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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Types of Research – Tips and Examples

Published by Carmen Troy at August 16th, 2021 , Revised On May 8, 2024

“Research is an investigation conducted to seek knowledge and find solutions to scientific and social problems.”

It includes the collection of information from various sources. New research can contribute to existing knowledge.

The types of research can be categorised from the following perspectives;

• Application of the study
• Aim of the research
• Mode of Inquiry
• Research approach

Types of Research According to the Application Perspective

The different types of research, according to the application perspective, include the following.

Basic Research

Primary research is conducted to increase knowledge. It is also known as theoretical research, pure research, and fundamental research. It provides in-depth knowledge about the scientific and logical explanations and their conclusions.

The results of the primary research are used as the base of applied research. It is based on  experiments  and observation. The results of basic research are published in peer-reviewed journals.

• What is global warming?
• How did the Universe begin?
• What do humans get stress?

Applied Research

Applied research is conducted to find solutions for practical problems. It uses the outcomes of basic research as its base. The results of applied research are applied immediately. It includes case studies and experimental research.

Example: Finding the solution to control air pollution.

Descriptive Research

Descriptive research is carried out to describe current issues and programs and provides information about the issue through surveys and various fact-finding methods.

It includes co-relational and comparative methods of research. It follows the Ex post facto research, which predicts the possible reasons behind the situation that has already occurred.

A researcher cannot control its variables and can report only about the current situation and its occurring.

Example: The widespread contaminated diseases in a specific area of the town. An investigation revealed that there was no trash removal system in that area. A researcher can hypothesise that improper trash removal leads to widespread contaminated diseases.

Analytical Research

In analytical research, a researcher can use the existing data, facts, and knowledge and critically analyse and evaluate the sources and material. It attempts to describe why a specific situation exists.

Example: Impact of video games on teenagers.

Explanatory

Explanatory research is conducted to know why and how two or more variables are interrelated. Researchers usually conduct experiments to know the effect of specific changes among two or more variables.

Example: A study to identify the impact of a nutritious diet on pregnant women.

Exploratory

Exploratory research is conducted to understand the nature of the problem. It does not focus on finding evidence or a conclusion of the problem. It studies the problem to explore the research in-depth and covers topics that have not been studied before.

Example: An investigation into the growing crimes against women in India.

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Types of Research According to the Mode of Inquiry

Qualitative research.

Qualitative research  is based on quality, and it looks in-depth at non-numerical data. It enables us to understand the comprehensive details of the problem. The researcher prepares open-ended questions to gather as much information as possible.

• Stress level among men and women.
• The obesity rate among teenagers.

Quantitative Research

Quantitative research is associated with the aspects of measurement, quantity, and extent. It follows the statistical, mathematical, and computational techniques in the form of numerical data such as percentages and statistics. The type of research is conducted on a large population.

• Find out the weight of students of the fifth standard
• Studying in government schools.

Types of Research According to the Research Approach

Longitudinal research.

Researchers collect the information at multiple points in time. Usually, a specific group of participants is selected and examined numerous times at various periods.

Example: If a researcher experiments on a group of women to find out the impact of a low-carb diet within six months. The women’s weight and health check-ups will be done multiple times to get the evidence for the study.

Cross-Sectional Research

Cross-sectional research gathers and compares the information from various groups of the population at the same point. It may not provide the exact reason and relationship between the subjects, but it gives a broad picture of studying multiple groups at the same time.

Example: If a researcher wants to know the number of students studying in a school, he will get to know about the age groups, height, weight, and gender of the students at the same time.

Conceptual Research

It is associated with the concept and theory that describes the hypothesis being studied. It is based on  the inductive  approach of reasoning. It does not follow practical experiments. Philosophers, thinkers, logicians, and theorists use such research to discover new concepts and understand existing knowledge.

Example: Discoveries of Sir Isaac Newton and Einstein.

Empirical Research

It is also known as experimental research, which depends on observation and experience. It is based on the  deductive  approach of reasoning . A researcher focuses on gathering information about the facts and their sources and investigating the existing knowledge. Example: Is intermittent fasting the healthy weight loss option for women?

The researcher can come up with the result that a certain number of women lost weight, and it improved their health. On the other hand, a certain number of women suffering from low blood pressure and diabetes didn’t lose weight, and they faced the negative impacts of intermittent fasting on their health.

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Mixed Methods of Research

When you combine quantitative and qualitative methods of research, the resulting approach becomes mixed methods of research.

Over the last few decades, much of the research in the world of academia has been conducted using mixed methods. Due to its greater legitimacy, this particular technique has gained for several reasons, including the feeling that combining the two types of research can provide holistic and more dependable results.

Here is what mixed methods of research involve:

• Interpreting and investigating the information gathered through quantitative and qualitative techniques.
• There could be more than one stage of research. Depending on the topic of research, occasionally, it would be more appropriate to perform qualitative research in the first stage to figure out and investigate a problem to unveil key themes and conduct quantitative research in stage two of the process for measuring relationships between the themes.

 Tips for Choosing the Right Type of Research

Choosing the right type of research is essential for producing relevant and actionable insights. The choice depends on your objectives, available resources, and the nature of the problem. Here are some tips to help you make the right decision:

Define your Research Objectives Clearly

• Descriptive Research: To describe the characteristics of certain phenomena.
• Exploratory Research: To explore a problem that hasn’t been studied in depth.
• Explanatory (or Causal) Research: To explain patterns of cause and effect.
• Predictive Research: To forecast future outcomes based on patterns.

Understand the Research Methods

• Quantitative Research: Employs structured data collection (e.g., surveys) to generate statistical data.
• Qualitative Research: Uses unstructured or semi-structured data collection methods (e.g., interviews, observations) to understand behaviour, motivations, etc.

Consider the Time Dimension

• Cross-sectional Studies: Capture data at a single point in time.
• Longitudinal Studies: Collect data over extended periods to observe changes.

Evaluate Available Resources

• Budget: Some research methods, like experimental research, may require more funding.
• Time: Exploratory or ethnographic studies may take longer than surveys.
• Expertise: Ensure you or your team possess the skills needed for your chosen research method.

Consider the Nature of the Problem

Complex problems may require mixed-methods research (a combination of qualitative and quantitative).

Review Existing Literature

Review existing literature before settling on a type to see what methodologies were previously employed for similar questions.

Think about Data Collection

Consider the best method to gather data: surveys, interviews, experiments, observations, etc. Your choice affects the research type.

Ethical Considerations:

Ensure your chosen method abides by ethical standards, especially when human subjects are involved.

Generalisability Vs. Depth

Quantitative methods often allow for generalisability, while qualitative methods provide depth and detail.

Pilot Testing

If unsure, run a pilot study to test your chosen method’s feasibility and utility.

Stay Open to Adaptation

Sometimes, initial research can lead to unforeseen insights or complexities. Be prepared to adjust your approach if needed.

Seek Feedback

Discuss your research approach with colleagues, mentors, or experts in the field. They might offer valuable insights or identify potential pitfalls.

Stay Updated

Research methods evolve. Stay updated with the latest techniques, tools, and best practices in your field.

Frequently Asked Questions

What is research.

Research is a systematic inquiry aimed at discovering, interpreting, and revising knowledge about specific phenomena. It involves formulating hypotheses, collecting data, and analysing results to generate new insights or validate existing theories. Conducted in various fields, research can be empirical, theoretical, or experimental and is fundamental for informed decision-making.

What are the different Types of Research?

Different types of research include:

• Descriptive: Describe and analyse phenomena.
• Experimental: Manipulate variables to establish causation.
• Correlational: Examine relationships between variables.
• Qualitative: Gather insights and understanding.
• Quantitative: Use numerical data for analysis.
• Case study, survey, ethnography, and more.

What is research design?

Research design is a structured blueprint for conducting a study, outlining how data will be collected, analysed, and interpreted. It determines the overall strategy and approach to obtain valid, accurate, and reliable results. Research design encompasses choices about type (e.g., experimental, observational), method (qualitative, quantitative), and data collection procedures.

What is survey?

A survey is a research method used to gather data from a predefined group by asking specific questions. Surveys can be conducted using various mediums, such as face-to-face interviews, phone calls, or online questionnaires. They are valuable for collecting descriptive, quantitative, or qualitative information and gauging public opinion or behaviours.

What is research method?

A research method is a systematic approach used by researchers to gather, analyse, and interpret data relevant to their study. It dictates how information is collected and evaluated to answer specific research questions. Methods can be qualitative, quantitative, or mixed and include techniques like surveys, experiments, case studies, and interviews.

What is exploratory research?

Exploratory research is an initial study designed to clarify and define the nature of a problem. It’s used when researchers have a limited understanding of the topic. Instead of seeking definitive answers, it aims to identify patterns, ideas, or hypotheses. Methods often include literature reviews, qualitative interviews, and observational studies.

What is the purpose of research?

The purpose of research is to discover, interpret, or revise knowledge on specific topics or phenomena. It seeks to answer questions, validate theories, or find solutions to problems. Research enhances understanding, informs decision-making, guides policies, drives innovation, and contributes to academic, scientific, and societal advancement. It’s fundamental for evidence-based practices.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst... there’s more!

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

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Neag School of Education

Educational Research Basics by Del Siegle

Types of Research

How do we know something exists? There are a numbers of ways of knowing…

• -Sensory Experience
• -Agreement with others
• -Expert Opinion
• -Scientific Method (we’re using this one)

The Scientific Process (replicable)

• Identify a problem
• Clarify the problem
• Determine what data would help solve the problem
• Organize the data
• Interpret the results

General Types of Educational Research

• Descriptive — survey, historical, content analysis, qualitative (ethnographic, narrative, phenomenological, grounded theory, and case study)
• Associational — correlational, causal-comparative
• Intervention — experimental, quasi-experimental, action research (sort of)

Researchers Sometimes Have a Category Called Group Comparison

• Ex Post Facto (Causal-Comparative): GROUPS ARE ALREADY FORMED
• Experimental: RANDOM ASSIGNMENT OF INDIVIDUALS
• Quasi-Experimental: RANDOM ASSIGNMENT OF GROUPS (oversimplified, but fine for now)

General Format of a Research Publication

• Background of the Problem (ending with a problem statement) — Why is this important to study? What is the problem being investigated?
• Review of Literature — What do we already know about this problem or situation?
• Methodology (participants, instruments, procedures) — How was the study conducted? Who were the participants? What data were collected and how?
• Analysis — What are the results? What did the data indicate?
• Results — What are the implications of these results? How do they agree or disagree with previous research? What do we still need to learn? What are the limitations of this study?

Del Siegle, PhD [email protected]

Last modified 6/18/2019

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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What Is Research? Types and Methods

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Forage puts students first. Our blog articles are written independently by our editorial team. They have not been paid for or sponsored by our partners. See our full  editorial guidelines .

Research is the process of examining a hypothesis to make discoveries. Practically every career involves research in one form or another. Accountants research their client’s history and financial documents to understand their financial situation, and data scientists perform research to inform data-driven decisions. 

