider this topic in my upcoming posts.

A model is only as good as the data used to create it. You must therefore make sure that your data is accurate and reliable for better predictive outcomes.

• Bland, C.J., Center, B.A., Finstad, D.A., Risbey, K.R., and J. G. Staples. (2005). A Theoretical, Practical, Predictive Model of Faculty and Department Research Productivity.  Academic Medicine , Vol. 80, No. 3, 225-237.
• Stevens, J. 2002. Applied multivariate statistics for the social sciences, 3rd ed . New Jersey: Lawrence Erlbaum Publishers. p. 72.

Updated May 6, 2022 © P. A. Regoniel

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A critique on the cooperative writing response groups, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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The Four Types of Research Paradigms: A Comprehensive Guide

5-minute read

• 22nd January 2023

In this guide, you’ll learn all about the four research paradigms and how to choose the right one for your research.

Introduction to Research Paradigms

A paradigm is a system of beliefs, ideas, values, or habits that form the basis for a way of thinking about the world. Therefore, a research paradigm is an approach, model, or framework from which to conduct research. The research paradigm helps you to form a research philosophy, which in turn informs your research methodology.

Your research methodology is essentially the “how” of your research – how you design your study to not only accomplish your research’s aims and objectives but also to ensure your results are reliable and valid. Choosing the correct research paradigm is crucial because it provides a logical structure for conducting your research and improves the quality of your work, assuming it’s followed correctly.

Three Pillars: Ontology, Epistemology, and Methodology

Before we jump into the four types of research paradigms, we need to consider the three pillars of a research paradigm.

Ontology addresses the question, “What is reality?” It’s the study of being. This pillar is about finding out what you seek to research. What do you aim to examine?

Epistemology is the study of knowledge. It asks, “How is knowledge gathered and from what sources?”

Methodology involves the system in which you choose to investigate, measure, and analyze your research’s aims and objectives. It answers the “how” questions.

Let’s now take a look at the different research paradigms.

1.   Positivist Research Paradigm

The positivist research paradigm assumes that there is one objective reality, and people can know this reality and accurately describe and explain it. Positivists rely on their observations through their senses to gain knowledge of their surroundings.

In this singular objective reality, researchers can compare their claims and ascertain the truth. This means researchers are limited to data collection and interpretations from an objective viewpoint. As a result, positivists usually use quantitative methodologies in their research (e.g., statistics, social surveys, and structured questionnaires).

This research paradigm is mostly used in natural sciences, physical sciences, or whenever large sample sizes are being used.

2.   Interpretivist Research Paradigm

Interpretivists believe that different people in society experience and understand reality in different ways – while there may be only “one” reality, everyone interprets it according to their own view. They also believe that all research is influenced and shaped by researchers’ worldviews and theories.

As a result, interpretivists use qualitative methods and techniques to conduct their research. This includes interviews, focus groups, observations of a phenomenon, or collecting documentation on a phenomenon (e.g., newspaper articles, reports, or information from websites).

3.   Critical Theory Research Paradigm

The critical theory paradigm asserts that social science can never be 100% objective or value-free. This paradigm is focused on enacting social change through scientific investigation. Critical theorists question knowledge and procedures and acknowledge how power is used (or abused) in the phenomena or systems they’re investigating.

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Researchers using this paradigm are more often than not aiming to create a more just, egalitarian society in which individual and collective freedoms are secure. Both quantitative and qualitative methods can be used with this paradigm.

4.   Constructivist Research Paradigm

Constructivism asserts that reality is a construct of our minds ; therefore, reality is subjective. Constructivists believe that all knowledge comes from our experiences and reflections on those experiences and oppose the idea that there is a single methodology to generate knowledge.

This paradigm is mostly associated with qualitative research approaches due to its focus on experiences and subjectivity. The researcher focuses on participants’ experiences as well as their own.

Choosing the Right Research Paradigm for Your Study

Once you have a comprehensive understanding of each paradigm, you’re faced with a big question: which paradigm should you choose? The answer to this will set the course of your research and determine its success, findings, and results.

To start, you need to identify your research problem, research objectives , and hypothesis . This will help you to establish what you want to accomplish or understand from your research and the path you need to take to achieve this.

You can begin this process by asking yourself some questions:

• What is the nature of your research problem (i.e., quantitative or qualitative)?
• How can you acquire the knowledge you need and communicate it to others? For example, is this knowledge already available in other forms (e.g., documents) and do you need to gain it by gathering or observing other people’s experiences or by experiencing it personally?
• What is the nature of the reality that you want to study? Is it objective or subjective?

Depending on the problem and objective, other questions may arise during this process that lead you to a suitable paradigm. Ultimately, you must be able to state, explain, and justify the research paradigm you select for your research and be prepared to include this in your dissertation’s methodology and design section.

Using Two Paradigms

If the nature of your research problem and objectives involves both quantitative and qualitative aspects, then you might consider using two paradigms or a mixed methods approach . In this, one paradigm is used to frame the qualitative aspects of the study and another for the quantitative aspects. This is acceptable, although you will be tasked with explaining your rationale for using both of these paradigms in your research.

Choosing the right research paradigm for your research can seem like an insurmountable task. It requires you to:

●  Have a comprehensive understanding of the paradigms,

●  Identify your research problem, objectives, and hypothesis, and

●  Be able to state, explain, and justify the paradigm you select in your methodology and design section.

Although conducting your research and putting your dissertation together is no easy task, proofreading it can be! Our experts are here to make your writing shine. Your first 500 words are free !

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Following the people and events that make up the research community at Duke

What is a Model?

By Nina Cervantes

On February 26, 2018

In Art , Computers/Technology , Data , Lecture , Statistics

When you think of the word “model,” what do you think?

Wharton began the talk by defining the term “model,” knowing that it can often times be rather ambiguous. She stated the observation that models are “a prolific class of things,” from architectural models, to video game models, to runway models. Some of these types of things seem unrelated, but Wharton, throughout her talk, pointed out the similarities between them and ultimately tied them together as all being models.

The word “model” itself has become a heavily loaded term. According to Wharton, the dictionary definition of “model” is 9 columns of text in length. Wharton then stressed that a model “is an autonomous agent.” This implies that models must be independent of the world and from theory, as well as being independent of their makers and consumers. For example, architecture, after it is built, becomes independent of its architect.

Next, Wharton outlined different ways to model. They include modeling iconically, in which the model resembles the actual thing, such as how the video game Assassins Creed models historical architecture. Another way to model is indexically, in which parts of the model are always ordered the same, such as the order of utensils at a traditional place setting. The final way to model is symbolically, in which a model symbolizes the mechanism of what it is modeling, such as in a mathematical equation.

Wharton then discussed the difference between a “strong model” and a “weak model.” A strong model is defined as a model that determines its weak object, such as an architect’s model or a runway model. On the other hand, a “weak model” is a copy that is always less than its archetype, such as a toy car. These different classifications include examples we are all likely aware of, but weren’t able to explicitly classify or differentiate until now.

She detailed how the model that provides the best sense of the building without being there is found in a surprising place, an Assassin’s Creed video game. This model is not only very much resembles the actual Hagia Sophia, but is also an experiential and immersive model. Wharton joked that even better, the model allows explorers to avoid tourists, unlike in the actual Hagia Sophia.

Wharton described why the Assassin’s Creed model is a highly effective agent. Not only does the model closely resemble the actual architecture, but it also engages history by being surrounded by a historical fiction plot. Further, Wharton mentioned how the perceived freedom of the game is illusory, because the course of the game actually limits players’ autonomy with code and algorithms.

After Wharton’s talk, it’s clear that models are definitely “a prolific class of things.” My big takeaway is that so many thing in our everyday lives are models, even if we don’t classify them as such. Duke’s East Campus is a model of the University of Virginia’s campus, subtraction is a model of the loss of an entity, and an academic class is a model of an actual phenomenon in the world. Leaving my first Friday Visualization Forum, I am even more positive that models are powerful, and stretch so far beyond the statistical models in my Economics classes.

