what is research study example

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

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

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

Types of Research Methods

Research process: how to conduct research, top 10 best practices for conducting research in 2023, what is research.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base.

This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

Enhance Your Research

Collect feedback and conduct research with IdeaScale’s award-winning software

Elevate Research And Feedback With Your IdeaScale Community!

IdeaScale is an innovation management solution that inspires people to take action on their ideas. Your community’s ideas can change lives, your business and the world. Connect to the ideas that matter and start co-creating the future.

Copyright © 2024 IdeaScale

Privacy Overview

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

Affiliations

Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
• Criterion sampling-selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling-selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

• Internal validity--- Credibility
• External validity---Transferability
• Reliability---Dependability
• Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
• Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

• Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

• Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

In this Page

Bulk download.

• Bulk download StatPearls data from FTP

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Similar articles in PubMed

• Suicidal Ideation. [StatPearls. 2024] Suicidal Ideation. Harmer B, Lee S, Duong TVH, Saadabadi A. StatPearls. 2024 Jan
• Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. [Cochrane Database Syst Rev. 2022] Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, et al. Cochrane Database Syst Rev. 2022 Feb 1; 2(2022). Epub 2022 Feb 1.
• Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012). [Phys Biol. 2013] Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012). Foffi G, Pastore A, Piazza F, Temussi PA. Phys Biol. 2013 Aug; 10(4):040301. Epub 2013 Aug 2.
• Review Evidence Brief: The Effectiveness Of Mandatory Computer-Based Trainings On Government Ethics, Workplace Harassment, Or Privacy And Information Security-Related Topics [ 2014] Review Evidence Brief: The Effectiveness Of Mandatory Computer-Based Trainings On Government Ethics, Workplace Harassment, Or Privacy And Information Security-Related Topics Peterson K, McCleery E. 2014 May
• Review Public sector reforms and their impact on the level of corruption: A systematic review. [Campbell Syst Rev. 2021] Review Public sector reforms and their impact on the level of corruption: A systematic review. Mugellini G, Della Bella S, Colagrossi M, Isenring GL, Killias M. Campbell Syst Rev. 2021 Jun; 17(2):e1173. Epub 2021 May 24.

Recent Activity

• Qualitative Study - StatPearls Qualitative Study - StatPearls

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

What (Exactly) Is A Research Proposal?

A simple explainer with examples + free template.

By: Derek Jansen (MBA) | Reviewed By: Dr Eunice Rautenbach | June 2020 (Updated April 2023)

Whether you’re nearing the end of your degree and your dissertation is on the horizon, or you’re planning to apply for a PhD program, chances are you’ll need to craft a convincing research proposal . If you’re on this page, you’re probably unsure exactly what the research proposal is all about. Well, you’ve come to the right place.

Overview: Research Proposal Basics

• What a research proposal is
• What a research proposal needs to cover
• How to structure your research proposal
• Example /sample proposals
• Proposal writing FAQs
• Key takeaways & additional resources

What is a research proposal?

Simply put, a research proposal is a structured, formal document that explains what you plan to research (your research topic), why it’s worth researching (your justification), and how  you plan to investigate it (your methodology). 

The purpose of the research proposal (its job, so to speak) is to convince  your research supervisor, committee or university that your research is  suitable  (for the requirements of the degree program) and  manageable  (given the time and resource constraints you will face). 

The most important word here is “ convince ” – in other words, your research proposal needs to  sell  your research idea (to whoever is going to approve it). If it doesn’t convince them (of its suitability and manageability), you’ll need to revise and resubmit . This will cost you valuable time, which will either delay the start of your research or eat into its time allowance (which is bad news). 

What goes into a research proposal?

A good dissertation or thesis proposal needs to cover the “ what “, “ why ” and” how ” of the proposed study. Let’s look at each of these attributes in a little more detail:

Your proposal needs to clearly articulate your research topic . This needs to be specific and unambiguous . Your research topic should make it clear exactly what you plan to research and in what context. Here’s an example of a well-articulated research topic:

An investigation into the factors which impact female Generation Y consumer’s likelihood to promote a specific makeup brand to their peers: a British context

As you can see, this topic is extremely clear. From this one line we can see exactly:

• What’s being investigated – factors that make people promote or advocate for a brand of a specific makeup brand
• Who it involves – female Gen-Y consumers
• In what context – the United Kingdom

So, make sure that your research proposal provides a detailed explanation of your research topic . If possible, also briefly outline your research aims and objectives , and perhaps even your research questions (although in some cases you’ll only develop these at a later stage). Needless to say, don’t start writing your proposal until you have a clear topic in mind , or you’ll end up waffling and your research proposal will suffer as a result of this.

Need a helping hand?

As we touched on earlier, it’s not good enough to simply propose a research topic – you need to justify why your topic is original . In other words, what makes it  unique ? What gap in the current literature does it fill? If it’s simply a rehash of the existing research, it’s probably not going to get approval – it needs to be fresh.

But,  originality  alone is not enough. Once you’ve ticked that box, you also need to justify why your proposed topic is  important . In other words, what value will it add to the world if you achieve your research aims?

As an example, let’s look at the sample research topic we mentioned earlier (factors impacting brand advocacy). In this case, if the research could uncover relevant factors, these findings would be very useful to marketers in the cosmetics industry, and would, therefore, have commercial value . That is a clear justification for the research.

So, when you’re crafting your research proposal, remember that it’s not enough for a topic to simply be unique. It needs to be useful and value-creating – and you need to convey that value in your proposal. If you’re struggling to find a research topic that makes the cut, watch  our video covering how to find a research topic .

It’s all good and well to have a great topic that’s original and valuable, but you’re not going to convince anyone to approve it without discussing the practicalities – in other words:

• How will you actually undertake your research (i.e., your methodology)?
• Is your research methodology appropriate given your research aims?
• Is your approach manageable given your constraints (time, money, etc.)?

While it’s generally not expected that you’ll have a fully fleshed-out methodology at the proposal stage, you’ll likely still need to provide a high-level overview of your research methodology . Here are some important questions you’ll need to address in your research proposal:

• Will you take a qualitative , quantitative or mixed -method approach?
• What sampling strategy will you adopt?
• How will you collect your data (e.g., interviews, surveys, etc)?
• How will you analyse your data (e.g., descriptive and inferential statistics , content analysis, discourse analysis, etc, .)?
• What potential limitations will your methodology carry?

So, be sure to give some thought to the practicalities of your research and have at least a basic methodological plan before you start writing up your proposal. If this all sounds rather intimidating, the video below provides a good introduction to research methodology and the key choices you’ll need to make.

How To Structure A Research Proposal

Now that we’ve covered the key points that need to be addressed in a proposal, you may be wondering, “ But how is a research proposal structured? “.

While the exact structure and format required for a research proposal differs from university to university, there are four “essential ingredients” that commonly make up the structure of a research proposal:

• A rich introduction and background to the proposed research
• An initial literature review covering the existing research
• An overview of the proposed research methodology
• A discussion regarding the practicalities (project plans, timelines, etc.)

In the video below, we unpack each of these four sections, step by step.

Research Proposal Examples/Samples

In the video below, we provide a detailed walkthrough of two successful research proposals (Master’s and PhD-level), as well as our popular free proposal template.

Proposal Writing FAQs

How long should a research proposal be.

This varies tremendously, depending on the university, the field of study (e.g., social sciences vs natural sciences), and the level of the degree (e.g. undergraduate, Masters or PhD) – so it’s always best to check with your university what their specific requirements are before you start planning your proposal.

As a rough guide, a formal research proposal at Masters-level often ranges between 2000-3000 words, while a PhD-level proposal can be far more detailed, ranging from 5000-8000 words. In some cases, a rough outline of the topic is all that’s needed, while in other cases, universities expect a very detailed proposal that essentially forms the first three chapters of the dissertation or thesis.

The takeaway – be sure to check with your institution before you start writing.

How do I choose a topic for my research proposal?

Finding a good research topic is a process that involves multiple steps. We cover the topic ideation process in this video post.

How do I write a literature review for my proposal?

While you typically won’t need a comprehensive literature review at the proposal stage, you still need to demonstrate that you’re familiar with the key literature and are able to synthesise it. We explain the literature review process here.

How do I create a timeline and budget for my proposal?

We explain how to craft a project plan/timeline and budget in Research Proposal Bootcamp .

Which referencing format should I use in my research proposal?

The expectations and requirements regarding formatting and referencing vary from institution to institution. Therefore, you’ll need to check this information with your university.

What common proposal writing mistakes do I need to look out for?

We’ve create a video post about some of the most common mistakes students make when writing a proposal – you can access that here . If you’re short on time, here’s a quick summary:

• The research topic is too broad (or just poorly articulated).
• The research aims, objectives and questions don’t align.
• The research topic is not well justified.
• The study has a weak theoretical foundation.
• The research design is not well articulated well enough.
• Poor writing and sloppy presentation.
• Poor project planning and risk management.
• Not following the university’s specific criteria.

Key Takeaways & Additional Resources

As you write up your research proposal, remember the all-important core purpose:  to convince . Your research proposal needs to sell your study in terms of suitability and viability. So, focus on crafting a convincing narrative to ensure a strong proposal.

At the same time, pay close attention to your university’s requirements. While we’ve covered the essentials here, every institution has its own set of expectations and it’s essential that you follow these to maximise your chances of approval.

By the way, we’ve got plenty more resources to help you fast-track your research proposal. Here are some of our most popular resources to get you started:

• Proposal Writing 101 : A Introductory Webinar
• Research Proposal Bootcamp : The Ultimate Online Course
• Template : A basic template to help you craft your proposal

If you’re looking for 1-on-1 support with your research proposal, be sure to check out our private coaching service , where we hold your hand through the proposal development process (and the entire research journey), step by step.

Psst… there’s more!

This post is an extract from our bestselling short course, Research Proposal Bootcamp . If you want to work smart, you don't want to miss this .

You Might Also Like:

51 Comments

I truly enjoyed this video, as it was eye-opening to what I have to do in the preparation of preparing a Research proposal.

I would be interested in getting some coaching.

I real appreciate on your elaboration on how to develop research proposal,the video explains each steps clearly.

Thank you for the video. It really assisted me and my niece. I am a PhD candidate and she is an undergraduate student. It is at times, very difficult to guide a family member but with this video, my job is done.

In view of the above, I welcome more coaching.

Wonderful guidelines, thanks

This is very helpful. Would love to continue even as I prepare for starting my masters next year.