In this guide, we’ll go over: 

Research Definition

Types of research , research methods, careers in research, showing research skills on resumes.

Research is an investigation into a topic or idea to discover new information. There’s no all-encompassing definition for research because it’s an incredibly varied approach to finding discoveries. For example, research can be as simple as seeking to answer a question that already has a known answer, like reading an article to learn why the sky is blue. 

Research can also be much broader, seeking to answer questions that have never before been asked. For instance, a lot of research looks for ways to deepen our collective understanding of social, physical, and biological phenomena. Besides broadening humanity’s knowledge, research is a great tool for businesses and individuals to learn new things.

Why Does Research Matter?

While some research seeks to uncover ground-breaking information on its own, other research forms building blocks that allow for further development. For example, Tony Gilbert of the Masonic Medical Research Institute (MMRI) says that Dr. Gordon K. Moe, a co-founder and director of research at MMRI, led early studies of heart rhythms and arrhythmia.  

Gilbert notes that this research “allowed other scientists and innovators to develop inventions like the pacemaker and defibrillator (AED). So, while Dr. Moe did not invent the pacemaker or the AED, the basic research produced at the MMRI lab helped make these devices possible, and this potentially benefitted millions of people.”

Of course, not every researcher is hunting for medical innovations and cures for diseases. In fact, most companies, regardless of industry or purpose, use research every day.  

“Access to the latest information enables you to make informed decisions to help your business succeed,” says Andrew Pickett, trial attorney at Andrew Pickett Law, PLLC.

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Scientific Research

Scientific research utilizes a systematic approach to test hypotheses. Researchers plan their investigation ahead of time, and peers test findings to ensure the analysis was performed accurately. 

Foundational research in sciences, often referred to as “basic science,” involves much of the research done at medical research organizations. Research done by the MMRI falls into this category, seeking to uncover “new information and insights for scientists and medical researchers around the world.”

Scientific research is a broad term; studies can be lab-based, clinical, quantitative, or qualitative. Studies can also switch between different settings and methods, like translational research. 

“Translational research moves research from lab-settings to the settings in which they will provide direct impact (for example, moving bench science to clinical settings),” says Laren Narapareddy, faculty member and researcher at Emory University.

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Historical Research

Historical research involves studying past events to determine how they’ve affected the course of time, using historical data to explain or anticipate current and future events, and filling in gaps in history. Researchers can look at past socio-political events to hypothesize how similar events could pan out in the future.  

However, historical research can also focus on figuring out what actually happened at a moment in time, like reading diary entries to better understand life in England in the 14th century. 

In many ways, research by data, financial, and marketing analysts can be considered historical because these analysts look at past trends to predict future outcomes and make business decisions. 

User Research

User research is often applied in business and marketing to better understand a customer base. Researchers and analysts utilize surveys, interviews, and feedback channels to evaluate their clients’ and customers’ wants, needs, and motivations. Analysts may also apply user research techniques to see how customers respond to a product’s user experience (UX) design and test the efficacy of marketing campaigns. 

>>MORE: See how user and market research inform marketing decisions with Lululemon’s Omnichannel Marketing Job Simulation .

Market Research

Market research utilizes methods similar to user research but seeks to look at a customer base more broadly. Studies of markets take place at an intersection between economic trends and customer decision-making. 

Market research “allows you to stay up-to-date with industry trends and changes so that you can adjust your business strategies accordingly,” says Pickett. 

A primary goal in market research is finding competitive advantages over other businesses. Analysts working in market research may conduct surveys, focus groups, or historical analysis to predict how a demographic will act (and spend) in the future. 

Other Types of Research

The world of research is constantly expanding. New technologies bring new ways to ask and answer unique questions, creating the need for different types of research. Additionally, certain studies or questions may not be easily answered by one kind of research alone, and researchers can approach hypotheses from a variety of directions. So, more niche types of research seek to solve some of the more complex questions. 

For instance, “multidisciplinary research brings experts in different disciplines together to ask and answer questions at the intersection of their fields,” says Narapareddy.

Research doesn’t happen in a bubble, though. To foster better communication between researchers and the public, types of research exist that bring together both scientists and non-scientists. 

“Community-based participatory research is a really important and equitable model of research that involves partnerships among researchers, communities and organizations at all stages of the research process,” says Narapareddy.

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Regardless of the type of research or the study’s primary goal, researchers usually use quantitative or qualitative methods. 

Qualitative Methods

Qualitative research focuses on descriptive information, such as people’s beliefs and emotional responses. Researchers often use focus groups, interviews, and surveys to gather qualitative data. 

This approach to research is popular in sociology, political science, psychology, anthropology, and software engineering . For instance, determining how a user feels about a website’s look isn’t easily put into numbers (quantitative data). So, when testing UX designs, software engineers rely on qualitative research. 

Quantitative Methods

Quantitative research methods focus on numerical data like statistics, units of time, or percentages. Researchers use quantitative methods to determine concrete things, like how many customers purchased a product. Analysts and researchers gather quantitative data using surveys, censuses, A/B tests, and random data sampling. 

Practically every industry or field uses quantitative methods. For example, a car manufacturer testing the effectiveness of new airbag technology looks for quantitative data on how often the airbags deploy properly. Additionally, marketing analysts look for increased sales numbers to see if a marketing campaign was successful. 

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Mixed-Methods

Answering a question or testing a hypothesis may require a mixture of qualitative and quantitative methods. To see if your customers like your website, for instance, you’ll likely apply qualitative methods, like asking them how they feel about the site’s look and visual appeal, and quantitative methods, like seeing how many customers use the website daily. Research that involves qualitative and quantitative methods is called mixed-method research. 

Researching ideas and hypotheses is a common task in many different careers. For example, working in sales requires understanding quantitative research methods to determine if certain actions improve sales numbers. Some research-intensive career paths include:

• Data science
• Investment banking
• Product management
• Civil rights law
• Actuarial science  

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Working in Research

Once you have the fundamentals of researching down, the subject matter may evolve or change over the course of your career. 

“My first research experience was assessing fall risk in firefighters — and I now use multi-omic methods [a type of molecular cell analysis] to understand fertility and reproductive health outcomes in women,” notes Narapareddy.

For those considering a career in research, it’s important to “take the time to explore different research methods and techniques to gain a better understanding of what works best for them,” says Pickett. 

Remember that research is exploratory by nature, so don’t be afraid to fail. 

“The work of scientists who came before us helps guide the path for future research, including both their hits and misses,” says Gilbert.

You can show off your research skills on your resume by listing specific research methods in your skills section. You can also call out specific instances you used research skills, and the impact your research had, in the description of past job or internship experiences. For example, you could talk about a time you researched competitors’ marketing strategies and used your findings to suggest a new campaign. 

Your cover letter is another great place to discuss your experience with research. Here, you can talk about large-scale research projects you completed during school or at previous jobs and explain how your research skills would help you in the job you’re applying for. If you have experience collecting and collating data from research surveys during college, for instance, that can translate into data analysis and organizational skills. 

Grow your skills and get job-ready with Forage’s free job simulations . 

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Types of Research – Methods Explained with Examples

In the ever-evolving world of academia and professional inquiry, understanding the various types of research is crucial for anyone looking to delve into a new study or project. Research, a systematic investigation aimed at discovering and interpreting facts, plays a pivotal role in expanding our knowledge across various fields.

From qualitative research , known for its in-depth analysis of non-numerical data, to quantitative research , which focuses on numerical data and statistical approaches, the spectrum of research types is broad and diverse. We also explore descriptive research , which aims to accurately and systematically describe a population, situation, or phenomenon, and analytical research, which goes a step further to understand the ‘why’ and ‘how’ of a subject.

What is Research?

Research is the process of studying a subject in detail to discover new information or understand it better. This can be anything from studying plants or animals, to learning how people think and behave, to finding new ways to cure diseases. People do research by asking questions, collecting information, and then looking at that information to find answers or learn new things.

Types of Researches Glance

This table provides a quick reference to understand the key aspects of each research type.

Types of Researches Methodology

1. qualitative.

Qualitative research is a methodological approach primarily used in fields like social sciences, anthropology, and psychology. It’s aimed at understanding human behavior and the motivations behind it. Unlike quantitative research that focuses on numbers and statistics, qualitative research delves into the nature of phenomena through detailed, in-depth exploration. Here’s a more detailed explanation:

Definition and Approach: Qualitative research focuses on understanding human behavior and the reasons that govern such behavior. It involves in-depth analysis of non-numerical data like texts, videos, or audio recordings.

Key Features:

• Emphasis on exploring complex phenomena
• Involves interviews, focus groups , and observations
• Generates rich, detailed data that are often subjective

Applications: Widely used in social sciences, marketing, and user experience research.

2. Quantitative Research

Quantitative research is a systematic approach used in various scientific fields to quantify data and generalize findings from a sample to a larger population. This type of research is fundamentally different from qualitative research in several ways:

Definition and Approach: Quantitative research is centered around quantifying data and generalizing results from a sample to the population of interest. It involves statistical analysis and numerical data .

• Relies on structured data collection instruments
• Large sample sizes for generalizability
• Statistical methods to establish relationships between variables

Applications: Common in natural sciences, economics, and market research.

3. Descriptive Research

Definition and Approach: This research type aims to accurately describe characteristics of a particular phenomenon or population.

• Provides detailed insights without explaining why or how something happens
• Involves surveys and observations
• Often used as a preliminary research method

Applications: Used in demographic studies, census, and organizational reporting.

4. Analytical Research

Definition and Approach: Analytical research goes beyond description to understand the underlying reasons or causes.

• Involves comparing data and facts to make evaluations
• Critical thinking is a key component
• Often hypothesis-driven

Applications: Useful in scientific research, policy analysis, and business strategy.

5. Applied Research

Definition and Approach: Applied research focuses on finding solutions to practical problems.

• Direct practical application
• Often collaborative , involving stakeholders
• Results are immediately applicable

Applications: Used in healthcare, engineering, and technology development.

6. Fundamental Research

Definition and Approach: Also known as basic or pure research, it aims to expand knowledge without a direct application in mind.

• Theoretical framework
• Focus on understanding fundamental principles
• Long-term in nature

Applications: Foundational in fields like physics, mathematics, and social sciences.

7. Exploratory Research

Definition and Approach: This type of research is conducted for a problem that has not been clearly defined.

• Flexible and unstructured
• Used to identify potential hypotheses
• Relies on secondary research like reviewing available literature

Applications: Often the first step in social science research and product development.

8. Conclusive Research

Definition and Approach: Conclusive research is designed to provide information that is useful in decision-making.

• Structured and methodical
• Aims to test hypotheses
• Involves experiments, surveys, and testing

Applications: Used in market research, clinical trials, and policy evaluations.

Difference between Qualitative And Quantitative Research

Here is detailed difference between the qualitative and quantitative research –

Understanding the different types of research is crucial for anyone embarking on a research project. Each type has its unique approach, methodology, and application area, making it essential to choose the right type for your specific research question or problem. This guide serves as a starting point for researchers to explore and select the most suitable research method for their needs, ensuring effective and reliable outcomes.