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Organizing Your Social Sciences Research Paper

• The C.A.R.S. Model
• Purpose of Guide
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• Glossary of Research Terms
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• Narrowing a Topic Idea
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Introduction

The Creating a Research Space [C.A.R.S.] Model was developed by John Swales based upon his analysis of journal articles representing a variety of discipline-based writing practices. His model attempts to explain and describe the organizational pattern of writing the introduction to scholarly research studies. Following the C.A.R.S. Model can be useful approach because it can help you to: 1) begin the writing process [getting started is often the most difficult task]; 2) understand the way in which an introduction sets the stage for the rest of your paper; and, 3) assess how the introduction fits within the larger scope of your study. The model assumes that writers follow a general organizational pattern in response to two types of challenges [“competitions”] relating to establishing a presence within a particular domain of research: 1) the competition to create a rhetorical space and, 2) the competition to attract readers into that space. The model proposes three actions [Swales calls them “moves”], accompanied by specific steps, that reflect the development of an effective introduction for a research paper. These “moves” and steps can be used as a template for writing the introduction to your own social sciences research papers.

"Introductions." The Writing Lab and The OWL. Purdue University; Coffin, Caroline and Rupert Wegerif. “How to Write a Standard Research Article.” Inspiring Academic Practice at the University of Exeter; Kayfetz, Janet. "Academic Writing Workshop." University of California, Santa Barbara, Fall 2009; Pennington, Ken. "The Introduction Section: Creating a Research Space CARS Model." Language Centre, Helsinki University of Technology, 2005; Swales, John and Christine B. Feak. Academic Writing for Graduate Students: Essential Skills and Tasks. 2nd edition. Ann Arbor, MI: University of Michigan Press, 2004.

Creating a Research Space Move 1: Establishing a Territory [the situation] This is generally accomplished in two ways: by demonstrating that a general area of research is important, critical, interesting, problematic, relevant, or otherwise worthy of investigation and by introducing and reviewing key sources of prior research in that area to show where gaps exist or where prior research has been inadequate in addressing the research problem. The steps taken to achieve this would be:

• Step 1 -- Claiming importance of, and/or  [writing action = describing the research problem and providing evidence to support why the topic is important to study]
• Step 2 -- Making topic generalizations, and/or  [writing action = providing statements about the current state of knowledge, consensus, practice or description of phenomena]
• Step 3 -- Reviewing items of previous research  [writing action = synthesize prior research that further supports the need to study the research problem; this is not a literature review but more a reflection of key studies that have touched upon but perhaps not fully addressed the topic]

Move 2: Establishing a Niche [the problem] This action refers to making a clear and cogent argument that your particular piece of research is important and possesses value. This can be done by indicating a specific gap in previous research, by challenging a broadly accepted assumption, by raising a question, a hypothesis, or need, or by extending previous knowledge in some way. The steps taken to achieve this would be:

• Step 1a -- Counter-claiming, or  [writing action = introduce an opposing viewpoint or perspective or identify a gap in prior research that you believe has weakened or undermined the prevailing argument]
• Step 1b -- Indicating a gap, or  [writing action = develop the research problem around a gap or understudied area of the literature]
• Step 1c -- Question-raising, or  [writing action = similar to gap identification, this involves presenting key questions about the consequences of gaps in prior research that will be addressed by your study. For example, one could state, “Despite prior observations of voter behavior in local elections in urban Detroit, it remains unclear why do some single mothers choose to avoid....”]
• Step 1d -- Continuing a tradition  [writing action = extend prior research to expand upon or clarify a research problem. This is often signaled with logical connecting terminology, such as, “hence,” “therefore,” “consequently,” “thus” or language that indicates a need. For example, one could state, “Consequently, these factors need to examined in more detail....” or “Evidence suggests an interesting correlation, therefore, it is desirable to survey different respondents....”]

Move 3: Occupying the Niche [the solution] The final "move" is to announce the means by which your study will contribute new knowledge or new understanding in contrast to prior research on the topic. This is also where you describe the remaining organizational structure of the paper. The steps taken to achieve this would be:

• Step 1a -- Outlining purposes, or  [writing action = answering the “So What?” question. Explain in clear language the objectives of your study]
• Step 1b -- Announcing present research [writing action = describe the purpose of your study in terms of what the research is going to do or accomplish. In the social sciences, the “So What?” question still needs to addressed]
• Step 2 -- Announcing principle findings  [writing action = present a brief, general summary of key findings written, such as, “The findings indicate a need for...,” or “The research suggests four approaches to....”]
• Step 3 -- Indicating article structure  [writing action = state how the remainder of your paper is organized]

"Introductions." The Writing Lab and The OWL. Purdue University; Atai, Mahmood Reza. “Exploring Subdisciplinary Variations and Generic Structure of Applied Linguistics Research Article Introductions Using CARS Model.” The Journal of Applied Linguistics 2 (Fall 2009): 26-51; Chanel, Dana. "Research Article Introductions in Cultural Studies: A Genre Analysis Explorationn of Rhetorical Structure." The Journal of Teaching English for Specific and Academic Purposes 2 (2014): 1-20; Coffin, Caroline and Rupert Wegerif. “How to Write a Standard Research Article.” Inspiring Academic Practice at the University of Exeter; Kayfetz, Janet. "Academic Writing Workshop." University of California, Santa Barbara, Fall 2009; Pennington, Ken. "The Introduction Section: Creating a Research Space CARS Model." Language Centre, Helsinki University of Technology, 2005; Swales, John and Christine B. Feak. Academic Writing for Graduate Students: Essential Skills and Tasks . 2nd edition. Ann Arbor, MI: University of Michigan Press, 2004; Swales, John M. Genre Analysis: English in Academic and Research Settings . New York: Cambridge University Press, 1990; Chapter 5: Beginning Work. In Writing for Peer Reviewed Journals: Strategies for Getting Published . Pat Thomson and Barbara Kamler. (New York: Routledge, 2013), pp. 93-96.

Writing Tip

Swales showed that establishing a research niche [move 2] is often signaled by specific terminology that expresses a contrasting viewpoint, a critical evaluation of gaps in the literature, or a perceived weakness in prior research. The purpose of using these words is to draw a clear distinction between perceived deficiencies in previous studies and the research you are presenting that is intended to help resolve these deficiencies. Below is a table of common words used by authors.

NOTE : You may prefer not to adopt a negative stance in your writing when placing it within the context of prior research. In such cases, an alternative approach is to utilize a neutral, contrastive statement that expresses a new perspective without giving the appearance of trying to diminish the validity of other people's research. Examples of how to take a more neutral contrasting stance can be achieved in the following ways, with A representing the findings of prior research, B representing your research problem, and X representing one or more variables that have been investigated.

• Prior research has focused primarily on A , rather than on B ...
• Prior research into A can be beneficial but to rectify X , it is important to examine B ...
• These studies have placed an emphasis in the areas of A as opposed to describing B ...
• While prior studies have examined A , it may be preferable to contemplate the impact of B ...
• After consideration of A , it is important to also distinguish B ...
• The study of A has been thorough, but changing circumstances related to X support a need for examining [or revisiting] B ...
• Although research has been devoted to A , less attention has been paid to B ...
• Earlier research offers insights into the need for A , though consideration of B would be particularly helpful to...

In each of these example statements, what follows the ellipsis is the justification for designing a study that approaches the problem in the way that contrasts with prior research but which does not devalue its ongoing contributions to current knowledge and understanding.

