analytical vs descriptive research

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Analytical vs. Descriptive

What's the difference.

Analytical and descriptive are two different approaches used in various fields of study. Analytical refers to the process of breaking down complex ideas or concepts into smaller components to understand their underlying principles or relationships. It involves critical thinking, logical reasoning, and the use of evidence to support arguments or conclusions. On the other hand, descriptive focuses on providing a detailed account or description of a particular phenomenon or event. It aims to present facts, observations, or characteristics without any interpretation or analysis. While analytical aims to uncover the "why" or "how" behind something, descriptive aims to provide a comprehensive picture of what is being studied. Both approaches have their own merits and are often used in combination to gain a deeper understanding of a subject matter.

Further Detail

Introduction.

When it comes to research and data analysis, two common approaches are analytical and descriptive methods. Both methods have their own unique attributes and serve different purposes in understanding and interpreting data. In this article, we will explore the characteristics of analytical and descriptive approaches, highlighting their strengths and limitations.

Analytical Approach

The analytical approach focuses on breaking down complex problems or datasets into smaller components to gain a deeper understanding of the underlying patterns and relationships. It involves the use of logical reasoning, critical thinking, and statistical techniques to examine data and draw conclusions. The primary goal of the analytical approach is to uncover insights, identify trends, and make predictions based on the available information.

One of the key attributes of the analytical approach is its emphasis on hypothesis testing. Researchers using this method formulate hypotheses based on existing theories or observations and then collect and analyze data to either support or refute these hypotheses. By systematically testing different variables and their relationships, the analytical approach allows researchers to make evidence-based claims and draw reliable conclusions.

Another important attribute of the analytical approach is its reliance on quantitative data. This method often involves the use of statistical tools and techniques to analyze numerical data, such as surveys, experiments, or large datasets. By quantifying variables and measuring their relationships, the analytical approach provides a rigorous and objective framework for data analysis.

Furthermore, the analytical approach is characterized by its focus on generalizability. Researchers using this method aim to draw conclusions that can be applied to a broader population or context. By using representative samples and statistical inference, the analytical approach allows researchers to make inferences about the larger population based on the analyzed data.

However, it is important to note that the analytical approach has its limitations. It may overlook important contextual factors or qualitative aspects of the data that cannot be easily quantified. Additionally, the analytical approach requires a strong understanding of statistical concepts and techniques, making it more suitable for researchers with a background in quantitative analysis.

Descriptive Approach

The descriptive approach, on the other hand, focuses on summarizing and presenting data in a meaningful and informative way. It aims to provide a clear and concise description of the observed phenomena or variables without necessarily seeking to establish causal relationships or make predictions. The primary goal of the descriptive approach is to present data in a manner that is easily understandable and interpretable.

One of the key attributes of the descriptive approach is its emphasis on data visualization. Researchers using this method often employ charts, graphs, and other visual representations to present data in a visually appealing and accessible manner. By using visual aids, the descriptive approach allows for quick and intuitive understanding of the data, making it suitable for a wide range of audiences.

Another important attribute of the descriptive approach is its flexibility in dealing with different types of data. Unlike the analytical approach, which primarily focuses on quantitative data, the descriptive approach can handle both quantitative and qualitative data. This makes it particularly useful in fields where subjective opinions, narratives, or observations play a significant role.

Furthermore, the descriptive approach is characterized by its attention to detail. Researchers using this method often provide comprehensive descriptions of the variables, including their distribution, central tendency, and variability. By presenting detailed summaries, the descriptive approach allows for a thorough understanding of the data, enabling researchers to identify patterns or trends that may not be immediately apparent.

However, it is important to acknowledge that the descriptive approach has its limitations as well. It may lack the rigor and statistical power of the analytical approach, as it does not involve hypothesis testing or inferential statistics. Additionally, the descriptive approach may be more subjective, as the interpretation of the data relies heavily on the researcher's judgment and perspective.

In conclusion, the analytical and descriptive approaches have distinct attributes that make them suitable for different research purposes. The analytical approach emphasizes hypothesis testing, quantitative data analysis, and generalizability, allowing researchers to draw evidence-based conclusions and make predictions. On the other hand, the descriptive approach focuses on data visualization, flexibility in handling different data types, and attention to detail, enabling researchers to present data in a clear and concise manner. Both approaches have their strengths and limitations, and the choice between them depends on the research objectives, available data, and the researcher's expertise. By understanding the attributes of each approach, researchers can make informed decisions and employ the most appropriate method for their specific research needs.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Critical Writing 101

Descriptive vs analytical vs critical writing.

By: Derek Jansen (MBA) | Expert Reviewed By: Dr Eunice Rautenbach | April 2017

Across the thousands of students we work with , descriptive writing (as opposed to critical or analytical writing) is an incredibly pervasive problem . In fact, it’s probably the biggest killer of marks in dissertations, theses and research papers . So, in this post, we’ll explain the difference between descriptive and analytical writing in straightforward terms, along with plenty of practical examples.

Descriptive vs Analytical Writing

Writing critically is one of the most important skills you’ll need to master for your academic journey, but what exactly does this mean?

Well, when it comes to writing, at least for academic purposes, there are two main types – descriptive writing and critical writing. Critical writing is also sometimes referred to as analytical writing, so we’ll use these two terms interchangeably.

To understand what constitutes critical (or analytical) writing, it’s useful to compare it against its opposite, descriptive writing. At the most basic level, descriptive writing merely communicates the “ what ”, “ where ”, “ when ” or “ who ”. In other words, it describes a thing, place, time or person. It doesn’t consider anything beyond that or explore the situation’s impact, importance or meaning. Here’s an example of a descriptive sentence:

  “Yesterday, the president unexpectedly fired the minister of finance.”

As you can see, this sentence just states what happened, when it happened and who was involved. Classic descriptive writing.

Contrasted to this, critical writing takes things a step further and unveils the “ so what? ” – in other words, it explains the impact or consequence of a given situation. Let’s stick with the same event and look at an example of analytical writing:

“The president’s unexpected firing of the well-respected finance minister had an immediate negative impact on investor confidence. This led to a sharp decrease in the value of the local currency, especially against the US dollar. This devaluation means that all dollar-based imports are now expected to rise in cost, thereby raising the cost of living for citizens, and reducing disposable income.”

As you can see in this example, the descriptive version only tells us what happened (the president fired the finance minister), whereas the critical version goes on to discuss some of the impacts of the president’s actions.

Ideally, critical writing should always link back to the broader objectives of the paper or project, explaining what each thing or event means in relation to those objectives. In a dissertation or thesis, this would involve linking the discussion back to the research aims, objectives and research questions – in other words, the golden thread .

Sounds a bit fluffy and conceptual? Let’s look at an example:

If your research aims involved understanding how the local environment impacts demand for specialty imported vegetables, you would need to explain how the devaluation of the local currency means that the imported vegetables would become more expensive relative to locally farmed options. This in turn would likely have a negative impact on sales, as consumers would turn to cheaper local alternatives.

As you can see, critical (or analytical) writing goes beyond just describing (that’s what descriptive writing covers) and instead focuses on the meaning of things, events or situations, especially in relation to the core research aims and questions.

Need a helping hand?

But wait, there’s more.

This “ what vs so what”  distinction is important in understanding the difference between description and analysis, but it is not the only difference – the differences go deeper than this. The table below explains some other key differences between descriptive and analytical writing.

Should I avoid descriptive writing altogether?

Not quite. For the most part, you’ll need some descriptive writing to lay the foundation for the critical, analytical writing. In other words, you’ll usually need to state the “what” before you can discuss the “so what”. Therefore, description is simply unavoidable and in fact quite essential , but you do want to keep it to a minimum and focus your word count on the analytical side of things.

As you write, a good rule of thumb is to identify every what (in other words, every descriptive point you make) and then check whether it is accompanied by a so what (in other words, a critical conclusion regarding its meaning or impact).

Of course, this won’t always be necessary as some conclusions are fairly obvious and go without saying. But, this basic practice should help you minimise description, maximise analysis, and most importantly, earn you marks!

Let’s recap.

So, the key takeaways for this post are as follows:

• Descriptive writing focuses on the what , while critical/analytical writing focuses on the so what .
• Analytical writing should link the discussion back to the research aims, objectives or research questions (the golden thread).
• Some amount of description will always be needed, but aim to minimise description and maximise analysis to earn higher marks.

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 ...

19 Comments

Thank you so much. This was helpful and a switch from the bad writing habits to the good habits.

Great to hear that, Sarah. Glad you found it useful!

I am currently working on my Masters Thesis and found this extremely informative and helpful. Thank you kindly.

I’m currently a University student and this is so helpful. Thank you.

It really helped me to get the exact meaning of analytical writing. Differences between the two explains it well

Thank you! this was very useful

With much appreciation, I say thank you. Your explanations are down to earth. It has been helpful.

Very helpful towards my theses journey! Many thanks 👍

very helpful

very helpful indeed

Thanks Derek for the useful coaching

Thank you for sharing this. I was stuck on descriptive now I can do my corrections. Thank you.

I was struggling to differentiate between descriptive and analytical writing. I googled and found this as it is so helpful. Thank you for sharing.

I am glad to see this differences of descriptive against analytical writing. This is going to improve my masters dissertation

Thanks in deed. It was helpful

Thank you so much. I’m now better informed

Busy with MBA in South Africa, this is very helpful as most of the writing requires one to expound on the topics. thanks for this, it’s a salvation from watching the blinking cursor for hours while figuring out what to write to hit the 5000 word target 😂

It’s been fantastic and enriching. Thanks a lot, GRAD COACH.

Wonderful explanation of descriptive vs analytic writing with examples. This is going to be greatly helpful for me as I am writing my thesis at the moment. Thank you Grad Coach. I follow your YouTube videos and subscribed and liked every time I watch one.

Very useful piece. thanks

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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

QuestionPro offers solutions for every issue and industry, making it more than just survey software. For handling data, we also have systems like our InsightsHub research library.

You may make crucial decisions quickly while using QuestionPro to understand your clients and other study subjects better. Make use of the possibilities of the enterprise-grade research suite right away!

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Guilherme Mazui

• What is the Difference Between Analytical and Descriptive?

The main difference between analytical and descriptive research lies in their purpose and approach. Here are the key differences between the two:

• Descriptive research aims to describe a situation, problem, or phenomenon accurately, providing a snapshot of specific phenomena at a particular point in time.
• Analytical research goes beyond description to analyze and interpret data, unearth insights, understand underlying relationships, or solve problems.
• Descriptive research collects data to portray or snapshot the subject matter accurately, classifying, describing, comparing, and measuring data.
• Analytical research uses data to conduct deeper analysis, identify patterns, and explore cause-effect relationships, focusing on cause and effect.
• Descriptive research provides a clear and detailed picture of the situation or issue.
• Analytical research offers insights, explanations, or solutions based on a thorough analysis.
• Complexity :
• Descriptive research is generally more straightforward, as it only describes the existing state of affairs.
• Analytical research is more complex, involving critically examining data to draw meaningful conclusions.

In summary, descriptive research focuses on accurately portraying the current state of variables or conditions, while analytical research delves deeper to understand, interpret, or explain why and how certain phenomena occur, aiming to uncover underlying relationships and causality between variables.

Comparative Table: Analytical vs Descriptive

The main difference between analytical and descriptive writing lies in their focus and purpose. Here is a table comparing the two:

Descriptive writing focuses on providing clear descriptions of facts or things that have happened, while analytical writing evaluates information and draws conclusions based on the data and context. In most cases, both types of writing are used in combination to effectively communicate ideas and findings.

• Descriptive vs Analytic Epidemiology
• Prescriptive vs Descriptive
• Descriptive vs Correlational Research
• Descriptive vs Exploratory Research
• Narrative vs Descriptive Essay
• Descriptive vs Inferential Statistics
• Descriptive vs Experimental Research
• Case Study vs Descriptive Approach to Research
• Summary vs Analysis
• Description vs Definition
• Qualitative Analysis vs Quantitative Analysis
• Explain vs Describe
• Job Analysis vs Job Description
• Analysis vs Synthesis
• Narration vs Description
• Analysis vs Analyses
• Quantitative vs Qualitative
• Prescriptivism vs Descriptivism
• Predictive vs Prescriptive Analytics

• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

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Study designs: Part 3 - Analytical observational studies

Priya ranganathan.

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

Rakesh Aggarwal

1 Director, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India

In analytical observational studies, researchers try to establish an association between exposure(s) and outcome(s). Depending on the direction of enquiry, these studies can be directed forwards (cohort studies) or backwards (case–control studies). In this article, we examine the key features of these two types of studies.

INTRODUCTION

In a previous article[ 1 ] in this series, we looked at descriptive observational studies, namely case reports, case series, cross-sectional studies, and ecological studies. As compared to descriptive studies which merely describe one or more variables in a sample (or occasionally population), analytical studies attempt to quantify a relationship or association between two variables – an exposure and an outcome. As discussed previously, in observational analytical studies, the exposure is naturally determined as opposed to experimental studies where an investigator assigns each subject to receive or not receive a particular exposure.

COHORT STUDIES

A cohort is defined as a “group of people with a shared characteristic.” In cohort studies, different groups of people with varying levels of exposure are followed over time to evaluate the occurrence of an outcome. These participants have to be free of the outcome at baseline. The presence or absence of the risk factor (exposure) in each subject is recorded. The subjects are then followed up over time (longitudinally) to determine the occurrence of the outcome. Thus, cohort studies are forward-direction studies (moving from exposure to outcome) and are typically prospective studies (the outcome has not occurred at the start of the study).

An example of cohort study design is a study by Viljakainen et al ., which investigated the relation between maternal vitamin D levels during pregnancy and the bone health in their newborns.[ 2 ] Maternal blood vitamin D levels were estimated during pregnancy. Children born to these mothers were then followed up until 14 months of age, and bone parameters were evaluated. Based on the maternal serum 25-hydroxy vitamin D levels during pregnancy, children were divided into two groups – those born to mothers with normal blood vitamin D and those born to mothers with low blood vitamin D. The authors found that children born to mothers with low vitamin D levels had persistent bone abnormalities.

Advantages of cohort studies

• For an exposure to be causative, it must precede the outcome. In a cohort study, one starts with subjects who are known to have or not have the exposure and are free of the outcome at the start of the study, and the outcome develops later. Hence, one is certain that the exposure preceded the outcome, and temporality (and therefore probable causality) can be established. In the above example, one can be certain that the maternal vitamin D deficiency preceded the bone abnormalities.
• For a given exposure, more than one outcome can be studied. In the above example, the authors compared not only bone growth but also the age at which the babies born to low and high vitamin D mothers started walking independently.
• In cohort studies, often several exposures can be studied simultaneously. For this, the investigators begin by assessing several 'exposures', for example, age, sex, smoking status, diabetes, and obesity/overweight status in every member of a population. The entire population is then followed for the outcome of interest, for example, coronary artery disease. At the end of the follow-up, the data can then be analyzed for several contrasting cohorts defined by levels of each “exposure” – old/young, male/female, smoker/nonsmoker, diabetic/nondiabetic, and underweight/ideal body weight/overweight/obese, etc.

Limitations of cohort studies

• Cohort studies often require a long duration of follow-up to determine whether outcome will occur or not. This duration depends on the exposure-outcome pair. In the above example, a follow-up of at least 14 months was used. An even longer follow-up over several years or decades may be necessary – for instance, in the above example, if the investigators wanted to study whether maternal vitamin D levels influence the final height of a person, they would have needed to follow the babies till adolescence. During such follow-up, losses to follow-up, and logistic and cost issues pose major challenges.
• It is not uncommon for one or more unknown confounding factors to affect the occurrence of outcome. For example, in a cohort study looking at coffee drinking as a risk factor for pancreatic cancer, people who drink a large amount of coffee may also be consuming alcohol. In such cases, the finding that coffee drinkers have an increased occurrence of pancreatic cancer may lead the investigator to incorrectly conclude that drinking coffee increases the risk of pancreatic cancer, whereas it is the consumption of alcohol which is the true risk factor. Similarly, in the above study, the mothers with low and high vitamin D levels could have been different in another factor, e.g. overall nutrition or socioeconomic status, and that could be the real reason for the differences in the babies' bone health.

Uses of cohort studies

• Since cohort study design closely resembles the experimental design with the only difference being lack of random assignment to exposure, it is considered as having a greater validity compared to the other observational study designs.
• Since one starts with subjects known to have or not have exposure, one can determine the risk of outcome among exposed persons and unexposed persons, as also the relative risk.
• In situations where experimental studies are not feasible (e.g., when it is either unethical to randomize participants to a potentially harmful intervention, such as smoking, or impractical to create an exposure, such as diabetes or hypertension), cohort studies are a reasonable and arguably the best alternative.