Thanks for the work done, the text was helpful to me

Bundle of thanks to you for the research proposal guide it was really good and useful if it is possible please send me the sample of research proposal

You’re most welcome. We don’t have any research proposals that we can share (the students own the intellectual property), but you might find our research proposal template useful: https://gradcoach.com/research-proposal-template/

Cheruiyot Moses Kipyegon

Thanks alot. It was an eye opener that came timely enough before my imminent proposal defense. Thanks, again

thank you very much your lesson is very interested may God be with you

I am an undergraduate student (First Degree) preparing to write my project,this video and explanation had shed more light to me thanks for your efforts keep it up.

Very useful. I am grateful.

this is a very a good guidance on research proposal, for sure i have learnt something

Wonderful guidelines for writing a research proposal, I am a student of m.phil( education), this guideline is suitable for me. Thanks

You’re welcome 🙂

Thank you, this was so helpful.

A really great and insightful video. It opened my eyes as to how to write a research paper. I would like to receive more guidance for writing my research paper from your esteemed faculty.

Thank you, great insights

Thank you, great insights, thank you so much, feeling edified

Wow thank you, great insights, thanks a lot

Thank you. This is a great insight. I am a student preparing for a PhD program. I am requested to write my Research Proposal as part of what I am required to submit before my unconditional admission. I am grateful having listened to this video which will go a long way in helping me to actually choose a topic of interest and not just any topic as well as to narrow down the topic and be specific about it. I indeed need more of this especially as am trying to choose a topic suitable for a DBA am about embarking on. Thank you once more. The video is indeed helpful.

Have learnt a lot just at the right time. Thank you so much.

thank you very much ,because have learn a lot things concerning research proposal and be blessed u for your time that you providing to help us

Hi. For my MSc medical education research, please evaluate this topic for me: Training Needs Assessment of Faculty in Medical Training Institutions in Kericho and Bomet Counties

I have really learnt a lot based on research proposal and it’s formulation

Thank you. I learn much from the proposal since it is applied

Your effort is much appreciated – you have good articulation.

You have good articulation.

I do applaud your simplified method of explaining the subject matter, which indeed has broaden my understanding of the subject matter. Definitely this would enable me writing a sellable research proposal.

This really helping

Great! I liked your tutoring on how to find a research topic and how to write a research proposal. Precise and concise. Thank you very much. Will certainly share this with my students. Research made simple indeed.

Thank you very much. I an now assist my students effectively.

Thank you very much. I can now assist my students effectively.

I need any research proposal

Thank you for these videos. I will need chapter by chapter assistance in writing my MSc dissertation

Very helpfull

the videos are very good and straight forward

thanks so much for this wonderful presentations, i really enjoyed it to the fullest wish to learn more from you

Thank you very much. I learned a lot from your lecture.

I really enjoy the in-depth knowledge on research proposal you have given. me. You have indeed broaden my understanding and skills. Thank you

interesting session this has equipped me with knowledge as i head for exams in an hour’s time, am sure i get A++

This article was most informative and easy to understand. I now have a good idea of how to write my research proposal.

Thank you very much.

Wow, this literature is very resourceful and interesting to read. I enjoyed it and I intend reading it every now then.

Thank you for the clarity

Thank you. Very helpful.

Thank you very much for this essential piece. I need 1o1 coaching, unfortunately, your service is not available in my country. Anyways, a very important eye-opener. I really enjoyed it. A thumb up to Gradcoach

What is JAM? Please explain.

Thank you so much for these videos. They are extremely helpful! God bless!

very very wonderful…

thank you for the video but i need a written example

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Educational resources and simple solutions for your research journey

What is a Research Paradigm? Types of Research Paradigms with Examples

If you’re a researcher, you’ve probably heard the term “ research paradigm .” And, if you are a researcher, especially if you haven’t been trained under the social sciences, you are probably confused by the concept of a research paradigm . What is a research paradigm? How does it apply to my research? Why is it important? 

Research paradigms refer to the beliefs and assumptions that provide the structure for your research. These can be characteristics of your discipline or even your personal beliefs. For example, if you are a physical scientist and you are conducting research on the performance of a newly developed catalyst for removing chemical impurities from drinking water, your study is probably based on the premise that there is one reality, and your results will show that the new product either works better or it doesn’t. However, if your research discipline is education and you’re looking at the effects of parental literacy rates on the literacy or academic success of their children, you will not expect a such a definite result, and you may be examining your topic from different viewpoints, such as cultural or socio-economic. Your findings will then depend on those assumptions, beliefs, and biases.  

The rest of this article will try to clarify the concept of research paradigms, provide a research paradigm definition, and offer some examples of different types of research paradigms . While years of study may not completely clear up your confusion about research paradigms , perhaps you will think a little better of them and how they can help you in your work and maybe even in your personal life.  

Table of Contents

What is a research paradigm?  

According to the Merriam-Webster Dictionary, a paradigm is “ a philosophical and theoretical framework of a scientific school or discipline within which theories, laws, and generalizations and the experiments performed in support of them are formulated. ” 1 As applied in the context of research, a research paradigm is a worldview or philosophical framework, including ideas, beliefs, and biases, that guides the research process. The research paradigm in which a study is situated helps determine the manner in which the research will be conducted.  

The research paradigm is the framework into which the theories and practices of your discipline fit to create the research plan. This foundation guides all areas of your research plan, including the aim of the study, research question, instruments or measurements used, and analysis methods.   

Most research paradigms are based on one of two model types: positivism or interpretivism. These guide the theories and methodologies used in the research project. In general, positivist research paradigms lead to quantitative studies and interpretivist research paradigms lead to qualitative studies. Of course, there are many variations of both of these research paradigm types, some of which lead to mixed-method studies.  

What are the three pillars of research paradigms ?     

So, now you may be asking, what makes up a research paradigm ? How are they formed and categorized? The research paradigm framework is supported by three pillars: ontology, epistemology, and methodology. Some scholars have recently begun adding another pillar to research paradigms : ethics or axiology. However, this article will only discuss the three traditional aspects, which together define the research paradigm and provide the base on which to build your research project.  

Ontology is the study of the nature of reality. Is there a single reality, multiple realities, or no reality at all? These are the questions that the philosophy of ontology attempts to answer. The oft-used example of an ontological question is “Does God exist?” Two possible single realities exist: yes or no.  

Think about your research project with this in mind; that is, does a single reality exist within your research? If you’re a medical researcher, the answer is probably yes. You’re looking for specific results that ideally have clear yes or no answers. If you’re an anthropologist, there probably isn’t one clear, specific answer to your research question but multiple possible realities, and the study results are interpreted through the researcher’s viewpoint or paradigm.  

Epistemology is the study of knowledge and how we can know reality. It incorporates the extent and ways to gain knowledge and how to validate that knowledge. A frequently used example question in epistemology is “How is it possible to know whether or not God exists?”  

The epistemology of your research project will help determine your approach to your study. For example, if the medical researcher believes there is one singular truth, an objective approach will be taken. On the other hand, if the anthropologist believes in multiple realities viewed through a cultural lens, the research results will be more subjective and understood only in the proper context. This difference divides research studies into those using quantitative and qualitative techniques.  

Methodology is the study of how one investigates the environment and validates the knowledge gained. It attempts to answer the question “how to go about discovering the answer/reality.” Addressing this pillar leads to specific data collection and analysis plans.   

The medical researcher may create a research plan that includes a clinical trial, during which blood tests that measure a specific protein are conducted. These results are then analyzed, with a focus on differences within groups. The anthropologist, on the other hand, may conduct observations, examine artifacts, or set up interviews to determine certain aspects of reality within the context of a group’s culture. In this situation, yes or no answers are not sought but a truth is discovered.  

What is the purpose of research paradigms ?  

Put all the information about the three pillars of a research paradigm together, and you can see the purpose of research paradigms . Research paradigms establish the structure and foundation for a research project.   

Once the research paradigm has been determined, an appropriate research plan can be created. The philosophical basis of the study guides what knowledge is sought, how that knowledge can be discovered, and how to form the collected information or data into the knowledge being sought. The research paradigm clearly outlines the path to investigate your topic. This brings clarity to your study and improves the quality of your methods and analysis.  

In addition, it is important for researchers to understand how their own beliefs, assumptions, and biases can affect the research process. The study’s data collection, analysis, and interpretation will be impacted by the worldview of the researcher. Knowing the underlying research paradigm and how it frames the study allows researchers to better understand the effect of their perspective on the study results.   

Types of research paradigms  

As mentioned previously, there are two basic types of research paradigms , from which other frequently used paradigms are derived. This section will briefly describe these two major research paradigms .   

Positivist paradigm – Proponents of a positivist paradigm believe that there is a single reality that can be measured and understood. Therefore, these researchers are likely to utilize quantitative methods in their studies. The research process for positivist paradigm studies tend to propose an empirical hypothesis, which is then supported or refuted through the data collection and analysis. Positivists approach research in an objective manner and statistically investigate the existence of quantitative relationships between variables instead of looking for the qualitative reason behind those relationships. Researchers who subscribe to this paradigm also believe that the results of one study can be generalized to similar situations. Positivist paradigms are most frequently used by physical scientists.  

Interpretivism paradigm – Interpretivists believe in the existence of multiple realities rather than a single reality. This is the research paradigm used by the majority of qualitative studies conducted in the social sciences. Interpretivism holds that because human behavior is so complex, it cannot be studied by probabilistic models, such as those used under positivist paradigms . Knowledge can only be created by interpreting the meanings that people put on behaviors and events. Therefore, studies employing this framework are necessarily subjective and are greatly affected by the researcher’s personal viewpoint. Interpretivist paradigm research is conducted within the reality of those being studied, not in a contrived environment such as a laboratory. Because of the nature of interpretivist studies, their results are only valid under the particular circumstances of the study and are usually not generalizable.   

Research paradigm examples    

Positivist and interpretivist research paradigms , sometimes referred to as quantitative and qualitative paradigms, are the two major approaches to research. However, many other variations of these have been used. Following are brief descriptions of some of the more popular of these research paradigm variations.  

Pragmatism paradigm – Pragmatists believe that reality is continually changing amid the flow of constantly changing situations. Therefore, rather than use a single research paradigm , they employ the framework that is most applicable to the research question they are examining. Both qualitative and quantitative techniques are often used as positivist and interpretivist approaches are combined. Pragmatists believe that the best research method is the one that will most effectively address the research question.  

Constructivist paradigm – Like interpretivists, constructivists believe that there are numerous realities, not a single reality. The constructivist paradigm holds that people construct their own understanding of the world through experiencing and reflecting on those experiences. Constructivist research seeks to understand the meanings that people attach to those experiences. Therefore, qualitative techniques, such as interviews and case studies, are frequently used. Constructivists are seeking the “why” of events. Constructivism is also a popular theory of learning that focuses on how children and other learners create knowledge from their experiences and learn better through experimentation than through direct instruction.  