Dretske, Fred I. “Contrastive Statements.” The Philosophical Review 81 (October 1972): 411-437; Kayfetz, Janet. "Academic Writing Workshop." University of California, Santa Barbara, Fall 2009; Pennington, Ken. "The Introduction Section: Creating a Research Space CARS Model." Language Centre, Helsinki University of Technology, 2005; Swales, John M. Genre Analysis: English in Academic and Research Settings . New York: Cambridge University Press, 1990

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National research center for distance education and technological advancements deta, research model.

A National Research Model for Online Learning By Tanya Joosten

Downloadable PDF version

In developing the grant proposal for the U.S. Department of Education’s Fund for Improvement in Postsecondary Education (FIPSE), several researchers from the University of Wisconsin-Milwaukee (UWM) spent a Friday afternoon discussing the types of research projects we would propose to be conducted by the new National Research Center for Distance Education and Technological Advancements (DETA).  What became clear in that meeting room was evidence of a broader issue in distance education research.  Individuals who are studying distance education, including eLearning, blended learning, and online learning, are heterogeneous.  These individuals represent an array of disciplines, including different paradigmatic, theoretical, and methodological approaches to studying distance education, just as we were witnessing in the room that day.  The opportunity of this diversity in research approaches has the potential to provide our higher education communities a greater understanding of the complexity of human interaction in distance education.  The opportunity identified also presented a new problem to solve – we don’t all speak the same language about research in distance education.  Evident from this discussion was a need for coherency about how to approach the study of this phenomenon.  

In distance education, a common language or ground has not yet been established.  Although existing scholarship attempts to establish an identity for teaching and learning on the fringe or margins (see Moore, 2013), such as distance education, there is still much work to be done.  It is common in other disciplines to struggle with finding this common ground as well (e.g., Corman & Poole, 2000).  Yet, unlike many other disciplines that have models illustrative of the phenomenon of interest or research models that guide the design of research, distance education has seen little traction in this area.  A cohesive approach to researching distance education from a transdisciplinary lens is pertinent.        

The lack of common language and work being conducted in disciplinary silos has led to a disregard or lack of acknowledgement of previous developments in the field.  Furthermore, the disconnect many times between the fast moving development of practice and redundant research of already proven practices is less than helpful to developing distance education. Several authors over the last several years have noted this dilemma.  Saba (2013) discusses that “authors, editors, and reviewers are not familiar with the historical origin and conceptual growth of the field of distance education…history starts from when they become interested in the field” (p. 50).   Dziuban and Picciano (2015) refer to Roberts (2007) and Diamond (1999) in describing this as a type of amnesia where “we tend to trust what we have seen for ourselves and dismiss events that have occurred in the distance past…we forget anything but what we are experiencing at the moment and assume that the present is a way it has always been” (p. 179).  Moore and Kiersey (2011) have discussed this tendency as a threat to good practice and good scholarship.  

Our initial goal, as outlined in the grant, is to solve this problem and create a language that will have sustainability across disciplines and temporal barriers.  At least in the first year, it was apparent that there was a need for grant efforts to focus on creating a language we can all understand as well as to engage distance education stakeholders from across the country in the attempt to create an interdisciplinary lens for examining distance education. In so doing, the aim is to facilitate research efforts regarding cross-institutional distance education research as a strategy for ensuring quality in teaching and learning for all students.  The research fellows on the grant team felt a desire to identify a model or models that represented research in distance education, in particular, with regard to the research that would be conducted as part of the grant activities.  Moreover, the development of a framework of inquiry that included detailed representations, which illustrates the varying levels of inquiry as characterized by input-throughput-output processes facilitating an interdisciplinary approach to studying distance education, was needed as well as research models.      

Development

The first goal of the grant activities is to develop research models for online learning that provide guidance in the practice of distance education research.  The models were intended to facilitate the exploration of instructional practices, inform future instructional practices, serve as a model for future research practices across educational institutions, and enhance consistency in the field.  In the development process, it became clear that a more general research model was needed to represent the various research designs that would be deployed as part of the DETA research efforts rather than several specific research models.  The development of this model included the following steps:

• Review of the literature on desired outcomes in distance education, including blended and online research, to determine key desired outcomes in practice and research in the field.       
• Identify and engaging with national experts, including researchers and practitioners, in the field to identify pertinent research questions and variables of interest for enhancing the understanding of the desired outcomes.  
• Review germane research and current national efforts to ensure alignment with the development of research model and the framework of inquiry, including identifying any gaps and future areas of research needed.
• Create research designs, including formulating measures, instrumentation, and coding to conduct cross-institutional research within the framework of inquiry.
• Develop a research model for online learning appropriate for interdisciplinary research and diverse methodologies to be brought to fruition in the development and use of research toolkits by researchers and practitioners across the country.

The National DETA Research Model for Online Learning

Prior to the DETA national summit, held at the 2015 EDUCAUSE Learning Initiative (ELI)  meeting, the DETA Research Center reviewed pertinent literature and documents in developing the desired outcomes (see https://uwm.edu/deta/desired-outcomes/).  These desired outcomes were published on the DETA community site and feedback was solicited from the national experts who participated in the summit.  The desired outcomes guiding the activities at the DETA national summit are also appended.

Participants at the DETA national summit (see https://uwm.edu/deta/summit/ ) were asked to participate in two key sets of activities related to developing and prioritizing research questions  and the process of creating a framework of inquiry to guide current and future research by identifying key variables for research model.   

The research questions and associated votes were statistically analyzed for prioritization.  The top research questions were identified by highlighting those that were one standard deviation at or above the mean.  The top research questions can be viewed at: https://uwm.edu/deta/top-research-questions/ .  Additionally, the variables were examined to identify conceptual alignment with existing literature and to sort based on level of inquiry, which resulted in the framework of inquiry (see Figure 1, General Framework of Inquiry).  The detailed version of the framework of inquiry, including variables, can be viewed here .

Figure 1, General Framework of Inquiry

Situated within the framework of inquiry, several research designs were created, including formulating measures, developing instrumentation, and coding to conduct cross-institutional research within the framework of inquiry.  These research designs included experimental and survey study designs to address the top research questions.  Experimental designs included interventions identified for testing that burgeoned from discussions at the DETA national summit.  Survey studies and instrumentation (applicable to both survey and experimental studies) were developed from existing research at UWM and a review of the literature, including utilized instrumentation.  Survey studies included questions to gather qualitative data for analysis to address research questions of exploratory nature.  Both the survey and experimental research designs are complemented by data mining of student information systems to provide learner characteristics (low-income, minority, first generation, and disabled) and outcome data (grade, completion).      

Model Description

Taking a structured approach to model development, a research model for online learning appropriate for interdisciplinary research and diverse methodologies was derived from a grounded and theoretical approach (see Figure 2, Developing Research Model of Online Learning).  The model is considered grounded because it is a reflection of the research questions, framework of inquiry, including variables, and research designs developed as part of the grant activities.  The model is considered theoretical since social and learning theories inform the development.

Figure 2, Developing Research Model of Online Learning

There are four primary components that compose the research model for online learning.  The four components include (1) inputs and outputs, (2) process, (3) context, and (4) interventions.  The inputs and outputs include both agency and structural level inputs.  Agency level inputs include students (learners) and instructors.  Structural level inputs include the characteristics of the course, instruction, and the program that provide structure, rules, and resources to agents to facilitate online learning process.  The second component is the process, which includes in-class and out-of-class interactions that are online learning.  The third component is that of the context.  The context for the research of this grant is institutions of postsecondary higher education. Although much learning may happen in informal settings, it is not a focus of this model.  The final component of the model is intervention.  Interventions create variable conditions intended to result in a predetermined outcome, usually to increase student success.       