Variations of cohort studies

Sometimes, a researcher may look back at data which have already been collected. For example, let us think of a hospital that records every patient's smoking status at the time of the first visit. A researcher may use these records from 10 years ago, and then contact the persons today to check if any of them have already been diagnosed or currently have features of lung cancer. This is still a forward-direction study (exposure traced forward among exposed and unexposed to outcome) but is retrospective (since the outcome may have already occurred). Such studies are known as 'retrospective cohort studies'.

Large cohort studies, such as the Framingham Heart Study or the Nurses' Health Study, have yielded extremely useful information about risk factors for several chronic diseases.

CASE-CONTROL STUDIES

In case-control studies, the researcher first enrolls cases (participants with the outcome) and controls (participants without the outcome) and then tries to elicit a history of exposure in each group. Thus, these are backward-direction studies (looking from outcome to exposure) and are always retrospective (the outcome must have occurred when the study starts). Typically, cases are identified from hospital records, death certificates or disease registries. This is followed by the identification and enrolment of controls.

Identification of appropriate controls is a key element of the case-control study design and can influence the estimate of association between exposure and outcome (selection bias). The controls should resemble cases in all respects, except for the absence of disease. Thus, they should be representative of the population from which the cases were drawn. For instance, if cases are drawn from a community clinic, an outpatient clinic or an inpatient setting, the controls should also ideally be from the same setting.

Sometimes, controls are individually matched with cases for factors (except for the one which is the exposure of interest) which are considered important to the development of the outcome. For example, in a study on relation of smoking with lung cancer, for each case of lung cancer enrolled, one control with similar age and sex is enrolled. This would reduce the risk of confounding by age and sex – the factors used for matching. Sometimes, the number of controls per case may be larger (e.g. two, three, or more).

Furthermore, to minimize assessment bias, it is important that the person assessing the history of exposure (e.g., smoking in this case) is unaware of (blinded to) whether the participant being interviewed is a case or a control.

For example, Anderson et al . conducted a case–control study to look at risk factors for childhood fractures.[ 3 ] They recruited cases from a hospital fracture clinic and individually matched controls (children without fractures) from a primary care research network. The cases and controls were matched on age, sex, height, and season. They found that the history of previous use of vitamin D supplements was significantly higher in the children without fractures, suggesting an inverse association between vitamin D supplementation and incidence of fractures.

Advantages of case–control studies

• Case-control studies are often cheap, and less time-consuming than cohort studies.
• Once cases and controls are identified and enrolled, it is often easy to study the relationship of outcome with not one but several exposures.

Limitations of case–control studies

• In case-control studies, temporality (whether the outcome or exposure occurred first) is often difficult to establish.
• There may be a bias in selecting cases or controls. For instance, if the cases studied differ from the entire pool of cases of a disease in an important characteristic, then the results of the study may apply only to the selected type of cases and not to the entire population of cases. In the above example,[ 3 ] the cases and controls were derived from different sources, and it is possible that the children that attended the hospital fracture clinic had different socioeconomic backgrounds to those attending the primary care facility from where controls were enrolled.
• Confounding factors, as discussed in cohort studies, also apply to case-control studies. For instance, the children with fractures and controls could have had different overall food intake, milk intake, and outdoor play time. These factors could influence both the likelihood of prior use of vitamin D supplements (exposure) and the risk of fracture (outcome), affecting the measurement of their association.
• The determination of exposure relies on existing records or history taking. Either can be problematic. The records may not contain information on exposure or contain erroneous data (e.g., those collected perfunctorily). This is particularly challenging if the missing or unreliable data are more likely to be present in one of the two groups being compared – cases or controls (misinformation bias). During history taking, cases may be more likely to recall exposure than controls (recall bias), for example, the mother of a child with a congenital anomaly is more likely to recall drugs ingested during pregnancy than a mother with a normal child. In the study by Anderson et al,[ 3 ] the mothers of children with fractures could have underestimated the amount of vitamin D their children have received, believing that this was the reason for the occurrence of fracture.
• Finally, since case–control studies are backward-directed, there is no “at risk” group at the start of the study; therefore, the determination of “risk” (and relative risk or risk ratio) is not possible, and one can only estimate “odds” (and odds ratio). For a detailed discussion on this, please refer to a previous article.[ 4 ]

Uses of case–control studies

• Case-control studies are ideal for rare diseases, where identifying cases is easier than following up large numbers of exposed persons to determine outcome.
• Case-control studies, because of their simplicity and need for fewer resources, are often the initial study design used to assess the relationship of a particular exposure and an outcome. If this study is positive, then a study with more complex and robust study design (cohort or interventional) can be undertaken.

A special variation of case–control study design

Nested case-control design is a special type of case-control study design which is built into a cohort study. From the main cohorts, participants who develop the outcome (irrespective of whether exposed or unexposed) are chosen as cases. From among the remaining study participants who have not developed the outcome, a subset of matched controls are selected. The cases and controls are then compared with respect to exposure. This is still a backward-direction (since the enquiry begins with outcome and then proceeds toward exposure) and retrospective study (since outcomes have already occurred when the study starts). The main advantage is that since one knows that the outcome had not occurred when the cohorts were established, temporal relation of exposure and outcome is ensured.

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Conflicts of interest.

There are no conflicts of interest.

Analytical vs Descriptive: Which Should You Use In Writing?

Considering analyzing information and conveying it effectively, two key approaches come to mind: analytical and descriptive. But which one is the proper word to use? Well, the answer might surprise you—it could be both! Analytical and descriptive are both valid ways to describe different aspects of writing and communication. Analytical refers to the process of breaking down complex information into smaller parts to gain a deeper understanding, while descriptive focuses on providing vivid and detailed explanations. In this article, we will explore the characteristics of analytical and descriptive writing styles, their applications, and how they can be used together to create powerful and informative content.

The Definitions

In the realm of research and data analysis, two prominent approaches stand out: analytical and descriptive. Understanding the nuances of these methodologies is crucial for anyone seeking to delve into the world of data-driven decision-making. Let’s begin by defining these two terms.

Define Analytical

Analytical, in the context of data analysis, refers to the process of examining complex data sets to uncover patterns, relationships, and trends. This approach involves breaking down the data into its constituent parts and scrutinizing each component to gain a comprehensive understanding of the underlying factors at play.

When employing an analytical approach, researchers utilize various statistical techniques, mathematical models, and algorithms to extract meaningful insights from the data. By employing rigorous methodologies, analytical analysis aims to answer specific research questions or solve problems by examining the data from multiple angles.

For instance, in the field of market research, an analytical approach may involve dissecting sales data to identify the key drivers of consumer behavior, such as pricing strategies, promotional campaigns, or product features. By analyzing these factors, businesses can make informed decisions to optimize their marketing efforts and enhance their competitive edge.

Define Descriptive

On the other hand, descriptive analysis focuses on summarizing and presenting data in a meaningful and understandable manner. Rather than diving deep into the underlying causes or relationships within the data, the descriptive approach aims to provide a clear snapshot of the data’s characteristics and features.

Descriptive analysis involves organizing and summarizing data using various statistical measures such as averages, frequencies, percentages, and visual representations like charts or graphs. This approach helps researchers gain a preliminary understanding of the data and its distribution, allowing them to communicate key findings in a concise and accessible manner.

For instance, in the field of healthcare, a descriptive analysis of patient demographics may involve presenting information about age, gender, and geographic distribution. This type of analysis can help healthcare providers identify target populations, allocate resources effectively, and design tailored interventions to address specific health challenges.

While descriptive analysis does not delve into the underlying causes or relationships within the data, it serves as a crucial first step in any research process, providing a foundation for further investigation and analysis.

How To Properly Use The Words In A Sentence

When it comes to crafting well-structured sentences, understanding how to use the right words is crucial. In this section, we will explore the proper usage of two distinct words: analytical and descriptive.

How To Use “Analytical” In A Sentence

The term “analytical” refers to the process of examining something in detail, breaking it down into its constituent parts, and evaluating it systematically. When incorporating “analytical” into a sentence, it is important to consider the context and ensure its proper usage. Here are a few examples:

• She approached the problem with an analytical mindset, carefully dissecting each element before drawing a conclusion.
• The data scientist utilized analytical tools to uncover hidden patterns in the vast dataset.
• His analytical skills allowed him to identify the root cause of the issue and propose effective solutions.

By using “analytical” in these sentences, we convey the idea of a methodical approach, logical reasoning, and a keen eye for detail. It adds depth to the description and emphasizes the importance of critical thinking.

How To Use “Descriptive” In A Sentence

“Descriptive” is a term used to depict or provide detailed information about someone or something. It involves using vivid language and sensory details to paint a clear picture in the reader’s mind. Here are a few examples of how to use “descriptive” effectively:

• The author’s descriptive writing style transported the readers to a serene beach, with waves crashing against the shore and seagulls soaring above.
• The artist’s painting was incredibly descriptive, capturing the intricate details of every brushstroke and color gradient.
• In her travel blog, she vividly described the bustling streets of Tokyo, immersing her readers in the sights, sounds, and smells of the city.

By incorporating “descriptive” into these sentences, we evoke imagery and engage the reader’s senses. This word choice allows us to create a more immersive experience, making the subject matter come alive in the reader’s imagination.

More Examples Of Analytical & Descriptive Used In Sentences

When it comes to understanding the difference between analytical and descriptive writing, it can be helpful to see these concepts in action. In this section, we will provide examples of how both analytical and descriptive can be used in sentences, showcasing their distinct characteristics and purposes.

Examples Of Using “Analytical” In A Sentence:

• The research paper presented an analytical approach to understanding the effects of climate change on biodiversity.
• Through careful data analysis, the team was able to provide an analytical explanation for the sudden drop in sales.
• His analytical mind allowed him to break down complex problems into manageable components.
• By conducting an analytical review of the financial statements, the accountant identified areas of potential cost savings.
• The professor praised the student’s analytical skills, as demonstrated in their well-structured argument.
• In order to make informed decisions, policymakers rely on analytical reports that provide comprehensive insights.

Examples Of Using “Descriptive” In A Sentence:

• The author used vivid and descriptive language to paint a picture of the bustling city streets.
• In her descriptive essay, the writer beautifully described the breathtaking sunset over the ocean.
• With a keen eye for detail, the artist created a highly descriptive portrait capturing the subject’s unique features.
• The travel brochure provided a descriptive overview of the picturesque landscapes and cultural landmarks.
• Through the use of sensory details, the poet crafted a descriptive poem that evoked strong emotions in the reader.
• During the guided tour, the museum guide offered descriptive explanations of the historical artifacts on display.

These examples highlight the contrasting nature of analytical and descriptive writing. While analytical writing focuses on critical thinking, data analysis, and logical explanations, descriptive writing aims to create vivid imagery, evoke emotions, and provide detailed descriptions. Both styles serve distinct purposes and are valuable tools in various fields of study and communication.

Common Mistakes To Avoid

When it comes to discussing analytical and descriptive approaches, it is crucial to understand the key distinctions between the two. Unfortunately, many individuals tend to use these terms interchangeably, leading to confusion and misinterpretation of their true meanings. In order to prevent such errors, it is important to highlight some common mistakes people make when using analytical and descriptive interchangeably, along with explanations of why these mistakes are incorrect.

1. Treating Analytical And Descriptive As Synonyms

One of the most prevalent mistakes is mistakenly considering analytical and descriptive as synonyms. While both approaches involve examining and interpreting data, they differ significantly in terms of their objectives and methodologies. Descriptive analysis focuses on summarizing and presenting data in a clear and concise manner, providing a snapshot of the current state. On the other hand, analytical analysis goes beyond mere summarization and aims to uncover patterns, relationships, and insights within the data. It involves a more in-depth examination, often utilizing statistical techniques and models to uncover hidden trends or make predictions.

By treating these terms as interchangeable, individuals may fail to grasp the true essence of each approach, leading to erroneous conclusions and ineffective decision-making. It is crucial to recognize the unique purposes and methodologies associated with analytical and descriptive analysis in order to leverage their respective strengths.

2. Neglecting The Importance Of Context

Another common mistake is neglecting the importance of context when using analytical and descriptive approaches. Descriptive analysis provides a high-level overview of data, presenting facts and figures without delving into the underlying causes or implications. It aims to answer questions such as “What happened?” or “What is the current situation?” On the other hand, analytical analysis seeks to understand the “why” behind the observed phenomena, exploring relationships, causality, and potential future outcomes.

When individuals fail to consider the context in which their analysis is conducted, they risk drawing incorrect conclusions or making misguided decisions. For instance, relying solely on descriptive analysis without delving into the underlying factors may lead to superficial insights or misguided strategies. Conversely, solely relying on analytical analysis without understanding the current state may result in unrealistic or impractical recommendations.

3. Overlooking The Need For Data Quality

A crucial mistake that individuals often make when using analytical and descriptive interchangeably is overlooking the importance of data quality. Both approaches heavily rely on data, whether it is in the form of numbers, text, or other formats. However, the level of scrutiny and validation required for each approach differs.

Descriptive analysis typically deals with structured, aggregated, and well-organized data sets. While data quality is still important, it may be more forgiving in terms of missing values or minor discrepancies. Conversely, analytical analysis often involves more complex and diverse data sources, requiring rigorous data cleansing, validation, and transformation to ensure accurate and reliable results.

By neglecting the need for data quality, individuals may inadvertently introduce biases or inaccuracies into their analysis, leading to flawed conclusions or misleading interpretations. It is essential to recognize the distinct data requirements and quality considerations associated with each approach to ensure the integrity of the analysis.

4. Underestimating The Role Of Subjectivity

Lastly, a common mistake is underestimating the role of subjectivity in both analytical and descriptive analysis. While these approaches aim to provide objective insights and findings, they are not devoid of subjective interpretations and assumptions.

Descriptive analysis involves presenting facts and figures in an unbiased manner, but the selection of which data to include or exclude may introduce subjectivity. Similarly, analytical analysis relies on the researcher’s expertise, assumptions, and interpretation of the data, which can introduce subjectivity at various stages of the analysis.

By disregarding the role of subjectivity, individuals may mistakenly assume that their analysis is entirely objective, leading to unwarranted confidence in their findings. Recognizing and acknowledging the inherent subjectivity in both analytical and descriptive analysis allows for a more nuanced understanding of the limitations and potential biases that may be present.

Avoiding these common mistakes when discussing analytical and descriptive approaches is crucial for accurate and meaningful analysis. By understanding the distinctions between these two approaches, considering the importance of context and data quality, and acknowledging the role of subjectivity, individuals can leverage the strengths of each approach to derive valuable insights and make informed decisions.

Context Matters

When it comes to choosing between an analytical and descriptive approach, it is crucial to consider the context in which these methods are being used. The choice between the two depends on various factors such as the purpose of the analysis, the target audience, and the specific requirements of the situation at hand. Let’s explore a few different contexts to understand how the choice between analytical and descriptive approaches might change.

1. Scientific Research

In the realm of scientific research, the choice between analytical and descriptive methods depends on the nature of the study and the research question being addressed. For instance, if the goal is to understand the underlying mechanisms or relationships between variables, an analytical approach would be more appropriate. This involves a systematic examination of data, statistical analysis, and hypothesis testing to draw meaningful conclusions.

On the other hand, in certain situations where the primary objective is to describe a phenomenon or gather preliminary insights, a descriptive approach may be more suitable. This involves summarizing and presenting data in a straightforward manner without delving into complex statistical analyses. Descriptive studies can serve as a foundation for further analytical investigations.

2. Business Decision-making

In the business world, the choice between analytical and descriptive methods depends on the specific decision-making process and the available data. When faced with complex problems, an analytical approach can provide valuable insights by breaking down the problem into smaller components, analyzing data, and identifying patterns or trends. This enables organizations to make data-driven decisions based on a comprehensive understanding of the situation.

However, there are instances where a descriptive approach is more appropriate, especially when dealing with time-sensitive or urgent decisions. In such cases, descriptive methods allow for a quick overview of the current state of affairs without the need for extensive analysis. This approach can be particularly useful when there is limited data or when time constraints prevent a deep analytical dive.

3. Academic Writing

When it comes to academic writing, the choice between analytical and descriptive approaches depends on the specific requirements of the assignment or research paper. In some cases, an analytical approach is expected, where students are required to critically analyze existing literature, theories, or experimental data. This involves a thorough examination of various perspectives, identifying gaps or inconsistencies, and providing evidence-based arguments.

However, there are instances where a descriptive approach is more appropriate, particularly in certain disciplines such as anthropology or descriptive linguistics. In these cases, the focus is on providing a detailed account or description of a particular phenomenon, culture, or language without necessarily analyzing or interpreting the data extensively. Descriptive approaches allow for a comprehensive understanding of the subject matter, laying the groundwork for future analytical investigations.