Types of Research – FAQs

What are the 4 main types of research.

There are four main types of Quantitative research:  Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research . attempts to establish cause- effect relationships among the variables. These types of design are very similar to true experiments, but with some key differences.

What are the types of research PDF?

APPLIED RESEARCH BASIC RESEARCH CORRELATIONAL RESEARCH DESCRIPTIVE RESEARCH ETHNOGRAPHIC RESEARCH EXPERIMENTAL RESEARCH. EXPLORATORY RESEARCH

What are the 5 main purpose of research?

The primary purposes of basic research (as opposed to applied research) are  documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge .

Can I be sure that my assignment paper will be plagiarism-free?

You can be 100% sure about the content of your assignment when done by a professional and genuine assignment maker. Most companies focus on providing the best and unique content so that they can attract more returning customers.

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In brief: what types of studies are there.

Last Update: September 8, 2016 ; Next update: 2024.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

• What is the cause of the condition?
• What is the natural course of the disease if left untreated?
• What will change because of the treatment?
• How many other people have the same condition?
• How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

• Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

• How long the study should last
• How many participants are needed
• How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes .

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

• Is the new drug A better than the standard treatment for medical condition X?
• Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
• Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

• How does high blood pressure affect heart health?
• Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

• Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

• Do HPV infections increase the risk of cervical cancer ?
• Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

• Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

• How tall are German men and women at age 20?
• How many people have cancer screening?
• Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

• How do women experience a Cesarean section?
• What aspects of treatment are especially important to men who have prostate cancer ?
• How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs , are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

• German Network for Evidence-based Medicine. Glossar: Qualitative Forschung.  Berlin: DNEbM; 2011. 
• Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003. 
• Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
• Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
• Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. informedhealth.org can provide support for talks with doctors and other medical professionals, but cannot replace them. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

• Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. In brief: What types of studies are there? [Updated 2016 Sep 8].

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What is Quantitative Research Design? Definition, Types, Methods and Best Practices

By Nick Jain

Published on: July 7, 2023

Table of Contents

What is Quantitative Research Design?

Types of quantitative research design, quantitative research design methods, quantitative research design process: 10 key steps, top 11 best practices for quantitative research design.

Quantitative research design is defined as a research method used in various disciplines, including social sciences, psychology, economics, and market research. It aims to collect and analyze numerical data to answer research questions and test hypotheses.

Quantitative research design offers several advantages, including the ability to generalize findings to larger populations, the potential for statistical analysis and hypothesis testing, and the capacity to uncover patterns and relationships among variables. However, it also has limitations, such as the potential for oversimplification of complex phenomena and the reliance on predetermined categories and measurements.

Quantitative research design key elements

Quantitative research design typically follows a systematic and structured approach. It involves the following key elements:

• Research Question: The researcher formulates a clear and specific question that can be answered through quantitative research . The question should be measurable and objective
• Variables: The researcher identifies and defines the variables relevant to the research question. Variables are attributes or characteristics that can be measured or observed. They can be independent variables (factors that are manipulated or controlled) or dependent variables (outcomes or responses that are measured).
• Hypotheses: The researcher develops one or more hypotheses based on the research question. Hypotheses are verifiable statements that make predictions about the association between variables.
• Sampling: The researcher determines the target population and selects a representative sample from that population. The sample should be large enough to provide statistically significant results and should be chosen using appropriate sampling techniques.
• Data Collection: Quantitative research design relies on the collection of numerical data. This can be done through various methods such as surveys, experiments, quantitative observations , or secondary data analysis. Standardized instruments, such as questionnaires or scales, are often used to ensure consistency and reliability.
• Data Analysis: The collected data is analyzed using statistical methods and techniques. Descriptive statistics are used to summarize and describe the data, while inferential statistics are used to draw conclusions and make generalizations about the population based on the sample data.
• Results and Conclusions: The researcher interprets the findings and draws conclusions based on the analysis. The results are typically presented in the form of tables, graphs, and statistical measures, such as means, correlations, or regression coefficients.

There are several types of quantitative research designs, each suited for different research purposes and questions. Here are some common types of quantitative research designs:

• Experimental Design

Experimental design involves the manipulation of an independent variable to observe its effect on a dependent variable while controlling for other variables. Participants are typically randomly assigned to different groups, such as a control group and one or more experimental groups, to compare the outcomes. This approach enables the establishment of cause-and-effect relationships.

• Quasi-Experimental Design

Quasi-experimental design exhibits similarities to experimental design, yet it lacks the random assignment of participants to groups. The researcher takes advantage of naturally occurring groups or pre-existing conditions to compare the effects of an independent variable on a dependent variable. While it doesn’t establish causality as strongly as experimental design, it can still provide valuable insights.

• Survey Research

Survey research involves collecting data through questionnaires or interviews administered to a sample of participants. Surveys allow researchers to gather data on a wide range of variables and can be conducted in various settings, such as online surveys or face-to-face interviews. This design is particularly useful for studying attitudes, opinions, and behaviors within a population.

• Correlational Design

The correlational design investigates the association between two or more variables without engaging in their manipulation. Researchers measure variables and determine the degree and direction of their association using statistical techniques such as correlation analysis. However, correlational research cannot establish causality, only the strength and direction of the relationship.

• Longitudinal Design

Longitudinal design involves collecting data from the same individuals or groups over an extended period. This design allows researchers to study changes and patterns over time, providing insights into the stability and development of variables. Longitudinal studies can be conducted retrospectively (looking back) or prospectively (following participants into the future).

• Cross-sectional Design

Cross-sectional design collects data from a specific population at a single point in time. Researchers examine different variables simultaneously and analyze the relationships among them. This design is often used to gather data quickly and assess the prevalence of certain characteristics or behaviors within a population.

• Ex post facto Design

Ex post facto design involves studying the effects of an independent variable that is beyond the researcher’s control. The researcher selects participants based on their exposure to the independent variable, collecting data retrospectively. This design is useful when random assignment or manipulation of variables is not feasible or ethical.

Learn more: What is Quantitative Market Research?

Quantitative research design methods refer to the specific techniques and approaches used to collect and analyze numerical data in quantitative research . Below are several commonly utilized quantitative research methods:

• Surveys: Surveys involve administering questionnaires or structured interviews to gather data from a sample of participants. Surveys can be implemented through different channels, such as conducting them in person, over the phone, via mail, or utilizing online platforms. Researchers use various question types, such as multiple-choice, Likert scales, or rating scales, to collect quantitative data on attitudes, opinions, behaviors, and demographics.
• Experiments: Experiments involve manipulating one or more independent variables and measuring their effects on dependent variables. To compare outcomes, participants are assigned randomly to various groups, including control and experimental groups. Experimental designs allow researchers to establish cause-and-effect relationships by controlling for confounding factors.
• Observational Studies: Observational studies involve systematically observing and recording behavior, events, or phenomena in natural settings. Researchers can use structured or unstructured quantitative observation methods , depending on the research objectives. Quantitative data can be collected by counting the frequency of specific behaviors or by using coding systems to categorize and analyze observed data.
• Archival Research: Archival research involves analyzing existing data collected for purposes other than the current study. Researchers may use historical documents, government records, public databases, or organizational records to extract data through quantitative research . Archival research allows for large-scale data analysis and can provide insights into long-term trends and patterns.
• Secondary Data Analysis: Similar to archival research, secondary data analysis involves using existing datasets that were collected by other researchers or organizations. Researchers analyze the data to answer new research questions or test different hypotheses. Secondary data sources can include government surveys, social surveys, or market research data.
• Content Analysis: Content analysis is a method used to analyze textual or visual data to identify patterns, themes, or relationships. Researchers code and categorize the content of documents, interviews, articles, or media sources. The coded data is then quantified and statistically analyzed to draw conclusions. Content analysis can be both qualitative and quantitative , depending on the approach used.
• Psychometric Testing: Psychometric testing involves the development and administration of tests or scales to measure psychological constructs, such as intelligence, personality traits, or attitudes. Researchers use statistical techniques to analyze the test data, such as factor analysis, reliability analysis, or item response theory.

Learn more: What is Quantitative Observation?

The quantitative research design process typically involves several key steps to ensure a systematic and rigorous approach to data collection and analysis. While the specific steps may vary depending on the research context, here are the key stages commonly involved in quantitative research design:

1. Identify the Research Problem

Clearly define the research problem or objective. Determine the research question(s) and objectives that you want to address through your quantitative research study. Ensure that your research question is specific, measurable, and aligned with your research goals.

2. Review Existing Literature

Conduct a comprehensive review of existing literature and research on the topic. This helps you understand the current state of knowledge, identify gaps in the literature, and inform your research design. It also helps in selecting appropriate variables and developing hypotheses.

3. Determine Research Design

Based on your research question and objectives, determine the appropriate research design. Decide whether an experimental, quasi-experimental, correlational, or another design would best suit your research goals. Consider factors such as feasibility, ethical considerations, and resources available.

4. Define Variables and Hypotheses

Identify the variables that are pertinent to your research question. Clearly define each variable and its operational definitions (how they will be measured or observed). Develop hypotheses that state the expected relationships between variables based on existing theories or prior research.

5. Determine Sampling Strategy

Define the target population for your study and determine the sampling strategy. Decide on the sample size and the sampling method (e.g., random sampling, stratified sampling, convenience sampling). Ensure that your sample is representative of the population you want to generalize your findings to.

6. Select Data Collection Methods

Choose the appropriate data collection methods to gather data through quantitative research . This can include surveys, experiments, observations, or secondary data analysis. Develop or select validated instruments (e.g., questionnaires, scales) for data collection. Perform a pilot test on the instruments to ensure their reliability and validity.

7. Collect Data

Implement your data collection plan. Administer surveys, conduct experiments, observe participants, or extract data from existing sources. Ensure proper data management and organization to maintain accuracy and integrity. Consider ethical considerations and obtain necessary permissions or approvals.

8. Analyze Data

Perform data analysis using appropriate statistical techniques. Depending on your research design and data characteristics, apply descriptive statistics (e.g., means, frequencies) and inferential statistics (e.g., t-tests, ANOVA, regression analysis) to analyze relationships, test hypotheses, and draw conclusions. Use statistical software for efficient and accurate analysis.

9. Interpret Results

Interpret the findings of your data analysis. Examine statistical outputs, identify significant relationships or patterns, and relate them to your research question and hypotheses. Consider the limitations of your study and address any unexpected or contradictory results.

10. Communicate Findings

Prepare a research report or manuscript that summarizes your research process, findings, and conclusions. Present your results in a clear and understandable manner using appropriate visualizations (e.g., tables, graphs). Consider disseminating your findings through academic publications, conferences, or other appropriate channels.

To ensure the quality and validity of your quantitative research design, here are some best practices to consider:

1. Define Research Objectives Clearly: Initiate the process by providing a clear definition of your research objectives and formulating precise research questions. This clarity will guide your study design and data collection process.

2. Conduct a Comprehensive Literature Review: Thoroughly review existing literature and research on your topic to understand the current state of knowledge. This helps you identify research gaps, refine your research question, and avoid duplication of efforts.