There are three facets of the model that describe the relationship between and among the components of the model.  First, the model is cyclical in nature in that learning is conducted in cycles with each end playing the role of input and output through an interactive process representing a continuous lifecycle of online learning.  Second, the model is transactional .   This means that online learning is a simultaneous engagement of students and instructors in the learning process.  Students and instructors are linked reciprocally.  Third, the model can be structurational .   Courses, instructional, and program characteristics are outcomes from human action (instructors and staff) in design, development, and modification.  Also, these facilitate and constrain student interactions in online learning.  Furthermore, institutional properties influence individuals in their online learning interaction through instructional and professional norms, design standards, and available resources.  Likewise, the interactions in online learning will influence institutional properties through reinforcing or transforming structures.    

The proposed model describes a series of inputs that can have a relationship with online learning, which is a throughput or process, inside and outside the classroom within the contexts of institutions.  For DETA research the institutional context is postsecondary institutions of higher education.  The cyclical elements of the model are evident in the inputs, including the characteristics of students, instructors, course as well as instruction, and programs, may influence the online learning process, which, in return, will influence future inputs of online learning process in a cyclical fashion.  For instance, a course is designed by an instructor in such a way that it leads to increased rates of completion, which eventually can alter the program profile and potentially future course designs.  Therefore, the inputs will influence the online learning process, which will in return influence the inputs through a feedback loop process.  For example, students may become more confident and have a greater growth or mindset for achievement in future courses, instructors may learn from what works in the classroom and improve future instructional methods and course designs, and programs may have greater success.  Not only is there a lifecycle of online learning, but an important interplay between the success of students in a course and the continued development of courses and programs by instructors and staff within the institution.  

There are individual agents in the model, including students and instructors, that have characteristics of which have a relationship with online learning.  First, these students and instructors are agents within the context of institutions but have influences from beyond the institution, too.  The cognition and experiences (from within and outside of the institution) of students and instructors will potentially affect online learning interactions within and outside a class.  Second, there are also course, instructional, and program characteristics. The design of these, in particular, will have a relationship with and potentially enhancing or hindering the process of online learning.  These five inputs will have relationships with the online learning process.  

Interventions can be employed at any level of these input variables in order to enhance the probability that the online learning process will be positively influenced.  Interventions can be at the agent level to develop students or instructors, or at the course, instructional, or program levels to potentially improve the interactions of students and instructors to enhance online learning.  At the learner level, an intervention may be a workshop about taking an online course.  At the instructor level, an intervention may be a faculty development program for teaching online.  At the course and instructional level, an intervention may be focused on how content is designed to  meet the course learning outcomes to enhance the student-content interaction.  At the program level, an intervention may be the receipt of tutoring support during the course.  Interventions at the agent or structural levels are intended to increase student success by enhancing online learning.

The model represents an array of research designs, including experimental, quasi experimental, survey, and qualitative appropriate for DETA research.  Input variables, such as student or course characteristics, can be mined through institutional technology systems, such as student information systems, or can be reported on surveys.  This information can be used for all research designs.  Experimental or quasi experimental studies would focus on comparisons of the control and experimental condition based on the intervention applied usually through the comparison of student assessments.  Survey studies can examine the ability to predict student outcome variables based on the student self-report of instructional and program/institutional characteristics including reports of behaviors taking place or perceptions of in-class and out-of-class.  Finally, qualitative data can be collected through surveys and other methods to better understand or develop measurement for an array of constructs (e.g., student motivation, ecosystem components).    

This research model and the associated toolkits serves to guide research conducted across institutions and disciplines, including both experimental and survey studies.  The DETA Research Center will disseminate a call for proposals in the grant’s second year, October 2015, to identify partners across the country who are interested in using the research toolkits to gather data to better understand the key factors in distance education courses and programs that are impacting student success.  Once research has been conducted, an evaluation of the model and toolkits will be conducted to improve the quality of the grant products for dissemination in the final year of the grant.    

References:

Corman, S. R., & Poole, M. S. (2000) Perspectives on organizational communication: Finding common ground. Guilford Press.

Dziuban, C. D., & Picciano, A. G. (2015).  What the future might hold for online and blended learning research.  In Dziuban, C. D., Picciano, A. G., Graham, C. R., & Moskal, P. D. (Eds). Conducting research in online and blended learning environments.

Moore, M. G. (Ed.). (2013). Handbook of distance education. Routledge.

Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.

Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization science, 3(3), 398-427.

Saba, F. (2013).  Building the future: A theoretical perspective. In Moore, M. G. (Ed.). Handbook of distance education. Routledge.

Top Research Questions

Framework of Inquiry

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Key Research Questions and Variables

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• Open access
• Published: 19 April 2024

A longitudinal cohort study on the use of health and care services by older adults living at home with/without dementia before and during the COVID-19 pandemic: the HUNT study

• Tanja Louise Ibsen 1 ,
• Bjørn Heine Strand 1 , 2 , 3 ,
• Sverre Bergh 1 , 4 ,
• Gill Livingston 5 , 6 ,
• Hilde Lurås 7 , 8 ,
• Svenn-Erik Mamelund 9 ,
• Richard Oude Voshaar 10 ,
• Anne Marie Mork Rokstad 1 , 11 ,
• Pernille Thingstad 12 , 13 ,
• Debby Gerritsen 14 &
• Geir Selbæk 1 , 15 , 16  

BMC Health Services Research volume  24 , Article number:  485 ( 2024 ) Cite this article

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Metrics details

Older adults and people with dementia were anticipated to be particularly unable to use health and care services during the lockdown period following the COVID-19 pandemic. To better prepare for future pandemics, we aimed to investigate whether the use of health and care services changed during the pandemic and whether those at older ages and/or dementia experienced a higher degree of change than that observed by their counterparts.

Data from the Norwegian Trøndelag Health Study (HUNT4 70 + , 2017–2019) were linked to two national health registries that have individual-level data on the use of primary and specialist health and care services. A multilevel mixed-effects linear regression model was used to calculate changes in the use of services from 18 months before the lockdown, (12 March 2020) to 18 months after the lockdown.

The study sample included 10,607 participants, 54% were women and 11% had dementia. The mean age was 76 years (SD: 5.7, range: 68–102 years). A decrease in primary health and care service use, except for contact with general practitioners (GPs), was observed during the lockdown period for people with dementia ( p  < 0.001) and those aged ≥ 80 years without dementia ( p  = 0.006), compared to the 6-month period before the lockdown. The use of specialist health services decreased during the lockdown period for all groups ( p  ≤ 0.011), except for those aged < 80 years with dementia. Service use reached levels comparable to pre-pandemic data within one year after the lockdown.

Older adults experienced an immediate reduction in the use of health and care services, other than GP contacts, during the first wave of the COVID-19 pandemic. Within primary care services, people with dementia demonstrated a more pronounced reduction than that observed in people without dementia; otherwise, the variations related to age and dementia status were small. Both groups returned to services levels similar to those during the pre-pandemic period within one year after the lockdown. The increase in GP contacts may indicate a need to reallocate resources to primary health services during future pandemics.

Trial registration

The study is registered at ClinicalTrials.gov, with the identification number NCT 04792086.

Peer Review reports

In Norway, similar to most European countries [ 1 , 2 , 3 ], the first wave of the COVID-19 pandemic lasted from 12 March to 15 June 2020 [ 4 ]. During this period, strict infection control measures were introduced to minimise the number of infected people. Health and care services were reduced or locked down, because health professionals were transferred to COVID-19-related services, or hospital wards were reserved for COVID-19 patients. Facilities such as day care services were closed to prevent the spread of infection through social contact, and some services were employed with digital technology. People were urged to stay at home to maintain social distancing and prevent the spread of the virus [ 4 ].

The strict infection control measures aimed mainly to prevent people from hospitalisation and/or death by COVID-19. By 13 November 2022 (last published data), Norway recorded 4,399 cumulative COVID-19-related deaths, of which approximately two-thirds occurred in 2022 (in people of an average age of 85.6 years in 2022) [ 5 ]. From March 2020 to March 2021, compared to the mean all-cause mortality from 2016 to 2019 as a reference, Norway recorded significantly lower all-cause mortality than those recorded by other European Union countries [ 6 ], indicating that Norway had a successful public health strategy. The topic being raised in the present paper, is how infection control measures affected the use of health and care services by the older population, to better prepare ourselves for future health crisis like a pandemic.