4. Data Visualization

In the realm of data visualization, the choice between analytical and descriptive approaches depends on the intended purpose of the visual representation. If the goal is to present complex data sets in a visually appealing and informative manner, an analytical approach would be more suitable. This involves using advanced visualization techniques, such as scatter plots, heatmaps, or network diagrams, to uncover patterns, relationships, and outliers within the data.

However, there are situations where a descriptive approach is preferred, especially when the main objective is to provide a simple and intuitive overview of the data. This can involve creating basic charts, graphs, or infographics that summarize key findings without delving into intricate details. Descriptive visualizations are particularly useful when communicating with non-expert audiences or when time constraints limit the complexity of the analysis.

As we have seen, the choice between an analytical and descriptive approach depends on the context in which they are used. Whether it’s scientific research, business decision-making, academic writing, or data visualization, understanding the specific requirements and objectives of each context is essential in making the right choice. By considering these factors, individuals can effectively leverage the power of both analytical and descriptive methods to gain meaningful insights and make informed decisions.

Exceptions To The Rules

While the rules for using analytical and descriptive writing are generally straightforward, there are a few key exceptions where these rules may not apply. It is important to recognize these exceptions as they can significantly impact the effectiveness and clarity of your writing. Below, we will explore some of these exceptions, providing brief explanations and examples for each case.

1. Creative Writing

In the realm of creative writing, the strict boundaries between analytical and descriptive writing tend to blur. While descriptive writing is often associated with vivid imagery and sensory details, analytical writing focuses more on logical reasoning and critical analysis. However, in creative writing, authors often employ a combination of both styles to engage readers and evoke emotions.

For example, in a poetic piece, the writer may use descriptive language to paint a vivid picture of a beautiful sunset, while also incorporating analytical elements to explore the deeper meaning behind the scene. By intertwining descriptive and analytical writing techniques, creative writers can create a more immersive and thought-provoking experience for their audience.

2. Personal Narratives

When it comes to personal narratives, such as memoirs or autobiographies, the rules of analytical and descriptive writing can be bent to accommodate the author’s unique voice and perspective. In these cases, the focus is often on conveying personal experiences and emotions rather than strictly adhering to analytical or descriptive conventions.

For instance, in a memoir recounting a life-changing event, the author may use descriptive language to vividly depict the sensory details of the situation. At the same time, they might also incorporate analytical reflections to explore the impact of that event on their personal growth or worldview. By blending both styles, personal narratives can effectively convey the author’s story while providing deeper insights into their thoughts and feelings.

3. Persuasive Writing

Persuasive writing, which aims to convince the reader of a particular viewpoint or argument, often requires a strategic combination of analytical and descriptive elements. While analytical writing provides logical reasoning and evidence to support the argument, descriptive writing helps create a compelling narrative that engages the reader’s emotions and imagination.

For instance, in a persuasive essay advocating for environmental conservation, the writer may use analytical data and statistics to highlight the urgency of the issue. Simultaneously, they might employ descriptive language to vividly describe the devastating consequences of environmental degradation, evoking empathy and a sense of urgency in the reader. By skillfully blending these two styles, persuasive writing can effectively sway opinions and inspire action.

4. Hybrid Genres

Some genres defy categorization and combine elements of both analytical and descriptive writing. These hybrid genres often emerge in academic disciplines that require both critical analysis and detailed descriptions of phenomena. Examples include scientific papers, ethnographic studies, and historical analyses.

In a scientific paper, for instance, researchers must provide analytical explanations of their experimental methods and results while also employing descriptive language to accurately depict the observed phenomena. Similarly, in an ethnographic study, the researcher may use analytical frameworks to analyze cultural practices, but also rely on descriptive writing to capture the nuances of participants’ experiences. By embracing this hybrid approach, these genres effectively bridge the gap between analysis and description, offering comprehensive insights into complex subjects.

While these exceptions may challenge the traditional boundaries between analytical and descriptive writing, they serve as reminders of the versatility and adaptability of language. By understanding when and how to deviate from the rules, writers can harness the power of both styles to create engaging, informative, and impactful content.

The comparison between analytical and descriptive approaches has shed light on their distinct characteristics and applications in various fields. While analytical writing aims to dissect and analyze complex concepts, descriptive writing focuses on vividly portraying a subject or event. Both styles possess unique strengths and serve different purposes.

Analytical writing, with its emphasis on critical thinking and logical reasoning, is particularly valuable in academic and scientific contexts. It enables researchers to delve deep into a topic, unraveling its intricacies and uncovering hidden patterns. By employing data-driven methodologies and rigorous analysis, analytical writing fosters a comprehensive understanding of complex phenomena.

On the other hand, descriptive writing excels in capturing the essence of a subject through rich sensory details and evocative language. It finds its place in creative writing, literature, and storytelling, where the goal is to transport readers to different worlds and evoke emotional responses. Descriptive writing allows for the creation of vivid mental images, immersing readers in the experience and fostering a deeper connection with the content.

While analytical and descriptive writing may seem distinct, they are not mutually exclusive. In fact, a well-rounded piece of writing often incorporates elements of both styles. By blending analytical insights with descriptive storytelling, writers can engage readers intellectually while also appealing to their emotions.

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Shawn Manaher is the founder and creative force behind GrammarBeast.com. A seasoned entrepreneur and language enthusiast, he is dedicated to making grammar and spelling both fun and accessible. Shawn believes in the power of clear communication and is passionate about helping people master the intricacies of the English language.

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Descriptive vs. Analytical Research in Sociology: A Comparative Study

Table of Contents

When we delve into the world of research, particularly in fields like patterns of social relationships , social interaction, and culture.">sociology , we encounter a myriad of methods designed to uncover the layers of human society and behavior. Two of the most fundamental research methods are descriptive and analytical research . Each plays a crucial role in understanding our world, but they do so in distinctly different ways. So, what exactly are these methods, and how do they compare when applied in the realm of social studies? Let’s embark on a comparative journey to understand these methodologies better.

Understanding Descriptive Research

Descriptive research is akin to the meticulous work of an artist attempting to capture the intricate details of a landscape. It aims to accurately describe the characteristics of a particular population or phenomenon. By painting a picture of the ‘what’ aspect, this method helps researchers to understand the prevalence of certain attributes, behaviors, or issues within a group.

Key Features of Descriptive Research

• Snapshot in time: It often involves studying a single point or period, providing a snapshot rather than a motion picture.
• Surveys and observations : Common tools include surveys , observations, and case studies .
• Quantitative data: It leans heavily on quantitative data to present findings in numerical format.
• No hypothesis testing: Unlike other research types, it doesn’t typically involve hypothesis testing.

When to Use Descriptive Research

• Establishing a baseline : When there’s a need to set a reference point for future studies or track changes over time.
• Exploratory purposes: When little is known about a topic and there’s a need to gather initial information that could inform future research.
• Policy-making: When organizations or government bodies need factual data to inform decisions and policies.

Exploring Analytical Research

On the flip side, analytical research steps beyond mere description to explore the ‘why’ and ‘how’. It’s like a detective piecing together clues to not just recount events, but to understand the relationships and causations behind them. Analytical researchers critically evaluate information to draw conclusions and generalizations that extend beyond the immediate data.

Key Characteristics of Analytical Research

• Critical evaluation: It involves a deep analysis of the available information to form judgments.
• Qualitative and quantitative data: Uses both numerical data and non-numerical data for a more comprehensive analysis.
• Hypothesis-driven: This method often starts with a hypothesis that the research is designed to test.
• Seeking patterns: Aims to identify patterns, relationships, and causations.

When to Opt for Analytical Research

• Understanding complexities: When the research question is complex and requires understanding the interplay of various factors.
• Building upon previous research: When expanding on existing knowledge or challenging prevailing theories.
• Recommendations for action: When research is aimed at providing actionable insights or solutions to problems.

Comparing Descriptive and Analytical Research in Real-World Scenarios

Imagine a sociologist aiming to tackle a pressing social issue, such as the dynamics of homelessness in urban areas. Descriptive research would enable them to establish the scale and scope of homelessness, identifying key demographics and patterns. Analytical research, however, would take these findings and probe deeper into the causes, examining the social, economic, and political factors that contribute to the situation and what can be done to alleviate it.

Advantages and Limitations

Each research type has its own set of strengths and weaknesses. Descriptive research is powerful for mapping out the landscape but may fall short in explaining the underlying reasons for observed phenomena. Analytical research, with its depth, can provide those explanations, but it may be more time-consuming and complex to conduct.

Choosing the Right Approach

Deciding between descriptive and analytical research often comes down to the specific objectives of the study. It’s not uncommon for researchers to employ both methods within the same broader research project to maximize their understanding of a topic.

In conclusion, descriptive and analytical research are two sides of the same coin, offering different lenses through which we can view and interpret the intricacies of social phenomena. By understanding their distinctions and applications, researchers can better design studies that yield rich, actionable insights into the fabric of society.

What do you think? Could a blend of both descriptive and analytical research provide a more holistic understanding of social issues? Are there situations where one method is clearly preferable over the other?

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Research Methodologies & Methods

1 Logic of Inquiry in Social Research

• A Science of Society
• Comte’s Ideas on the Nature of Sociology
• Observation in Social Sciences
• Logical Understanding of Social Reality

2 Empirical Approach

• Empirical Approach
• Rules of Data Collection
• Cultural Relativism
• Problems Encountered in Data Collection
• Difference between Common Sense and Science
• What is Ethical?
• What is Normal?
• Understanding the Data Collected
• Managing Diversities in Social Research
• Problematising the Object of Study
• Conclusion: Return to Good Old Empirical Approach

3 Diverse Logic of Theory Building

• Concern with Theory in Sociology
• Concepts: Basic Elements of Theories
• Why Do We Need Theory?
• Hypothesis Description and Experimentation
• Controlled Experiment
• Designing an Experiment
• How to Test a Hypothesis
• Sensitivity to Alternative Explanations
• Rival Hypothesis Construction
• The Use and Scope of Social Science Theory
• Theory Building and Researcher’s Values

4 Theoretical Analysis

• Premises of Evolutionary and Functional Theories
• Critique of Evolutionary and Functional Theories
• Turning away from Functionalism
• What after Functionalism
• Post-modernism
• Trends other than Post-modernism

5 Issues of Epistemology

• Some Major Concerns of Epistemology
• Rationalism
• Phenomenology: Bracketing Experience

6 Philosophy of Social Science

• Foundations of Science
• Science, Modernity, and Sociology
• Rethinking Science
• Crisis in Foundation

7 Positivism and its Critique

• Heroic Science and Origin of Positivism
• Early Positivism
• Consolidation of Positivism
• Critiques of Positivism

8 Hermeneutics

• Methodological Disputes in the Social Sciences
• Tracing the History of Hermeneutics
• Hermeneutics and Sociology
• Philosophical Hermeneutics
• The Hermeneutics of Suspicion
• Phenomenology and Hermeneutics

9 Comparative Method

• Relationship with Common Sense; Interrogating Ideological Location
• The Historical Context
• Elements of the Comparative Approach

10 Feminist Approach

• Features of the Feminist Method
• Feminist Methods adopt the Reflexive Stance
• Feminist Discourse in India

11 Participatory Method

• Delineation of Key Features

12 Types of Research

• Basic and Applied Research
• Descriptive and Analytical Research
• Empirical and Exploratory Research
• Quantitative and Qualitative Research
• Explanatory (Causal) and Longitudinal Research
• Experimental and Evaluative Research
• Participatory Action Research

13 Methods of Research

• Evolutionary Method
• Comparative Method
• Historical Method
• Personal Documents

14 Elements of Research Design

• Structuring the Research Process

15 Sampling Methods and Estimation of Sample Size

• Classification of Sampling Methods
• Sample Size

16 Measures of Central Tendency

• Relationship between Mean, Mode, and Median
• Choosing a Measure of Central Tendency

17 Measures of Dispersion and Variability

• The Variance
• The Standard Deviation
• Coefficient of Variation

18 Statistical Inference- Tests of Hypothesis

• Statistical Inference
• Tests of Significance

19 Correlation and Regression

• Correlation
• Method of Calculating Correlation of Ungrouped Data
• Method Of Calculating Correlation Of Grouped Data

20 Survey Method

• Rationale of Survey Research Method
• History of Survey Research
• Defining Survey Research
• Sampling and Survey Techniques
• Operationalising Survey Research Tools
• Advantages and Weaknesses of Survey Research

21 Survey Design

• Preliminary Considerations
• Stages / Phases in Survey Research
• Formulation of Research Question
• Survey Research Designs
• Sampling Design

22 Survey Instrumentation

• Techniques/Instruments for Data Collection
• Questionnaire Construction
• Issues in Designing a Survey Instrument

23 Survey Execution and Data Analysis

• Problems and Issues in Executing Survey Research
• Data Analysis
• Ethical Issues in Survey Research

24 Field Research – I

• History of Field Research
• Ethnography
• Theme Selection
• Gaining Entry in the Field
• Key Informants
• Participant Observation

25 Field Research – II

• Interview its Types and Process
• Feminist and Postmodernist Perspectives on Interviewing
• Narrative Analysis
• Interpretation
• Case Study and its Types
• Life Histories
• Oral History
• PRA and RRA Techniques

26 Reliability, Validity and Triangulation

• Concepts of Reliability and Validity
• Three Types of “Reliability”
• Working Towards Reliability
• Procedural Validity
• Field Research as a Validity Check
• Method Appropriate Criteria
• Triangulation
• Ethical Considerations in Qualitative Research

27 Qualitative Data Formatting and Processing

• Qualitative Data Processing and Analysis
• Description
• Classification
• Making Connections
• Theoretical Coding
• Qualitative Content Analysis

28 Writing up Qualitative Data

• Problems of Writing Up
• Grasp and Then Render
• “Writing Down” and “Writing Up”
• Write Early
• Writing Styles
• First Draft

29 Using Internet and Word Processor

• What is Internet and How Does it Work?
• Internet Services
• Searching on the Web: Search Engines
• Accessing and Using Online Information
• Online Journals and Texts
• Statistical Reference Sites
• Data Sources
• Uses of E-mail Services in Research

30 Using SPSS for Data Analysis Contents

• Introduction
• Starting and Exiting SPSS
• Creating a Data File
• Univariate Analysis
• Bivariate Analysis

31 Using SPSS in Report Writing

• Why to Use SPSS
• Working with SPSS Output
• Copying SPSS Output to MS Word Document

32 Tabulation and Graphic Presentation- Case Studies

• Structure for Presentation of Research Findings
• Data Presentation: Editing, Coding, and Transcribing
• Case Studies
• Qualitative Data Analysis and Presentation through Software
• Types of ICT used for Research

33 Guidelines to Research Project Assignment

• Overview of Research Methodologies and Methods (MSO 002)
• Research Project Objectives
• Preparation for Research Project
• Stages of the Research Project
• Supervision During the Research Project
• Submission of Research Project
• Methodology for Evaluating Research Project

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Analytical vs. Descriptive Writing: Definitions and Examples

Scholars at all levels are expected to write. People who are not students or scholars often engage in writing for work, or to communicate with friends, family, and strangers through email, text messages, and social media. Academia recognizes two major types of writing—descriptive writing and analytical writing—which are both used in non-academic situations as well. As you might expect, descriptive writing focuses on clear descriptions of facts or things that have happened, while analytical writing provides additional analysis.

Descriptive writing is the most straightforward type of academic writing. It provides accurate information about "who", "what", "where", and "when". Examples of descriptive writing include:

• Summarizing an article (without offering additional insight)
• Stating the results of an experiment (without analyzing the implications)
• Describing a newsworthy event (without discussing possible long-term consequences)

High school students and undergraduates are most commonly asked to write descriptively, to show that they understand the key points of a specific topic (e.g. the major causes of World War II).

Analytical writing goes beyond summarizing information and instead provides evaluation, comparison, and possible conclusions. It addresses the questions of "why?", "so what?", and "what next?". Examples of analytical writing include:

• The discussion section of research papers
• Opinion pieces about the likely consequences of newsworthy events and the steps that should be taken in response.

High school students and undergraduates are sometimes asked to write analytically to "stretch their thinking". Possible topics might include "Could World War II have been avoided?" and "How can CRISPR-Cas9 technology improve human health?". The value of any such analysis is entirely dependent on the writer's ability to understand and clearly explain relevant information, which would be explained through descriptive writing. For graduate students and professional researchers, the quality of their work is at least partially based on the quality of their analysis.

The following table from The Study Skills Handbook by Stella Cottrell (2013, 4th edition, Palgrave Macmillan, page 198) is commonly used to summarize the differences between descriptive writing and analytical writing.

Description and analysis are also used in spoken communication such as presentations and conversations, and in visual communication such as diagrams and memes. In all of these cases, it is important to communicate clearly and effectively, and to use reliable sources of information.