Post-positivism paradigm – Post-positivists veer away from the concept of reality as being an absolute certainty and view it instead in a more probabilistic manner, thus taking a more subjective viewpoint. They believe that research outcomes can never be totally objective and a researcher’s worldview and biases can never be completely removed from the research results.  

Transformative paradigm – Proponents of transformative research reject both positivism and interpretivism, believing that these frameworks do not accurately represent the experiences of marginalized communities. Transformative researchers generally use both qualitative and quantitative techniques to better understand the disparities in community relationships, support social justice, and ultimately ensure transformative change.    

Combining research paradigms    

While most research is based on either a positivist (quantitative) or interpretivist (qualitative) foundations, some studies combine both. For example, quantitative and qualitative techniques are frequently used together in psychology studies. These types of studies are referred to as mixed-method research. Some research paradigms are themselves combinations of other paradigms and frequently employ all the associated research methods. Post-positivism combines the paradigms of positivism and interpretivism.  

5 steps to a paradigm shift  

Research studies aren’t the only things that can be considered to have paradigms. Researchers themselves bring a specific worldview to their work and produce higher quality work when they are aware of the effect their perspective has on their results. Understanding all the aspects of a personal paradigm, including beliefs, habits, and behaviors, can make it possible for that paradigm to be changed. Here are suggested steps to successfully shift your personal paradigm and increase the quality of your research 2 .  

• Identify the paradigm element you want to change – what part of your worldview do you want to change? What habitual or hidden behavior may be adversely affecting your research or your life? 
• Write down your goals – setting specific desired outcomes and putting them down on paper sets them in your subconscious.   
• Adjust your mindset – intentionally influencing your thoughts to support your goals can motivate you to create the change you want. Some suggested activities to help with this include journaling, reading motivational books, and spending time with like-minded people.  
• Do uncomfortable things – you need to get out of your comfort zone to effect real change. This will get your subconscious out of its usual habits and move you toward your goal.  
• Practice being who you want to be – the change you want will become solidified and part of your new paradigm once you break out of your old habit and keep repeating the new behavior so as to cement it in your subconscious.  

References:  

• Merriam-Webster Dictionary. https://www.merriam-webster.com/dictionary/paradigm [Accessed March 10, 2023]  
• What is research paradigm – explanation and examples. Peachy Essay. https://peachyessay.com/blogs/what-is-research-paradigm/ [Accessed March 10, 2023]  

Researcher.Life is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Researcher.Life All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

Related Posts

Single-Blind vs Double-Blind Peer Review

How to Read, Write, Promote Your Research Effectively: Timesaving Solution for Busy Researchers

Get science-backed answers as you write with Paperpal's Research feature

What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

Let Paperpal help you write the perfect research methods section. Start now!

What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

Got writer’s block? Kickstart your research paper writing with Paperpal now!

How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

Streamline Your Research Paper Writing Process with Paperpal

The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.  

• Generate an outline: Input some details about your research to instantly generate an outline for your methods section 
• Develop the section: Use the outline and suggested sentence templates to expand your ideas and develop the first draft.  
• P araph ras e and trim : Get clear, concise academic text with paraphrasing that conveys your work effectively and word reduction to fix redundancies. 
• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

You can repeat this process to develop each section of your research manuscript, including the title, abstract and keywords. Ready to write your research papers faster, better, and without the stress? Sign up for Paperpal and start writing today!

Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

Accelerate your research paper writing with Paperpal. Try for free now!

• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Dangling Modifiers and How to Avoid Them in Your Writing 
• Webinar: How to Use Generative AI Tools Ethically in Your Academic Writing
• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot
• How to Paraphrase Research Papers Effectively

Language and Grammar Rules for Academic Writing

Climatic vs. climactic: difference and examples, you may also like, what is academic writing: tips for students, what is hedging in academic writing  , how to use ai to enhance your college..., how to use paperpal to generate emails &..., ai in education: it’s time to change the..., is it ethical to use ai-generated abstracts without..., do plagiarism checkers detect ai content, word choice problems: how to use the right..., how to avoid plagiarism when using generative ai..., what are journal guidelines on using generative ai....

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Sample: Definition, Types, Formula & Examples

How often do researchers look for the right survey respondents, either for a market research study or an existing survey in the field? The sample or the respondents of this research may be selected from a set of customers or users that are known or unknown.

You may often know your typical respondent profile but don’t have access to the respondents to complete your research study. At such times, researchers and research teams reach out to specialized organizations to access their panel of respondents or buy respondents from them to complete research studies and surveys.

These could be general population respondents that match demographic criteria or respondents based on specific criteria. Such respondents are imperative to the success of research studies.

This article discusses in detail the different types of samples, sampling methods, and examples of each. It also mentions the steps to calculate the size, the details of an online sample, and the advantages of using them.

Content Index

• What is a sample?

Probability sampling methodologies with examples

Non-probability sampling methodologies with examples.

• How to determine a sample size
• Calculating sample size
• Sampling advantages

What is a Sample?

A sample is a smaller set of data that a researcher chooses or selects from a larger population using a pre-defined selection bias method. These elements are known as sample points, sampling units, or observations.

Creating a sample is an efficient method of conducting research . Researching the whole population is often impossible, costly, and time-consuming. Hence, examining the sample provides insights the researcher can apply to the entire population.

For example, if a cell phone manufacturer wants to conduct a feature research study among students in US Universities. An in-depth research study must be conducted if the researcher is looking for features that the students use, features they would like to see, and the price they are willing to pay.

This step is imperative to understand the features that need development, the features that require an upgrade, the device’s pricing, and the go-to-market strategy.

In 2016/17 alone, there were 24.7 million students enrolled in universities across the US. It is impossible to research all these students; the time spent would make the new device redundant, and the money spent on development would render the study useless.

Creating a sample of universities by geographical location and further creating a sample of these students from these universities provides a large enough number of students for research.

Typically, the population for market research is enormous. Making an enumeration of the whole population is practically impossible. The sample usually represents a manageable size of this population. Researchers then collect data from these samples through surveys, polls, and questionnaires and extrapolate this data analysis to the broader community.

LEARN ABOUT: Survey Sampling

Types of Samples: Selection methodologies with examples

The process of deriving a sample is called a sampling method. Sampling forms an integral part of the research design as this method derives the quantitative and qualitative data that can be collected as part of a research study. Sampling methods are characterized into two distinct approaches: probability sampling and non-probability sampling.

Probability sampling is a method of deriving a sample where the objects are selected from a population-based on probability theory. This method includes everyone in the population, and everyone has an equal chance of being selected. Hence, there is no bias whatsoever in this type of sample.

Each person in the population can subsequently be a part of the research. The selection criteria are decided at the outset of the market research study and form an important component of research.

LEARN ABOUT:   Action Research

Probability sampling can be further classified into four distinct types of samples. They are:

• Simple random sampling: The most straightforward way of selecting a sample is simple random sampling . In this method, each member has an equal chance of participating in the study. The objects in this sample population are chosen randomly, and each member has the same probability of being selected. For example, if a university dean would like to collect feedback from students about their perception of the teachers and level of education, all 1000 students in the University could be a part of this sample. Any 100 students can be selected randomly to be a part of this sample.
• Cluster sampling: Cluster sampling is a type of sampling method where the respondent population is divided into equal clusters. Clusters are identified and included in a sample based on defining demographic parameters such as age, location, sex, etc. This makes it extremely easy for a survey creator to derive practical inferences from the feedback. For example, if the FDA wants to collect data about adverse side effects from drugs, they can divide the mainland US into distinctive cluster analysis , like states. Research studies are then administered to respondents in these clusters. This type of generating a sample makes the data collection in-depth and provides easy-to-consume and act-upon, insights.
• Systematic sampling: Systematic sampling is a sampling method where the researcher chooses respondents at equal intervals from a population. The approach to selecting the sample is to pick a starting point and then pick respondents at a pre-defined sample interval. For example, while selecting 1,000 volunteers for the Olympics from an application list of 10,000 people, each applicant is given a count of 1 to 10,000. Then starting from 1 and selecting each respondent with an interval of 10, a sample of 1,000 volunteers can be obtained.
• Stratified random sampling: Stratified random sampling is a method of dividing the respondent population into distinctive but pre-defined parameters in the research design phase. In this method, the respondents don’t overlap but collectively represent the whole population. For example, a researcher looking to analyze people from different socioeconomic backgrounds can distinguish respondents by their annual salaries. This forms smaller groups of people or samples, and then some objects from these samples can be used for the research study.

LEARN ABOUT: Purposive Sampling

The non-probability sampling method uses the researcher’s discretion to select a sample. This type of sample is derived mostly from the researcher’s or statistician’s ability to get to this sample.

This type of sampling is used for preliminary research where the primary objective is to derive a hypothesis about the topic in research. Here each member does not have an equal chance of being a part of the sample population, and those parameters are known only post-selection to the sample.

We can classify non-probability sampling into four distinct types of samples. They are:

• Convenience sampling: Convenience sampling , in easy terms, stands for the convenience of a researcher accessing a respondent. There is no scientific method for deriving this sample. Researchers have nearly no authority over selecting the sample elements, and it’s purely done based on proximity and not representativeness.

This non-probability sampling method is used when there is time and costs limitations in collecting feedback. For example, researchers that are conducting a mall-intercept survey to understand the probability of using a fragrance from a perfume manufacturer. In this sampling method, the sample respondents are chosen based on their proximity to the survey desk and willingness to participate in the research.

• Judgemental/purposive sampling: The judgemental or purposive sampling method is a method of developing a sample purely on the basis and discretion of the researcher purely, based on the nature of the study along with his/her understanding of the target audience. This sampling method selects people who only fit the research criteria and end objectives, and the remaining are kept out.

For example, if the research topic is understanding what University a student prefers for Masters, if the question asked is “Would you like to do your Masters?” anything other than a response, “Yes” to this question, everyone else is excluded from this study.

• Snowball sampling: Snowball sampling or chain-referral sampling is defined as a non-probability sampling technique in which the samples have rare traits. This is a sampling technique in which existing subjects provide referrals to recruit samples required for a research study.

For example, while collecting feedback about a sensitive topic like AIDS, respondents aren’t forthcoming with information. In this case, the researcher can recruit people with an understanding or knowledge of such people and collect information from them or ask them to collect information.

• Quota sampling: Quota sampling is a method of collecting a sample where the researcher has the liberty to select a sample based on their strata. The primary characteristic of this method is that two people cannot exist under two different conditions. For example, when a shoe manufacturer would like to understand millennials’ perception of the brand with other parameters like comfort, pricing, etc. It selects only females who are millennials for this study as the research objective is to collect feedback about women’s shoes.

How to determine a Sample Size

As we have learned above, the right sample size determination is essential for the success of data collection in a market research study. But is there a correct number for the sample size? What parameters decide the sample size? What are the distribution methods of the survey?