Older adults are particularly vulnerable to COVID-19 and at a higher risk of hospitalisation and death [ 7 ]. People with dementia are anticipated to have an even higher risk of mortality than that of people without dementia, because of an impaired immune system [ 8 ]. Fearing the virus, some older adults personally imposed strict infection control measures and cancelled scheduled healthcare appointments. A German study, including participants aged ≥ 73 years, has reported that approximately 30% of the participants reduced or cancelled their medical consultations during the first wave of the pandemic [ 1 ]. A qualitative study including participants aged 65–79 years from Portugal, Brazil, and the United Kingdom has reported that the majority refrained from face-to-face contact with their family doctors in the first wave of the pandemic, as it implied using public transport making social distancing difficult [ 2 ]. Some health and care services have been replaced with online or telephone consultations, which have been beneficial for some parts of the population and challenging for others, especially older adults [ 2 , 3 , 9 ].

People with dementia often need health and care services and practical assistance in their homes to manage their everyday lives [ 10 ]. A Norwegian study including 105 caregivers of people with dementia has reported that 60% experienced a reduction or full cessation of formal care during the first wave of the pandemic as the services were cancelled by the service provider [ 11 ]. This is in line with studies from Sweden and the USA, which reported a significant drop in the use of health and care services during this period [ 12 , 13 ]. However, how the use of primary and specialist healthcare services affected older adults, including people with dementia, as society began a cautious reopening after the first wave of the pandemic remains unclear. A study from the USA conducted a predictive analysis for the post-lockdown period (June 2020–October 2021) on inpatient, outpatient, and emergency services. They found that people with mild cognitive impairment (MCI), Alzheimer’s disease, and related dementia experienced greater and more sustained disruptions in primary and specialist health and care service use than those experienced by people without MCI or dementia [ 13 ].

In the present study, we used a large population-based dataset from the Norwegian Trøndelag Health Study (HUNT) [ 14 ], linked to national registry data on primary and specialist health and care services, to investigate whether the use of health and care services changed during the pandemic, and those with older ages and/or dementia had a higher degree of change than that observed in their counterparts.

Study design and setting

We used a longitudinal cohort design, linking participant data on sex, year of birth, and cognitive status from the HUNT4 70 + survey with later registry data on the use of health and care services from 12 September 2018 to 11 September 2021. This time period equals 18 months before- and 18 months after the Norwegian lockdown on 12 March 2020. This 36-month period was grouped into six periods of six months each, including three pre-lockdown periods (pre1, pre2, and pre3), one lockdown period, and two post-lockdown periods (post1 and post2) (Fig.  1 ). We included a longer lockdown period than the generally denoted period from March to June 2020, as the reopening started slowly, and many older adults imposed strict social distancing on themselves. The next period, 12 September 2020 to 11 March 2021 also included periods with restrictions on social life and activity, such as a maximum of five people gathering and recommendations for wearing a face mask where maintaining distance is difficult. In the last period from 12 March to 11 September 2021, all infection control measures were gradually lifted until Norway was completely reopened on 25 September 2021 [ 4 ]. Trøndelag, the county where the study was conducted, followed national infection control regulations.

Flow-chart of the study periods

Participants

The study included participants aged > 70 years in the fourth wave of the HUNT Study (HUNT4 70 +), which took place between September 2017 and March 2019. The HUNT is a population-based study that has invited the entire adult population from the same geographic area, North-Trøndelag, in four waves, first time in 1984 [ 14 ]. As North-Trøndelag does not comprise any large cities, a random sample of people aged > 70 years from a city in Trondheim (212,000 inhabitants) was also invited. In total, 11,675 participants were included, with 9,930 from North-Trøndelag (response rate 51%) and 1,745 from Trondheim (response rate 34%). We do not judge that there is likely to be any systematic bias introduced by the difference in response rates in different municipalities as the people living at home are similar populations.”. The participants answered a questionnaire that included socio-demographic and clinical data, and they attended a comprehensive clinical evaluation by health professionals [ 15 ]. Participants without sufficient information on their cognitive status ( n  = 202) and nursing home residents ( n  = 866) were excluded (Fig.  2 ). The mean age (76 years, SD 5.7 years) of those included was lower than that of those excluded (82 years, SD 7.9) ( p  < 0.001). The study population included 10,607 participants with complete data on cognitive status. We do not have information on dementia status on the population not included in HUNT4 70 + .

Flow-chart of included participants. HUNT4 70 + : The fourth wave of the Trøndelag health study, 70 year and older cohort

Dementia diagnosis

Two specialists from a diagnostic workgroup of nine medical doctors with comprehensive scientific and clinical expertise (geriatrics, old-age psychiatry, or neurology) independently diagnosed each patient with dementia using the Diagnostic and Statistical Manual of Mental Disorders-5 [ 16 ]. Discrepancies were resolved and consensuses were obtained by the involvement of a third expert. During the diagnostic process, the experts had access to all relevant information from the HUNT4 70 + dataset, such as education, function in activities of daily living, neuropsychiatric symptoms, onset and course of cognitive symptoms, cognitive tests (the Montreal Cognitive Assessment (MoCA) scale [ 17 ] and the Word List Memory Task (WLMT) [ 18 ], and structured interviews with the closest family proxy. A more comprehensive description of the diagnostic process has been published [ 15 ].

Health and care services

Data from two national registries were collected for the entire study period, from September 2018 to September 2021. Health and care services used in primary health care were obtained from The Norwegian Registry of Primary Health Care [ 19 ]. This registry includes individual-level data on municipal health services (contacts with general practitioners (GPs), emergency rooms, and physiotherapists) and care services (care, such as home nurses, and practical assistance in the recipient’s home, day care, respite services and short-term nursing home stays, municipal housing, and nursing home admission) [ 20 ]. Information on the use of specialist health services was based on data from the Norwegian Patient Registry (NPR) [ 21 ]. The NPR holds individual-level data on patients’ use of specialist health services (contacts with somatic hospitals, mental health care, and rehabilitation institutions). The NPR also registers whether the contact was an outpatient consultation, hospitalisation, or day-treatment [ 20 ].

Data were analysed using the STATA 16 software [ 22 ]. Participant characteristics are reported as means with SD, frequencies, or percentages, as appropriate. Those who were admitted to a nursing home ( n  = 364) or died ( n  = 821) during the study period were censored and participated in only half of the person-time during the study period. Duplicates were removed (3,293 observations). The mean number of health and care services per person in each period (with 95% confidence interval [CI]) was predicted from a multilevel mixed-effects linear regression model with random intercept, where random effects varied across the individuals. In the regression model, the number of services per person was the outcome variable and sex, age, cognitive status (no dementia/dementia), and period were covariates.Age and cognitive status are relevant confounders to address the aim of the present study, and sex is included as a key sociodemographic measure in epidemiological research. [ 23 , 24 ]. To allow for different time trends by cognitive status group, the interaction term period by cognitive status was included in the regression model. In the predictions, the adjusted variables were fixed at their mean values. The significance level was set at p  < 0.05. To investigate the use of health and care services before and during the pandemic, the number of care services implemented within each period and the number of contacts within each period for primary and specialist health services were aggregated. Hence, for care services, we used the date on which the service was implemented, for example the date on which practical assistance at home was implemented. For health services, we used the date when the service occurred, for example, the date a person had contact with a GP or the date a person had contact with a hospital, either for outpatient consultation, hospitalisation, or day-treatment.

In the Results section, we report significant differences between the lockdown period and all the pre- and post-lockdown periods, and between pre2 and post2, as these periods comprise the same seasonal months, one year before and one year after the lockdown, respectively.