Descriptive writing and analytical writing are often used in combination. In job application cover letters and essays for university admission, adding analytical text can provide context for otherwise unremarkable statements.

• Descriptive text: "I graduated from Bear University in 2020 with a B.S. in Chemistry and a cumulative GPA of 3.056."
• Analytical text: "While I struggled with some of my introductory courses, I proactively sought help to fill gaps in my understanding, and earned an "A" grade for all five of my senior year science courses. Therefore, I believe I am a strong candidate for . . ."

Combining description and analysis can also be very effective when discussing the significance of research results.

• Descriptive text: "Our study found significant (>2 ug/L) concentrations of polyfluoroalkyl substances (PFAS) in blood samples from all 5,478 study participants."
• Analytical text: "These results are alarming because the sample population included people who range in age from 1 month old to 98 years old, who live on five different continents, who reside in extremely rural areas and in urban areas, and who have little to no direct contact with products containing PFAS. PFAS are called "forever chemicals" because they are estimated to take hundreds or thousands of years to degrade. According to the US Centers for Disease Control (CDC), PFAS can move through soils to contaminate drinking water, and bioaccumulate in animals. Further research is urgently needed to better understand the adverse effects that PFAS have on human health, to identify the source of PFAS in rural communities, and to develop a method to sequester or destroy PFAS that have already entered the environment."

In both of the examples above, the analytical text includes additional facts (e.g. "A" grade for senior science courses; 1 month old to 98 years old) that help strengthen the argument. The student's transcript and the research paper's results section would contain these same facts—along with many others—written descriptively or presented in graphs, tables, or lists. For the analytical text, the author is trying to persuade the reader, and has therefore selected relevant facts to support their argument.

In the example about PFAS, the author's argument is further strengthened by citing additional information from a reputable source (the CDC). In reports where the author is supposed to be unbiased (e.g. a journalist writing descriptively), a similar effect can be obtained by quoting reputable sources. For example, "Professor of environmental science Kim Lee explains that PFAS are. . ." In these situations, it is often appropriate to present opposing views, as long as they come from reputable sources. This strategy of quoting or citing reputable sources can also be effective for students and professionals who do not have strong credentials in the topic under discussion.

Analytical writing supports a point of view

People cannot choose their own facts, but the same facts can be used to support very different points of view. Let's consider some different points of view that can be supported by the PFAS example from above.

• Scientific point of view: "Further research is urgently needed to better understand the adverse effects that PFAS have on human health, to identify the source of PFAS in rural communities, and to develop a method to sequester or destroy PFAS that have already entered the environment."
• Policy point of view: "Legislative action is urgently needed to ban the use of all PFAS, instead of banning new PFAS one at a time. Abundant and reliable data strongly indicates that all PFAS have similar effects, even if they have small differences in chemical composition. Given such evidence, the impetus must be on the chemical industry to prove safety, rather than on the general public to prove harm."
• Legal point of view: "Chemical companies have known about the danger of PFAS for years, but hid the evidence and continued to use these chemicals. Therefore, individuals and communities who have been harmed have the right to sue for damages."

These three points of view focus on three different fields (science, policy, and law), but all have a negative view of PFAS. The next example shows how the same factual information can be used to support opposing views.

• Descriptive text: " According to Data USA , the average fast food worker in 2019 was 26.1 years old, and earned a salary of $12,294 a year."
• Point of view #1: "These data show why raising the minimum wage is unnecessary. Most fast food workers are young, with many being teenagers who are making extra money while living with their parents. The majority will eventually transition to jobs that require more skills, and that are rewarded with higher pay. If we mandate that companies pay low-skill workers more than required by the free market, then more highly skilled workers will also demand a pay raise. This will hurt businesses, contribute to inflation, and have no net benefit."
• Point of view #2: "These data show why raising the minimum wage is so important. On average, for every 16-year-old working in fast food for extra money, there is a 36-year-old trying to make ends meet. As factory jobs have moved overseas, employees without specialized skills have turned to fast food for steady employment. According to the UC Berkeley Labor Center , for families with someone working full-time (40 hours/week) in fast food, more than half are enrolled in public assistance programs. These include Medicaid, food stamps, and the Earned Income Tax Credit. Therefore, taxpayers are subsidizing companies that pay poverty wages, so that their employees can have access to basic necessities like food and healthcare."

A primary purpose of analytical writing is to show how facts (explained through descriptive writing) support a particular conclusion or a particular path forward. This often requires explaining why an alternative interpretation is less satisfactory. This is how scholarly work—and good discussions in less formal situations—contribute to our collective understanding of the world.

Educational resources and simple solutions for your research journey

What is Descriptive Research? Definition, Methods, Types and Examples

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.

Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.

After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.

Table of Contents

What is descriptive research?

If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.

Importance of descriptive research

Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:

Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.

Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.

Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.

Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.

Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.

When to use descriptive research design?

Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.

Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:

• In the last two decades, what changes have occurred in patterns of urban gardening in Mumbai?
• What are the differences in climate change perceptions of farmers in coastal versus inland villages in the Philippines?

Characteristics of descriptive research

Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.

• Quantitative nature: Some descriptive research types involve quantitative research methods to gather quantifiable information for statistical analysis of the population sample.
• Qualitative nature: Some descriptive research examples include those using the qualitative research method to describe or explain the research problem.
• Observational nature: This approach is non-invasive and observational because the study variables remain untouched. Researchers merely observe and report, without introducing interventions that could impact the subject(s).
• Cross-sectional nature: In descriptive research, different sections belonging to the same group are studied, providing a “snapshot” of sorts.
• Springboard for further research: The data collected are further studied and analyzed using different research techniques. This approach helps guide the suitable research methods to be employed.

Types of descriptive research

There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.

• Surveys: This involves collecting data through questionnaires or interviews to gather qualitative and quantitative data.
• Observational studies: This involves observing and collecting data on a particular population or phenomenon without influencing the study variables or manipulating the conditions. These may be further divided into cohort studies, case studies, and cross-sectional studies:
• Cohort studies: Also known as longitudinal studies, these studies involve the collection of data over an extended period, allowing researchers to track changes and trends.
• Case studies: These deal with a single individual, group, or event, which might be rare or unusual.
• Cross-sectional studies : A researcher collects data at a single point in time, in order to obtain a snapshot of a specific moment.
• Focus groups: In this approach, a small group of people are brought together to discuss a topic. The researcher moderates and records the group discussion. This can also be considered a “participatory” observational method.
• Descriptive classification: Relevant to the biological sciences, this type of approach may be used to classify living organisms.

Descriptive research methods

Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.

• Surveys: This method involves the collection of data through questionnaires or interviews. Surveys may be done online or offline, and the target subjects might be hyper-local, regional, or global.
• Observational studies: These entail the direct observation of subjects in their natural environment. These include case studies, dealing with a single case or individual, as well as cross-sectional and longitudinal studies, for a glimpse into a population or changes in trends over time, respectively. Participatory observational studies such as focus group discussions may also fall under this method.

Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.

Examples of descriptive research

Now, let’s consider some descriptive research examples.

• In social sciences, an example could be a study analyzing the demographics of a specific community to understand its socio-economic characteristics.
• In business, a market research survey aiming to describe consumer preferences would be a descriptive study.
• In ecology, a researcher might undertake a survey of all the types of monocots naturally occurring in a region and classify them up to species level.

These examples showcase the versatility of descriptive research across diverse fields.

Advantages of descriptive research

There are several advantages to this approach, which every researcher must be aware of. These are as follows:

• Owing to the numerous descriptive research methods and types, primary data can be obtained in diverse ways and be used for developing a research hypothesis .
• It is a versatile research method and allows flexibility.
• Detailed and comprehensive information can be obtained because the data collected can be qualitative or quantitative.
• It is carried out in the natural environment, which greatly minimizes certain types of bias and ethical concerns.
• It is an inexpensive and efficient approach, even with large sample sizes

Disadvantages of descriptive research

On the other hand, this design has some drawbacks as well:

• It is limited in its scope as it does not determine cause-and-effect relationships.
• The approach does not generate new information and simply depends on existing data.
• Study variables are not manipulated or controlled, and this limits the conclusions to be drawn.
• Descriptive research findings may not be generalizable to other populations.
• Finally, it offers a preliminary understanding rather than an in-depth understanding.

To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.

Frequently asked questions

When should researchers conduct descriptive research.

Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.

What is the difference between descriptive and exploratory research?

Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.

What is the difference between descriptive and experimental research?

Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.

Is descriptive research only for social sciences?

No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.

How important is descriptive research?

The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.

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Descriptive vs Analytical

Explaining & comparing both methods, descriptive research.

Descriptive research is defined as a research method that describes the characteristics of the population or phenomenon that is being studied. This methodology focuses more on the “what” of the research subject rather than the “why” of the research subject.

In other words, descriptive research primarily focuses on describing the nature of a demographic segment, without focusing on “why” a certain phenomenon occurs. That means, it “describes” the subject of the research, without covering “why” it happens.

Types of Descriptive Research

Naturalistic observation.

Naturalistic observation is, in contrast to analog observation, a research tool in which a subject is observed in its natural habitat without any manipulation by the observer. During naturalistic observation, researchers take great care to avoid interfering with the behavior they are observing by using unobtrusive methods.

Naturalistic observation involves two main differences that set it apart from other forms of data gathering. In the context of a naturalistic observation, the environment is in no way being manipulated by the observer nor was it created by the observer.

The essence of survey research can be explained as “questioning individuals on a topic or topics and then describing their responses”. Survey research is often used to assess thoughts, opinions, and feelings. Surveys can be specific and limited, or they can have more global, widespread goals.

Case Studies

A case study is a research method involving an up-close, in-depth, and detailed examination of a subject of study (the case), as well as its related contextual conditions. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

Analytical Research

In Analytical Research, the researcher has to use facts or information already available, and analyze them to make a critical evaluation of the material.

It involves the in-depth study and evaluation of available information in an attempt to explain complex phenomenon.

Analytical Researches primarily concerned with testing hypothesis and specifying and interpreting relationships, by analyzing the facts or information already available.

Types of Analytical Research

Historical research.

It is the study of past records and other information sources, with a view to find the origin and development of a phenomenon and to discover the trends in the past, in order to understand the present and to anticipate the future.

Philosophical Research

It is the research of the fundamental nature of knowledge, reality and existence. It is the research of the theoretical basis of a branch of knowledge or experience.

It is the process of a formal assessment of a research with the intention of instituting or making any change in it if necessary.

Research Synthesis

It is the process through which two or more research studies are assessed with the objective of summarizing the evidence relating to a particular question.

More Informative Resources

• DESCRIPTIVE RESEARCH DESIGN
• DESCRIPTIVE RESEARCH DEFINITION
• OVERVIEW OF DESCRIPTIVE RESEARCH
• WHAT IS ANALYTICAL RESEARCH?
• DESCRIPTIVE AND ANALYTICAL RESEARCH
• ANALYTICAL RESEARCH FORUM 2018 (ARF18)

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Descriptive Analytics – Methods, Tools and Examples

Table of Contents

Descriptive Analytics

Definition:

Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization tools to represent the data in a way that is easy to interpret.

Descriptive Analytics in Research

Descriptive analytics plays a crucial role in research, helping investigators understand and describe the data collected in their studies. Here’s how descriptive analytics is typically used in a research setting:

• Descriptive Statistics: In research, descriptive analytics often takes the form of descriptive statistics . This includes calculating measures of central tendency (like mean, median, and mode), measures of dispersion (like range, variance, and standard deviation), and measures of frequency (like count, percent, and frequency). These calculations help researchers summarize and understand their data.
• Visualizing Data: Descriptive analytics also involves creating visual representations of data to better understand and communicate research findings . This might involve creating bar graphs, line graphs, pie charts, scatter plots, box plots, and other visualizations.
• Exploratory Data Analysis: Before conducting any formal statistical tests, researchers often conduct an exploratory data analysis, which is a form of descriptive analytics. This might involve looking at distributions of variables, checking for outliers, and exploring relationships between variables.
• Initial Findings: Descriptive analytics are often reported in the results section of a research study to provide readers with an overview of the data. For example, a researcher might report average scores, demographic breakdowns, or the percentage of participants who endorsed each response on a survey.
• Establishing Patterns and Relationships: Descriptive analytics helps in identifying patterns, trends, or relationships in the data, which can guide subsequent analysis or future research. For instance, researchers might look at the correlation between variables as a part of descriptive analytics.

Descriptive Analytics Techniques

Descriptive analytics involves a variety of techniques to summarize, interpret, and visualize historical data. Some commonly used techniques include:

Statistical Analysis

This includes basic statistical methods like mean, median, mode (central tendency), standard deviation, variance (dispersion), correlation, and regression (relationships between variables).

Data Aggregation

It is the process of compiling and summarizing data to obtain a general perspective. It can involve methods like sum, count, average, min, max, etc., often applied to a group of data.

Data Mining

This involves analyzing large volumes of data to discover patterns, trends, and insights. Techniques used in data mining can include clustering (grouping similar data), classification (assigning data into categories), association rules (finding relationships between variables), and anomaly detection (identifying outliers).

Data Visualization

This involves presenting data in a graphical or pictorial format to provide clear and easy understanding of the data patterns, trends, and insights. Common data visualization methods include bar charts, line graphs, pie charts, scatter plots, histograms, and more complex forms like heat maps and interactive dashboards.

This involves organizing data into informational summaries to monitor how different areas of a business are performing. Reports can be generated manually or automatically and can be presented in tables, graphs, or dashboards.

Cross-tabulation (or Pivot Tables)

It involves displaying the relationship between two or more variables in a tabular form. It can provide a deeper understanding of the data by allowing comparisons and revealing patterns and correlations that may not be readily apparent in raw data.

Descriptive Modeling

Some techniques use complex algorithms to interpret data. Examples include decision tree analysis, which provides a graphical representation of decision-making situations, and neural networks, which are used to identify correlations and patterns in large data sets.

Descriptive Analytics Tools

Some common Descriptive Analytics Tools are as follows:

Excel: Microsoft Excel is a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.

Tableau: Tableau is a data visualization tool that is used to represent data in a graphical or pictorial format. It can handle large data sets and allows for real-time data analysis.

Power BI: Power BI, another product from Microsoft, is a business analytics tool that provides interactive visualizations with self-service business intelligence capabilities.

QlikView: QlikView is a data visualization and discovery tool. It allows users to analyze data and use this data to support decision-making.

SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis. It’s widely used in social sciences research but also in other industries.

Google Analytics: For web data, Google Analytics is a popular tool. It allows businesses to analyze in-depth detail about the visitors on their website, providing valuable insights that can help shape the success strategy of a business.

R and Python: Both are programming languages that have robust capabilities for statistical analysis and data visualization. With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics.

Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.

When to use Descriptive Analytics

Descriptive analytics forms the base of the data analysis workflow and is typically the first step in understanding your business or organization’s data. Here are some situations when you might use descriptive analytics:

Understanding Past Behavior: Descriptive analytics is essential for understanding what has happened in the past. If you need to understand past sales trends, customer behavior, or operational performance, descriptive analytics is the tool you’d use.

Reporting Key Metrics: Descriptive analytics is used to establish and report key performance indicators (KPIs). It can help in tracking and presenting these KPIs in dashboards or regular reports.

Identifying Patterns and Trends: If you need to identify patterns or trends in your data, descriptive analytics can provide these insights. This might include identifying seasonality in sales data, understanding peak operational times, or spotting trends in customer behavior.

Informing Business Decisions: The insights provided by descriptive analytics can inform business strategy and decision-making. By understanding what has happened in the past, you can make more informed decisions about what steps to take in the future.

Benchmarking Performance: Descriptive analytics can be used to compare current performance against historical data. This can be used for benchmarking and setting performance goals.

Auditing and Regulatory Compliance: In sectors where compliance and auditing are essential, descriptive analytics can provide the necessary data and trends over specific periods.

Initial Data Exploration: When you first acquire a dataset, descriptive analytics is useful to understand the structure of the data, the relationships between variables, and any apparent anomalies or outliers.

Examples of Descriptive Analytics

Examples of Descriptive Analytics are as follows:

Retail Industry: A retail company might use descriptive analytics to analyze sales data from the past year. They could break down sales by month to identify any seasonality trends. For example, they might find that sales increase in November and December due to holiday shopping. They could also break down sales by product to identify which items are the most popular. This analysis could inform their purchasing and stocking decisions for the next year. Additionally, data on customer demographics could be analyzed to understand who their primary customers are, guiding their marketing strategies.

Healthcare Industry: In healthcare, descriptive analytics could be used to analyze patient data over time. For instance, a hospital might analyze data on patient admissions to identify trends in admission rates. They might find that admissions for certain conditions are higher at certain times of the year. This could help them allocate resources more effectively. Also, analyzing patient outcomes data can help identify the most effective treatments or highlight areas where improvement is needed.