3. Use Validated Measures: When selecting or developing measurement instruments, ensure that they have established validity and reliability. Use validated scales, questionnaires, or tests that have been previously tested and proven to measure the constructs of interest accurately.

4. Pilot Testing: Before implementing your data collection, conduct pilot testing to evaluate the effectiveness of your research instruments and procedures. Pilot testing helps identify any issues or shortcomings and allows for adjustments before the main data collection.

5. Ensure Sample Representativeness: Pay attention to sample selection to ensure it is representative of the target population. Use appropriate sampling techniques and consider factors such as sample size, demographics, and relevant characteristics to enhance generalizability.

6. Minimize Nonresponse Bias: Address potential nonresponse bias by employing strategies to maximize response rates, such as providing clear instructions, using follow-up reminders, and ensuring confidentiality. Analyze nonresponse patterns to assess potential bias and consider appropriate weighting techniques if needed.

7. Maintain Data Quality: Implement robust data management practices to ensure data quality and integrity. Conduct data cleaning, perform checks for outliers and missing values, and document any data transformations or manipulations. Document your data collection procedures thoroughly to facilitate replication and transparency.

8. Employ Appropriate Statistical Analysis: Choose statistical techniques that align with your research design and data characteristics. Use appropriate descriptive and inferential statistics to analyze relationships, test hypotheses, and draw valid conclusions. Ensure proper interpretation and reporting of statistical results.

9. Address Potential Confounding Factors: Identify potential confounding variables that may influence the relationship between your independent and dependent variables. Consider controlling for these factors through study design or statistical techniques to isolate the effects of the variables of interest.

10. Consider Ethical Considerations: Adhere to ethical guidelines and obtain necessary approvals or permissions before conducting your research. Protect participants’ rights, ensure informed consent, maintain confidentiality, and handle data responsibly.

11. Document and Report: Document your research design, data collection, and analysis procedures thoroughly. This helps ensure the transparency and reproducibility of your study. Prepare a comprehensive research report or manuscript that clearly presents your methodology, findings, limitations, and implications.

Learn more: What is Quantitative Research?

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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What Is Data Analysis? (With Examples)

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it? 

Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs). 

Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions? 

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate . Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, L earn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate :

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. 

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months. 

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more : Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].

Get started with Coursera

If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate . Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization . Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate . In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Where is data analytics used ‎.

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance. ‎

What are the top skills for a data analyst? ‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python),  machine learning, and spreadsheets.

Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022 ‎

What is a data analyst job salary? ‎

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location. ‎

Do data analysts need to be good at math? ‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

Learn more: Data Analyst vs. Data Scientist: What’s the Difference? ‎

Article sources

World Economic Forum. " The Future of Jobs Report 2023 , https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.

McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 19, 2024.

Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed March 19, 2024.

Keep reading

Coursera staff.

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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New Research Shows Cancer Patients May Need Less of These Types of Treatment for Better Results

The motto “less is more” may have found some scientific backing when it comes to cancer treatments, according to reports that have recently emerged from the American Society of Clinical Oncology conference in Chicago, hailed as the world’s largest cancer conference.

According to the Associated Press , reports presented at the conference over the weekend showed that scaling back treatment for three types of cancer — ovarian, esophageal, and Hodgkin lymphoma — can improve patient quality of life without compromising outcomes.

This reportedly includes conducting less surgery, less chemotherapy or less radiation.

The study found that reducing these treatments could help patients live longer and feel better.

It’s a far cry from nearly 30 years ago when the focus of cancer research was on increasing treatment intensity.

A notable example involved women with advanced breast cancer undergoing extreme chemotherapy and bone marrow transplants, which proved no more effective than standard chemotherapy and caused significant suffering.

“The good news is that cancer treatment is not only becoming more effective, it’s becoming easier to tolerate and associated with less short-term and long-term complications,” said Dr. William G. Nelson from Johns Hopkins School of Medicine, who was reportedly not involved in the recent research.

According to the American Cancer Society , in 2024, the United States is expected to see over 2 million new cancer cases.

This translates to roughly 5,500 new diagnoses each day.

The National Cancer Institute’s Surveillance, Epidemiology, and End Results Program projects that more than 611,700 people will die from cancer in the United States in 2024 alone.

That is over 1,600 deaths per day​.

The study’s findings, which were revealed during the conference, noted that French researchers found that it’s safe to avoid removing healthy-looking lymph nodes during surgery for advanced ovarian cancer.

The researchers conducted a study — funded by the National Institute of Cancer in France — which included 379 patients in which half had their lymph nodes removed while the other half did not.

It found that after nine years, survival rates were similar between the two groups, but those who underwent less-extensive surgery experienced fewer complications, including the need for blood transfusions.

A German study — funded by the German Research Foundation — involving 438 esophageal cancer patients compared two treatment plans over three years. Half of the participants received chemotherapy and surgery, while the other half received chemotherapy, surgery, and radiation. After the three years, 57 percent of those who received chemotherapy and surgery were alive, compared to 51 percent of those who also received radiation.

Another study — funded by Takeda Oncology — compared two chemotherapy regimens for advanced Hodgkin lymphoma and found that less-intensive treatment was more effective and caused fewer side effects. It included 1,482 people from nine countries. After four years, the disease was controlled in 94 percent of patients receiving the less harsh chemotherapy, compared to 91 percent of those receiving the more intense treatment.

Last month, renowned Australian pathologist and oncologist, Richard Scolyer, announced that he is cancer-free a year after using his own experimental immunotherapy to combat glioblastoma, a typically fatal brain cancer.

His treatment, adapted from his successful work in immunotherapy for melanoma, reportedly led to this remarkable outcome.

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What is peer review?

Reviewers play a pivotal role in scholarly publishing. The peer review system exists to validate academic work, helps to improve the quality of published research, and increases networking possibilities within research communities. Despite criticisms, peer review is still the only widely accepted method for research validation and has continued successfully with relatively minor changes for some 350 years.

Elsevier relies on the peer review process to uphold the quality and validity of individual articles and the journals that publish them.

Peer review has been a formal part of scientific communication since the first scientific journals appeared more than 300 years ago. The Philosophical Transactions opens in new tab/window of the Royal Society is thought to be the first journal to formalize the peer review process opens in new tab/window under the editorship of Henry Oldenburg (1618- 1677).

Despite many criticisms about the integrity of peer review, the majority of the research community still believes peer review is the best form of scientific evaluation. This opinion was endorsed by the outcome of a survey Elsevier and Sense About Science conducted in 2009 opens in new tab/window and has since been further confirmed by other publisher and scholarly organization surveys. Furthermore, a  2015 survey by the Publishing Research Consortium opens in new tab/window , saw 82% of researchers agreeing that “without peer review there is no control in scientific communication.”

To learn more about peer review, visit Elsevier’s free e-learning platform  Researcher Academy opens in new tab/window and see our resources below.

The peer review process

Types of peer review.

Peer review comes in different flavours. Each model has its own advantages and disadvantages, and often one type of review will be preferred by a subject community. Before submitting or reviewing a paper, you must therefore check which type is employed by the journal so you are aware of the respective rules. In case of questions regarding the peer review model employed by the journal for which you have been invited to review, consult the journal’s homepage or contact the editorial office directly.  

Single anonymized review

In this type of review, the names of the reviewers are hidden from the author. This is the traditional method of reviewing and is the most common type by far. Points to consider regarding single anonymized review include:

Reviewer anonymity allows for impartial decisions , as the reviewers will not be influenced by potential criticism from the authors.

Authors may be concerned that reviewers in their field could delay publication, giving the reviewers a chance to publish first.

Reviewers may use their anonymity as justification for being unnecessarily critical or harsh when commenting on the authors’ work.

Double anonymized review

Both the reviewer and the author are anonymous in this model. Some advantages of this model are listed below.

Author anonymity limits reviewer bias, such as on author's gender, country of origin, academic status, or previous publication history.

Articles written by prestigious or renowned authors are considered based on the content of their papers, rather than their reputation.

But bear in mind that despite the above, reviewers can often identify the author through their writing style, subject matter, or self-citation – it is exceedingly difficult to guarantee total author anonymity. More information for authors can be found in our  double-anonymized peer review guidelines .

Triple anonymized review

With triple anonymized review, reviewers are anonymous to the author, and the author's identity is unknown to both the reviewers and the editor. Articles are anonymized at the submission stage and are handled in a way to minimize any potential bias towards the authors. However, it should be noted that: 

The complexities involved with anonymizing articles/authors to this level are considerable.

As with double anonymized review, there is still a possibility for the editor and/or reviewers to correctly identify the author(s) from their writing style, subject matter, citation patterns, or other methodologies.

Open review

Open peer review is an umbrella term for many different models aiming at greater transparency during and after the peer review process. The most common definition of open review is when both the reviewer and author are known to each other during the peer review process. Other types of open peer review consist of:

Publication of reviewers’ names on the article page 

Publication of peer review reports alongside the article, either signed or anonymous 

Publication of peer review reports (signed or anonymous) with authors’ and editors’ responses alongside the article 

Publication of the paper after pre-checks and opening a discussion forum to the community who can then comment (named or anonymous) on the article 

Many believe this is the best way to prevent malicious comments, stop plagiarism, prevent reviewers from following their own agenda, and encourage open, honest reviewing. Others see open review as a less honest process, in which politeness or fear of retribution may cause a reviewer to withhold or tone down criticism. For three years, five Elsevier journals experimented with publication of peer review reports (signed or anonymous) as articles alongside the accepted paper on ScienceDirect ( example opens in new tab/window ).

Read more about the experiment

More transparent peer review

Transparency is the key to trust in peer review and as such there is an increasing call towards more  transparency around the peer review process . In an effort to promote transparency in the peer review process, many Elsevier journals therefore publish the name of the handling editor of the published paper on ScienceDirect. Some journals also provide details about the number of reviewers who reviewed the article before acceptance. Furthermore, in order to provide updates and feedback to reviewers, most Elsevier journals inform reviewers about the editor’s decision and their peers’ recommendations. 

Article transfer service: sharing reviewer comments

Elsevier authors may be invited to  transfer  their article submission from one journal to another for free if their initial submission was not successful. 

As a referee, your review report (including all comments to the author and editor) will be transferred to the destination journal, along with the manuscript. The main benefit is that reviewers are not asked to review the same manuscript several times for different journals. 

Tools and resources

Interesting reads.

Chapter 2 of Academic and Professional Publishing, 2012, by Irene Hames in 2012 opens in new tab/window

"Is Peer Review in Crisis?" Perspectives in Publishing No 2, August 2004, by Adrian Mulligan opens in new tab/window

“The history of the peer-review process” Trends in Biotechnology, 2002, by Ray Spier opens in new tab/window

Reviewers’ Update articles

Peer review using today’s technology

Lifting the lid on publishing peer review reports: an interview with Bahar Mehmani and Flaminio Squazzoni

How face-to-face peer review can benefit authors and journals alike

Innovation in peer review: introducing “volunpeers”

Results masked review: peer review without publication bias

Elsevier Researcher Academy modules

The certified peer reviewer course opens in new tab/window

Transparency in peer review opens in new tab/window

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is cybercrime? How to protect yourself

What is cybercrime?