To understand all of this and make an informed calculation of the right sample size, it is first essential to understand four important variables that form the basic characteristics of a sample. They are:

• Population size: The population size is all the people that can be considered for the research study. This number, in most cases, runs into huge amounts. For example, the population of the United States is 327 million. But in market research, it is impossible to consider all of them for the research study.
• The margin of error (confidence interval): The margin of error is depicted by a percentage that is a statistical inference about the confidence of what number of the population depicts the actual views of the whole population. This percentage helps towards the statistical analysis in selecting a sample and how much sampling error in this would be acceptable.

LEARN ABOUT: Research Process Steps

• Confidence level: This metric measures where the actual mean falls within a confidence interval. The most common confidence intervals are 90%, 95%, and 99%.
• Standard deviation: This metric covers the variance in a survey. A safe number to consider is .5, which would mean that the sample size has to be that large.

Calculating Sample Size

To calculate the sample size, you need the following parameters.

• Z-score: The Z-score value can be found   here .
• Standard deviation
• Margin of error
• Confidence level

To calculate use the sample size, use this formula:

Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Consider the confidence level of 90%, standard deviation of .6 and margin of error, +/-4%

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603 respondents are needed and that becomes your sample size.

Try our sample size calculator to give population, margin of error calculator , and confidence level.

LEARN MORE: Population vs Sample

Sampling Advantages

As shown above, there are many advantages to sampling. Some of the most significant advantages are:

• Reduced cost & time: Since using a sample reduces the number of people that have to be reached out to, it reduces cost and time. Imagine the time saved between researching with a population of millions vs. conducting a research study using a sample.
• Reduced resource deployment: It is obvious that if the number of people involved in a research study is much lower due to the sample, the resources required are also much less. The workforce needed to research the sample is much less than the workforce needed to study the whole population .
• Accuracy of data: Since the sample indicates the population, the data collected is accurate. Also, since the respondent is willing to participate, the survey dropout rate is much lower, which increases the validity and accuracy of the data.
• Intensive & exhaustive data: Since there are lesser respondents, the data collected from a sample is intense and thorough. More time and effort are given to each respondent rather than collecting data from many people.
• Apply properties to a larger population: Since the sample is indicative of the broader population, it is safe to say that the data collected and analyzed from the sample can be applied to the larger population, which would hold true.

To collect accurate data for research, filter bad panelists, and eliminate sampling bias by applying different control measures. If you need any help arranging a sample audience for your next market research project, contact us at [email protected] . We have more than 22 million panelists across the world!

In conclusion, a sample is a subset of a population that is used to represent the characteristics of the entire population. Sampling is essential in research and data analysis to make inferences about a population based on a smaller group of individuals. There are different types of sampling, such as probability sampling, non-probability sampling, and others, each with its own advantages and disadvantages.

Choosing the right sampling method depends on the research question, budget, and resources is important. Furthermore, the sample size plays a crucial role in the accuracy and generalizability of the findings.

This article has provided a comprehensive overview of the definition, types, formula, and examples of sampling. By understanding the different types of sampling and the formulas used to calculate sample size, researchers and analysts can make more informed decisions when conducting research and data unit of analysis .

Sampling is an important tool that enables researchers to make inferences about a population based on a smaller group of individuals. With the right sampling method and sample size, researchers can ensure that their findings are accurate and generalizable to the population.

Utilize one of QuestionPro’s many survey questionnaire samples to help you complete your survey.

When creating online surveys for your customers, employees, or students, one of the biggest mistakes you can make is asking the wrong questions. Different businesses and organizations have different needs required for their surveys.

If you ask irrelevant questions to participants, they’re more likely to drop out before completing the survey. A questionnaire sample template will help set you up for a successful survey.

LEARN MORE         SIGN UP FREE

MORE LIKE THIS

Customer Communication Tool: Types, Methods, Uses, & Tools

Apr 23, 2024

Top 12 Sentiment Analysis Tools for Understanding Emotions

QuestionPro BI: From research data to actionable dashboards within minutes

Apr 22, 2024

21 Best Customer Experience Management Software in 2024

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Designing the Theoretical Framework

What is it.

• A foundational review of existing theories. 
• Serves as a roadmap or blueprint for developing arguments and supporting research.
• Overview of the theory that the research is based on.
• Can be made up of theories, principles, and concepts.

What does it do?

• Explains the why and how of a particular phenomenon within a particular body of literature.
• Connects the research subject with the theory.
• Specifies the study’s scope; makes it more valuable and generalizable.
• Guides further actions like framing the research questions, developing the literature review, and data collection and analyses.

What should be in it?

• Theory or theories that the researcher considers relevant for their research, principles, and concepts.

Theoretical Framework Guide

• Theoretical Framework Guide Use this guide to determine the guiding framework for your theoretical dissertation research.

Making a Theoretical Framework

How to make a theoretical framework.

• Specify research objectives.
• Note the prominent variables under the study.
• Explore and review the literature through keywords identified as prominent variables.
• Note the theories that contain these variables or the keywords.
• Review all selected theories again in the light of the study’s objectives, and the key variables identified.
• Search for alternative theoretical propositions in the literature that may challenge the ones already selected.
• Ensure that the framework aligns with the study’s objectives, problem statement, the main research question, methodology, data analysis, and the expected conclusion.
• Decide on the final framework and begin developing.

Example Framework

• Theoretical Framework Example for a Thesis or Dissertation This link offers an example theoretical framework.

Additional Framework Resources

Some additional helpful resources in constructing a theoretical framework for study:.

• https://www.scribbr.com/dissertation/theoretical-framework/
• https://www.scribbr.com/dissertation/theoretical-framework-example/
• https://www.projectguru.in/how-to-write-the-theoretical-framework-of-research/

Theoretical Framework Research

The term conceptual framework and theoretical framework are often and erroneously used interchangeably (Grant & Osanloo, 2014). A theoretical framework provides the theoretical assumptions for the larger context of a study, and is the foundation or ‘lens’ by which a study is developed. This framework helps to ground the research focus understudy within theoretical underpinnings and to frame the inquiry for data analysis and interpretation.  The application of theory in traditional theoretical research is to understand, explain, and predict phenomena (Swanson, 2013).

Casanave, C.P.,& Li,Y.(2015). Novices’ struggles with conceptual and theoretical framing in writing  dissertations and papers for publication. Publications,3 (2),104-119.doi:10.3390/publications3020104

Grant, C., & Osanloo, A. (2014). Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House. ” Administrative Issues Journal: Connecting Education, Practice, and Research, 4(2), 12–26

Swanson, R. (2013). Theory building in applied disciplines . San Francisco: Berrett-Koehler Publishers.

• << Previous: Conceptual Framework
• Next: Quantitative Research Questions >>
• Last Updated: Apr 24, 2024 11:06 AM
• URL: https://resources.nu.edu/researchtools

What is Marketing Research? Examples and Best Practices

12 min read

Marketing research is essentially a method utilized by companies to collect valuable information regarding their target market. Through the common practice of conducting market research, companies gather essential information that enables them to make informed decisions and develop products that resonate with consumers. It encompasses the gathering, analysis, and interpretation of data, which aids in identifying consumer demands, anticipating market trends, and staying ahead of the competition.

Exploratory research is one of the initial steps in the marketing research process. It helps businesses gain broad insights when specific information is unknown. If you are seeking insight into how marketing research can influence the trajectory of your SaaS, then you have come to the right place!

• Market research is a systematic and objective process crucial for understanding target markets, refining business strategies, and informing decisions, which includes collecting, analyzing, and interpreting data on customers, competitors, and the industry.
• Primary market research gathers specific data directly from the target audience using tools like surveys and focus groups, while secondary market research utilizes existing data from various sources to provide broader market insights.
• Effective market research combines both qualitative methods, which explore consumer motivations, and quantitative methods, which provide measurable statistics, to create comprehensive insights that guide business strategy and decision-making.

Try Userpilot and Take Your Product Marketing to the Next Level

• 14 Day Trial
• No Credit Card Required

Defining marketing research

Launching a product without knowing what your target audience wants is like walking in the dark. Market research lights the way, helping you collect, analyze, and understand information about your target market. This allows you to refine your business strategies and make decisions based on solid evidence.

Gone are the days when just intuition or subjective judgment was enough. Objective insights from market research help avoid costly mistakes and meet consumer needs by identifying trends and changes in the market. This is crucial for assessing a product’s potential success, optimizing marketing strategies, and preparing for market shifts.

Market research is a systematic approach that provides essential information, helping businesses navigate the complexities of the commercial world. Partnering with market research companies can offer additional benefits, leveraging their expertise in understanding market demands, trends, market size, economic indicators, location, market saturation, and pricing. Whether starting a new business, developing products, or updating marketing plans, understanding how to conduct effective market research is key to success.

To conduct market research effectively, businesses must determine study goals, identify target consumers, collect and analyze data, and use the findings to make informed decisions. This process is vital for evaluating past performance, measuring changes over time, and addressing specific business needs. It guides businesses in product development, marketing strategies, and overall decision-making, ensuring a better ROI and providing an eye-opening view of the market through various research methods, whether conducted in-house or outsourced.

The purpose of marketing research

Conducting marketing research is more than just gathering data; it’s about turning that data into actionable insights to refine your business strategies. This process helps you understand what motivates your customers, enabling you to tailor your products and services to minimize risks from the start. Importantly, market research plays a pivotal role in measuring and enhancing customer satisfaction and loyalty, which are critical for understanding key demographics, improving user experience, designing better products, and driving customer retention. Customer satisfaction is measured as a key outcome, directly linked to the success of marketing strategies and business activities.

For SaaS product managers, market research, including competitive analysis, is crucial. It evaluates past strategies and gauges the potential success of new offerings. This research provides essential insights into brand strength, consumer behavior, and market position, which are vital for teams focused on sales, marketing, and product development.

A key aspect of market research is analyzing customer attitudes and usage. This analysis offers detailed insights into what customers want, the choices they make, and the challenges they face. It helps identify opportunities in the market and aids in formulating effective strategies for market entry.

Overall, market research equips SaaS entrepreneurs with the knowledge to meet their target audience’s needs effectively, guiding product adjustments and innovations based on informed decisions.

Key components of market research

Conducting market research is analogous to preparing a cake, requiring precise ingredients in specific quantities to achieve the intended outcome. Within this realm, necessary components consist of primary and secondary data gathering, thorough analysis, and insightful interpretation.

Primary research techniques such as exploratory studies, product evolution inquiries, estimations of market dimensions and shares, and consumer behavior examinations play a crucial role in collecting targeted information that can be directly applied. These methods afford a deeper understanding of your target demographic, allowing for customized strategy development.