Finance Industry: A financial firm might use descriptive analytics to analyze historical market data. They could look at trends in stock prices, trading volume, or economic indicators to inform their investment decisions. For example, analyzing the price-earnings ratios of stocks in a certain sector over time could reveal patterns that suggest whether the sector is currently overvalued or undervalued. Similarly, credit card companies can analyze transaction data to detect any unusual patterns, which could be signs of fraud.

Advantages of Descriptive Analytics

Descriptive analytics plays a vital role in the world of data analysis, providing numerous advantages:

• Understanding the Past: Descriptive analytics provides an understanding of what has happened in the past, offering valuable context for future decision-making.
• Data Summarization: Descriptive analytics is used to simplify and summarize complex datasets, which can make the information more understandable and accessible.
• Identifying Patterns and Trends: With descriptive analytics, organizations can identify patterns, trends, and correlations in their data, which can provide valuable insights.
• Inform Decision-Making: The insights generated through descriptive analytics can inform strategic decisions and help organizations to react more quickly to events or changes in behavior.
• Basis for Further Analysis: Descriptive analytics lays the groundwork for further analytical activities. It’s the first necessary step before moving on to more advanced forms of analytics like predictive analytics (forecasting future events) or prescriptive analytics (advising on possible outcomes).
• Performance Evaluation: It allows organizations to evaluate their performance by comparing current results with past results, enabling them to see where improvements have been made and where further improvements can be targeted.
• Enhanced Reporting and Dashboards: Through the use of visualization techniques, descriptive analytics can improve the quality of reports and dashboards, making the data more understandable and easier to interpret for stakeholders at all levels of the organization.
• Immediate Value: Unlike some other types of analytics, descriptive analytics can provide immediate insights, as it doesn’t require complex models or deep analytical capabilities to provide value.

Disadvantages of Descriptive Analytics

While descriptive analytics offers numerous benefits, it also has certain limitations or disadvantages. Here are a few to consider:

• Limited to Past Data: Descriptive analytics primarily deals with historical data and provides insights about past events. It does not predict future events or trends and can’t help you understand possible future outcomes on its own.
• Lack of Deep Insights: While descriptive analytics helps in identifying what happened, it does not answer why it happened. For deeper insights, you would need to use diagnostic analytics, which analyzes data to understand the root cause of a particular outcome.
• Can Be Misleading: If not properly executed, descriptive analytics can sometimes lead to incorrect conclusions. For example, correlation does not imply causation, but descriptive analytics might tempt one to make such an inference.
• Data Quality Issues: The accuracy and usefulness of descriptive analytics are heavily reliant on the quality of the underlying data. If the data is incomplete, incorrect, or biased, the results of the descriptive analytics will be too.
• Over-reliance on Descriptive Analytics: Businesses may rely too much on descriptive analytics and not enough on predictive and prescriptive analytics. While understanding past and present data is important, it’s equally vital to forecast future trends and make data-driven decisions based on those predictions.
• Doesn’t Provide Actionable Insights: Descriptive analytics is used to interpret historical data and identify patterns and trends, but it doesn’t provide recommendations or courses of action. For that, prescriptive analytics is needed.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Table of Contents

Data is growing exponentially in today’s digital age. Organizations produces and collect large amounts of data through various online activities, transactions, social media, mobile devices, sensors, etc. Read on to understand the key distinctions between data science and data analytics regarding their focus areas, required skill sets, job roles, and career prospects.

Data Science vs Data Analytics: An Overview  

Data science adopts a broader scope, leveraging predictive modeling, machine learning, advanced statistics, and algorithms to discover hidden insights and patterns from raw data. The focus is on forecasting and predictive analysis to drive innovation and optimize processes. 

Data scientists work at the intersection of programming, mathematics, statistics, database engineering, and business to uncover actionable intelligence.

In contrast, data analytics applies basic statistical and quantitative analysis to interpret data for historical insights, diagnose inefficiencies, and provide recommendations based on the findings. Data analysts utilize BI tools and SQL queries for reporting and visualization. 

Key Differences Between Data Science and Data Analytics

The following are key factors of differences: 

1. Focus Areas

Master’s in Data Science involves predictive modeling and forecasting through advanced machine learning and statistical methods. The focus is on innovation and optimizing products, services and processes.

In contrast, Data Analytics applies descriptive and diagnostic analysis to provide insights into past performance and trends. The goal is to enhance existing operational processes.

2. Techniques and Tools

Data Science leverages more complex techniques like machine learning, artificial intelligence, neural networks, algorithms and programming languages like Python and R. Data Scientists also use Big Data platforms like Hadoop and Spark.

Meanwhile, Data Analytics relies more on conventional statistical analysis, data mining and business intelligence tools like Excel, Tableau, SQL and Power BI for data workflow and visualization.

3. Types of Analytics

Master’s in Data Science deals with sophisticated predictive analytics and prescriptive recommendations based on future probabilities. Data analytics is confined to descriptive analytics, which describes what has already happened through historical data reporting.

4. Job Roles

Data Scientists perform predictive analysis to identify trends and patterns. They also develop ML models, optimize data pipelines, and translate analysis into solutions.

Data Analysts collect, clean, analyze, and visualize data to interpret historical trends and provide recommendations to business stakeholders.

5. Experience Levels

Data Science requires advanced skillsets and an academic background, such as a Master’s or PhD in quantitative disciplines and programming. However, Data Analytics is an entry-level field accessible to graduates with a bachelor’s degree in mathematics, statistics, or computer science.

6. Salary Range

Data Science offers a significantly higher salary range than Data Analytics at all stages of career progression.

7. Key Responsibilities

Data Scientists focus more on predictive modeling and devising data products and solutions to unlock innovation. Data Analysts present insights from historical data to enhance and diagnose issues in existing business processes.

Data analytics focuses on historical data analysis, while data science leverages advanced analytics techniques for predictive modeling and devising data solutions. Though some of the underlying skills overlap, the roles, responsibilities, and salaries of data scientists and data analysts differ markedly. Understanding these core distinctions provides clarity for aspiring professionals on which career path best aligns with their interests and strengths.

To learn it in depth, explore upGrad’s industry-relevant PG programs in Data Science and Analytics, which are approved in partnership with top universities like Liverpool John Moores University.

1. Is data analytics a part of data science?

Data analytics can be considered a subset of data science focused on descriptive and diagnostic analysis, while it also encompasses predictive analytics and machine learning.

2. Is a data analyst an entry-level job compared to a data scientist? 

Yes, data analysis is generally an entry-level job compared to a data scientist’s more advanced and specialized role.

3. How much higher are data science salaries compared to data analytics?

On average, data scientists earn 30-50% higher salaries than data analysts, and the gap widens as they gain more experience.

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• Published: 07 August 2024

Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study

• Rebecca Barry   ORCID: orcid.org/0000-0003-2272-4694 1 ,
• Elyce Green   ORCID: orcid.org/0000-0002-7291-6419 1 ,
• Kristy Robson   ORCID: orcid.org/0000-0002-8046-7940 1 &
• Melissa Nott   ORCID: orcid.org/0000-0001-7088-5826 1  

BMC Health Services Research volume  24 , Article number:  908 ( 2024 ) Cite this article

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Metrics details

The use of telehealth has proliferated to the point of being a common and accepted method of healthcare service delivery. Due to the rapidity of telehealth implementation, the evidence underpinning this approach to healthcare delivery is lagging, particularly when considering the uniqueness of some service users, such as those in rural areas. This research aimed to address the current gap in knowledge related to the factors critical for the successful delivery of telehealth to rural populations.

This research used a qualitative descriptive design to explore telehealth service provision in rural areas from the perspective of clinicians and describe factors critical to the effective delivery of telehealth in rural contexts. Semi-structured interviews were conducted with clinicians from allied health and nursing backgrounds working in child and family nursing, allied health services, and mental health services. A manifest content analysis was undertaken using the Framework approach.

Sixteen health professionals from nursing, clinical psychology, and social work were interviewed. Participants mostly identified as female (88%) and ranged in age from 26 to 65 years with a mean age of 47 years. Three overarching themes were identified: (1) Navigating the role of telehealth to support rural healthcare; (2) Preparing clinicians to engage in telehealth service delivery; and (3) Appreciating the complexities of telehealth implementation across services and environments.

Conclusions

This research suggests that successful delivery of telehealth to rural populations requires consideration of the context in which telehealth services are being delivered, particularly in rural and remote communities where there are challenges with resourcing and training to support health professionals. Rural populations, like all communities, need choice in healthcare service delivery and models to increase accessibility. Preparation and specific, intentional training for health professionals on how to transition to and maintain telehealth services is a critical factor for delivery of telehealth to rural populations. Future research should further investigate the training and supports required for telehealth service provision, including who, when and what training will equip health professionals with the appropriate skill set to deliver rural telehealth services.

Peer Review reports

Introduction

Telehealth is a commonly utilised application in rural health settings due to its ability to augment service delivery across wide geographical areas. During the COVID-19 pandemic, the use of telehealth became prolific as it was rapidly adopted across many new fields of practice to allow for healthcare to continue despite requirements for physical distancing. In Australia, the Medicare Benefits Scheme (MBS) lists health services that are subsidised by the federal government. Telehealth items were extensively added to these services as part of the response to COVID-19 [ 1 ]. Although there are no longer requirements for physical distancing in Australia, many health providers have continued to offer services via telehealth, particularly in rural areas [ 2 , 3 ]. For the purpose of this research, telehealth was defined as a consultation with a healthcare provider by phone or video call [ 4 ]. Telehealth service provision in rural areas requires consideration of contextual factors such as access to reliable internet, community members’ means to finance this access [ 5 ], and the requirement for health professionals to function across a broad range of specialty skills. These factors present a case for considering the delivery of telehealth in rural areas as a unique approach, rather than one portion of the broader use of telehealth.

Research focused on rural telehealth has proliferated alongside the rapid implementation of this service mode. To date, there has been a focus on the impact of telehealth on areas such as client access and outcomes [ 2 ], client and health professional satisfaction with services and technology [ 6 ], direct and indirect costs to the patient (travel cost and time), healthcare service provider staffing, lower onsite healthcare resource utilisation, improved physician recruitment and retention, and improved client access to care and education [ 7 , 8 ]. In terms of service implementation, these elements are important but do not outline the broader implementation factors critical to the success of telehealth delivery in rural areas. One study by Sutarsa et al. explored the implications of telehealth as a replacement for face-to-face services from the perspectives of general practitioners and clients [ 9 ] and articulated that telehealth services are not a like-for-like service compared to face-to-face modes. Research has also highlighted the importance of understanding the experience of telehealth in rural Australia across different population groups, including Aboriginal and Torres Strait Islander peoples, and the need to consider culturally appropriate services [ 10 , 11 , 12 , 13 ].

Research is now required to determine what the critical implementation factors are for telehealth delivery in rural areas. This type of research would move towards answering calls for interdisciplinary, qualitative, place-based research [ 12 ] that explores factors required for the sustainability and usability of telehealth in rural areas. It would also contribute to the currently limited understanding of implementation factors required for telehealth delivery to rural populations [ 14 ]. There is a reasonable expectation that there is consistency in the way health services are delivered, particularly across geographical locations. Due to the rapid implementation of telehealth services, there was limited opportunity to proactively identify factors critical for successful telehealth delivery in rural areas and this has created a lag in policy, process, and training. This research aimed to address this gap in the literature by exploring and describing rural health professionals’ experiences providing telehealth services. For the purpose of this research, rural is inclusive of locations classified as rural or remote (MM3-6) using the Modified Monash Model which considers remoteness and population size in its categorisation [ 15 ].

This research study adopted a qualitative descriptive design as described by Sandelowski [ 16 ]. The purpose of a descriptive study is to document and describe a phenomenon of interest [ 17 ] and this method is useful when researchers seek to understand who was involved, what occurred, and the location of the phenomena of interest [ 18 ]. The phenomenon of interest for this research was the provision of telehealth services to rural communities by health professionals. In line with this, a purposive sampling technique was used to identify participants who have experience of this phenomenon [ 19 ]. This research is reported in line with the consolidated criteria for reporting qualitative research [ 20 ] to enhance transparency and trustworthiness of the research process and results [ 21 ].

Research aims

This research aimed to:

Explore telehealth service provision in rural areas from the perspective of clinicians.

Describe factors critical to the successful delivery of telehealth in rural contexts.

Participant recruitment and data collection

People eligible to participate in the research were allied health (using the definition provided by Allied Health Professions Australia [ 22 ]) or nursing staff who delivered telehealth services to people living in the geographical area covered by two rural local health districts in New South Wales, Australia (encompassing rural areas MM3-6). Health organisations providing telehealth service delivery in the southwestern and central western regions of New South Wales were identified through the research teams’ networks and invited to be part of the research.

Telehealth adoption in these organisations was intentionally variable to capture different experiences and ranged from newly established (prompted by COVID-19) to well established (> 10 years of telehealth use). Organisations included government, non-government, and not-for-profit health service providers offering child and family nursing, allied health services, and mental health services. Child and family nursing services were delivered by a government health service and a not-for-profit specialist service, providing health professional advice, education, and guidance to families with a baby or toddler. Child and family nurses were in the same geographical region as the families receiving telehealth. Transition to telehealth services was prompted by the COVID-19 pandemic. The participating allied health service was a large, non-government provider of allied health services to regional New South Wales. Allied health professionals were in the same region as the client receiving telehealth services. Use of telehealth in this organisation had commenced prior to the COVID-19 pandemic. Telehealth mental health services were delivered by an emergency mental health team, located at a large regional hospital to clients in another healthcare facility or location to which the health professional could not be physically present (typically a lower acuity health service in a rural location).

Once organisations agreed to disseminate the research invitation, a key contact person employed at each health organisation invited staff to participate via email. Staff were provided with contact details of the research team in the email invitation. All recruitment and consent processes were managed by the research team to minimise risk of real or perceived coercion between staff and the key contact person, who was often in a supervisory or managerial position within the organisation. Data were collected using semi-structured interviews using an online platform with only the interviewer and participant present. Interviews were conducted by a research team member with training in qualitative data collection during November and December 2021 and were transcribed verbatim by a professional transcribing service. All participants were offered the opportunity to review their transcript and provide feedback, however none opted to do so. Data saturation was not used as guidance for participant numbers, taking the view of Braun and Clarke [ 23 ] that meaning is generated through the analysis rather than reaching a point of saturation.

Data analysis

Researchers undertook a manifest content analysis of the data using the Framework approach developed by Ritchie and Spencer [ 24 ]. All four co-authors were involved in the data analysis process. Framework uses five stages for analysis including (1) familiarisation (2) identifying a thematic framework based on emergent overarching themes, (3) application of the coding framework to the interview transcripts [indexing], (4) reviewing and charting of themes and subthemes, and (5) mapping and interpretation [ 24 , p. 178]. The research team analysed a common interview initially, identified codes and themes, then independently applied these to the remaining interviews. Themes were centrally recorded, reviewed, and discussed by the research team prior to inclusion into the thematic framework. Final themes were confirmed via collaborative discussion and consensus. The iterative process used to review and code data was recorded into an Excel spreadsheet to ensure auditability and credibility, and to enhance the trustworthiness of the analysis process.

This study was approved by the Greater Western NSW Human Research Ethics Committee and Charles Sturt University Human Research Ethics Committee (approval numbers: 2021/ETH00088 and H21215). All participants provided written consent.

Eighteen health professionals consented to be interviewed. Two were lost to follow-up, therefore semi-structured interviews were conducted with 16 of these health professionals, the majority of which were from the discipline of nursing ( n  = 13, 81.3%). Participant demographics and their pseudonyms are shown in Table  1 .

Participants mostly identified as female ( n  = 14, 88%) and ranged in age from 26 to 65 years with a mean age of 47 years. Participants all delivered services to rural communities in the identified local health districts and resided within the geographical area they serviced. The participants resided in areas classified as MM3-6 but were most likely to reside in an area classified MM3 (81%). Average interview time was 38 min, and all interviews were conducted online via Zoom.

Three overarching themes were identified through the analysis of interview transcripts with health professionals. These themes were: (1) Navigating the role of telehealth to support rural healthcare; (2) Preparing clinicians to engage in telehealth service delivery; and (3) Appreciating the complexities of telehealth implementation across services and environments.