Cybercrime is criminal activity that either targets or uses a computer, a computer network or a networked device. Most cybercrime is committed by cybercriminals or hackers who want to make money. However, occasionally cybercrime aims to damage computers or networks for reasons other than profit. These could be political or personal.

Cybercrime can be carried out by individuals or organizations. Some cybercriminals are organized, use advanced techniques and are highly technically skilled. Others are novice hackers.

What are the types of cybercrime?

Types of cybercrime include:

• Email and internet fraud.
• Identity fraud (where personal information is stolen and used).
• Theft of financial or card payment data.
• Theft and sale of corporate data.
• Cyberextortion (demanding money to prevent a threatened attack).
• Ransomware attacks (a type of cyberextortion).
• Cryptojacking (where hackers mine cryptocurrency using resources they do not own).
• Cyberespionage (where hackers access government or company data).
• Interfering with systems in a way that compromises a network.
• Infringing copyright.
• Illegal gambling.
• Selling illegal items online.
• Soliciting, producing, or possessing child pornography.

Cybercrime involves one or both of the following:

• Criminal activity targeting computers using viruses and other types of malware .
• Criminal activity using  computers to commit other crimes.

Cybercriminals that target computers may infect them with malware to damage devices or stop them working. They may also use malware to delete or steal data. Or cybercriminals may stop users from using a website or network or prevent a business providing a software service to its customers, which is called a Denial-of-Service (DoS) attack.

Cybercrime that  uses  computers to commit other crimes may involve using computers or networks to spread malware, illegal information or illegal images.

Cybercriminals are often doing both at once. They may target computers with viruses first and then use them to spread malware to other machines or throughout a network. Some jurisdictions recognize a third category of cybercrime which is where a computer is used as an accessory to crime. An example of this is using a computer to store stolen data.

Examples of cybercrime

Here are some famous examples of different types of cybercrime attack used by cybercriminals:

1. Malware attacks

A malware attack is where a computer system or network is infected with a computer virus or other type of malware. A computer compromised by malware could be used by cybercriminals for several purposes. These include stealing confidential data, using the computer to carry out other criminal acts, or causing damage to data.

A famous example of a malware attack was the WannaCry ransomware attack, a global cybercrime committed in May 2017. WannaCry is a type of ransomware, malware used to extort money by holding the victim’s data or device to ransom. The ransomware targeted a vulnerability in computers running Microsoft Windows.

When the WannaCry ransomware attack hit, 230,000 computers were affected across 150 countries. Users were locked out of their files and sent a message demanding that they pay a Bitcoin ransom to regain access.

Worldwide, the WannaCry cybercrime is estimated to have caused $4 billion in financial losses. To this day, the attack stands out for its sheer size and impact.

2. Phishing

A phishing campaign is when spam emails, or other forms of communication, are sent with the intention of tricking recipients into doing something that undermines their security. Phishing campaign messages may contain infected attachments or links to malicious sites, or they may ask the receiver to respond with confidential information.

A famous example of a phishing scam took place during the World Cup in 2018. According to our report, 2018 Fraud World Cup , the World Cup phishing scam involved emails that were sent to football fans. These spam emails tried to entice fans with fake free trips to Moscow, where the World Cup was being hosted. People who opened and clicked on the links contained in these emails had their personal data stolen. 

Another type of phishing campaign is known as spear-phishing . These are targeted phishing campaigns which try to trick specific individuals into jeopardizing the security of the organization they work for. 

Unlike mass phishing campaigns, which are very general in style, spear-phishing messages are typically crafted to look like messages from a trusted source. For example, they are made to look like they have come from the CEO or the IT manager. They may not contain any visual clues that they are fake.

3. Distributed DoS attacks

Distributed DoS attacks (DDoS) are a type of cybercrime attack that cybercriminals use to bring down a system or network. Sometimes connected IoT (Internet of Things) devices are used to launch DDoS attacks.

A DDoS attack overwhelms a system by using one of the standard communication protocols it uses to spam the system with connection requests. Cybercriminals who are carrying out cyberextortion may use the threat of a DDoS attack to demand money. Alternatively, a DDoS may be used as a distraction tactic while another type of cybercrime takes place.

A famous example of this type of attack is the  2017 DDoS attack on the UK National Lottery website . This brought the lottery’s website and mobile app offline, preventing UK citizens from playing. The reason behind the attack remains unknown, however, it is suspected that the attack was an attempt to blackmail the National Lottery.

Impact of cybercrime

Generally, cybercrime is on the rise. According to Accenture’s State of Cybersecurity Resilience 2021 report , security attacks increased 31% from 2020 to 2021. The number of attacks per company increased from 206 to 270 year on year. Attacks on companies affect individuals too since many of them store sensitive data and personal information from customers.

A single attack – whether it’s a data breach, malware, ransomware or DDoS attack - costs companies of all sizes an average of $200,000, and many affected companies go out of business within six months of the attack, according to  insurance company Hiscox .

Javelin Strategy & Research published an Identity Fraud Study in 2021 which found that identity fraud losses for the year totalled $56 billion.

For both individuals and companies, the impact of cybercrime can be profound – primarily financial damage, but also loss of trust and reputational damage.

How to report a cybercrime

File a report with the Internet Crime Complaint Center (IC3) as soon as possible. Visit ic3.gov for more information.

Contact Action Fraud as soon as possible – find out more on their website here.

Europol has a useful website here which collates the relevant cybercrime reporting links for each EU member state.

You can find information about how to report cybercrime in the UAE on this official website here .

The Australian Cyber Security Centre has information about how to report a cybercrime here.

• How to protect yourself against cybercrime

Given its prevalence, you may be wondering how to stop cybercrime? Here are some sensible tips to protect your computer and your personal data from cybercrime:

1. Keep software and operating system updated

Keeping your software and operating system up to date ensures that you benefit from the latest security patches to protect your computer.

2. Use anti-virus software and keep it updated

Using anti-virus or a comprehensive internet security solution like  Kaspersky Premium is a smart way to protect your system from attacks. Anti-virus software allows you to scan, detect and remove threats before they become a problem. Having this protection in place helps to protect your computer and your data from cybercrime, giving you piece of mind. Keep your antivirus updated to receive the best level of protection.

3. Use strong passwords

Be sure to use strong passwords that people will not guess and do not record them anywhere. Or use a reputable password manager to generate strong passwords randomly to make this easier.

4. Never open attachments in spam emails

A classic way that computers get infected by malware attacks and other forms of cybercrime is via email attachments in spam emails. Never open an attachment from a sender you do not know.

5. Do not click on links in spam emails or untrusted websites

Another way people become victims of cybercrime is by clicking on links in spam emails or other messages, or unfamiliar websites. Avoid doing this to stay safe online.

6. Do not give out personal information unless secure

Never give out personal data over the phone or via email unless you are completely sure the line or email is secure. Make certain that you are speaking to the person you think you are. 

7. Contact companies directly about suspicious requests

If you are asked for personal information or data from a company who has called you, hang up. Call them back using the number on their official website to ensure you are speaking to them and not a cybercriminal. Ideally, use a different phone because cybercriminals can hold the line open. When you think you’ve re-dialed, they can pretend to be from the bank or other organization that you think you are speaking to.

8. Be mindful of which website URLs you visit

Keep an eye on the URLs you are clicking on. Do they look legitimate? Avoid clicking on links with unfamiliar or URLs that look like spam. If your internet security product includes functionality to secure online transactions, ensure it is enabled before carrying out financial transactions online.

9. Keep an eye on your bank statements

Spotting that you have become a victim of cybercrime quickly is important. Keep an eye on your bank statements and query any unfamiliar transactions with the bank. The bank can investigate whether they are fraudulent.

A good antivirus will protect you from the threat of cybercrime.  Learn more about Kaspersky Premium.

Further reading:

• How to protect your data online by using a password manager
• What to do if you’ve been a victim of a phishing attack
• Ransomware protection: how to keep your data safe in 2024

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Takeda’s TAK-861 Phase 2b Late-Breaking Data Presentations at SLEEP 2024 Demonstrate Clinically Meaningful Impact of Oral Orexin Agonist in Narcolepsy Type 1 Compared to Placebo

Phase 2b Trial Demonstrated Statistically Significant and Clinically Meaningful Improvements Across Primary and all Secondary Endpoints up to 8 Weeks

TAK-861 is the First Oral Orexin Receptor 2 Agonist to Potentially Address the Underlying Pathophysiology of NT1

Safety Results Indicated TAK-861 is Generally Safe and Well Tolerated

Phase 3 Trials of TAK-861 to be Initiated in 1H FY2024

OSAKA, Japan and CAMBRIDGE, Massachusetts, June 3, 2024 – Takeda ( TSE: 4502/NYSE:TAK ) will present today positive results from its Phase 2b trial of TAK-861 in narcolepsy type 1 (NT1) as late-breaking data presentations at SLEEP 2024, the 38th annual meeting of the American Academy of Sleep Medicine and the Sleep Research Society. TAK-861 is an investigational oral orexin receptor 2 (OX2R) agonist and, based on the results, has the potential to provide transformative efficacy in addressing the overall disease burden in people with NT1. The randomized, double-blind, placebo-controlled, multiple dose trial, TAK-861-2001 ( NCT05687903 Go to https://classic.clinicaltrials.gov/ct2/show/NCT05687903?term=TAK-861&draw=2&rank=3 ), in 112 patients with NT1 demonstrated statistically significant and clinically meaningful improvements across primary and secondary endpoints, with efficacy sustained over 8 weeks of treatment.*

NT1 is a chronic, rare neurological central disorder of hypersomnolence caused by a significant loss of orexin neurons, resulting in low levels of orexin neuropeptides in the brain and cerebrospinal fluid. No currently approved treatments target the underlying pathophysiology of NT1. People with NT1 suffer from excessive daytime sleepiness (EDS), cataplexy (sudden loss of muscle tone), disrupted nighttime sleep, hypnagogic and hypnopompic hallucinations and sleep paralysis. These debilitating symptoms lead to a markedly reduced quality of life and can severely impact job performance, academic achievement and personal relationships. TAK-861 is designed to address the orexin deficiency in NT1 by selectively stimulating the orexin receptor 2.

The presentation highlights results from the Phase 2b trial including:

The primary endpoint demonstrated statistically significant and clinically meaningful increased sleep latency on the Maintenance of Wakefulness Test (MWT) versus placebo across all doses (LS mean difference versus placebo all p ≤0.001). Improvements were sustained over 8 weeks.

Consistent results were achieved in the key secondary endpoints including the Epworth Sleepiness Scale (ESS) and Weekly Cataplexy Rate (WCR), demonstrating significantly improved subjective measures of sleepiness and cataplexy (sudden loss of muscle tone) frequency versus placebo that were also sustained over 8 weeks.

The majority of NT1 patients in the trial were found to be within normative ranges for MWT and ESS by the end of the 8-week treatment period as a result of these sustained improvements.