The study included 10,607 participants, of whom 54% were women, and 11% had dementia (Table  1 ). The mean age of the participants on 1 January 2017 was 76 years (SD 5.7, range: 68–102 years), and 7,769 participants (73%) were < 80 years old. During the 36-month follow-up period, the study sample was reduced by 10% (from 10,607 to 9,568) due to censoring for death and/or nursing home admission (Table  2 ). The dropout rate was higher in those with dementia than in those without dementia (37% vs. 7%, p  < 0.001). During these 36-months, the total number of contacts with primary health services was 554,061, which corresponded to 9.2 contacts per person per 6-month period (Table  3 ). People with dementia had more contact with health services in the municipality than the contact made by those without dementia (11.3 vs. 8.8 contacts per person per 6-month period, p  < 0.001). The total number of care services implemented for our study population was 20,411, which corresponded to 0.3 care services per person per 6-month period. People with dementia received more care services than those received by people without dementia (1.2 vs. 0.2 care services per person per 6-month period, p  < 0.001). The total number of contacts with specialist health services was 141,994, which corresponded to 2.3 contacts per person per 6-month period. People with dementia had less contact with specialist health services than the contact made by those without dementia (2.2 vs. 2.6 contacts per person per 6-month period, p  < 0.001).

Primary health and care services

Health services.

During the 36-month study period, contact with GPs was the most used health service (66%), followed by physiotherapy services (30%), and contact with GPs in emergency rooms (4%).

The following model only presents contact with GPs, including GPs in emergency rooms, as contact with GPs was the most frequently used primary health service.

The age- and sex-adjusted model (Fig.  3 ) shows that for those aged < 80 years with dementia, the mean number of GP contacts during the lockdown period was higher than that during pre1 (1.27, p  < 0.001) and pre3 (0.82, p  = 0.002) and lower than that during post1 (1.67, p  < 0.001) and post2 (0.84, p  < 0.002). The mean number of GP contacts during post2 was higher than that during pre2 (0.32, p  < 0.001).

Mean number of registered contacts with general practitioners (GPs) per period, pre-lockdown, during lockdown and post-lockdown, including GPs at emergency rooms, for participants < 80 versus ≥ 80 years, divided in people with- or without dementia. Mean number of contacts was predicted in a mixed-effects linear regression model adjusted by period, cognitive status, sex, age, and the interaction period*cognitive status. In the predictions, the adjustment variables age and sex were fixed at the mean values

For those without dementia, the mean number of GP contacts during the lockdown was higher than that during pre1 (0.45, p  < 0.001) and pre2 (0.51, p  < 0.001) and lower than that during post1 (1.18, p  < 0.001) and post2 (0.59, p  < 0.001). The mean number of GP contacts during post2 was higher than that during pre2 (1.11, p  < 0.001).

For those aged ≥ 80 years with dementia, the mean number of GP contacts during the lockdown was higher than that during pre1 (1.45, p  < 0.001) and pre2 (0.96, p  = 0.015) and lower than that during post1 (2.31, p  < 0.001). The mean number of GP contacts during post2 was higher than that during pre2 (1.72, p  < 0.001).

For those without dementia, the mean number of GP contacts during the lockdown was higher than that during pre1 (1.15, p  < 0.001) and pre2 (0.91, p  < 0.001) and lower than that during post1 (1.86, p  < 0.001) and post2 (0.60, p  < 0.002). The mean number of GP contacts during post2 was higher than that during pre2 (1.51, p  < 0.001).

Care services

During the 36-month study period, care and practical assistance at home represented the largest service group (69%), followed by short-term nursing home stays and respite services (21%), nursing home admissions (4%), municipal housing (3%), and day care services (4%). The following model presents all combined care services.

The age- and sex-adjusted model (Fig.  4 ) shows that for those aged < 80 years with dementia, the mean number of care services implemented during the lockdown was lower than that during pre3 (0.37, p  < 0.001) and post1 (0.43, p  < 0.001). The mean number of care services implemented in post2 was higher than that during pre2 (0.13, p  = 0.039).

Mean number of care services implemented per period, pre-lockdown, during lockdown and post-lockdown, as health care and practical assistance in the home, day- and respite services, short-term institutional stay, and nursing home admission, for participants < 80 versus ≥ 80 years, divided in people with- and without dementia. Mean number of care services implemented was predicted in a mixed-effects linear regression model adjusted by period, cognitive status, sex, age, and the interaction period*cognitive status. In the predictions, the adjustment variables age and sex were fixed at the mean values

For those without dementia, the mean number of care services implemented during the lockdown was higher than that during pre1 (0.5, p  = 0.001) and pre2 (0.04, p  = 0.005) and lower than that during post1 (0.03, p  = 0.044). The mean number of care services implemented during post2 was higher than that during pre2 (0.07, p  < 0.001).

For those aged ≥ 80 years with dementia, the mean number of care services implemented during the lockdown was lower than that during pre3 (0.76, p  < 0.001).

For those without dementia, the mean number of care services implemented during the lockdown was higher than that during pre1 (0.22, p  = 0.001) and pre2 (0.17, p  = 0.011) and lower than that during pre3 (0.18, p  = 0.006) and post1 (0.18, p  = 0.007). The mean number of care services implemented during post2 was higher than that during pre2 (0.24, p  < 0.001).

Specialist health services

During the study period, service provision from somatic hospitals was the most used service (96%), followed by mental health care (3%), and treatment at a rehabilitation institution (1%). Somatic hospital services included outpatient consultations (88%), hospitalisation (9%), and daily treatment (3%). The following model only presents contacts with somatic hospital services, as this is the most frequently used specialist health service.

The age- and sex-adjusted models (Fig.  5 ) show that for those aged < 80 years with dementia, the mean number of contacts with somatic hospital services during the lockdown was lower than that during post1 (0.67, p  = 0.002) and post2 (0.48, p  = 0.025). The mean number of contacts with somatic hospital services in post2 was higher than that during pre2 (0.61, p  = 0.004).

Mean number of registered contacts with somatic hospital services per period, pre-lockdown, during lockdown and post-lockdown, for participants < 80 versus ≥ 80 years, divided in people with- or without dementia. Mean number of contacts was predicted in a mixed-effects linear regression model adjusted by period, cognitive status, sex, age, and the interaction period*cognitive status. In the predictions, the adjustment variables age and sex were fixed at the mean values

For those without dementia, the mean number of contacts with somatic hospital services during the lockdown was lower than that during pre1 (0.16, p  = 0.002), pre3 (0.40, p  < 0.001), post1 (0.43, p  < 0.001), and post2 (0.34, p  < 0.001). The mean number of contacts with somatic hospital services in post2 was higher than that during pre2 (0.25, p  < 0.001).

For those aged ≥ 80 years with dementia, the mean number of contacts with somatic hospital services during the lockdown was lower than that during pre2 (0.54, p  = 0.003), pre3 (0.46, p  = 0.011), post1 (0.44, p  = 0.022), and post2 (0.42, p  = 0.040).

For those without dementia, the mean number of contacts with somatic hospital services during the lockdown was lower than that during pre3 (0.49, p  < 0.001), post1 (0.41, p  < 0.001), and post2 (0.41, p  < 0.001). The mean number of contacts with somatic hospital services in post2 was higher than that during pre2 (0.29, p  = 0.001).

This population-based study revealed that people with dementia experienced a larger decrease in the use of primary care services implemented during the lockdown than that experienced by people without dementia. Contact with GPs was maintained at a normal level or increased in both groups during the lockdown. The use of specialist health services decreased in both groups during the lockdown period except for those aged < 80 years with dementia. The use of primary health and care services, and specialist health services was at the same or higher-level post-lockdown (post2) as pre-lockdown (pre2). Collectively, these results indicate an increased burden on primary health services during the lockdown.