Theme 1: navigating the role of telehealth to support rural healthcare

The first theme described clinicians’ experiences of using telehealth to deliver healthcare to rural communities, including perceived benefits and challenges to acceptance, choice, and access. Interview participants identified several factors that impacted on or influenced the way they could deliver telehealth, and these were common across the different organisational structures. Clinicians highlighted the need to consider how to effectively navigate the role of telehealth in supporting their practice, including when it would enhance their practice, and when it might create barriers. The ability to improve rural service provision through greater access was commonly discussed by participants. In terms of factors important for telehealth delivery in rural contexts, the participants demonstrated that knowledge of why and how telehealth was used were important, including the broadened opportunity for healthcare access and an understanding of the benefits and challenges of providing these services.

Access to timely and specialist healthcare for rural communities

Participants described a range of benefits using telehealth to contact small, rural locations and facilitate greater access to services closer to home. This was particularly evident when there was lack of specialist support in these areas. These opportunities meant that rural people could receive timely care that they required, without the burden of travelling significant distances to access health services.

The obvious thing in an area like this, is that years ago, people were being transported three hours just to see us face to face. It’s obviously giving better, more timely access to services. (Patrick)

Staff access to specialist support was seen as an important aspect for rural healthcare by participants, because of the challenges associated with lack of staffing and resources within these areas which potentially increased the risks for staff in these locations, particularly when managing clients with acute mental illnesses.

Within the metro areas they’ve got so many staff and so many hospitals and they can manage mental health patients quite well within those facilities, but with us some of these hospitals will have one RN on overnight and it’s just crappy for them, and so having us able to do video link, it kind of takes the pressure off and we’re happy to make the decisions and the risky decisions for what that person needs. (Tracey)

Participants described how the option to use telehealth to provide specialised knowledge and expertise to support local health staff in rural hospitals likely led to more appropriate outcomes for clients wanting to be able to remain in their community. Conversely, Amber described the implications if telehealth was not available.

If there was some reason why the telehealth wasn’t available… quite often, I suppose the general process be down to putting the pressure on the nursing and the medical staff there to make a decision around that person, which is not a fair or appropriate thing for them to do. (Amber)

Benefits and challenges to providing telehealth in rural communities

Complementing the advantage of reduced travel time to access services, was the ability for clients to access additional support via telehealth, which was perceived as a benefit. For example, one participant described how telehealth was useful for troubleshooting client’s problems rather than waiting for their next scheduled appointment.

If a mum rings you with an issue, you can always say to them “are you happy to jump onto My Virtual Care with me now?” We can do that, do a consult over My Virtual Care. Then I can actually gauge how mum is. (Jade)

While accessibility was a benefit, participants highlighted that rural communities need to be provided with choice, rather than the assumption that telehealth be the preferred option for everyone, as many rural clients want face-to-face services.

They’d all prefer, I think, to be able to see someone in person. I think that’s generally what NSW rural [want] —’cause I’m from country towns as well—there’s no substitute, like I said, for face-to-face assessment. (Adam)

Other, more practical limitations of broad adoption of telehealth raised by the participants included issues with managing technology and variability in internet connectivity.

For many people in the rural areas, it’s still an issue having that regular [internet] connection that works all the time. I think it’s a great option but I still think it’s something that some rural people will always have some challenges with because it’s not—there’s so many black spots and so many issues still with the internet connection in rural areas. Even in town, there’s certain areas that are still having lots of problems. (Chloe)

Participants also identified barriers related to assumptions that all clients will have access to technology and have the necessary data to undertake a telehealth consultation, which wasn’t always the case, particularly with individuals experiencing socioeconomic disadvantage.

A lot of [Aboriginal] families don’t actually have access to telehealth services. Unless they use their phone. If they have the technology on their phones. I found that was a little bit of an issue to try and help those particular clients to get access to the internet, to have enough data on their phone to make that call. There was a lot of issues and a lot of things that we were putting in complaints about as they were going “we’re using up a lot of these peoples’ data and they don’t have internet in their home.” (Evelyn).

Other challenges identified by the participants were related to use of telehealth for clients that required additional support. Many participants talked about the complexities of using an interpreter during a telehealth consultation for culturally and linguistically diverse clients.

Having interpreters, that’s another element that’s really, really difficult because you’re doing video link, but then you’ve also got the phone on speaker and you’re having this three-way conversation. Even that, in itself, that added element on video link is really, really tough. It’s a really long process. (Tracey)

In summary, this theme described some of the benefits and constraints when using telehealth for the delivery of rural health services. The participants demonstrated the importance of understanding the needs and contexts of individual clients, and accounting for this when making decisions to incorporate telehealth into their service provision. Understanding how and why telehealth can be implemented in rural contexts was an important foundation for the delivery of these services.

Theme 2: preparing clinicians to engage in telehealth service delivery

The preparation required for clinicians to engage with telehealth service delivery was highlighted and the participants described the unique set of skills required to effectively build rapport, engage, and carry out assessments with clients. For many participants who had not routinely used telehealth prior to the COVID-19 pandemic, the transition to using telehealth had been rapid. The participants reflected on the implications of rapidly adopting these new practices and the skills they required to effectively deliver care using telehealth. These skills were critical for effective delivery of telehealth to rural communities.

Rapid adoption of new skills and ways of working

The rapid and often unsupported implementation of telehealth in response to the COVID-19 pandemic resulted in clinicians needing to learn and adapt to telehealth, often without being taught or with minimal instruction.

We had to do virtual, virtually overnight we were changed to, “Here you go. Do it this way,” without any real education. It was learned as we went because everybody was in the same boat. Everyone was scrabbling to try and work out how to do it. (Chloe)

In addition to telehealth services starting quickly, telehealth provision requires clinicians to use a unique set of skills. Therapeutic interventions and approaches were identified as being more challenging when seeing a client through a screen, compared to being physically present together in a room.

The body language is hidden a little bit when you’re on teleconference, whereas when you’re standing up face to face with someone, or standing side by side, the person can see the whole picture. When you’re on the video link, the patient actually can’t—you both can’t see each other wholly. That’s one big barrier. (Adam)

There was an emphasis on communication skills such as active listening and body language that were required when engaging with telehealth. These skills were seen as integral to building rapport and connection. The importance of language in an environment with limited visualisation of body language, is further demonstrated by one participant describing how they tuned into the timing and flow of the conversation to avoid interrupting and how these skills were pertinent for using telehealth.

In the beginning especially, we might do this thing where I think they’ve finished or there’s a bit of silence, so I go to speak and then they go to speak at the same time, and that’s different because normally in person you can really gauge that quite well if they’ve got more to say. I think those little things mean that you’ve got to work a bit harder and you’ve got to bring those things to the attention of the client often. (Robyn)

Preparing clinicians to engage in telehealth also required skills in sharing clear and consistent information with clients about the process of interacting via telehealth. This included information to reassure the client that the telehealth appointment was private as well as prepare them for potential interruptions due to connection issues.

I think being really explicitly clear about the fact that with our setups we have here, no one can dial in, no one else is in my room even watching you. We’re not recording, and there’s a lot of extra information, I think around that we could be doing better in terms of delivering to the person. (Amber)

Becoming accustomed to working through the ‘window’

Telehealth was often described as a window and not a view of the whole person which presented limitations for clinicians, such as seeing nuance of expression. Participants described the difficulties of assessing a client using telehealth when you cannot see the whole picture such as facial expressions, movement, behaviour, interactions with others, dress, and hygiene.

I found it was quite difficult because you couldn’t always see the actual child or the baby, especially if they just had their phone. You couldn’t pick up the body language. You couldn’t always see the facial expressions. You couldn’t see the child and how the child was responding. It did inhibit a lot of that side of our assessing. Quite often you’d have to just write, “Unable to view child.” You might be able to hear them but you couldn’t see them. (Chloe)

Due to the window view, the participants described how they needed to pay even greater attention to eye contact and tone of voice when engaging with clients via telehealth.

I think the eye contact is still a really important thing. Getting the flow of what they’re comfortable with a little bit too. It’s being really careful around the tone of voice as well too, because—again, that’s the same for face-to-face, but be particularly careful of it over telehealth. (Amber)

This theme demonstrates that there are unique and nuanced skills required by clinicians to effectively engage in provision of rural healthcare services via telehealth. Many clinicians described how the rapid uptake of telehealth required them to quickly adapt to providing telehealth services, and they had to modify their approach rather than replicate what they would do in face-to-face contexts. Appreciating the different skills sets required for telehealth practice was perceived as an important element in supporting clinicians to deliver quality healthcare.

Theme 3: appreciating the complexities of telehealth implementation across services and environments

It was commonly acknowledged that there needed to be an appreciation by clinicians of the multiple different environments that telehealth was being delivered in, as well as the types of consultations being undertaken. This was particularly important when well-resourced large regional settings were engaging with small rural services or when clinicians were undertaking consultations within a client’s home.

Working from a different location and context

One of the factors identified as important for the successful delivery of services via telehealth was an understanding of the location and context that was being linked into. Participants regularly talked about the challenges when undertaking a telehealth consultation with clients at home, which impacted the quality of the consultation as it was easy to “ lose focus” (Kelsey) and become distracted.

Instead of just coming in with one child, they had all the kids, all wanting their attention. I also found that babies and kids kept pressing the screen and would actually disconnect us regularly. (Chloe)

For participants located in larger regional locations delivering telehealth services to smaller rural hospitals, it was acknowledged that not all services had equivalent resources, skills, and experience with this type of healthcare approach.

They shouldn’t have to do—they’ve gotta double-click here, login there. They’re relying on speakers that don’t work. Sometimes they can’t get the cameras working. I think telehealth works as long as it’s really user friendly. I think nurses—as a nurse, we’re not supposed to be—I know IT’s in our job criteria, but not to the level where you’ve got to have a degree in technology to use it. (Adam)

Participants also recognised that supporting a client through a telehealth consultation adds workload stress as rural clinicians are often having pressures with caseloads and are juggling multiple other tasks while trying to trouble shoot technology issues associated with a telehealth consultation.

Most people are like me, not great with computers. Sometimes the nurse has got other things in the Emergency Department she’s trying to juggle. (Eleanor)

Considerations for safety, privacy, and confidentiality

Participants talked about the challenges that arose due to inconsistencies in where and how the telehealth consultation would be conducted. Concerns about online safety and information privacy were identified by participants.

There’s the privacy issue, particularly when we might see someone and they might be in a bed and they’ve got a laptop there, and they’re not given headphones, and we’re blaring through the speaker at them, and someone’s three meters away in another bed. That’s not good. That’s a bit of a problem. (Patrick)

When telehealth was offered as an option to clients at a remote healthcare site, clinicians noted that some clients were not provided with adequate support and were left to undertake the consultation by themselves which could cause safety risks for the client and an inability for the telehealth clinician to control the situation.

There were some issues with patients’ safety though. Where the telehealth was located was just in a standard consult room and there was actually a situation where somebody self-harmed with a needle that was in a used syringe box in that room. Then it was like, you just can’t see high risk—environment. (Eleanor)

Additionally, participants noted that they were often using their own office space to conduct telehealth consultations rather than a clinical room which meant there were other considerations to think about.

Now I always lock my room so nobody can enter. That’s a nice little lesson learnt. I had a consult with a mum and some other clinicians came into my room and I thought “oh my goodness. I forgot to lock.” I’m very mindful now that I lock. (Jade)

This theme highlights the complexities that exist when implementing telehealth across a range of rural healthcare settings and environments. It was noted by participants that there were variable skills and experience in using telehealth across staff located in smaller rural areas, which could impact on how effective the consultation was. Participants identified the importance of purposely considering the environment in which the telehealth consultation was being held, ensuring that privacy, safety, and distractibility concerns have been adequately addressed before the consultation begins. These factors were considered important for the successful implementation of telehealth in rural areas.

This study explored telehealth service delivery in various rural health contexts, with 16 allied health and nursing clinicians who had provided telehealth services to people living in rural communities prior to, and during the COVID-19 pandemic. Reflections gained from clinicians were analysed and reported thematically. Major themes identified were clinicians navigating the role of telehealth to support rural healthcare, the need to prepare clinicians to engage in telehealth service delivery and appreciating the complexities of telehealth implementation across services and environments.

The utilisation of telehealth for health service delivery has been promoted as a solution to resolve access and equity issues, particularly for rural communities who are often impacted by limited health services due to distance and isolation [ 6 ]. This study identified a range of perceived benefits for both clients and clinicians, such as improved access to services across large geographic distances, including specialist care, and reduced travel time to engage with a range of health services. These findings are largely supported by the broader literature, such as the systematic review undertaken by Tsou et al. [ 25 ] which found that telehealth can improve clinical outcomes and increase the timeliness to access services, including specialist knowledge. Clinicians in our study also noted the benefits of using telehealth for ad hoc clinical support outside of regular appointment times, which to date has not been commonly reported in the literature as a benefit. Further investigation into this aspect may be warranted.

The findings from this study identify a range of challenges that exist when delivering health services within a virtual context. It was common for participants to highlight that personal preference for face-to-face sessions could not always be accommodated when implementing telehealth services in rural areas. The perceived technological possibilities to improve access can have unintended consequences for community members which may contribute to lack of responsiveness to community needs [ 12 ]. It is therefore important to understand the client and their preferences for using telehealth rather than making assumptions on the appropriateness of this type of health service delivery [ 26 ]. As such, telehealth is likely to function best when there is a pre-established relationship between the client and clinician, with clients who have a good knowledge of their personal health and have access to and familiarity with digital technology [ 13 ]. Alternatively, it is appropriate to consider how telehealth can be a supplementary tool rather than a stand-alone service model replacing face-to-face interactions [ 13 ].

As identified in this study, managing technology and internet connectivity are commonly reported issues for rural communities engaging in telehealth services [ 27 , 28 ]. Additionally, it was highlighted that within some rural communities with higher socioeconomic disadvantage, limited access to an appropriate level of technology and the required data to undertake a telehealth consult was a deterrent to engage in these types of services. Mathew et al. [ 13 ] found in their study that bandwidth impacted video consultations, which was further compromised by weather conditions, and clients without smartphones had difficulty accessing relevant virtual consultation software.

The findings presented here indicate that while telehealth can be a useful model, it may not be suitable for all clients or client groups. For example, the use of interpreters in telehealth to support clients was a key challenge identified in this study. This is supported by Mathew et al. [ 13 ] who identified that language barriers affected the quality of telehealth consultations and accessing appropriate interpreters was often difficult. Consideration of health and digital literacy, access and availability of technology and internet, appropriate client selection, and facilitating client choice are all important drivers to enhance telehealth experiences [ 29 ]. Nelson et al. [ 6 ] acknowledged the barriers that exist with telehealth, suggesting that ‘it is not the groups that have difficulty engaging, it is that telehealth and digital services are hard to engage with’ (p. 8). There is a need for telehealth services to be delivered in a way that is inclusive of different groups, and this becomes more pertinent in rural areas where resources are not the same as metropolitan areas.

The findings of this research highlight the unique set of skills required for health professionals to translate their practice across a virtual medium. The participants described these modifications in relation to communication skills, the ability to build rapport, conduct healthcare assessments, and provide treatment while looking at a ‘window view’ of a person. Several other studies have reported similar skillsets that are required to effectively use telehealth. Uscher-Pines et al. [ 30 ] conducted research on the experiences of psychiatrists moving to telemedicine during the COVID-19 pandemic and noted challenges affecting the quality of provider-patient interactions and difficulty conducting assessment through the window of a screen. Henry et al. [ 31 ] documented a list of interpersonal skills considered essential for the use of telehealth encompassing attributes related to set-up, verbal and non-verbal communication, relationship building, and environmental considerations.

Despite the literature uniformly agreeing that telehealth requires a unique skill set there is no agreement on how, when and for whom education related to these skills should be provided. The skills required for health professionals to use telehealth have been treated as an add-on to health practice rather than as a specialty skill set requiring learning and assessment. This is reflected in research such as that by Nelson et al. [ 6 ] who found that 58% of mental health professionals using telehealth in rural areas were not trained to use it. This gap between training and practice is likely to have arisen from the rapid and widespread implementation of telehealth during the COVID-19 pandemic (i.e. the change in MBS item numbers [ 1 ]) but has not been addressed in subsequent years. For practice to remain in step with policy and funding changes, the factors required for successful implementation of telehealth in rural practice must be addressed.

The lack of clarity around who must undertake training in telehealth and how regularly, presents a challenge for rural health professionals whose skill set has been described as a specialist-generalist that covers a significant breadth of knowledge [ 32 ]. Maintaining knowledge currency across this breadth is integral and requires significant resources (time, travel, money) in an environment where access to education can be limited [ 33 ]. There is risk associated with continually adding skills on to the workload of rural health professionals without adequate guidance and provision for time to develop and maintain these skills.

While the education required to equip rural health professionals with the skills needed to effectively use telehealth in their practice is developing, until education requirements are uniformly understood and made accessible this is likely to continue to pose risk for rural health professionals and the community members accessing their services. Major investment in the education of all health professionals in telehealth service delivery, no matter the context, has been identified as critical [ 6 ].