The majority of the participants who completed the trial enrolled in the long-term extension (LTE) study with some patients reaching one year of treatment.

The trial also included additional exploratory endpoints that showed meaningful improvements in narcolepsy symptoms and functioning according to most participants. These data will also be presented in poster presentations at SLEEP and at future scientific congresses.

The dataset showed that TAK-861 was generally safe and well tolerated during the study, with no treatment-related serious treatment-emergent adverse events (TEAEs) or discontinuations due to TEAEs.

No cases of hepatotoxicity or visual disturbances were reported in the Phase 2b trial or in the ongoing LTE study. The most common TEAEs were insomnia, urinary urgency and frequency, and salivary hypersecretion. Most TEAEs were mild to moderate in severity, and most started within 1-2 days of treatment and were transient.

“In this trial, TAK-861's profile balanced efficacy and safety with the potential to establish a new standard of care for people with NT1,” said Sarah Sheikh, M.D., M.Sc., B.M., B.Ch., MRCP, Head, Neuroscience Therapeutic Area Unit and Head, Global Development at Takeda. “We are dedicated to investigating the full potential of orexin biology and advancing TAK-861 to late-stage clinical trials, with the ultimate goal of delivering a potential first-in-class treatment that can make a meaningful difference for patients.”

Based on these results, and in consultation with global health authorities, Takeda plans to initiate global Phase 3 trials of TAK-861 in NT1 in the first half of its fiscal year 2024. The Phase 2b data also supported the recent Breakthrough Therapy designation for TAK-861 for the treatment of EDS in NT1 from the U.S. Food and Drug Administration (FDA). Breakthrough Therapy designation is a process designed to expedite the development and review of a drug that is intended to treat a serious or life-threatening condition, for which preliminary clinical evidence indicates that the drug may demonstrate substantial improvement over available therapies on at least one clinically significant endpoint.

Takeda will be hosting a call to discuss these data this evening, June 3, at 7:30 p.m. CT for investors and analysts. Presentation slides and a virtual meeting link will be available here .

Additional presentations on TAK-861 will be shared during the SLEEP 2024 poster presentation session on Tuesday, June 4, from 10:00 to 11:45 a.m. CT, assessing function and health-related quality of life in individuals with NT1, as well as patient satisfaction with TAK-861 treatment. There is no change in Takeda’s full year consolidated forecast for the fiscal year ending March 31, 2025 (FY2024), announced on May 9, 2024.

About Takeda’s Orexin Franchise

Takeda is advancing the field of orexin therapeutics with a multi-asset franchise offering tailored treatments to unlock the full potential of orexin. Orexin is a key regulator of the sleep-wake cycle and is involved in other essential functions, including respiration and metabolism. TAK-861 is the leading program in this franchise. The company is also progressing multiple orexin agonists in patient populations with normal levels of orexin neuropeptides and other indications where orexin biology is implicated. This includes TAK-360, an oral OX2R agonist being investigated for narcolepsy type 2 and idiopathic hypersomnia, which recently initiated a Phase 1 trial and received Fast Track designation from the U.S. FDA, and danavorexton (TAK-925), an intravenously administered OX2R agonist being investigated in a Phase 2 trial in patients with moderate to severe obstructive sleep apnea undergoing general anesthesia.

About Takeda

Takeda is focused on creating better health for people and a brighter future for the world. We aim to discover and deliver life-transforming treatments in our core therapeutic and business areas, including gastrointestinal and inflammation, rare diseases, plasma-derived therapies, oncology, neuroscience and vaccines. Together with our partners, we aim to improve the patient experience and advance a new frontier of treatment options through our dynamic and diverse pipeline. As a leading values-based, R&D-driven biopharmaceutical company headquartered in Japan, we are guided by our commitment to patients, our people and the planet. Our employees in approximately 80 countries and regions are driven by our purpose and are grounded in the values that have defined us for more than two centuries. For more information, visit www.takeda.com .

* The topline results were announced on February 8, 2024, via a press release, “Takeda Intends to Rapidly Initiate the First Global Phase 3 Trials of TAK-861, an Oral Orexin Agonist, in Narcolepsy Type 1 in First Half of Fiscal Year 2024."

Media Contacts:

Japanese media.

Yuko Yoneyama

[email protected]

+81 70-2610-6609

U.S. and International Media

Rachel Wallace

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Forward-Looking Statements

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Medical Information

This press release contains information about products that may not be available in all countries, or may be available under different trademarks, for different indications, in different dosages, or in different strengths. Nothing contained herein should be considered a solicitation, promotion or advertisement for any prescription drugs including the ones under development.

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Research, Documentation and Knowledge Management Intern

New Delhi, INDIA

Type of Contract :

Starting date :.

27-Jun-2024

Application Deadline :

15-Jun-24 (Midnight New York, USA)

Post Level :

Duration of initial contract :.

3 months (extendable up to 6 months)

Time left :

Languages required :.

English  

Expected Duration of Assignment :

UNDP is committed to achieving workforce diversity in terms of gender, nationality and culture. Individuals from minority groups, indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the strictest confidence. UNDP does not tolerate sexual exploitation and abuse, any kind of harassment, including sexual harassment, and discrimination. All selected candidates will, therefore, undergo rigorous reference and background checks.

Organizational Context

UNDP has been working in India since 1951 in almost all areas of human development. Together with the Government of India and development partners, we have worked towards eradicating poverty, reducing inequalities, strengthening local governance, enhancing community resilience, protecting the environment, supporting policy initiatives and institutional reforms, and accelerating sustainable development for all.

With projects and programmes in every state and union territory in India, UNDP works with national and subnational government, and diverse development actors to deliver people-centric results, particularly for the most vulnerable and marginalized communities. As the integrator for collective action on the Sustainable Development Goals (SDGs) within the UN system, we are committed to supporting the Government of India’s national development vision and priorities and accelerating the achievement of the SDGs for the people and the planet. UNDP India’s new Country Programme (2023-2027) builds on our prior work and aims to provide an integrated approach to development solutions in three strategic portfolios: 

• Strong, accountable and evidence-led institutions for accelerated achievement of the SDGs.
• Enhanced economic opportunities and social protection to reduce inequality, with a focus on the marginalized.
• Climate-smart solutions, sustainable ecosystems and resilient development for reduced vulnerability.

South-South cooperation, gender equality and social inclusion are promoted across the pillars. The programme is supported by a framework of renewed partnerships and blended finance solutions, strategic innovation and accelerator labs, and data and digital architecture.

You are invited to join a team of future-smart development professionals to support India in achieving the national and globally agreed goals. As part of the UNDP team, your focus will be to work with diverse stakeholders to find country-specific solutions that lead to sustainable development and reach those furthest behind first.

Project Background:  The United Nzations Development Programme (UNDP) under its Business and Human Rights (B+HR) project aims to enable Sustainable Economic Development, through the Protect, Respect and Remedy Framework of the UN Guiding Principles (UNGPs) on Business & Human Rights (BHR). The project promotes the agenda on BHR by working with governments, businesses and civil society in the implementation of the UNGPs at the national level, through dialogue, training, research, civil society action, awareness raising and capacity building activities. The overall objective of the project is to prevent, mitigate and address human rights abuses in business operations and enable a more level playing field for businesses that demonstrate respect for human rights. In so doing, the action aims to strengthen human rights conditions in business operations and supply chains, facilitating sustainable economic growth while also promoting multilateralism. 

Supervisor: Business and Human Rights Specialist 

Duties and Responsibilities

Duties and Responsibilities:

Research Analysis and Programme Support

• Conduct research to develop a blueprint for business and human rights (BHR)/responsible business priorities in India, vis-à-vis uptake and promotion of responsible business practices and sustainability in line with India’s national and international obligations.
• Conduct supervised secondary research on specific topics in the area of sustainability, green economy, just transition and related topics of relevance to the BHR agenda;
• Provide support in writing of project related documents (concept notes, proposals, etc.), and synthesize documents and other relevant knowledge products;
• Identify key areas of intervention for engagement with Private Sector (with particular focus on MSMEs) on decent
• Work, sustainable economic growth, human rights and environmental sustainability and reducing inequalities.
• Identify opportunities for engagement on environment and human rights due diligence tools and processes with the Private Sector in particular MSMEs, sector-wise; and
• Work closely with UN agencies working in India to implement and further responsible business framework.

Communications and Knowledge Management:

• Contribute to development of policy briefs, speeches, talking points and background/communications materials and other advocacy activities for strengthening communication and relationship with varied stakeholders on the BHR agenda;
• Proof reading and editing of reports, books, booklets or any other type of publications/communications material;
• Develop and implement the social media strategy for the programme; Initiate and/or assist in submitting best practices, stories and experiences on responsible business practices for wide dissemination through multiple channels;
• Prepare PowerPoint presentations, visual representations, infographics as required;
• Assist in the organization of webinars/conferences/events/forums; and
• Support the team with other relevant tasks.

Engagement with Private Sector, with focus on MSMEs:

• Build networks for UNDP and support work with Industry Associations, national and regional (FISME, CII, FICCI etc.) on Outreach, training, monitoring, reporting and evaluation as well as any other relevant processes; Coordinate and support capacity building efforts (including conceptualization) on responsible and sustainable business practices for private sector including roll-out and implementation including facilitating awareness on national and international policies; and
• Work closely with other UN system-wide coordination groups, industry associations and civil society actors on the agenda. 

Competencies

Competencies:

• Strong sense of collaborative work, excellent communication and interpersonal skills;
• Keen attention to detail;
• Works collaboratively with colleagues to achieve organizational goals;solicits input by genuinely valuing others’ ideas and expertise;
• Is willing to learn from others and take initiative;
• Proactively works on tasks and delivers under given timelines. 

Required Skills and Experience

Applicants to the UNDP internship programme must at the time of application meet one of the following requirements:

• Currently in the final year of a Bachelor’s degree; or
• Currently enrolled in a postgraduate programme (such as a Master’s programme or higher); or have graduated no longer than 1 year ago from a university degree or equivalent studies.
• Field of study: Law, International Development, Human Rights, Social Sciences or equivalent
• Has sound knowledge of Microsoft office (Office 365, SharePoint Online)
• Ability to produce high quality documents with quick turnaround time.
• Good team player and ability to adapt to change in work environment.
• Ability to work within tight deadlines and adjust accordingly as new priorities arise.
• Familiarity with use of Canva and other design and data analysis tools. 

Language Requirements: 

• Fluency in English, while understanding of Hindi is desirable.

Application procedure:

The application should contain: Current and complete CV in English; Details of 3 Academic Referees and recommendation letter from the University Candidates who are selected must submit the following documents, upon selection: Official letter from the University confirming enrolment in their undergraduate or graduate-level degree programme.

Proof of medical and life/accident insurance valid for the location in which the internship will be carriedout. Selected intern must have medical and life insurance. Kindly note, UNDP only accepts interns for a minimum of 6 weeks and a maximum of 6 months.