In contrast, secondary research enriches the specificity of primary findings by adding wider context. It taps into external resources encompassing works from other investigators, sector-specific reports, and demographics data, which provide an expansive yet less particularized landscape view of the marketplace.

The subsequent phase involves meticulous analysis of collated data offering unbiased perspectives critical for identifying deficiencies while recognizing emerging patterns. Technological progress now facilitates examination efforts on both structured and unstructured datasets effectively addressing large-scale analytical complexities.

Ultimately, it’s through expert-led interpretation that value transcends raw figures, yielding strategies grounded in deep comprehension. Akin to decoding recipes using selected ingredients—this interpretative step enables crafting optimal business maneuvers just as one would bake their ideal confectionery creation utilizing proper culinary guidance.

Types of market research: primary and secondary

Now that you know the importance of clear research objectives, let’s explore the different types of market research and the techniques available to achieve these goals. Market research methods can be divided into two main categories: primary research and secondary research . The choice between these depends on factors like your budget, time constraints, and whether you need exploratory data or definitive answers.

Primary research involves collecting new data directly from sources. This process is like mining for precious metals, as it requires using various methods to gather fresh insights.

• Surveys (here – in-app survey templates from Userpilot ).

• Interviews.

• Focus groups.
• Product trials.

This approach gives you first-hand insight into your target audience.

Conversely, secondary research uses already established datasets of primary data – which can add depth and reinforcement to your firsthand findings.

Conducting your own market research using primary research tools can be a cost-effective strategy, allowing businesses to gather valuable insights directly and tailor their research to specific needs.

Let’s look a bit deeper into them now.

What is primary market research?

Market research uses primary market research as an essential tool. This involves collecting new data directly from your target audience using various methods, such as surveys , focus groups, and interviews.

Each method has its benefits. For example, observational studies allow you to see how consumers interact with your product.

There are many ways to conduct primary research.

Focus Groups : Hold discussions with small groups of 5 to 10 people from your target audience. These discussions can provide valuable feedback on products, perceptions of your company’s brand name, or opinions on competitors. Additionally, these discussions can help understand the characteristics, challenges, and buying habits of target customers, optimizing brand strategy.

Interviews : Have one-on-one conversations to gather detailed information from individuals in your target audience.

Surveys : These are a common tool in primary market research and can be used instead of focus groups to understand consumer attitudes. Surveys use structured questions and can reach a broad audience efficiently.

Navigating secondary market research

While marketing research using primary methods is like discovering precious metals, secondary market research technique is like using a treasure map. This approach uses data collected by others from various sources, providing a broad industry view. These sources include market analyses from agencies like Statista, historical data such as census records, and academic studies.

Secondary research provides the basic knowledge necessary for conducting primary market research goals but may lack detail on specific business questions and could also be accessible to competitors.

To make the most of secondary market research, it’s important to analyze summarized data to identify trends, rely on reputable sources for accurate data, and remain unbiased in data collection methods.

The effectiveness of secondary research depends significantly on how well the data is interpreted, ensuring that this information complements the insights from primary research.

Qualitative vs quantitative research

Market research employs both qualitative and quantitative methods, offering distinct insights that complement each other. Qualitative research aims to understand consumer behaviors and motivations through detailed analysis, while quantitative research collects measurable data for statistical analysis.

The selection of qualitative or quantitative methods should align with your research goals. If you need to uncover initial insights or explore deep consumer motivations, qualitative techniques like surveys or interviews are ideal.

On the other hand, if you need data that can be measured and analyzed for reliability, quantitative methods are more suitable.

However, these approaches don’t have to be used separately. Combining qualitative and quantitative methods in mixed-method studies allows you to capture both detailed exploratory responses and concrete numerical data. This integration offers a comprehensive view of the market, leveraging the strengths of both approaches to provide a fuller understanding of market conditions.

Implementing market research tools: Userpilot’s role

Similar to how a compass is essential for navigation at sea, businesses need appropriate instruments to carry out effective market research. Userpilot’s suite of product analytics and in-app engagement tools are critical components for this purpose.

Acting as a Buyer Persona Research instrument, Userpilot’s product analytics provide key quantitative research capabilities. This helps clearly define and comprehend the attributes and behaviors of potential customers, providing you with insights into your ICP (Ideal Customer Persona), user preferences, and product-market fit.

Beyond product analytics, Userpilot offers robust in-app engagement features such as modals and surveys that support real time collection of market research information. These interactive features work synergistically with the analytical tools to enable companies to gather detailed data and feedback crucial for informed business decision-making.

Marketing research process: Step-by-step guide

Marketing research conists of several critical stages:

• Defining precise goals.
• Delving into the knowledge of your target demographic.
• Collecting and scrutinizing data.
• Revealing insights that can be translated into tangible actions.

Following these steps allows you to gather critical information that guides business decisions.

An effective research strategy is crucial and involves:

• Properly allocating funds.
• Formulating testable hypotheses.
• Choosing appropriate methods for the study.
• Determining the number of study participants.
• Considering external variables.

A well-planned strategy ensures that your market research is focused, efficient, and produces useful outcomes.

After collecting data, the next step is to analyze it. This involves comparing the data to your initial questions to draw conclusions relevant to your business strategies.

Userpilot makes your data analysis easier by providing handy analytics dashboards for key user metrics such as activation, engagement, core feature adoption, and retention out of the box:

Finally, you report the findings and the process, providing recommendations based on the evidence. This is like solving a puzzle: each piece helps to complete the overall picture.

Challenges and best practices in market research

Delving into market research comes with its own set of hurdles. Those conducting the research must deliver more profound insights within increasingly shorter timespans, and they need to cultivate strategic, continuous research methods to stay abreast of an ever-changing business landscape.

Ensuring high-quality data can be demanding due to issues such as disjointed tools or insufficient analytical expertise. New solutions like Userpilot are surfacing that make these obstacles less daunting by offering accessible and user-friendly options. Maintaining clear lines of communication with your market research team is crucial for achieving both punctuality and quality in outcomes.

The advantages of engaging in marketing research cannot be overstated.

Real-life examples of successful market research

Real-life examples of market research in the SaaS industry often showcase innovative approaches to understanding customer needs and product-market fit.

For instance, Slack, the communication platform, utilized extensive market research to identify gaps in communication tools and understand the workflows of teams. This led to the development of features that seamlessly integrated with other tools and catered to the needs of various team sizes and structures.

Another example is HubSpot, which conducted market research to understand the pain points of small to medium-sized businesses in managing customer relationships. The insights gained helped shape their all-in-one inbound marketing, sales, and service platform, which has become integral to their users’ daily operations. These examples demonstrate how SaaS companies can employ market research to inform product development, improve user experience, and strategically position themselves in a competitive market.

Choosing the right market research tools

For B2B SaaS product managers aiming to do market research, having the right set of tools can make a significant difference. Here’s a list of valuable SaaS tools that can be leveraged for effective market research:

• Userpilot : A comprehensive Product Growth Platform offering in-depth product analytics, a code-free in-app experience builder, bespoke in-app survey capabilities, and robust integration options with platforms like Salesforce and Hubspot. This tool is particularly useful for understanding user behavior, enhancing user engagement, and gathering targeted feedback.
• Qualtrics : Known for its powerful survey tools, Qualtrics helps businesses gather and analyze customer feedback effectively. Its advanced analytics features are ideal for testing market hypotheses and understanding customer sentiments.
• SurveyMonkey : A versatile tool that enables product managers to create, send, and analyze surveys quickly and easily. SurveyMonkey is suitable for gauging customer satisfaction and collecting feedback on potential new features.
• Mixpanel : Specializes in user behavior analytics, offering detailed insights into how users interact with your product. This is essential for identifying patterns and optimizing product features.
• Hotjar : Combines analytics and feedback tools to give teams insights into user behavior and preferences. Hotjar’s heatmaps and session recordings are invaluable for understanding the user experience on a deeper level.
• Tableau : A leading platform for business intelligence and data visualization, Tableau allows product managers to create comprehensive visual reports that can inform strategic decisions based on user data analysis.

Each of these tools provides unique functionalities that can assist SaaS product managers in conducting thorough market research, thereby ensuring that their products are perfectly aligned with user needs and market demands.

Measuring the impact of market research

The pivotal challenge for market research lies in demonstrating its return on investment (ROI) and overall influence on corporate success sufficiently enough to justify regular financial commitment from company leaders. The worth attributed to a market research firm hinges not only on their ability to deliver relevant and high-caliber information, but also on their pricing structures and their contribution towards propelling organizational growth.

To gauge how effectively business choices made based on market research findings succeed, various metrics and key performance indicators (KPIs) are utilized. These numerical tools act as navigational aids directing enterprises toward achieving objectives while simultaneously verifying that efforts invested in conducting market analysis are yielding fruitful guidance.

Throughout our look at market research, we’ve seen its importance and impact. Our discussion covered the basics of market research, its key components, and different types, including both qualitative and quantitative methods, and the role of Userpilot’s tools. We’ve examined the details of the market research process, tackled challenges, identified best practices, and shared success stories. We also provided advice on choosing the right market research partner and how to measure the effectiveness of your market research.

In today’s data-driven world, comprehensive market research is crucial for companies that want to succeed. It acts like a guide, helping businesses navigate the complex market landscape. Start your own detailed research today, supported by insightful analytics to help you succeed.

Frequently asked questions

What is market research and why is it important.

Understanding your target market, honing business strategies, and making informed decisions are all essential components that depend heavily on effective market research. It offers objective insights to help avoid expensive errors and foresees the needs of customers .

What is the difference between primary and secondary market research?

Primary market research is characterized by the direct gathering of data, in contrast to secondary market research which leverages existing information from alternative sources for addressing research inquiries.

Such a distinction can guide you in selecting an approach that aligns with your precise needs for conducting specific research.

What are some examples of successful market research?

Examples of successful market research are evident in the operations of well-known companies such as Starbucks, Apple, and McDonald’s. They have harnessed this tool to fine-tune their business strategies and make decisions based on solid information.

By employing market research, these businesses have managed to gain insight into their customers’ desires and needs, which has contributed significantly to their success.

How can I choose the right market research partner?

Selecting an ideal market research ally involves identifying a firm that resonates with your project requirements, financial plan, and corporate goals while also verifying their track record of dependability and consistency via reviews from previous clients.

Best wishes on your endeavor!

How is the impact of market research measured?

The effectiveness of market research hinges on the precision, representativeness, and pertinence of its data, along with how successful business decisions are when they’re based on the findings from this research. These elements define the impact of the research conducted.

Leave a comment Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Get The Insights!

The fastest way to learn about Product Growth,Management & Trends.

The coolest way to learn about Product Growth, Management & Trends. Delivered fresh to your inbox, weekly.

The fastest way to learn about Product Growth, Management & Trends.