This research highlights that the experience of using telehealth in rural communities is unique and thus a ‘one size fits all’ approach is not helpful and can overlook the individual needs of a community. Participants described experiences of using telehealth that were different between rural communities, particularly for smaller, more remote rural locations where resources and staff support and experience using telehealth were not always equivalent to larger rural locations. Research has indicated the need to invest in resourcing and education to support expansion of telehealth, noting this is particularly important in rural, regional, and remote areas [ 34 ]. Our study recognises that this is an ongoing need as rural communities continue to have diverse experiences of using telehealth services. Careful consideration of the context of individual rural health services, including the community needs, location, and resource availability on both ends of the consultation is required. Use of telehealth cannot have the same outcomes in every area. It is imperative that service providers and clinicians delivering telehealth from metropolitan areas to rural communities appreciate and understand the uniqueness of every community, so their approach is tailored and is helpful rather than hindering the experience for people in rural communities.

Limitations

There are a number of limitations inherent to the design of this study. Participants were recruited via their workplace and thus although steps were taken to ensure they understood the research would not affect their employment, it is possible some employees perceived an association between the research and their employment. Health professionals who had either very positive or very negative experiences with telehealth may have been more likely to participate, as they may be more likely to want to discuss their experiences. In addition to this, only health services that were already connected with the researchers’ networks were invited to participate. Other limitations include purposive sampling, noting that the opinions of the participants are not generalisable. The participant group also represented mostly nursing professionals whose experiences with telehealth may differ from other health disciplines. Finally, it is important to acknowledge that the opinions of the health professionals who participated in the study, may not represent, or align with the experience and opinions of service users.

This study illustrates that while telehealth has provided increased access to services for many rural communities, others have experienced barriers related to variability in connectivity and managing technology. The results demonstrated that telehealth may not be the preferred or appropriate option for some individuals in rural communities and it is important to provide choice. Consideration of the context in which telehealth services are being delivered, particularly in rural and remote communities where there are challenges with resourcing and training to support health professionals, is critical to the success of telehealth service provision. Another critical factor is preparation and specific, intentional training for health professionals on how to transition to manage and maintain telehealth services effectively. Telehealth interventions require a unique skill set and guidance pertaining to who, when and what training will equip health professionals with the appropriate skill set to deliver telehealth services is still to be determined.

Data availability

The qualitative data collected for this study was de-identified before analysis. Consent was not obtained to use or publish individual level identified data from the participants and hence cannot be shared publicly. The de-identified data can be obtained from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge Georgina Luscombe, Julian Grant, Claire Seaman, Jennifer Cox, Sarah Redshaw and Jennifer Schwarz who contributed to various elements of the project.

The study authors are employed by Three Rivers Department of Rural Health. Three Rivers Department of Rural Health is funded by the Australian Government under the Rural Health Multidisciplinary Training (RHMT) Program.

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RB & EG contributed to the conceptualisation of the study and methodological design. RB & MN collected the research data. RB, EG, MN, KR contributed to analysis and interpretation of the research data. RB, EG, MN, KR drafted the manuscript. All authors provided feedback on the manuscript and approved the final submitted manuscript.

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Barry, R., Green, E., Robson, K. et al. Factors critical for the successful delivery of telehealth to rural populations: a descriptive qualitative study. BMC Health Serv Res 24 , 908 (2024). https://doi.org/10.1186/s12913-024-11233-3

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• Munehiro Seki 4 ,
• Elizabeth A. Duncan 5 ,
• Karin Bowen 6 ,
• Jonathan Marshall 7 ,
• Ayman Megally 8 &
• Mehul Patel   ORCID: orcid.org/0000-0003-0435-858X 9  

Respiratory Research volume  25 , Article number:  297 ( 2024 ) Cite this article

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Metrics details

Japanese guidelines recommend triple inhaled corticosteroid (ICS)/long-acting muscarinic antagonist (LAMA)/long-acting β 2 -agonist (LABA) therapy in patients with chronic obstructive pulmonary disease (COPD) and no concurrent asthma diagnosis who experience frequent exacerbations and have blood eosinophil (EOS) count ≥ 300 cells/mm 3 , and in patients with COPD and asthma with continuing/worsening symptoms despite receiving dual ICS/LABA therapy. These post-hoc analyses of the KRONOS study in patients with COPD and without an asthma diagnosis, examine the effects of fixed-dose triple therapy with budesonide/glycopyrronium/formoterol fumarate dihydrate (BGF) versus dual therapies on lung function and exacerbations based on blood EOS count – focusing on blood EOS count 100 to < 300 cells/mm 3 – as a function of exacerbation history and COPD severity.

In KRONOS, patients were randomized to receive treatments that included BGF 320/14.4/10 µg, glycopyrronium/formoterol fumarate dihydrate (GFF) 14.4/10 µg, or budesonide/formoterol fumarate dihydrate (BFF) 320/10 µg via metered dose inhaler (two inhalations twice-daily for 24 weeks). These post-hoc analyses assessed changes from baseline in morning pre-dose trough forced expiratory volume in 1 s (FEV 1 ) over 12–24 weeks and moderate or severe COPD exacerbations rates over 24 weeks. The KRONOS study was not prospectively powered for these subgroup analyses.

Among patients with blood EOS count 100 to < 300 cells/mm 3 , least squares mean treatment differences for lung function improvement favored BGF over BFF in patients without an exacerbation history in the past year and in patients with moderate and severe COPD, with observed differences ranging from 62 ml to 73 ml across populations. In this same blood EOS population, moderate or severe exacerbation rates were reduced for BGF relative to GFF by 56% in patients without an exacerbation history in the past year, by 47% in patients with moderate COPD, and by 50% in patients with severe COPD.

Conclusions

These post-hoc analyses of patients with moderate-to-very severe COPD from the KRONOS study seem to indicate clinicians may want to consider a step-up to triple therapy in patients with persistent/worsening symptoms with blood EOS count > 100 cells/mm 3 , even if disease severity is moderate and there is no recent history of exacerbations.

Trial registration

ClinicalTrials.gov registry number NCT02497001 (registration date, 13 July 2015).

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, with economic and social burdens that are both substantial and increasing [ 1 , 2 ]. Three fixed-dose triple therapies with an inhaled corticosteroid (ICS), a long-acting muscarinic antagonist (LAMA), and a long-acting β 2 -agonist (LABA) are approved for the maintenance treatment of COPD [ 3 , 4 , 5 ].

The Global Initiative For Chronic Obstructive Lung Disease (GOLD) 2023 report recommends triple therapy with an ICS/LAMA/LABA be considered as an initial treatment option in patients with blood eosinophil (EOS) count ≥ 300 cells/mm 3 with frequent (≥ 2/year) moderate exacerbations or (≥ 1) exacerbation leading to hospitalization [ 2 ]. A step up to ICS/LAMA/LABA triple therapy is also recommended in patients with blood EOS count ≥ 100 cells/mm 3 who experience exacerbations despite receiving LAMA/LABA dual therapy [ 2 ]. According to COPD treatment guidelines in Japan [ 6 ], ICS-containing treatment is recommended for patients with COPD and a clinical asthma diagnosis when dual therapy is not sufficient; however, in patients with COPD and no asthma diagnosis, ICS/LAMA/LABA triple therapy is only recommended for those who experience frequent exacerbations (≥ 2 moderate or ≥ 1 severe per year) and have blood EOS count ≥ 300 cells/mm 3 .

In ETHOS (NCT02465567), a study of patients with moderate-to-very severe COPD with exacerbations and receiving at least two inhaled maintenance therapies at screening, the fixed-dose triple combination therapy budesonide/glycopyrronium/formoterol fumarate dihydrate (BGF) 320/14.4/10 µg significantly reduced the annual rate of moderate or severe exacerbations (the primary study end point) [ 7 ] and significantly improved lung function (pulmonary function test sub-study primary endpoint) versus glycopyrronium/formoterol fumarate dihydrate (GFF) and budesonide/formoterol fumarate dihydrate (BFF) [ 8 ]. Similarly, in KRONOS (NCT02497001), a study of patients with moderate-to-very severe COPD and no requirement for prior exacerbations, BGF 320/14.4/10 significantly improved lung function versus GFF, BFF, and open-label budesonide/formoterol fumarate dry-powder inhaler (BUD/FORM), and significantly reduced the rate of moderate or severe exacerbations versus GFF [ 9 ].

Importantly, benefits of BGF over dual therapy were observed across a range of blood EOS counts in post-hoc analyses of ETHOS and KRONOS [ 10 , 11 , 12 ]. Given recommendations in the GOLD 2023 report [ 2 ], current Japanese treatment guidelines [ 6 ], and evidence for benefits of BGF over dual therapy across a range of blood EOS counts (including below 300 cells/mm 3 ) in patients with COPD [ 10 , 11 ], post-hoc analyses of the KRONOS study were conducted to further examine the effects of BGF versus dual LAMA/LABA and ICS/LABA therapies on lung function and exacerbation rates in patients with COPD based on blood EOS count (100 to < 300 and ≥ 100 cells/mm³) as a function of exacerbation history (exacerbations in the past year and no exacerbations in the past year) and COPD severity (moderate, severe, very severe).

Study design

A detailed description of the study design and patient population in KRONOS (ClinicalTrials.gov registry number NCT02497001; registration date, 13 July 2015), including inclusion and exclusion criteria, has been previously published [ 9 ]. In brief, KRONOS was a 24-week, double-blind, parallel-group, phase III randomized controlled study conducted at 215 sites across four countries (Canada, China, Japan, and the United States).

At screening, eligible patients discontinued current COPD medications (i.e., LAMA, LABA, or both) for the study duration and received open-label ipratropium bromide four times daily as COPD maintenance therapy. ICS use was permitted during screening, provided patients were on a stable dose for at least 4 weeks before screening; however, both ipratropium and ICS were stopped before randomization. Rescue use of salbutamol was permitted throughout the study.

After screening, patients were randomized 2:2:1:1 to receive BGF 320/14.4/10 µg, GFF 14.4/10 µg, or BFF 320/10 µg via a single Aerosphere ™ metered dose inhaler, or open-label BUD/FORM 400/12 µg via a dry powder inhaler (Symbicort ® Turbuhaler ® ), as two inhalations twice-daily for 24 weeks. As BFF was not an approved COPD therapy at the time KRONOS was conducted, BUD/FORM (which was already approved for COPD treatment) was included as an active comparator to support BFF as a comparator for BGF. However, for the purposes of this post-hoc analysis, only data for BFF and GFF are reported.

The study was conducted in accordance with Good Clinical Practice, including the Declaration of Helsinki. The protocol and informed consent form were approved by appropriate institutional review boards or independent ethics committees prior to the start of the study (a full listing of appropriate institutional review boards or independent ethics committees has been published [ 9 ]). All patients provided written informed consent before screening.

Key inclusion criteria for the KRONOS study have been described in detail previously [ 9 ]. Eligible patients were aged 40–80 years; were current or former smokers (smoking history of ≥ 10 pack-years); had an established COPD clinical history, as defined by the American Thoracic Society/European Respiratory Society [ 13 ] or Japanese local guidelines [ 14 ]; had moderate-to-very severe COPD, defined as post-bronchodilator FEV 1 of 25–80% of predicted normal values based on National Health and Nutrition Examination Survey III reference equations [ 15 ] or applicable local reference norms [ 14 , 15 , 16 , 17 ]; and were symptomatic (as defined by a COPD Assessment Test score ≥ 10) despite treatment with ≥ 2 inhaled maintenance therapies for ≥ 6 weeks before screening. Patients were not required to have a history of COPD exacerbations in the previous 12 months and were excluded if they had a current diagnosis of asthma or any respiratory disease other than COPD, evaluated by the investigator, that could affect study results.

In the KRONOS study, the primary lung function endpoint, according to the Japanese/Chinese regulatory approach, was change from baseline in morning pre-dose trough FEV 1 over 12–24 weeks; the rate of moderate or severe COPD exacerbations over 24 weeks was a secondary efficacy endpoint [ 12 ].

A COPD exacerbation was defined as a change in the patient’s usual COPD symptoms lasting for ≥ 2 days that was beyond normal day-to-day variation, acute in onset, and may have warranted a change in regular medication. An exacerbation was considered moderate if it resulted in systemic corticosteroid and/or antibiotic use for at least 3 days, and as severe if it resulted in an inpatient COPD-related hospitalization or death.

Data presentation and statistical analyses

For the current post-hoc analyses, change from baseline in morning pre-dose trough FEV 1 over 12–24 weeks and the rate of moderate or severe COPD exacerbations over 24 weeks were analyzed in patients with blood EOS counts of 100 to < 300 cells/mm 3 and ≥ 100 cells/mm 3 as a function of exacerbation history (any moderate or severe exacerbations in the past year; no exacerbations in the past year) and COPD severity (moderate [FEV 1 50–<80% predicted], severe [FEV 1 30–<50% predicted], very severe [FEV 1  < 30% predicted]). Analyses were conducted in the modified intention-to-treat (mITT) population, which included all patients with post-randomization data obtained before treatment discontinuation.

The primary baseline EOS subgroup of interest included those with blood EOS count 100 to < 300 cells/mm 3 , as assessment of this subgroup will provide insight into the benefits of BGF among patients with blood EOS count < 300 cells/mm 3 . The blood EOS count ≥ 100 cells/mm 3 subgroup was included to provide supportive evidence that inclusion of patients with blood EOS count > 300 cells/mm 3 in the analysis did not result in substantively different findings. Patients with blood EOS count < 100 cells/mm 3 were not included in the post-hoc analyses because the population size would be small and the published literature supports greater ICS benefits with higher EOS count [ 7 , 9 , 18 , 19 , 20 , 21 ] and lesser ICS efficacy with low blood EOS count [ 2 , 8 , 20 ].

Demographic and clinical characteristics are reported descriptively across treatment arms for each subgroup. Change from baseline in morning pre-dose trough FEV 1 over 12–24 weeks in each EOS subgroup by exacerbation history in the preceding 12 months or COPD severity was assessed using a linear repeated measures model that included baseline FEV 1 , percent reversibility to salbutamol, and baseline blood EOS count as continuous covariates and visit, treatment, treatment-by-visit interaction, and ICS use at screening (yes or no), as categorical covariates. Data reported includes the least squares (LS) mean change from baseline with 95% confidence intervals (CIs) for each treatment and LS mean differences with 95% CIs in the change from baseline for each treatment versus BGF.

The rate of moderate or severe exacerbations over 24 weeks in each EOS subgroup by exacerbation history in the preceding 12 months or COPD severity was assessed using negative binomial regression; treatments were compared with adjustment for baseline post-bronchodilator percent predicted FEV 1 , baseline COPD exacerbation history (0, 1, or ≥ 2) in the preceding 12 months, log baseline blood EOS count, region, and ICS use at screening (yes or no). The logarithm of the time at risk of experiencing an exacerbation was used as an offset variable in the model. The data reported includes the number (%) of patients with exacerbations, the total time at risk for an exacerbation, and the adjusted (standard error [SE]) rate of moderate or severe exacerbations; treatment differences between BGF and the other treatment arms are reported using rate ratios (RR) with 95% CIs. As the KRONOS study was not prospectively powered for any of the reported post-hoc analyses, reported P -values are nominal, unadjusted for multiplicity, and provided for descriptive purposes only.

Patient disposition and characteristics

The disposition and demographic/clinical characteristics of patients in the KRONOS study has been described in detail previously [ 9 ]. In brief, of 1902 randomized patients, 1896 were included in the mITT population (BGF, n  = 639; GFF, n  = 625; BFF, n  = 314). Across treatment groups in the overall mITT population, the average age was approximately 65 years, and the median blood EOS count was approximately 150 cells/mm 3 ; approximately 74% of patients did not report having an exacerbation in the preceding 12 months.

Demographic and clinical characteristics in patients with blood EOS count 100 to < 300 cells/mm 3 with and without exacerbations in the preceding 12 months are summarized in Table  1 and in patients categorized based on COPD severity in Additional file 1 supplementary Table S1 . Across treatment groups, demographic and clinical characteristics within each exacerbation history subgroup and COPD severity subgroup were well balanced, with the exception of those variables associated with categorization (i.e., exacerbation history or FEV 1 % predicted). Similarly, among patients with blood EOS count ≥ 100 cells/mm 3 , patient characteristics in each exacerbation history subgroup (Additional file 1 supplementary Table S2 ) or COPD severity subgroup (Additional file 1 supplementary Table S3 ) were also well balanced across treatment groups.