Working arrangements:

• Interns will be paid stipend according to UNDP Internship Policy, if it is not financially supported by any institution or programme, such as a university, government foundation orscholarship programme. A stipend intended to help cover basic daily expenses related to the internship, such as meals and transportation at the duty station. The stipend will be paid on monthly basis and part-time internship arrangements are prorated accordingly.
• Interns are responsible for securing adequate medical insurance for the duration of the internship. UNDP accepts no responsibility for costs arising from accidents and/or illness or death incurred duringthe internship. Interns must provide proof of enrolment in health insurance plan. Interns are expected to work full time, but flexibility is allowed for education programmes.
• The intern is responsible for obtaining necessary visas and arranging travel to and from the duty stationwhere the internship will be performed.
• Interns are not eligible to apply for, or be appointed to, any post in UNDP during the period of the internship.
• Interns are not staff members and may not represent UNDP in any official capacity.
• The purpose of the internship is not to lead to further employment with UNDP, but to complement an intern’s studies. Therefore, there should be no expectation of employment at the end of an internship.
• The intern will be evaluated at the end of the internship.

IMPORTANT: Family relationships are required to be disclosed in order to avoid real or perceived family influence or conflict of interest, within UNDP. If the candidate/ hiring manager has not disclosedthat he/she was related to an individual employed by UNDP in whatever contractual modality and irrespective of the nature of the family relationship, this may constitute a basis for withdrawing the offer of internship or, ifthe internship hasstarted, to terminate it without notice or indemnity.

How Technology Marketers Lead the Way in AI Experimentation [New Research]

• by Robert Rose
• | Published: February 21, 2024
• | Trends and Research

In a 20-minute demo, the sales engineer deftly clicked through the interface, configured a new image set, assigned the metadata, and set the rights-management properties. He logged in as different users to demonstrate a sophisticated workflow. Then, he published an asset and showed how the system presented it in channel-specific formats.

No fewer than five times, he mentioned how “easy” it was for the business user to do what once only experts could do.

I interrupted him. “Here’s the thing,” I said, “That isn’t easy for someone who doesn’t understand what you’re doing.”  

As technology marketers, what you offer to the world seems simple from the outside. You provide a new tool to help your customers do something they couldn’t before acquiring it. But the more amazing the thing they can now do, the more skilled they usually need to be at using it.

Said another way, a chainsaw in the hands of a lumberjack is a simple tool. But in my hands? It’s an ER trip waiting to happen.  

Today, businesses work with some of the most sophisticated digital technology and interfaces in any industry. But that doesn’t make technology easier to market. It still involves a complex and difficult journey made more challenging by how quickly things change.

We looked at the answers of 272 technology marketers who responded to CMI’s July 2023 survey to find out. (For more information about the full study of 1,084 marketers, see B2B Content Marketing Benchmarks, Budgets, and Trends .)

One not-too-surprising finding: Tech content marketers outpace their marketing peers in AI use. More than two-thirds (79%) of tech marketers say they use AI compared with 72% of B2B marketers as a whole and only 58% of enterprise marketers. 

What else to expect this year? Technology marketers say they’ll focus on these things in 2024:

• Increasing traffic, leads, and sales
• Nurturing existing clients
• Leveraging AI for content creation while ensuring authentic, quality content
• Enhancing content creation processes and systems
• Focusing on thought leadership
• Measuring content performance and value.

The most common trends mentioned center around:

• AI proliferation in content creation — with concerns about authenticity and oversaturation.
• Authenticity and uniqueness — valuing human-created content that stands out from AI-generated noise, prioritizing quality over quantity.
• AI’s impact on SEO and content ranking — changes in SEO strategies to accommodate AI algorithms, emphasizing FAQ-oriented and thought leadership content.
• Increased personalization — hyper-personalized content delivery using AI-driven tools to cater to individual personas or niche segments.

Let’s look deeper into the research sponsored by Foundry , an IDG, Inc. company .

Table of contents

Team structure

Content marketing challenges

Use of content types, distribution channels, and paid channels

Social media use

Content management and operations

Measurement and goals

Success factors

Budgets and spending

Action steps

Methodology

Ai use: 79% of technology marketers use generative tools.

Many respondents predicted a rise in the use of AI to generate content . In fact, 79% say they already use AI for content-related tasks. How?

More than half (53%) use generative AI to brainstorm new topics. Around half use the tools to write drafts (48%) and research headlines and keywords (43%). Fewer said they use AI to outline assignments (29%), proofread (19%), generate graphics/images (10%), and create videos (7%) and audio (7%).

Most don’t pay for generative AI tools (yet)

Of those using generative AI tools, 88% use free tools (e.g., ChatGPT ). Thirty-seven percent use tools embedded in their content creation/management systems, and 30% pay for tools like Writer and Jasper .

AI in content remains mostly ungoverned

When asked if their organizations have guidelines for using generative AI tools, 26% said yes, 63% said no, and 11% were unsure.

“Change, especially rapid change, is not something most organizations adapt to quickly,” says Yadin Porter de León , global content marketing executive. “The capabilities of generative AI tools currently represent a form of rapid change that very few people can even grasp. So, it’s no surprise that very few companies have created or communicated guidelines for its use … because they don’t know how.” 

Yadin says marketers should:

• Educate your team members so that they can be, at the very least, AI-literate.
• Establish an AI council to organize activities across the organization.
• Establish clear policies and guidelines for using AI.
• Identify use cases for the business and run pilot projects guided by those principles.

How AI is changing SEO

In the open-ended responses, several respondents predicted AI’s significant impact on SEO. How will AI’s integration in search engines shift technology marketers’ SEO strategy? Here’s what we found:

Twenty-seven percent say they’re not doing any of those things, while 29% say they’re unsure, suggesting that many may be doing little to nothing.

Now is the time to act.

Ryan Brock , chief solution officer at DemandJump, says, “The days of building a keyword list based on metrics like search volume are over … at least for now. Until the dust settles and we collectively figure out what kinds of answers we trust Bard (now known as Gemini) with and which ones will always require a more thoughtful comparison of sources to find, we’ve got to use topical authority as the North Star for our tactical content decisions.”

Ryan thinks of it this way: “I’m still going to be working to answer basic questions as part of my pillar content strategy, but I also acknowledge that answering them works more to build a foundation of topical authority than to drive immediate, convertible traffic.

“Those traffic and conversion-driving queries will become harder to come by than they’ve ever been, so when I find one I need to rank well for, I should be able to do so quickly and efficiently. Competing on a query-by-query level just doesn’t work when every business in a sector sees the same dwindling number of targets.

“Building interconnected, ‘choose-your-own-adventure’ style networks of pillar content is the best way to lay the proper topical authority foundation so you can rank fast when you find a term that’s ripe for true thought leadership.”

Team structure: How does the work get done?

Generative AI isn’t the only issue affecting content marketing these days. We also asked marketers about how they organize their teams .

Among larger technology companies (100-plus employees), more than half (54%) say content requests go through a centralized content team. Others say each department/brand produces its own content (22%), and the departments/brand/products share responsibility (20%). Three percent indicate other, while 1% say they outsource it.

Content strategies integrate with marketing, comms, and sales

Seventy percent say their organizations integrate content strategy into the overall marketing sales/communication/strategy, and 2% say it’s integrated into another strategy. Fourteen percent say content in marketing is a stand-alone strategy, and 4% say it’s a stand-alone strategy for all content produced by the company. Eight percent say they don’t have a content strategy. The remaining 2% say other or are unsure.

Employee churn means new teammates; content teams experience enlightened leadership

Thirty-three percent of technology marketers say team members resigned in the last year, 28% say team members were laid off, and about half (51%) say they had new team members acclimating to their ways of working.

While team members come and go, the understanding of content doesn’t. Fifty percent strongly agree, and 30% somewhat agree that the leader to whom their content team reports understands the work they do. Only 14% disagree. The remaining 6% neither agree nor disagree.

And remote work seems well-tolerated: Only 21% say collaboration is challenging due to remote or hybrid work.

Content marketing challenges: The right content, lack of resources

Most technology marketers (61%) cite creating the right content for their audience as a challenge.

Other content creation challenges include differentiating content (58%), creating content consistently (49%), creating quality content (43%), optimizing for SEO (43%), creating enough content to keep up with internal demand (40%), and creating content that requires technical skills (36%). One in four (25%) say they are challenged to create enough content to keep up with external demand.

Other hurdles

The most frequently cited non-creation challenge, by far, is a lack of resources (66%), followed by aligning content efforts across sales and marketing (52%) and aligning content with the buyer’s journey (52%). Forty-five percent say they have difficulty accessing subject matter experts, and 44% say they are challenged with workflow issues/content approval processes. Only 28% cite keeping up with new technologies as a challenge, 27% pick a lack of strategy, 12% say keeping up with privacy rules, and 13% point to tech integration issues.

Use of content types, distribution channels, and paid channels: Staying at the top

We asked technology marketers about the types of content they produce, their distribution channels , and paid content promotion. We also asked which formats and channels produce the best results.

Popular content types and formats

As in the previous year, the three most popular content types are short articles/posts (96%), case studies/customer stories (93%), and videos (90%). Eighty-two percent use thought leadership e-books/white papers, 81% use long articles/posts, 63% use data visualizations/visual content, 62% use product/technical data sheets, and 56% use research reports. Less than half of technology marketers use brochures (45%), interactive content (35%), livestreaming content (34%), and audio content (31%).

Effective content types and formats

Which formats are most effective?

Fifty-nine percent say case studies/customer stories deliver some of the best results. Almost as many (57%) name thought leadership e-books/white papers. Slightly more than half say research reports (53%) and videos (51%).

Popular content distribution channels

Regarding the channels used to distribute content, 90% use blogs and social media platforms (organic), followed by webinars (79%), email newsletters (78%), and email (74%). Sixty-four percent use in-person events, and 58% use digital events.

Less frequently used channels include:

• Microsites (40%)
• Podcasts (30%)
• Hybrid events (24%)
• Branded online communities (23%)
• Digital magazines (21%)
• Direct mail (19%)
• Online learning platform (18%)
• Print magazines (12%)
• Mobile apps (7%)
• Separate content brands (3%).

Effective content distribution channels

Which channels perform the best? Most surveyed tech marketers point to webinars (56%) and in-person events (53%). Forty-four percent say blogs, 43% pick email, and 37% say social media platforms (organic).

Popular paid content channels

When technology marketers pay to promote content , which channels do they invest in? Ninety-three percent use paid content distribution channels.

Of those, 77% use social media advertising/promoted posts, 71% use sponsorships, 70% use search engine marketing/pay-per-click, and 66% use digital display advertising. Around one in three use native advertising (38%) and partner emails (33%). Far fewer invest in print display advertising (11%).

Effective paid content channels

Search engine marketing and pay-per-click produce good results, according to 61% of tech marketers. Fifty-three percent say sponsorships deliver good results, followed by social media advertising/promoted posts (43%) and partner emails (34%).

Social media use: One platform rises way above

When asked which organic social media platforms deliver the best value for their organization, technology marketers (92%) pick LinkedIn. Twenty-seven percent cite YouTube as a top performer, 18% say Facebook, and 10% pick Instagram and Twitter. Only 1% cite TikTok.