Both cognitive groups had a similar number of GP contacts during lockdown as pre-lockdown. Those aged < 80 years with dementia experienced an increased number of GP contacts during the lockdown compared to the numbers during the 6-month period before the lockdown (pre3). Furthermore, all the groups had an increased number of GP contacts in the first 6-months period post-lockdown (post1). Unfortunately, we were unable to identify whether the consultations were digital in our material; however, digital consultations may have contributed to maintaining contact with GPs during the pandemic. This corresponds with the results of a previous study which has reported that the Norwegian population experienced an increased use of telephone and video consultations during the pandemic [ 3 ]. However, a survey during the pandemic in the same study population as that of the present study (HUNT4 70 +) revealed that only 8% reported contact with healthcare professionals via screen-based media or telephone at least once a month during the pandemic [ 9 ]. In addition, a survey of video consultations among Norwegian GPs during the pandemic revealed that video consultations were unsuitable for the oldest population [ 25 ].

The results of the present study may indicate that GPs managed to serve older adults in Norway during the pandemic and that the cancellations of medical consultations described among older adults in other countries [ 1 , 2 ] have been less extensive in Norway. Meanwhile, contact with GPs may have shifted towards more severe cases, where patients in need of specialist health services who postponed contact because of COVID-19 used the primary care service. In addition, the increase in GP contact post-lockdown may imply an increased stress level among older adults and an increase in health problems during the lockdown, which will be discussed in more detail in a later section.

Our finding that people with dementia experienced a larger decrease in the number of care services implemented during the lockdown than that experienced by people without dementia is in line with those of earlier studies [ 11 , 13 ]. This is most likely a consequence of the fact that people with dementia use care services more often and thus, are more affected when such services are reduced or locked down. Interestingly, those with dementia in both age groups experienced a significant increase in new services implemented in the 6-month period before the lockdown (pre3). However, the possible cause for the increase in care services implemented, such as a reduction in other services or societal changes during this period, remains unconfirmed. The most likely explanation is an increase in service needs related to dementia progression, although some random fluctuations cannot be ruled out.

Care service providers have reported a deterioration in older adults’ health during the pandemic related to the absence of social support, which, in turn, has led to less support with meals, practical help, and physical activity [ 26 ]. Next of kin reported that people with dementia had a reduction in cognitive- and functional abilities because of the limited possibility of meaningful activities and mental stimulation when they had to stay at home [ 27 , 28 ]. Furthermore, a lack of social connections [ 29 ] and perceived social support [ 30 ] are associated with cognitive decline and depression. Based on these findings, it can be assumed that the need for care services may be the same or higher post-lockdown than that in the 6-month period before the pandemic (pre3). However, the number of care services implemented post-lockdown (post2) was at the same level as that at pre-lockdown (pre2).

This study revealed that somatic hospital services for those aged ≥ 80 years were the only services with a lower level of contact during the lockdown period than during the comparable pre-lockdown period (pre2). Both those with and without dementia had a decrease in somatic hospital services during the lockdown period, compared to the 6-months period before the lockdown. This corresponds with findings from an Italian study conducted in the autumn of 2020, reporting that hospitalisations and outpatient visits among older adults aged ≥ 65 years were reduced by 18.3% during the pandemic [ 31 ].

The decrease in the use of somatic hospital services during the lockdown observed in the present study was most likely related to strict infection control measures that prevented a widespread COVID-19 outbreak. Furthermore, it may be interpreted as a precautionary measure taken to minimize the risk of exposing older adults to hospitals, where a considerable number were affected by COVID-19. Hospital services experienced the greatest decline in activity during the lockdown due to preparedness for COVID-19 patients [ 32 ]. In the present study, all the groups returned to the same or a higher level of contact with somatic hospital services post-lockdown (post2), than they had pre-lockdown (pre2). Conversely, a study from the USA has suggested that people with dementia or MCI would experience more sustained disruption in primary and specialist health services than that experienced by people without such diagnoses [ 13 ]. Another study from the USA has revealed that those with comorbidities, often present among people with dementia, were at a higher risk of delayed or missed care during the pandemic [ 33 ]. The contrast in the findings may be related to differences in the healthcare system. In addition, the World Health Organization has reported disruptions in both primary and specialist health services worldwide two years into the pandemic. High-income countries reported fewer service disruptions than those reported by low-income ones [ 34 ]. The increase in GP contact post-lockdown in the present study may indicate that primary health services have been able to relieve specialist health services in Norway, so that people with dementia and others in need of specialist health services may be prioritised.

The variation in the frequency of contact with both somatic hospital services and GPs may be observed in the context of normal seasonal variations, where contact might be higher in the autumn and winter months (pre1, pre3, and post2) than in the spring and summer months (pre2, lockdown, and post2). However, the Norwegian Institute of Public Health has reported that the seasonal flu outbreak from December 2019 to March 2020, which corresponds with the 6-month period before the lockdown (pre3), was limited compared to those in previous years [ 35 ]. Thus, normal variations due to seasonal flu cannot provide a full explanation for more contact with GPs and somatic hospital services in the 6-month period before lockdown (pre3). The next seasonal flu, expected from December 2020 to March 2021 (post1), did not appear as expected, most likely because of the infection control measures in connection with the COVID-19 outbreak [ 36 , 37 ]. The increase in the frequency of contact with GPs and somatic hospital services detected in the 6-month period after the lockdown (post1) may be explained by the fact that people had less contact with these services for diseases other than COVID-19 during the first wave of the pandemic [ 32 ], and that these consultations accumulated when society started reopening. Furthermore, the increase in contact with GPs and somatic hospital services after the lockdown may be explained by the increased contact between people, which may have caused an increased spread of infections [ 37 ].

Finally, the increase in mental health problems during the pandemic [ 27 , 28 , 30 ], may have required additional medical supervision. Studies have reported an increase in depression among older adults during the pandemic, a related increase in the prescription of antidepressant medication [ 30 , 38 ], and the need for primary health services, such as GPs, and specialist services, such as hospital services [ 38 ].

Strength and limitations

The main strength of the present study is its large population-based survey sample merged with unique national registry data on primary and specialist health care services. This provided objective data regarding the participants’ service use. Despite the large study sample, all the participants were from the middle region of Norway, which may differ from the population in other parts of the country and outside Norway. Furthermore, the study sample was a homogenous group of participants mainly born in Norway, and the results cannot be generalised to other ethnic groups. Although the diagnostic process for dementia was thorough, the diagnosis was based on collected research data without access to imaging or biomarker data which may have caused misclassification. As our goal was to estimate the actual change in service use based on dementia status among younger and older adults, the analysis does not include health-related covariates such as comorbidity and functional level. Finally, the information on dementia status was collected from 2017 to 2019 and may have changed during the study period from September 2018 to September 2021.

The use of primary care and specialist health services was immediately reduced during the COVID-19 lockdown period. Within primary care services, people with dementia experienced a more pronounced reduction than that experienced by people without dementia; however, age and dementia status only demonstrated small variations. One year after the lockdown, service provisions returned to a level similar to or higher than that of one year before the lockdown for all groups. Our findings indicate that infection control and management limited the scope of action within care services and specialist health services during the lockdown, leaving GPs on the front line to manage medical problems and psychological stress in the population. In any future pandemic, the reallocation of resources for primary health services could make us better equipped to meet the needs of the population.

Availability of data and materials

The data that support the findings of this study are available from the HUNT database and the Norwegian registry database, Helsedata, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the HUNT database and the Norwegian registry database Helsedata.

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Acknowledgements

HUNT is a collaborative project between the HUNT Research Centre at the Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, the Trøndelag County Council, the Central Norway Regional Health Authority and the Norwegian Institute of Public Health. We would like to thank everyone who participated in HUNT 70+ for their valuable contributions to this research.

This study was supported by the Norwegian Health Association (grant no. 22687).

