example of descriptive vs analytical research

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 (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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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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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.

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/methodology/types-of-research/

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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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What are Analytical Study Designs?

• Research Process
• Peer Review

Analytical study designs can be experimental or observational and each type has its own features. In this article, you'll learn the main types of designs and how to figure out which one you'll need for your study.

Updated on September 19, 2022

A study design is critical to your research study because it determines exactly how you will collect and analyze your data. If your study aims to study the relationship between two variables, then an analytical study design is the right choice.

But how do you know which type of analytical study design is best for your specific research question? It's necessary to have a clear plan before you begin data collection. Lots of researchers, sadly, speed through this or don't do it at all.

When are analytical study designs used?

A study design is a systematic plan, developed so you can carry out your research study effectively and efficiently. Having a design is important because it will determine the right methodologies for your study. Using the right study design makes your results more credible, valid, and coherent.

Descriptive vs. analytical studies

Study designs can be broadly divided into either descriptive or analytical.

Descriptive studies describe characteristics such as patterns or trends. They answer the questions of what, who, where, and when, and they generate hypotheses. They include case reports and qualitative studies.

Analytical study designs quantify a relationship between different variables. They answer the questions of why and how. They're used to test hypotheses and make predictions.

Experimental and observational

Analytical study designs can be either experimental or observational. In experimental studies, researchers manipulate something in a population of interest and examine its effects. These designs are used to establish a causal link between two variables.

In observational studies, in contrast, researchers observe the effects of a treatment or intervention without manipulating anything. Observational studies are most often used to study larger patterns over longer periods.

Experimental study designs

Experimental study designs are when a researcher introduces a change in one group and not in another. Typically, these are used when researchers are interested in the effects of this change on some outcome. It's important to try to ensure that both groups are equivalent at baseline to make sure that any differences that arise are from any introduced change.

In one study, Reiner and colleagues studied the effects of a mindfulness intervention on pain perception . The researchers randomly assigned participants into an experimental group that received a mindfulness training program for two weeks. The rest of the participants were placed in a control group that did not receive the intervention.

Experimental studies help us establish causality. This is critical in science because we want to know whether one variable leads to a change, or causes another. Establishing causality leads to higher internal validity and makes results reproducible.

Experimental designs include randomized control trials (RCTs), nonrandomized control trials (non-RCTs), and crossover designs. Read on to learn the differences.

Randomized control trials

In an RCT, one group of individuals receives an intervention or a treatment, while another does not. It's then possible to investigate what happens to the participants in each group.

Another important feature of RCTs is that participants are randomly assigned to study groups. This helps to limit certain biases and retain better control. Randomization also lets researchers pinpoint any differences in outcomes to the intervention received during the trial. RTCs are considered the gold standard in biomedical research and are considered to provide the best kind of evidence.

For example, one RCT looked at whether an exercise intervention impacts depression . Researchers randomly placed patients with depressive symptoms into intervention groups containing different types of exercise (i.e., light, moderate, or strong). Another group received usual medications or no exercise interventions.

Results showed that after the 12-week trial, patients in all exercise groups had decreased depression levels compared to the control group. This means that by using an RCT design, researchers can now safely assume that the exercise variable has a positive impact on depression.

However, RCTs are not without drawbacks. In the example above, we don't know if exercise still has a positive impact on depression in the long term. This is because it's not feasible to keep people under these controlled settings for a long time.

Advantages of RCTs

• It is possible to infer causality
• Everything is properly controlled, so very little is left to chance or bias
• Can be certain that any difference is coming from the intervention

Disadvantages of RCTs

• Expensive and can be time-consuming
• Can take years for results to be available
• Cannot be done for certain types of questions due to ethical reasons, such as asking participants to undergo harmful treatment
• Limited in how many participants researchers can adequately manage in one study or trial
• Not feasible for people to live under controlled conditions for a long time

Nonrandomized controlled trials

Nonrandomized controlled trials are a type of nonrandomized controlled studies (NRS) where the allocation of participants to intervention groups is not done randomly . Here, researchers purposely assign some participants to one group and others to another group based on certain features. Alternatively, participants can sometimes also decide which group they want to be in.