It makes sense that 73% say they increased their use of LinkedIn over the last 12 months, while only 36% boosted their YouTube presence, 19% increased Instagram use, 15% grew their Facebook presence, 12% increased X use, and 9% increased TikTok use.

Which platforms are marketers giving up? Did you guess X? You’re right — 34% of marketers say they decreased their X use. Twenty-four percent reduced their use of Facebook, with 14% decreasing on Instagram and YouTube, 3% pulling back on TikTok, and only 2% decreasing their use of LinkedIn.

Interestingly, we saw a significant rise in technology marketers who use TikTok: 17% say they use the platform, which is triple from last year (5%).

Content management and operations: The right tech isn’t a guarantee

To explore how teams manage content, we asked tech marketers about their technology use and investments and the challenges they face scaling their content .

Content management technology

Among the technologies used to manage content, technology marketers point to:

• Analytics tools (82%)
• Social media publishing/analytics (73%)
• Email marketing software (71%)
• Content creation/calendaring/collaboration/workflow (66%)
• Content management system (58%)
• Customer relationship management system (57%)
• Marketing automation system (38%)
• Sales enablement platform (30%)
• Digital asset management (DAM) system (24%)

But having technology doesn’t mean it’s the right technology (or its capabilities are used). Only 29% say they have the right tech to manage content across the organization. Thirty-two percent say they have the technology but aren’t using its potential, and 28% say they haven’t acquired the right technology. Eleven percent are unsure.

Even so, 40% of technology marketers say their organization is likely to invest in new technology in 2024; however, another 39% say it’s unlikely. Twenty-one percent say their organization is neither likely nor unlikely to invest.

Scaling content production

This year, we introduced a new question to understand what challenges technology marketers face while scaling content production .

Almost half (49%) say it’s a lack of communication across silos, and the same number say it’s not enough content repurposing. Thirty-one percent say they have no structured content production process, and 29% say they lack an editorial calendar with clear deadlines. Six percent say scaling is not a current focus.

Among the other hurdles are difficulty locating digital content assets (19%), translation/localization issues (17%), technology issues (15%), and no style guide (13%).

Measurement and goals: Generating sales and revenue rises

Almost half (43%) of technology marketers agree their organization measures content performance effectively — but the same amount (43%) disagree. Thirteen percent neither agree nor disagree. Only 1% say they don’t measure content performance.

The four most frequently used metrics to assess content performance are conversions (77%), website traffic (73%), email engagement (72%), and website engagement (70%). Sixty percent say they rely on quality of leads, 58% use social media analytics, 55% rely on search ratings, and 52% say quantity of leads. Less than half use tracking the cost to acquire a lead, subscriber, and/or customer (32%) and email subscribers (31%).

The most common challenge measuring content performance experienced by technology marketers is integrating/correlating data across multiple platforms (88%), followed by extracting insights from data (82%), tying performance data to goals (81%), organizational goal setting (73%), and lack of training (71%).

Among the goals assisted by content marketing, 82% of technology marketers say it created brand awareness in the last 12 months. Eighty percent say it helped generate demands/leads, 71% say it helped nurture subscribers/audiences/leads, and 61% say it helped generate sales revenue (up from 48% the previous year).

Less than half say it helped grow loyalty with existing clients/customers (46%), grow a subscribed audience (42%), and reduce customer support costs (14%).

Success factors: Know your audience

To separate top performers from the pack, we asked technology marketers to assess the success of their content marketing.

Twenty-seven percent rate the organization’s success as extremely or very successful. Another 58% report moderate success, and 15% feel minimally or not at all successful.

The most common factor for successful technology marketers is knowing their audience (81%).

That success factor makes sense because “creating the right content for our audience” is the top challenge. Top-performing content marketers prioritize knowing their audiences to create the right content for those audiences.

Top performers also set goals that align with their organization’s objectives (74%), have a documented strategy (67%), and collaborate with other teams (64%). Thought leadership (62%) and effectively measuring and demonstrating content performance (59%) also help top technology performers reach content marketing success.

Several other dimensions identify the differentiators of top technology performers:

• 75% of the most successful say they’re backed by leaders who understand their work. In contrast, just 50% of all tech respondents feel their leaders understand.
• They’re less likely to report a lack of resources (57% of top performers say they lack resources vs. 66% of all tech content marketers).
• They’re more likely to have the right content management technologies. About half (49%) of the top performers say they have the technology they need, compared with 29% of all tech marketers.
• Nearly three-fourths (74%) of top performers say they measure content performance effectively, compared with 43% of the whole set of tech marketers.
• They are more likely to use content marketing successfully to create brand awareness (96% vs. 82%), nurture subscribers/audiences/leads (82% vs. 71%), generate sales/revenue (81% vs. 61%), grow loyalty with existing customers/clients (64% vs. 46%), and grow a subscribed audience (57% vs. 42%).

Little difference exists between top performers and all respondents when it comes to the adoption of generative AI tools and related guidelines.

Budgets and spending: Holding steady

To explore budget plans for 2024, we asked technology marketers about their knowledge of their organization’s budget/budgeting process for content marketing. Of the 53% who have knowledge of their budgets, we followed up to assess the specifics.

Content marketing as a percentage of total marketing spend

Here’s what they say about the total marketing budget (excluding salaries):

• 18% say content marketing consumes at least one-fourth of the total marketing budget.
• More than one in three (37%) indicate that 10% to 24% of the marketing budget goes to content marketing.
• Just under half (45%) say less than 10% of the marketing budget goes to content marketing.

Content marketing budget outlook for 2024

Forty-eight percent think their content marketing budget will increase this year compared with 2023, whereas 39% think it will stay the same. Only 7% think it will decrease, and 6% are unsure.

Where will the budget go?

Next, we asked where respondents plan to increase their spending.

Sixty-nine percent of technology marketers say they would increase their investment in video, followed by in-person events (60%), thought leadership content (54%), webinars (41%), paid advertising (40%), online community building (27%), audio content (22%), digital events (21%), and hybrid events (11%).

Of course, content doesn’t exist in a vacuum.

Kami Buckner , HPC solutions marketing manager at Dell Technologies, notes that content must be integrated into a larger plan and support the customer journey by driving them to other content.

“Videos, in-person events, and thought leadership content may rank similarly in this survey because they are often developed to complement each other,” she says. “Thought leadership content is an important component of any event plan, and videos are an effective peripheral asset that can engage an audience to generate interest in downloading long-form thought leadership pieces, generate excitement before and after events, and be displayed at the event.”

For example, Dell developed a 15-second video to use on social media to drive viewers to a landing page, which hosted the 60-second sizzle reel to promote an upcoming event. We also:

• Launched thought leadership content around the video and event.
• Created and displayed another video at the event highlighting key points in the new thought leadership content that could be shared post-event.
• Developed a virtual reality experience for the event that built credibility for Dell Technologies, both hosting and serving thought leadership content.

Action steps: What tech marketers should do

These results from tech marketers reflect what we find across other B2B organizations. You should know your audience, lean into brand awareness, integrate data across the buyer’s journey, and invest more in thought leadership, events, and video.

But what should you prioritize as a technology marketer? Given where you are in 2024 and your relationship with modern technology, put these three things at the top of your list:

• Lean into the brand and develop relationships early and often . Marketing pundits laugh at the idea of technology companies developing “relationships” with audiences and buyers. They cynically surmise, “Nobody wants a relationship with their hydraulic actuator provider.” That may be true, but it doesn’t relieve you from trying. Today’s world makes it more imperative that technology companies differentiate, not just by providing the fastest, cheapest, easiest, or most scalable product on the market. You must also differentiate by helping customers be the best versions of themselves. As I used to say to my marketing team, “Our competitive advantage isn’t that we help people become better digital asset managers; it’s that we help digital asset managers become better people.” That leads to the second action.
• Grow owned media’s importance in your products and services. A differentiating strategy provides a reason for people to engage with you outside the small portion of their lives that goes into their buying journey. Owned media experiences create an ecosystem of value for your customers in the pre- and post-buying journey, foster a competitive advantage, and celebrate the complexity your products inherently induce. Yes, your tools are complex and sophisticated and do amazing things. Let you be the source of how to do those things better than anyone else.
• Connect first-party data. How you connect your buyers’ digital interactions will be the fabric that develops better relationships with your customers. If you understand their true intentions, needs, and wants, and more importantly, how they evolve, you can optimize every experience that leads and follows a sale. Of course, you likely must make big changes to implement any one or all three of these action steps. An audit, where you examine all your customers’ content-driven experiences along their journey, can help you develop a plan for which ones to keep, which should change, and which should be sunset for good. Creating this ecosystem gives you the power to transform what is seen as overly complex and hard into a worthwhile evolution and innovation. Your technology is not there to make the customer’s journey “easy” — it’s there to make it “worth it!” 

For the 14 th annual content marketing survey, CMI and MarketingProfs surveyed 1,080 recipients around the globe in July 2023, representing a range of industries, functional areas, and company sizes. The survey was emailed to a sample of marketers using lists from CMI and MarketingProfs.

This article presents the findings from the 272 respondents, mostly from North America, who indicated their organization is a technology company and that they are either content marketers or work in marketing, communications, or other roles involving content.

Of this group, 84% represent B2B companies, while 13% work for B2B+B2C brands, and 3% say they work for a tech company of a different nature. Thirty-six percent work at businesses with more than 1,000 employees, 36% work at places with between 100 and 999 employees, 23% work for brands with 10 to 99 employees, and 5% work at tech companies with between one and nine employees.

Thanks to the survey participants who made this research possible and everyone who helped disseminate these findings throughout the content marketing industry.

Interested in sharing the key takeaways from this report with your team or associates? You can download a summary here (registration required).

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Cover image by Joseph Kalinowski/Content Marketing Institute

About Content Marketing Institute

Content Marketing Institute (CMI) exists to do one thing: advance the practice of content marketing through online education and in-person and digital events. We create and curate content experiences that teach marketers and creators from enterprise brands, small businesses, and agencies how to attract and retain customers through compelling, multichannel storytelling. Global brands turn to CMI for strategic consultation, training, and research. Organizations from around the world send teams to Content Marketing World, the largest content marketing-focused event, the Marketing Analytics & Data Science (MADS) conference, and CMI virtual events, including ContentTECH Summit. Our community of 215,000+ content marketers shares camaraderie and conversation. CMI is organized by Informa Connect. To learn more, visit www.contentmarketinginstitute.com .

About Foundry, an IDG Inc. company

Foundry helps companies bring their visions to reality through a combination of media, marketing technologies and proprietary data on a global scale. Our intent data and martech platforms are powered by data from an owned and operated ecosystem of global editorial brands, awards, and events, all engineered and integrated to drive marketing campaigns for technology companies. Foundry is dedicated to generating and innovating with data, driving demand for technology marketers with 38 offices in markets around the globe. Foundry is a wholly owned subsidiary of International Data Group, Inc. ( IDG ), the world’s leading tech media, data, research and marketing services company. To learn more about Foundry, visit www.foundryco.com .

Robert Rose