You might also be interested in ...

Funnel marketing: definition and strategies that work for saas, how to do market research: a step-by-step guide.

• Privacy Policy

Home » Significance of the Study – Examples and Writing Guide

Significance of the Study – Examples and Writing Guide

Table of Contents

Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

People think 'old age' starts later than it used to, study finds

Increases in life expectancy, later retirement could explain shift in public perception of when old age begins.

Middle-aged and older adults believe that old age begins later in life than their peers did decades ago, according to a study published by the American Psychological Association.

"Life expectancy has increased, which might contribute to a later perceived onset of old age. Also, some aspects of health have improved over time, so that people of a certain age who were regarded as old in the past may no longer be considered old nowadays," said study author Markus Wettstein, PhD, of Humboldt University in Berlin, Germany.

However, the study, which was published in the journal Psychology and Aging , also found evidence that the trend of later perceived old age has slowed in the past two decades.

Wettstein, along with colleagues at Stanford University, the University of Luxembourg and the University of Greifswald, Germany, examined data from 14,056 participants in the German Ageing Survey, a longitudinal study that includes people living in Germany born between 1911 and 1974. Participants responded to survey questions up to eight times over 25 years (1996-2021), when they were between 40 and 100 years old. Additional participants (40 to 85 years old) were recruited throughout the study period as later generations entered midlife and old age. Among the many questions survey participants answered was, "At what age would you describe someone as old?"

The researchers found that compared with the earliest-born participants, later-born participants reported a later perceived onset of old age. For example, when participants born in 1911 were 65 years old, they set the beginning of old age at age 71. In contrast, participants born in 1956 said old age begins at age 74, on average, when they were 65.

However, the researchers also found that the trend toward a later perceived onset of old age has slowed in recent years.

"The trend toward postponing old age is not linear and might not necessarily continue in the future," Wettstein said.

The researchers also looked at how individual participants' perceptions of old age changed as they got older. They found that as individuals aged, their perception of the onset of old age was pushed further out. At age 64, the average participant said old age started at 74.7. At age 74, they said old age started at 76.8. On average, the perceived onset of old age increased by about one year for every four to five years of actual aging.

Finally, the researchers examined how individual characteristics such as gender and health status contributed to differences in perceived onset of old age. They found that women, on average, said that old age started two years later than men -- and that the difference between men and women had increased over time. They also found that people who reported being more lonely, in worse health, and feeling older said old age began earlier, on average, than those who were less lonely, in better health, and felt younger.

The results may have implications for when and how people prepare for their own aging, as well as how people think about older adults in general, Wettstein said.

"It is unclear to what extent the trend towards postponing old age reflects a trend towards more positive views on older people and aging, or rather the opposite -- perhaps the onset of old age is postponed because people consider being old to be an undesirable state," Wettstein said.

Future research should examine whether the trend toward a "postponement" of old age continues and investigate more diverse populations in other countries, including non-Western countries, to understand how perceptions of aging vary by country and culture, according to the researchers.

• Healthy Aging
• Teen Health
• Children's Health
• K-12 Education
• Racial Issues
• Eustachian tube
• Child prodigy
• Developmental psychology
• Adult attention-deficit disorder
• Biological tissue

Story Source:

Materials provided by American Psychological Association . Note: Content may be edited for style and length.

Journal Reference :

• Markus Wettstein, Rinseo Park, Anna E. Kornadt, Susanne Wurm, Nilam Ram, Denis Gerstorf. Postponing old age: Evidence for historical change toward a later perceived onset of old age. . Psychology and Aging , 2024; DOI: 10.1037/pag0000812

Cite This Page :

Explore More

• Food in Sight? The Liver Is Ready!
• Acid Reflux Drugs and Risk of Migraine
• Do Cells Have a Hidden Communication System?
• Mice Given Mouse-Rat Brains Can Smell Again
• How Do Birds Flock? New Aerodynamics
• Cancer: Epigenetic Origin Without DNA Mutation
• Climate Change Driving Biodiversity Loss
• Why Can't Robots Outrun Animals?
• Evolution of Gliding in Marsupials
• Novel One-Dimensional Superconductor

Trending Topics

Strange & offbeat.

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

8 facts about atheists

Atheists make up 4% of U.S. adults, according to our 2023 National Public Opinion Reference Survey . That compares with 3% who described themselves as atheists in 2014 and 2% who did so in 2007 .

Here are some key facts about atheists in the United States and around the world, based on several Pew Research Center surveys.

This analysis draws on several Pew Research Center studies. Data on the share of atheists in the United States is from the  2023 National Public Opinion Reference Survey , as well as the Center’s  2007  and  2014 Religious Landscape Studies .

Other data on U.S. atheists comes from various waves of the American Trends Panel, collected in  September and December 2017 ,  February 2019 ,  September 2022 , and  July and August 2023 .

For data from countries other than the U.S., this analysis draws on nationally representative surveys conducted in 2019, 2022 and 2023. Read more details about our  international survey methodology and country-specific sample designs .

For the purposes of this analysis, “wealthy nations” are those that were classified as “high income” according to the  World Bank Income Classifications .

In the U.S., atheists are mostly men and are relatively young,  according to a Center survey conducted in summer 2023 . Around six-in-ten U.S. atheists are men (64%). And seven-in-ten are ages 49 or younger, compared with about half of U.S. adults overall (52%).

Atheists also are more likely than the general public to be White (77% vs. 62%) and have a college degree (48% vs. 34%). Roughly eight-in-ten atheists identify with or lean toward the Democratic Party.

Almost all U.S. atheists (98%) say religion is not too or not at all important in their lives, according to the same summer 2023 survey. An identical share say that they seldom or never pray.

At the same time, 79% of American atheists say they feel a deep sense of wonder about the universe at least several times a year. And 36% feel a deep sense of spiritual peace and well-being at least that often.

U.S. atheists and religiously affiliated Americans find meaning in their lives in some of the same ways. In a 2017 survey , we asked an open-ended question about this. Like a majority of Americans, most atheists mentioned family as a source of meaning.

However, atheists (26%) were far more likely than Christians (10%) to describe their hobbies as meaningful or satisfying. Atheists were also more likely than Americans overall to describe finances and money, creative pursuits, travel, and leisure activities as meaningful. Very few atheists (4%) said they found life’s meaning in spirituality.

Atheists make up a larger share of the population in many Western European countries than in the U.S.,  according to a spring 2023 Center survey that included 10 European countries. For example, nearly a quarter of French adults (23%) identify as atheists, as do 18% of adults in Sweden, 17% in the Netherlands and 12% in the United Kingdom.

Most U.S. atheists express concerns about the role religion plays in society. An overwhelming majority of atheists (94%) say that the statement “religion causes division and intolerance” describes their views a great deal or a fair amount, according to our summer 2023 survey. And 91% say the same about the statement “religion encourages superstition and illogical thinking.” Nearly three-quarters (73%) say religion does more harm than good in American society.

At the same time, 41% of atheists say religion helps society by giving people meaning and purpose in their lives, and 33% say it encourages people to treat others well.

Atheists may not believe religious teachings, but they are  quite informed about religion . In our 2019 religious knowledge survey , atheists were among the best-performing groups. On average, they answered about 18 out of 32 fact-based questions correctly, while U.S. adults overall got roughly 14 questions right. In particular, atheists were twice as likely as Americans overall to know that the U.S. Constitution says no religious test is necessary to hold public office.

Atheists were also at least as knowledgeable as Christians on Christianity-related questions. For example, roughly eight-in-ten in both groups knew that Easter commemorates the resurrection of Jesus.

Most Americans don’t think believing in God is necessary to be a good person, according to the summer 2023 survey. When we asked people which statement came closer to their views, 73% selected “it is possible to be moral and have good values without believing in God,” while 25% picked “it is necessary to believe in God in order to be moral and have good values.”

Adults in some other wealthy countries tend to agree with this sentiment, based on responses to a similar question we asked in 2019 and 2022 . For example, nine-in-ten Swedish adults say belief in God is not necessary to be moral and have good values, while 85% in Australia, 80% in the Czech Republic and 77% in France say this.

However, fewer than one-in-ten adults in some other countries surveyed say that a person can be moral without believing in God. That includes 5% of adults in Kenya, 4% in the Philippines and 2% in Indonesia. In all three nations, more than nine-in-ten say instead that a person must believe in God to be a moral person.

About three-quarters of U.S. atheists (77%) do not believe in God or a higher power  or in a spiritual force of any kind, according to our summer 2023 survey. At the same time, 23% say they do believe in a higher power of some kind, though fewer than 1% of U.S. atheists say they believe in “God as described in the Bible.”

This shows that not all self-described atheists fit the literal definition of “atheist,” which is “a person who does not believe in the existence of a god or any gods,”  according to Merriam-Webster .

Note: This is an update of a post originally published on Nov. 5, 2015. It was last updated Dec. 6, 2019.

• Atheism & Agnosticism
• Beliefs & Practices
• Religiously Unaffiliated
• Size & Demographic Characteristics of Religious Groups
• U.S. Religious Demographics

Michael Lipka is an associate director focusing on news and information research at Pew Research Center

Patricia Tevington is a research associate focusing on religion research at Pew Research Center

Kelsey Jo Starr is a research analyst focusing on religion at Pew Research Center

Around 4 in 10 Americans have become more spiritual over time; fewer have become more religious

Spirituality among americans, chinese communist party promotes atheism, but many members still partake in religious customs, many people in u.s., other advanced economies say it’s not necessary to believe in god to be moral, unlike other u.s. religious groups, most atheists and agnostics oppose the death penalty, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy

• Open access
• Published: 19 April 2024

Asparagine reduces the risk of schizophrenia: a bidirectional two-sample mendelian randomization study of aspartate, asparagine and schizophrenia

• Huang-Hui Liu 1 , 2   na1 ,
• Yao Gao 1 , 2   na1 ,
• Dan Xu 1 , 2 ,
• Xin-Zhe Du 1 , 2 ,
• Si-Meng Wei 1 , 2 ,
• Jian-Zhen Hu 1 , 2 ,
• Yong Xu 1 , 2 &
• Liu Sha 1 , 2  

BMC Psychiatry volume  24 , Article number:  299 ( 2024 ) Cite this article

363 Accesses

1 Altmetric

Metrics details

Despite ongoing research, the underlying causes of schizophrenia remain unclear. Aspartate and asparagine, essential amino acids, have been linked to schizophrenia in recent studies, but their causal relationship is still unclear. This study used a bidirectional two-sample Mendelian randomization (MR) method to explore the causal relationship between aspartate and asparagine with schizophrenia.