For example, in one study, clinicians were interested in the impact of stroke recovery after being in an enriched versus non-enriched hospital environment . Patients were selected for the trial if they fulfilled certain requirements common to stroke recovery. Then, the intervention group was given access to an enriched environment (i.e. internet access, reading, going outside), and another group was not. Results showed that the enriched group performed better on cognitive tasks.

NRS are useful in medical research because they help study phenomena that would be difficult to measure with an RCT. However, one of their major drawbacks is that we cannot be sure if the intervention leads to the outcome. In the above example, we can't say for certain whether those patients improved after stroke because they were in the enriched environment or whether there were other variables at play.

Advantages of NRS's

• Good option when randomized control trials are not feasible
• More flexible than RCTs

Disadvantages of NRS's

• Can't be sure if the groups have underlying differences
• Introduces risk of bias and confounds

Crossover study

In a crossover design, each participant receives a sequence of different treatments. Crossover designs can be applied to RCTs, in which each participant is randomly assigned to different study groups.

For example, one study looked at the effects of replacing butter with margarine on lipoproteins levels in individuals with cholesterol . Patients were randomly assigned to a 6-week butter diet, followed by a 6-week margarine diet. In between both diets, participants ate a normal diet for 5 weeks.

These designs are helpful because they reduce bias. In the example above, each participant completed both interventions, making them serve as their own control. However, we don't know if eating butter or margarine first leads to certain results in some subjects.

Advantages of crossover studies

• Each participant serves as their own control, reducing confounding variables
• Require fewer participants, so they have better statistical power

Disadvantages of crossover studies

• Susceptible to order effects, meaning the order in which a treatment was given may have an effect
• Carry-over effects between treatments

Observational studies

In observational studies, researchers watch (observe) the effects of a treatment or intervention without trying to change anything in the population. Observational studies help us establish broad trends and patterns in large-scale datasets or populations. They are also a great alternative when an experimental study is not an option.

Unlike experimental research, observational studies do not help us establish causality. This is because researchers do not actively control any variables. Rather, they investigate statistical relationships between them. Often this is done using a correlational approach.

For example, researchers would like to examine the effects of daily fiber intake on bone density . They conduct a large-scale survey of thousands of individuals to examine correlations of fiber intake with different health measures.

The main observational studies are case-control, cohort, and cross-sectional. Let's take a closer look at each one below.

Case-control study

A case-control is a type of observational design in which researchers identify individuals with an existing health situation (cases) and a similar group without the health issue (controls). The cases and the controls are then compared based on some measurements.

Frequently, data collection in a case-control study is retroactive (i.e., backwards in time). This is because participants have already been exposed to the event in question. Additionally, researchers must go through records and patient files to obtain the records for this study design.

For example, a group of researchers examined whether using sleeping pills puts people at risk of Alzheimer's disease . They selected 1976 individuals that received a dementia diagnosis (“cases”) with 7184 other individuals (“controls”). Cases and controls were matched on specific measures such as sex and age. Patient data was consulted to find out how much sleeping pills were consumed over the course of a certain time.

Case-control is ideal for situations where cases are easy to pick out and compare. For instance, in studying rare diseases or outbreaks.

Advantages of case-control studies

• Feasible for rare diseases
• Cheaper and easier to do than an RCT

Disadvantages of case-control studies

• Relies on patient records, which could be lost or damaged
• Potential recall and selection bias

Cohort study (longitudinal)

A cohort is a group of people who are linked in some way. For instance, a birth year cohort is all people born in a specific year. In cohort studies, researchers compare what happens to individuals in the cohort that have been exposed to some variable compared with those that haven't on different variables. They're also called longitudinal studies.

The cohort is then repeatedly assessed on variables of interest over a period of time. There is no set amount of time required for cohort studies. They can range from a few weeks to many years.

Cohort studies can be prospective. In this case, individuals are followed for some time into the future. They can also be retrospective, where data is collected on a cohort from records.

One of the longest cohort studies today is The Harvard Study of Adult Development . This cohort study has been tracking various health outcomes of 268 Harvard graduates and 456 poor individuals in Boston from 1939 to 2014. Physical screenings, blood samples, brain scans and surveys were collected on this cohort for over 70 years. This study has produced a wealth of knowledge on outcomes throughout life.