• Lung function

Across treatment groups, increases from baseline in morning pre-dose trough FEV 1 were observed over 12–24 weeks for all blood EOS counts by exacerbation history and COPD severity subgroups (Additional file 1 supplementary Table S4 ). Among patients with blood EOS count 100 to < 300 cells/mm 3 , improvement in lung function with BGF versus BFF was observed among those without an exacerbation history in the preceding 12 months (nominal P  < 0.0001; Fig.  1 A); treatment differences in the changes from baseline in morning pre-dose trough FEV 1 were not suggestive of differences between BGF and GFF (Fig.  1 A). Improvements in lung function with BGF versus BFF were observed among those with moderate and severe COPD (both nominal P  < 0.05; Fig.  1 B), with a similar trend among those with very severe COPD; treatment differences in the changes from baseline in morning pre-dose trough FEV 1 were not suggestive of differences between BGF and GFF (Fig.  1 B).

Lung function difference versus BGF a, b : EOS subgroups by exacerbation history or COPD severity, mITT population. a Change from baseline in morning pre-dose trough FEV 1 over 12–24 weeks. b From a linear repeated measures model which included the following covariates: baseline FEV 1 , percent reversibility to salbutamol, and baseline EOS count as continuous covariates and visit, treatment, treatment-by-visit interaction, and ICS use at screening (yes/no) as categorical covariates. Abbreviations: BFF: budesonide/formoterol fumarate dihydrate; BGF: budesonide/glycopyrronium/formoterol fumarate dihydrate; CI: confidence interval; COPD: chronic obstructive pulmonary disease; EOS: eosinophil; FEV 1 : forced expiratory volume in 1 s; GFF: glycopyrronium/formoterol fumarate dihydrate; ICS: inhaled corticosteroid; LS: least squares; mITT: modified intention-to-treat

Similarly, among patients with blood EOS count ≥ 100 cells/mm 3 , improvements in lung function with BGF versus BFF were observed among those without an exacerbation history in the preceding 12 months (nominal P  < 0.0001; Fig.  1 C); treatment differences in the change from baseline in morning pre-dose trough FEV 1 were not suggestive of differences between BGF and GFF (Fig.  1 C). Improvement in lung function with BGF versus BFF was observed regardless of COPD severity (all nominal P  < 0.05; Fig.  1 D). Treatment differences in the change from baseline in morning pre-dose trough FEV 1 were not suggestive of differences between BGF and GFF in any COPD severity subgroup (Fig.  1 D).

• Exacerbation rates

Across blood EOS counts by exacerbation history in the preceding 12 months or COPD severity, the adjusted rate of moderate or severe exacerbations was greater with GFF than any other treatment (Table  2 ). Among patients with blood EOS count 100 to < 300 cells/mm 3 , the risk of moderate or severe exacerbations was 56% lower for BGF versus GFF in patients without exacerbation history in the preceding 12 months (nominal P  < 0.0001; Fig.  2 A), with a similar trend observed in those with exacerbation history in the preceding 12 months. Risk of moderate or severe exacerbations were 47% and 50% lower, respectively, for BGF versus GFF in patients with moderate and severe COPD (both nominal P  < 0.05; Fig.  2 B), with a similar trend observed for very severe COPD. Examination of RRs for moderate or severe exacerbations between BGF versus BFF was not suggestive of treatment differences for either exacerbation history subgroup (Fig.  2 A) or COPD severity group (Fig.  2 B).

Moderate/severe exacerbation risk versus BGF a : EOS subgroups by exacerbation history or COPD severity, mITT population. a Treatments compared adjusting for baseline post-bronchodilator percent predicted FEV 1 , baseline COPD exacerbation history (0, 1, or ≥ 2) in the preceding 12 months, log baseline blood EOS count, region, and ICS use at screening (yes/no) using negative binomial regression; the logarithm of the time at risk of experiencing an exacerbation was used as an offset variable in the model. Abbreviations: BFF: budesonide/formoterol fumarate dihydrate; BGF: budesonide/glycopyrronium/formoterol fumarate dihydrate; CI: confidence interval; COPD: chronic obstructive pulmonary disease; EOS: eosinophil; FEV 1 : forced expiratory volume in 1 s; GFF: glycopyrronium/formoterol fumarate dihydrate; ICS: inhaled corticosteroid; mITT: modified intention-to-treat; RR: rate ratio

Among patients with blood EOS count ≥ 100 cells/mm 3 , similar trends were observed in patients with blood EOS count 100 to < 300 cells/mm 3 (Fig.  2 C-D). However, this is not surprising as those with blood EOS count 100 to < 300 cells/mm 3 constitute the majority of the sample; only 12.4% of patients in the KRONOS mITT had blood EOS count > 300 cells/mm 3 .

In this post-hoc analysis of the KRONOS study, lung function and exacerbation rates with BGF versus dual LAMA/LABA and ICS/LABA therapies were evaluated in patients with moderate-to-very severe COPD in blood EOS count subgroups, as a function of exacerbation history in the preceding 12 months and COPD severity. To the best of our knowledge, these are the first analyses to suggest that triple therapy is effective even in patients with no history of exacerbations and low levels of peripheral eosinophilia.

Triple therapy with BGF improved lung function, as measured by greater increases from baseline in morning pre-dose trough FEV 1 , versus dual ICS/LABA therapy with BFF, in patients with blood EOS count 100 to < 300 cells/mm 3 without an exacerbation history in the preceding 12 months and among patients with moderate and severe COPD. Similar findings were observed among patients with blood EOS count ≥ 100 cells/mm 3 , which included a relatively small number of patients with blood EOS count ≥ 300 cells/mm 3 . Additionally, triple therapy with BGF reduced the annual moderate or severe exacerbations rate versus LAMA/LABA dual therapy with GFF in patients with blood EOS count 100 to < 300 cells/mm 3 without an exacerbation history in the preceding 12 months and among those with moderate and severe COPD severity, with a similar trend observed for very severe COPD. Overall, these findings seem to indicate that benefits of triple BGF therapy versus dual LAMA/LABA and ICS/LABA therapy are observed across a range of blood EOS counts (even when blood EOS counts are 100 to < 300 cells/mm 3 ) and exacerbation histories (including in the absence of exacerbations in the past year), and COPD severity (including those with moderate COPD). These findings may suggest that triple therapy with BGF is more effective than treatment without ICS, i.e., LAMA/LABA, in terms of exacerbations, and more effective than treatment without LAMA, i.e., ICS/LABA, in terms of lung function in some patients.

The observation that BGF conveys benefits over dual ICS/LABA and LAMA/LABA therapy in patients with blood EOS count 100 to < 300 cells/mm 3 is consistent with previously published reports [ 9 , 10 , 20 ]. In post-hoc analyses of the 52-week ETHOS study, BGF improved morning pre-dose trough FEV 1 versus BFF and GFF as well as reduced moderate or severe exacerbation rates versus GFF across a range of blood EOS counts (≥ 100, ≥ 100−<300, and ≥ 300 cells/mm³) [ 10 ]. In the KRONOS study, change from morning pre-dose trough FEV 1 with BGF versus BFF and BUD/FORM, as well as reductions in the rate of moderate or severe exacerbations for BGF versus GFF, were observed in patients with blood EOS count < 150 cells/mm 3 [ 9 ]. Similarly, results of the triple therapy studied in the 52-week IMPACT trial indicated that moderate or severe exacerbation rates with fluticasone furoate/umeclidinium/vilanterol triple therapy were lower compared with dual LAMA/LABA therapy with umeclidinium/vilanterol across a range of blood EOS levels, including at blood EOS count of approximately 100 to 300 cells/mm 3 [ 20 ]. Although the duration of the intervention was not long enough, the reduction in exacerbation rate with BGF triple therapy may be considered clinically meaningful. The clinical significance of the improvement in respiratory function needs to be clarified in future studies.

In the KRONOS study, exacerbation history reported in the year before study entry was lower than the model-estimated rates observed during the study [ 9 ]. This suggests that there are other factors that lead to the risk of exacerbations, and not only exacerbation history in the preceding 12 months. Although, not having an exacerbation history in the preceding 12 months is not synonymous with reduced risk, it is widely accepted that those with a history of exacerbations are more likely to experience a future exacerbation [ 22 ]. This is supported by observations in the current analyses, as patients with an exacerbation history in the preceding 12 months before entering the study had numerically higher exacerbations rates during the study, irrespective of treatment arm or blood EOS level, compared with those without an exacerbation history in the preceding 12 months.

Current guidance in Japan recommends ICS/LAMA/LABA triple therapy in patients with COPD and no diagnosis of asthma who experience frequent exacerbations and have blood EOS count ≥ 300 cells/mm 3 , and in patients with COPD and features of asthma with continuing/worsening symptoms despite receiving dual ICS/LABA therapy [ 6 ]. Our analyses suggest BGF has beneficial effects on lung function versus dual ICS/LABA therapy and on moderate or severe exacerbation rates versus dual LAMA/LABA therapy in patients with and without recent exacerbation histories and among those with moderate and severe COPD who have blood EOS count 100 to < 300 cells/mm 3 . Similar results were generally observed for both exacerbation history and COPD severity in supportive analyses of patients with blood EOS count ≥ 100 cells/mm 3 (i.e., when patients with blood EOS count > 300 cells/mm 3 were included; BGF, n  = 55; GFF, n  = 56; BFF, n  = 32). However, treatment differences on exacerbation rate reductions for BGF versus GFF did appear more robust in this subgroup in some instances, with beneficial effects observed in those with and without exacerbation histories. This is expected since a threshold of blood EOS count > 300 cells/mm 3 identifies patients most likely to benefit from ICS [ 2 ].

ICS withdrawal has been raised as a concern in triple therapy studies among participants previously treated with an ICS who discontinued ICS following randomization to a non-ICS containing treatment arm [ 23 ]. In this regard, it is possible that those patients randomized to LAMA/LABA with GFF might have exhibited increased exacerbation rates due to removal of the ICS treatment component. However, a previously published post-hoc analysis of the ETHOS study, which examined the relationship between prior ICS use and benefits of BGF on exacerbations, symptoms, health-related quality of life, and lung function in patients with COPD, indicated there are benefits of BGF versus GFF regardless of ICS use within the 30 days before screening [ 24 ], suggesting ICS withdrawal may not account for the current findings.

Though the current findings seem to suggest benefits of ICS-containing triple therapy versus dual therapy on lung function and exacerbations, observations from a real-world observational study of triple therapy in COPD among ICS-naive patients highlight that triple therapy may have potential negative impacts, including increased incidence of severe pneumonia [ 25 ]. Other studies have also reported increased risk of other respiratory infections and pneumonia associated with ICS [ 26 , 27 , 28 ]. This emphasizes the importance of tailoring treatment plans to individual patient needs.

A few limitations of these analyses should be considered when interpreting these results. As the KRONOS study was not prospectively powered for any of the reported post-hoc analyses, reported P -values are nominal, unadjusted for multiplicity, and provided for descriptive purposes only. In addition, 74% of patients had no exacerbations in the last 12 months in the KRONOS study [ 9 ]. As such, sample sizes for post-hoc analyses of patients with an exacerbation history in the preceding 12 months were relatively small and subject to greater levels of variability. However, as the most compelling and clinically relevant findings from the perspective of current treatment guidelines relate to triple therapy use in patients without exacerbation history in the preceding 12 months, this limitation is not considered to be critical. It should be acknowledged that exacerbations are not a stable phenotype. Even though previous reports suggest the most important determinant and the singular predictive tool of frequent exacerbations is a history of exacerbations [ 29 ], there also patients who experience exacerbations in the previous year who do not experience exacerbations in the following year [ 29 ]. Therefore, when considering the exacerbation-suppressing effects of drug interventions, it is essential to consider the possibility some patients might not have experienced exacerbations even without drug intervention.

In post-hoc analyses of patients with moderate-to-very severe COPD from the KRONOS study, benefits of ICS/LAMA/LABA triple therapy with BGF were observed for lung function versus dual ICS/LABA therapy, and for exacerbation rates versus dual LAMA/LABA therapy in patients with blood EOS count 100 to < 300 cells/mm 3 who had less severe disease and no history of exacerbations in the last 12 months. Taken together, these data may suggest patients with blood EOS count > 100 cells/mm 3 without a recent history of exacerbations and those with moderate disease could benefit from ICS/LAMA/LABA triple therapy with BGF relative to dual therapy with ICS/LABA or LAMA/LABA. Therefore, clinicians should consider a step-up to triple therapy in patients with persistent/worsening symptoms whose blood EOS count is ≥ 100 cells/mm 3 , even if overall disease severity is moderate and there is no recent history of exacerbations. However, these findings require confirmation in adequately controlled studies that are statistically powered to assess these endpoints.

Data availability

Data underlying the findings described in this manuscript may be obtained in accordance with AstraZeneca’s data sharing policy described at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Data for studies directly listed on Vivli can be requested through Vivli at www.vivli.org. Data for studies not listed on Vivli could be requested through Vivli at https://vivli.org/members/enquiries-about-studies-not-listed-on-the-vivli-platform/. The AstraZeneca Vivli member page is also available outlining further details: https://vivli.org/ourmember/astrazeneca/.

Abbreviations

Budesonide/formoterol fumarate dihydrate

Budesonide/glycopyrronium/formoterol fumarate dihydrate

Budesonide/formoterol fumarate dihydrate (via dry-powder inhaler)

Confidence interval

Chronic obstructive pulmonary disease

Forced expiratory volume in 1 s

Global initiative for Chronic Obstructive Lung disease

Glycopyrronium/formoterol fumarate dihydrate

Inhaled corticosteroid

Long-acting β 2 -agonist

Long-acting muscarinic antagonist

Least squares

Modified intention-to-treat

Standard error

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Acknowledgements

The authors would like to thank all the patients and the investigators of the KRONOS study. Medical writing support, under the direction of the authors, was provided by Stephanie Lee, MSc, CMC Connect, a division of IPG Health Medical Communications, funded by AstraZeneca, in accordance with Good Publication Practice (GPP 2022) guidelines [ 30 ]. The sponsor was involved in the study design; the collection, analysis, and interpretation of data; the writing of the report; and in the decision to submit the article for publication.

The KRONOS study was sponsored by AstraZeneca.

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Department of Respiratory Medicine, Nara Medical University, 840 Shijo-cho, Nara, 634-8522, Japan

Shigeo Muro

Division of Respirology, Neurology and Rheumatology, Department of Medicine, Kurume University School of Medicine, Kurume, Japan

Tomotaka Kawayama

Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan

Hisatoshi Sugiura

Respiratory Inhalation, Medical Department, AstraZeneca K.K. Kita-ku, Osaka, Japan

Munehiro Seki

Formerly of AstraZeneca, Durham, NC, USA

Elizabeth A. Duncan

Late RIA Biometrics, AstraZeneca, Gaithersburg, MD, USA

Karin Bowen

BioPharmaceuticals, R&I Medical, AstraZeneca, Cambridge, UK

Jonathan Marshall

Late RIA, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA

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Contributions

SM, TK, HS, MS, JM, and MP are responsible for the conception of the analysis. MP, EAD, and AM contributed to formal analysis. SM and HS contributed to the investigation. SM, HS, and KB contributed to the study methodology. KB acquired resources for this analysis. SM, TK, HS, MS, MP, and KB supervised the analysis. SM, TK, HS, MS, MP, and AM validated the analysis. MP, EAD, and AM contributed to data visualization. SM, TK, HS, MS, MP, EAD, KB, JM, and AM critically reviewed and edited the manuscript.

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Correspondence to Shigeo Muro .

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Competing interests

SM has received lecture fees from AstraZeneca, GlaxoSmithKline, Nippon Boehringer Ingelheim, and Novartis Pharma. TK has received grants from Helios co. Ltd. and lecture fees from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Kyorin, Novartis, Sanofi, and Teijin healthcare. HS has received lecture fees from AstraZeneca, GlaxoSmithKline, Nippon Boehringer Ingelheim, Novartis Pharma, and Sanofi. MS is an employee of AstraZeneca K.K. Kita-ku and owns stock and/or stock options in the company. EAD is a former employee of AstraZeneca and owns stock and/or stock options in the company. KB, JM, AM, and MP are employees of AstraZeneca and own stock and/or stock options in the company.

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Muro, S., Kawayama, T., Sugiura, H. et al. Benefits of budesonide/glycopyrronium/formoterol fumarate dihydrate on lung function and exacerbations of COPD: a post-hoc analysis of the KRONOS study by blood eosinophil level and exacerbation history. Respir Res 25 , 297 (2024). https://doi.org/10.1186/s12931-024-02918-8

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Received : 28 March 2024

Accepted : 17 July 2024

Published : 05 August 2024

DOI : https://doi.org/10.1186/s12931-024-02918-8

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• Blood eosinophils
• Budesonide/glycopyrronium/formoterol fumarate dihydrate (BGF)
• Chronic obstructive pulmonary disease (COPD)
• Disease severity
• Fixed-dose triple therapy

Respiratory Research

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