This study employed summary data from genome-wide association studies (GWAS) conducted on European populations to examine the correlation between aspartate and asparagine with schizophrenia. In order to investigate the causal effects of aspartate and asparagine on schizophrenia, this study conducted a two-sample bidirectional MR analysis using genetic factors as instrumental variables.

No causal relationship was found between aspartate and schizophrenia, with an odds ratio (OR) of 1.221 (95%CI: 0.483–3.088, P -value = 0.674). Reverse MR analysis also indicated that no causal effects were found between schizophrenia and aspartate, with an OR of 0.999 (95%CI: 0.987–1.010, P -value = 0.841). There is a negative causal relationship between asparagine and schizophrenia, with an OR of 0.485 (95%CI: 0.262-0.900, P -value = 0.020). Reverse MR analysis indicates that there is no causal effect between schizophrenia and asparagine, with an OR of 1.005(95%CI: 0.999–1.011, P -value = 0.132).

This study suggests that there may be a potential risk reduction for schizophrenia with increased levels of asparagine, while also indicating the absence of a causal link between elevated or diminished levels of asparagine in individuals diagnosed with schizophrenia. There is no potential causal relationship between aspartate and schizophrenia, whether prospective or reverse MR. However, it is important to note that these associations necessitate additional research for further validation.

Peer Review reports

Introduction

Schizophrenia is a serious psychiatric illness that affects 0.5 -1% of the global population [ 1 ]. The burden of mental illness was estimated to account for 7% of all diseases worldwide in 2016, and nearly 20% of all years lived with disability [ 2 ]. The characteristics of schizophrenia are positive symptoms, negative symptoms, and cognitive symptoms, which are often severe functional impairments and significant social maladaptations for patients suffering from schizophrenia [ 3 ]. It is still unclear what causes schizophrenia and what the pathogenesis is. There are a number of hypotheses based on neurochemical mechanisms, including dopaminergic and glutamatergic systems [ 4 ]. Although schizophrenia research has made significant advances in the past, further insight into its mechanisms and causes is still needed.

Association genetics research and genome-wide association studies have successfully identified more than 24 candidate genes that serve as molecular biomarkers for the susceptibility to treatment- refractory schizophrenia (TRS). It is worth noting that some proteins in these genes are related to glutamate transfer, especially the N-methyl-D-aspartate receptor (NMDAR) [ 5 ]. It is thought that NMDARs are important for neural plasticity, which is the ability of the brain itself to adapt to new environments. With age, NMDAR function usually declines, which may lead to decreased plasticity, leading to learning and memory problems. Consequently, the manifestation of cognitive deficits observed in diverse pathologies, including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, Parkinson’s disease, schizophrenia, and major depression, can be attributed to the dysfunction of NMDAR [ 4 ]. There are two enantiomers of aspartate (Asp): L and D [ 6 ]. In the brain, D-aspartate (D-Asp) stimulates glutamate receptors and dopaminergic neurons through its direct NMDAR agonist action [ 7 ]. According to the glutamate theory of Sch, glutamate NMDAR dysfunction is a primary contributor to the development of this psychiatric disorder and TRS [ 8 ]. It has been shown in two autopsy studies that D-Asp of prefrontal cortex neurons in patients with schizophrenia are significantly reduced, which is related to an increased expression of D-Asp oxidase [ 9 ] or an increased activity of D-Asp oxidase [ 10 ]. Several studies in animal models and humans have shown that D-amino acids, particularly D-Ser and D-Asp [ 11 ], are able to modulate several NMDAR-dependent processes, including synaptic plasticity, brain development, cognition and brain ageing [ 12 ]. In addition, D-Asp is synthesized in hippocampal and prefrontal cortex neurons, which play an important role in the development of schizophrenia [ 13 ]. It has been reported that the precursor substance of asparagine (Asn), aspartate, activates the N-methyl-D-aspartate receptor [ 14 ]. Asparagine is essential for the survival of all cells [ 15 ], and it was decreased in schizophrenia patients [ 16 ]. Asparagine can cause metabolic disorders of alanine, aspartate, and glutamic acid, leading to dysfunction of the glutamine-glutamate cycle and further affecting it Gamma-Aminobutyric Acid(GABA) level [ 17 ].It is widely understood that the imbalance of GABA levels and NMDAR plays a crucial role in the pathogenesis of schizophrenia, causing neurotoxic effects, synaptic dysfunction, and cognitive impairments [ 18 ].Schizophrenic patients exhibited significantly higher levels of serum aspartate, glutamate, isoleucine, histidine and tyrosine and significantly lower concentrations of serum asparagine, tryptophan and serine [ 19 ]. Other studies have also shown that schizophrenics have higher levels of asparagine, phenylalanine, and cystine, and lower ratios of tyrosine, tryptophan, and tryptophan to competing amino acids, compared to healthy individuals [ 20 ]. Aspartate and asparagine’s association with schizophrenia is not fully understood, and their causal relationship remains unclear.

The MR method is a method that uses Mendelian independence principle to infer causality, which uses genetic variation to study the impact of exposure on outcomes. By using this approach, confounding factors in general research are overcome, and causal reasoning is provided on a reasonable temporal basis [ 21 ]. The instrumental variables for genetic variation that are chosen must adhere to three primary hypotheses: the correlation hypothesis, which posits a robust correlation between single nucleotide polymorphisms (SNPs) and exposure factors; the independence hypothesis, which asserts that SNPs are not affected by various confounding factors; the exclusivity hypothesis, which maintains that SNPs solely influence outcomes through on exposure factors. In a recent study, Mendelian randomization was used to reveal a causal connection between thyroid function and schizophrenia [ 22 ]. According to another Mendelian randomization study, physical activity is causally related to schizophrenia [ 23 ]. Therefore, this study used Mendelian randomization method to explore the causal effects of aspartate on schizophrenia and asparagine on schizophrenia.

To elucidate the causal effects of aspartate and asparagine on schizophrenia. This study used bidirectional MR analysis. In the prospective analysis of MR, the exposure factors under consideration were aspartate and asparagine, while the outcome of interest was the risk of schizophrenia. On the contrary, in the reverse MR analysis, schizophrenia was utilized as the exposure factor, with aspartate and asparagine being chosen as the outcomes.

Materials and methods

Obtaining data sources, select genetic tools closely related to aspartate or asparagine.

In this research, publicly accessible GWAS summary statistical datasets from the MR basic platform were utilized. These datasets consisted of 7721 individuals of European ancestry [ 24 ] for the exposure phenotype instrumental variable of aspartate, and 7761 individuals of European ancestry [ 24 ] for the exposure phenotype instrumental variable of asparagine.

Select genetic tools closely related to schizophrenia

Data from the MR basic platform was used in this study for GWAS summary statistics, which included 77,096 individuals of European ancestry [ 5 ], as instrumental variables related to schizophrenia exposure phenotype.

Obtaining result data

The publicly accessible GWAS summary statistical dataset for schizophrenia was utilized on the MR basic platform, with a sample size of 77096. Additionally, the summary level data for aspartate and asparagine were obtained from the publicly available GWAS summary dataset on the MR basic platform, with sample sizes of 7721 and 7761, respectively, serving as outcome variables.

Instrumental variable filtering

Eliminating linkage disequilibrium.

The selection criteria for identifying exposure related SNPs from the aggregated data of GWAS include: (1) Reaching a significance level that meets the threshold for whole genome research, expressed as P -value < 5 * 10 − 6 [ 25 ]; (2) Ensure the independence of the selected SNPs and eliminate linkage disequilibrium SNPs ( r 2  < 0.001, window size of 10000KB) [ 26 ]; (3) There are corresponding data related to the research results in the GWAS summary data.

Eliminating weak instruments

To evaluate whether the instrumental variables selected for this MR study have weak values, we calculated the F-statistic. If the F-value is greater than 10, it indicates that there are no weak instruments in this study, indicating the reliability of the study. Using the formula F =[(N-K-1)/K] × [R 2 /(1-R 2 )], where N denotes the sample size pertaining to the exposure factor, K signifies the count of instrumental variables, and R 2 denotes the proportion of variations in the exposure factor that can be elucidated by the instrumental variables.

The final instrumental variable obtained

As a result of removing linkage disequilibrium and weak instrumental variables, finally, 3 SNPs related to aspartate and 24 SNPs related to asparagine were selected for MR analysis.

Bidirectional MR analysis

Research design.

Figure  1 presents a comprehensive depiction of the overarching design employed in the MR analysis undertaken in this study. We ascertained SNPs exhibiting robust correlation with the target exposure through analysis of publicly available published data, subsequently investigating the existence of a causal association between these SNPs and the corresponding outcomes. This study conducted two bidirectional MR analyses, one prospective and reverse MR on the causal relationship between aspartate and schizophrenia, and the other prospective and reverse MR on the causal relationship between asparagine and schizophrenia.

A MR analysis of aspartate and schizophrenia (located in the upper left corner). B  MR analysis of schizophrenia and aspartate (located in the upper right corner). C  MR analysis of asparagine and schizophrenia (located in the lower left corner). D  MR analysis of schizophrenia and asparagine (located in the lower right corner)

Statistical analysis

Weighted median, weighted mode, MR Egger, and inverse variance weighting (IVW) were used to conduct a MR study. The primary research findings were derived from the results obtained through IVW, the results of sensitivity analysis using other methods to estimate causal effects are considered. Statistical significance was determined if the P -value was less than 0.05. To enhance the interpretation of the findings, this study converted the beta values obtained in to OR, accompanied by the calculation of a 95% confidence interval (CI).

Test for directional horizontal pleiotropy

This study used MR Egger intercept to test horizontal pleiotropy. If the P -value is greater than 0.05, it indicates that there is no horizontal pleiotropy in this study, meaning that instrumental variables can only regulate outcome variables through exposure factors.

Results of bidirectional MR analysis of aspartate and schizophrenia

Analysis results of aspartate and schizophrenia.

In prospective MR analysis, this study set aspartate as the exposure factor and schizophrenia as the outcome. We used 3 SNPs significantly associated with aspartate screened across the entire genome. The instrumental variables exhibited F-values exceeding 10, signifying the absence of weak instruments and thereby affirming the robustness of our findings. Through MR analysis (Fig.  2 A), we assessed the individual influence of each SNP locus on schizophrenia. The results of the IVW method indicate that no causal effect was found between aspartate and schizophrenia, with an OR of 1.221 (95%CI: 0.483–3.088, P -value = 0.674).

In addition, the analyses conducted using the weighted mode and weighted median methods yielded similar results, indicating the absence of a causal association between aspartate and schizophrenia. Furthermore, the MR Egger analysis demonstrated no statistically significant disparity in effectiveness between aspartate and schizophrenia, as evidenced by a P -value greater than 0.05 (Table  1 ; Fig.  2 B).