A cohort study design is a good option when you have a specific group of people you want to study over time. However, a major drawback is that they take a long time and lack control.

Advantages of cohort studies

• Ethically safe
• Allows you to study multiple outcome variables
• Establish trends and patterns

Disadvantages of cohort studies

• Time consuming and expensive
• Can take many years for results to be revealed
• Too many variables to manage
• Depending on length of study, can have many changes in research personnel

Cross-sectional study

Cross-sectional studies are also known as prevalence studies. They look at the relationship of specific variables in a population in one given time. In cross-sectional studies, the researcher does not try to manipulate any of the variables, just study them using statistical analyses. Cross-sectional studies are also called snapshots of a certain variable or time.

For example, researchers wanted to determine the prevalence of inappropriate antibiotic use to study the growing concern about antibiotic resistance. Participants completed a self-administered questionnaire assessing their knowledge and attitude toward antibiotic use. Then, researchers performed statistical analyses on their responses to determine the relationship between the variables.

Cross-sectional study designs are ideal when gathering initial data on a research question. This data can then be analyzed again later. By knowing the public's general attitudes towards antibiotics, this information can then be relayed to physicians or public health authorities. However, it's often difficult to determine how long these results stay true for.

Advantages of cross-sectional studies

• Fast and inexpensive
• Provides a great deal of information for a given time point
• Leaves room for secondary analysis

Disadvantages of cross-sectional studies

• Requires a large sample to be accurate
• Not clear how long results remain true for
• Do not provide information on causality
• Cannot be used to establish long-term trends because data is only for a given time

So, how about your next study?

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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

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

Rakesh Aggarwal

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

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

INTRODUCTION

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

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

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

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

Exposure (or intervention) and outcome variables

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

Observational versus interventional (or experimental) studies

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

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

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

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

Descriptive versus analytical studies

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

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

Directionality of study designs

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

Prospective versus retrospective study designs

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

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

Classification of study designs

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

Classification of research study designs

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

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

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Home » Descriptive Analytics – Methods, Tools and Examples

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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What Is Descriptive Analytics? 5 Examples

• 09 Nov 2021

Data analytics is a valuable tool for businesses aiming to increase revenue, improve products, and retain customers. According to research by global management consulting firm McKinsey & Company, companies that use data analytics are 23 times more likely to outperform competitors in terms of new customer acquisition than non-data-driven companies. They were also nine times more likely to surpass them in measures of customer loyalty and 19 times more likely to achieve above-average profitability.

Data analytics can be broken into four key types :

• Descriptive, which answers the question, “What happened?”
• Diagnostic , which answers the question, “Why did this happen?”
• Predictive , which answers the question, “What might happen in the future?”
• Prescriptive , which answers the question, “What should we do next?”

Each type of data analysis can help you reach specific goals and be used in tandem to create a full picture of data that informs your organization’s strategy formulation and decision-making.

Descriptive analytics can be leveraged on its own or act as a foundation for the other three analytics types. If you’re new to the field of business analytics, descriptive analytics is an accessible and rewarding place to start.

Access your free e-book today.

What Is Descriptive Analytics?

Descriptive analytics is the process of using current and historical data to identify trends and relationships. It’s sometimes called the simplest form of data analysis because it describes trends and relationships but doesn’t dig deeper.

Descriptive analytics is relatively accessible and likely something your organization uses daily. Basic statistical software, such as Microsoft Excel or data visualization tools , such as Google Charts and Tableau, can help parse data, identify trends and relationships between variables, and visually display information.

Descriptive analytics is especially useful for communicating change over time and uses trends as a springboard for further analysis to drive decision-making .

Here are five examples of descriptive analytics in action to apply at your organization.

Related: 5 Business Analytics Skills for Professionals

5 Examples of Descriptive Analytics

1. traffic and engagement reports.

One example of descriptive analytics is reporting. If your organization tracks engagement in the form of social media analytics or web traffic, you’re already using descriptive analytics.