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The Norwegian National Centre for Ageing and Health (Ageing and Health), Vestfold Hospital Trust, Tønsberg, Norway

Tanja Louise Ibsen, Bjørn Heine Strand, Sverre Bergh, Anne Marie Mork Rokstad & Geir Selbæk

Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway

Bjørn Heine Strand

Department of Physical Health and Ageing, Norwegian Institute of Public Health, Oslo, Norway

Research Centre for Age-Related Functional Decline and Disease (AFS), Innlandet Hospital Trust, Ottestad, Norway

Sverre Bergh

Division of Psychiatry, University College London, London, UK

Gill Livingston

Camden and Islington NHS Foundation Trust, London, UK

Health Services Research Unit, Akershus University Hospital, Oslo, Norway

Hilde Lurås

Institute of Clinical Medicine, University of Oslo, Oslo, Norway

Centre for Research On Pandemics & Society (PANSOC), at Oslo Metropolitan University, Oslo, Norway

Svenn-Erik Mamelund

Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

Richard Oude Voshaar

Faculty of Health Sciences and Social Care, Molde University College, Molde, Norway

Anne Marie Mork Rokstad

Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Trondheim, Norway

Pernille Thingstad

Department of Health and Social Services, Trondheim Municipality, Trondheim, Norway

Department of Primary and Community Care, Research Institute for Medical Innovation, Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, Netherlands

Debby Gerritsen

Geir Selbæk

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Contributions

GS led the study project and is responsible for the concept and design of the study, together with BHS, SB and TLI. BHS was a major contributor in the analysis prosses together with TLI. TLI, BHS, SB, GL, HL, SEM, ROV, AMMR, PT og GS contributed to interpreting the data. TLI drafted the paper, with substantially contributions from all the authors in revising the drafted work. DG made significant contributions on the revised version after peer review. All authors read and approved the final manuscript.

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Correspondence to Tanja Louise Ibsen .

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This study was approved by the Regional Committee for Medical and Health Research Ethics of Norway (REK Southeast B 182575). All methods were carried out in accordance with REK’s guidelines which correspond to the Declaration of Helsinki. The present study is part of a larger project registered at ClinicalTrials.gov (identification number: NCT 04792086). Informed written consent was obtained from all participants in the HUNT4 70 + study. Participants with reduced capacity to consent were included if they had a next of kin who consented on their behalf. In the consent form, it was thoroughly described that collected data can be linked to other registers in order to carry out approved research projects, as has been done in the present project.

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Ibsen, T.L., Strand, B.H., Bergh, S. et al. A longitudinal cohort study on the use of health and care services by older adults living at home with/without dementia before and during the COVID-19 pandemic: the HUNT study. BMC Health Serv Res 24 , 485 (2024). https://doi.org/10.1186/s12913-024-10846-y

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Roberts group publishes synthetic chemistry research in Science

MINNEAPOLIS / ST. PAUL (04/25/2024) – The Roberts group recently published a new paper in  Science that explores enabling the use of a previously inaccessible functional group for N-heteroaromatic compounds.  Science – the flagship journal for the American Association for the Advancement of Science (AAAS) – publishes groundbreaking research across the spectrum of scientific fields. 

N-Heteroaromatic are an important class of molecules which are key to elements of pharmaceutical, agrochemicals and materials. Efficient and innovative methods to make functionalized heteroarenes are needed to make these critical molecules more readily available. One attractive method for the synthesis of N-heteroaromatic compounds would be the use of a N-heteroaryne – an aromatic ring containing a nitrogen atom and a triple bond. N-heteroarynes within 6-membered rings have been used as key intermediates for synthetic chemists, however after 120 years of aryne research the use of 5-membered N-heteroarynes has remained elusive. Notably, a computational model has predicted these 5-membered N-heteroarynes to be “inaccessible”, meaning they cannot be accessed synthetically due to the excessive strain associated with forming a triple bond within a small 5-membered ring.

The Roberts group hypothesized by applying principles of organometallic chemistry, forming 5-membered N-heteroarynes at a metal center would alleviate strain through back-bonding and allow access to this previously inaccessible functional group.  In a report which was published in  Science , the Roberts group achieved the first synthesis of 7-azaindole-2,3-yne complexes using phosphine-ligated nickel complexes. The complexes were characterized by X-ray crystallography and spectroscopy. Additionally, the complexes showed ambiphilic reactivity, meaning they react with both nucleophiles and electrophiles, making them an exceptionally versatile tool for the synthesis of N-heteroaromatic compounds. This exciting research breakthrough will have important applications in expanding the “chemist’s toolbox” for developing new pharmaceuticals, agrochemicals, and materials, and also provide fundamental insights on accessing synthetically useful strained intermediates.

This new work from the Roberts group was enabled by the National Institutes of Health, and by a multitude of fellowships held by the paper’s collaborators. Fifth-year PhD candidate Erin Plasek is supported by the UMN Doctoral Dissertation Fellowship;  fifth-year student Jenna Humke is supported by the National Science Foundation Graduate Research Fellowship Program; both Plasek and Humke are supported by Department of Chemistry Fourth-Year Excellence Fellowships; and third-year graduate student Sallu Kargbo was supported by the Gleysteen Departmental First Year Fellowship. For leadership excellence of her research program, Courtney Roberts has been awarded the 3M Alumni Professorship, the McKnight Land-Grant Professorship, the Amgen Young Investigator Award, and the Thieme Chemistry Journal Award in the past year alone.

“It is incredibly exciting to see this work, which started out as a few lines in my initial job proposals, come to fruition because of the exceptional team of students and postdocs behind it. We are delighted to finally share this new functional group for 5-membered N-heterocycles with the synthetic community,” Roberts writes.

Founded in 2019, the Roberts group uses inorganic and organometallic chemistry and catalysis to solve fundamental problems in synthetic organic chemistry related to pharmaceuticals, agrochemicals and materials. They have published work related to early transition metal catalysis, photochemical reactions, and inducing regioselectivity in metal-mediated aryne reactions. The group now consists of 14 graduate students, two postdoctoral associates, and one undergraduate researcher from a range of organic and inorganic backgrounds, which allows the team to take a multidisciplinary approach to solving research problems. They value diversity, collaboration, inclusivity, and radical candor in everything they do.

Roberts Group Website

Science Vol. 384 Issue 6694

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About the FTC

Our mission is protecting the public from deceptive or unfair business practices and from unfair methods of competition through law enforcement, advocacy, research, and education.

Learn more about the FTC

Meet the Chair

Lina M. Khan was sworn in as Chair of the Federal Trade Commission on June 15, 2021.

Chair Lina M. Khan

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Fact Sheet on FTC’s Proposed Final Noncompete Rule

• Competition
• Office of Policy Planning
• Bureau of Competition

The following outline provides a high-level overview of the FTC’s proposed final rule :

• Specifically, the final rule provides that it is an unfair method of competition—and therefore a violation of Section 5 of the FTC Act—for employers to enter into noncompetes with workers after the effective date.
• Fewer than 1% of workers are estimated to be senior executives under the final rule.
• Specifically, the final rule defines the term “senior executive” to refer to workers earning more than $151,164 annually who are in a “policy-making position.”
• Reduced health care costs: $74-$194 billion in reduced spending on physician services over the next decade.
• New business formation: 2.7% increase in the rate of new firm formation, resulting in over 8,500 additional new businesses created each year.
• This reflects an estimated increase of about 3,000 to 5,000 new patents in the first year noncompetes are banned, rising to about 30,000-53,000 in the tenth year.
• This represents an estimated increase of 11-19% annually over a ten-year period.
• The average worker’s earnings will rise an estimated extra $524 per year. 

The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the  FTC on social media , read  consumer alerts  and the  business blog , and  sign up to get the latest FTC news and alerts .

Contact Information

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Victoria Graham Office of Public Affairs 415-848-5121