These reports are created by taking raw data—generated when users interact with your website, advertisements, or social media content—and using it to compare current metrics to historical metrics and visualize trends.

For example, you may be responsible for reporting on which media channels drive the most traffic to the product page of your company’s website. Using descriptive analytics, you can analyze the page’s traffic data to determine the number of users from each source. You may decide to take it one step further and compare traffic source data to historical data from the same sources. This can enable you to update your team on movement; for instance, highlighting that traffic from paid advertisements increased 20 percent year over year.

The three other analytics types can then be used to determine why traffic from each source increased or decreased over time, if trends are predicted to continue, and what your team’s best course of action is moving forward.

2. Financial Statement Analysis

Another example of descriptive analytics that may be familiar to you is financial statement analysis. Financial statements are periodic reports that detail financial information about a business and, together, give a holistic view of a company’s financial health.

There are several types of financial statements, including the balance sheet , income statement , cash flow statement , and statement of shareholders’ equity. Each caters to a specific audience and conveys different information about a company’s finances.

Financial statement analysis can be done in three primary ways: vertical, horizontal, and ratio.

Vertical analysis involves reading a statement from top to bottom and comparing each item to those above and below it. This helps determine relationships between variables. For instance, if each line item is a percentage of the total, comparing them can provide insight into which are taking up larger and smaller percentages of the whole.

Horizontal analysis involves reading a statement from left to right and comparing each item to itself from a previous period. This type of analysis determines change over time.

Finally, ratio analysis involves comparing one section of a report to another based on their relationships to the whole. This directly compares items across periods, as well as your company’s ratios to the industry’s to gauge whether yours is over- or underperforming.

Each of these financial statement analysis methods are examples of descriptive analytics, as they provide information about trends and relationships between variables based on current and historical data.

3. Demand Trends

Descriptive analytics can also be used to identify trends in customer preference and behavior and make assumptions about the demand for specific products or services.

Streaming provider Netflix’s trend identification provides an excellent use case for descriptive analytics. Netflix’s team—which has a track record of being heavily data-driven—gathers data on users’ in-platform behavior. They analyze this data to determine which TV series and movies are trending at any given time and list trending titles in a section of the platform’s home screen.

Not only does this data allow Netflix users to see what’s popular—and thus, what they might enjoy watching—but it allows the Netflix team to know which types of media, themes, and actors are especially favored at a certain time. This can drive decision-making about future original content creation, contracts with existing production companies, marketing, and retargeting campaigns.

4. Aggregated Survey Results

Descriptive analytics is also useful in market research. When it comes time to glean insights from survey and focus group data, descriptive analytics can help identify relationships between variables and trends.

For instance, you may conduct a survey and identify that as respondents’ age increases, so does their likelihood to purchase your product. If you’ve conducted this survey multiple times over several years, descriptive analytics can tell you if this age-purchase correlation has always existed or if it was something that only occurred this year.

Insights like this can pave the way for diagnostic analytics to explain why certain factors are correlated. You can then leverage predictive and prescriptive analytics to plan future product improvements or marketing campaigns based on those trends.

Related: What Is Marketing Analytics?

5. Progress to Goals

Finally, descriptive analytics can be applied to track progress to goals. Reporting on progress toward key performance indicators (KPIs) can help your team understand if efforts are on track or if adjustments need to be made.

For example, if your organization aims to reach 500,000 monthly unique page views, you can use traffic data to communicate how you’re tracking toward it. Perhaps halfway through the month, you’re at 200,000 unique page views. This would be underperforming because you’d like to be halfway to your goal at that point—at 250,000 unique page views. This descriptive analysis of your team’s progress can allow further analysis to examine what can be done differently to improve traffic numbers and get back on track to hit your KPI.

Using Data to Identify Relationships and Trends

“Never before has so much data about so many different things been collected and stored every second of every day,” says Harvard Business School Professor Jan Hammond in the online course Business Analytics . “In this world of big data, data literacy —the ability to analyze, interpret, and even question data—is an increasingly valuable skill.”

Leveraging descriptive analytics to communicate change based on current and historical data and as a foundation for diagnostic, predictive, and prescriptive analytics has the potential to take you and your organization far.

Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.

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