In order to test the reliability of the research results, this study used MR Egger intercept analysis to examine horizontal pleiotropy, and the result was P -value = 0.579 > 0.05, indicating the absence of level pleiotropy. Furthermore, a leave-one-out test was conducted to demonstrate that no single SNP had a substantial impact on the stability of the results, indicating that this study has considerable stability (Fig.  2 C). Accordingly, the MR analysis results demonstrate the conclusion that aspartate and schizophrenia do not exhibit a causal relationship.

Analysis results of schizophrenia and aspartate

Different from prospective MR studies, in reverse MR studies, schizophrenia was set as an exposure factor and aspartate was set as the outcome. Through MR analysis (Fig.  2 D), we assessed the individual influence of each SNP locus on aspartate .The results of the IVW method indicate that there is no causal effect between schizophrenia and aspartate, with an OR of 0.999(95%CI: 0.987–1.010, P -value = 0.841). Similarly, the weighted mode, weighted median methods also failed to demonstrate a causal link between schizophrenia and aspartate. Additionally, the MR Egger analysis did not reveal any statistically significant difference in effectiveness between schizophrenia and aspartate ( P -value > 0.05) (Table  1 and Fig . 2 E).

The MR Egger intercept was used to test horizontal pleiotropy, and the result was P -value = 0.226 > 0.05, proving that this study is not affected by horizontal pleiotropy. Furthermore, a leave-one-out test revealed that no individual SNP significantly influenced the robustness of the findings (Fig.  2 F).

Depicts the causal association between aspartate and schizophrenia through diverse statistical analyses, as well as the causal association between schizophrenia and aspartate through diverse statistical analyses. A The forest plot of aspartate related SNPs and schizophrenia analysis results, with the red line showing the MR Egger test and IVW method. B  Scatter plot of the analysis results of aspartate and schizophrenia, with the slope indicating the strength of the causal relationship. C  Leave-one-out test of research results on aspartate and schizophrenia. D The forest plot of schizophrenia related SNPs and aspartate analysis results, with the red line showing the MR Egger test and IVW method. E  Scatter plot of the analysis results of schizophrenia and aspartate, with the slope indicating the strength of the causal relationship. F  Leave-one-out test of research results on schizophrenia and aspartate

Results of bidirectional MR analysis of asparagine and schizophrenia

Analysis results of asparagine and schizophrenia.

In prospective MR studies, we used asparagine as an exposure factor and schizophrenia as a result to investigate the potential causal relationship between them. Through a rigorous screening process, we identified 24 genome-wide significant SNPs associated with asparagine. In addition, the instrumental variable F values all exceeded 10, indicating that this study was not affected by weak instruments, thus proving the stability of the results. This study conducted MR analysis to evaluate the impact of all SNP loci on schizophrenia. (Fig.  3 A). According to the results of IVW, a causal relationship was found between asparagine and schizophrenia, and the relationship is negatively correlated, with an OR of 0.485 (95%CI: 0.262-0.900, P -value = 0.020).

The weighted median results also showed a causal relationship between asparagine and schizophrenia, and it was negatively correlated. In the weighted mode method, asparagine and schizophrenia did not have a causal relationship, while in the MR Egger method, there was no statistically significant difference in efficacy between them ( P -value > 0.05) (Table  1 ; Fig.  3 B).

In order to examine the horizontal pleiotropy, the MR Egger intercept was applied, and P -value = 0.768 > 0.05 result proves that this study is not affected by horizontal pleiotropy Furthermore, a leave-one-out test was conducted to demonstrate that no individual SNP had a substantial impact on the stability of the results, indicating that this study has good stability. (Fig.  3 C). Therefore, MR analysis shows that asparagine is inversely proportional to schizophrenia.

Analysis results of schizophrenia and asparagine

In reverse MR analysis, schizophrenia is considered an exposure factor, and asparagine is considered the result, studying the causal effects of schizophrenia and asparagine. Through MR analysis (Fig.  3 D), we assessed the individual influence of each SNP locus on s asparagine. The IVW method results indicated no potential causal relationship between schizophrenia and asparagine, with an OR of 1.005(95%CI: 0.999–1.011, P -value = 0.132). The research results of weighted mode method and weighted median method did not find a causal effects of schizophrenia and asparagine. Additionally, the MR Egger analysis did not reveal any statistically significant difference in effectiveness between schizophrenia and asparagine ( P -value > 0.05) (Table  1 ; Fig.  3 E).

In order to examine the horizontal pleiotropy, the MR Egger intercept was applied, and the result was P -value = 0.474 > 0.05, proving that this study is not affected by horizontal pleiotropy. Furthermore, a leave-one-out test was conducted to demonstrate that no individual SNP had a substantial impact on the stability of the results, indicating that this study has good stability (Fig.  3 F).

Depicts the causal association between asparagine and schizophrenia through diverse statistical analyses, as well as the causal association between schizophrenia and asparagine through diverse statistical analyses. A  The forest plot of asparagine related SNPs and schizophrenia analysis results, with the red line showing the MR Egger test and IVW method. B  Scatter plot of the analysis results of asparagine and schizophrenia, with the slope indicating the strength of the causal relationship. C Leave-one-out test of research results on asparagine and schizophrenia. D  The forest plot of schizophrenia related SNPs and asparagine analysis results, with the red line showing the MR Egger test and IVW method. E  Scatter plot of the analysis results of schizophrenia and asparagine, with the slope indicating the strength of the causal relationship. F  Leave-one-out test of research results on schizophrenia and asparagine

In this study, the MR analysis results after sensitivity analysis suggested a causal relationship between asparagine and schizophrenia, which was negatively correlated. However, the reverse MR analysis did not reveal any potential relationship between schizophrenia and asparagine, no potential causal relationship between aspartate and schizophrenia was found in both prospective and reverse MR analyses (Fig.  4 ).

Summary of results from bidirectional two-sample MR study

The levels of asparagine in schizophrenia patients decrease, according to studies [ 16 ]. Based on the findings of the Madis Parksepp research team, a continuous five-year administration of antipsychotic drugs (AP) has been observed to induce significant metabolic changes in individuals diagnosed with schizophrenia. Significantly, the concentrations of asparagine, glutamine (Gln), methionine, ornithine, and taurine have experienced a substantial rise, whereas aspartate, glutamate (Glu), and alpha-aminoadipic acid(α-AAA) levels have demonstrated a notable decline. Olanzapine (OLZ) treatment resulted in significantly lower levels of Asn compared to control mice [ 27 ]. Asn and Asp play significant roles in various biological processes within the human body, such as participating in glycoprotein synthesis and contributing to brain functionality. It is worth noting that the ammonia produced in brain tissue needs to have a rapid excretion pathway in the brain. Asn plays a crucial role in regulating cellular function within neural tissues through metabolic control. This amino acid is synthesized by the combination of Asp and ammonia, facilitated by the enzyme asparagine synthase. Additionally, the brain effectively manages ammonia elimination by producing glutamine Gln and Asn. This may be an explanation for the significant increase in Asn and Gln levels (as well as a decrease in Asp and Glu levels) during 5 years of illness and after receiving AP treatment [ 28 ]. The study by Marie Luise Rao’s team compared unmedicated schizophrenic patients, healthy individuals and patients receiving antipsychotic treatment. Unmedicated schizophrenics had higher levels of asparagine, citrulline, phenylalanine, and cysteine, while the ratios of tyrosine, tryptophan, and tryptophan to competing amino acids were significantly lower than in healthy individuals [ 29 ].

The findings of our study demonstrate an inverse association between asparagine levels and the susceptibility to schizophrenia, suggesting that asparagine may serve as a protective factor against the development of this psychiatric disorder. However, we did not find a causal relationship between schizophrenia and asparagine. Consequently, additional investigation and scholarly discourse are warranted to gain a comprehensive understanding of this complex association.

Two different autopsy studies measured D-ASP levels in two different brain samples from patients with schizophrenia and a control group [ 14 ]. The first study, which utilized a limited sample size (7–10 subjects per diagnosis), demonstrated a reduction in D-ASP levels within the prefrontal cortex (PFC) postmortem among individuals diagnosed with schizophrenia, amounting to approximately 101%. This decrease was found to be correlated with a notable elevation in D-aspartate oxidase (DDO) mRNA levels within the same cerebral region [ 30 ]. In addition, the second study was conducted on a large sample size (20 subjects/diagnosis/brain regions). The findings of this study indicated a noteworthy decrease of approximately 30% in D-ASP selectivity within the dorsal lateral PFC (DLPFC) of individuals diagnosed with schizophrenia, when compared to corresponding brain regions of individuals without schizophrenia. However, no significant reduction in D-ASP was observed in the hippocampus of patients with schizophrenia. The decrease in D-Asp content was associated with a significant increase (about 25%) in DDO enzyme activity in the DLPFC of schizophrenia patients. This observation highlights the existence of a dysfunctional metabolic process in DDO activity levels in the brains of schizophrenia patients [ 31 ].

Numerous preclinical investigations have demonstrated the influence of D-Asp on various phenotypes reliant on NMDAR, which are linked to schizophrenia. After administering D-Asp to D-Asp oxidase gene knockout mice, the abnormal neuronal pre-pulse inhibition induced by psychoactive drugs such as MK-801 and amphetamine was significantly reduced by the sustained increase in D-Asp [ 32 ]. According to a review, free amino acids, specifically D-Asp and D-Ser (D-serine), have been identified as highly effective and safe nutrients for promoting mental well-being. These amino acids not only serve as integral components of the central nervous system’s structural proteins, but also play a vital role in maintaining optimal functioning of the central nervous system. This is due to their essential role in regulating neurotransmitter levels, including dopamine, norepinephrine, serotonin, and others. For many patients with schizophrenia, a most persistent and effective improvement therapy may be supplementing amino acids, which can improve the expected therapeutic effect of AP and alleviate positive and negative symptoms of schizophrenia [ 33 ].

Numerous studies have demonstrated a plausible correlation between aspartate and schizophrenia; however, our prospective and reverse MR investigations have failed to establish a causal link between aspartate and schizophrenia. This discrepancy may be attributed to the indirect influence of aspartate on the central nervous system through the stimulation of NMDAR, necessitating further investigation to elucidate the direct relationship between aspartate and schizophrenia.

This study used a bidirectional two-sample MR analysis method to explore the causal relationship between aspartate and asparagine with schizophrenia, as well as its inverse relationship [ 34 ]. The utilization of MR analysis presents numerous benefits in the determination of causality [ 35 ]. Notably, the random allocation of alleles to gametes within this method permits the assumption of no correlation between instrumental variables and confounding factors. Consequently, this approach effectively alleviates bias stemming from confounding factors during the inference of causality. Furthermore, the study’s utilization of a substantial sample size in the GWAS summary data engenders a heightened level of confidence in the obtained results [ 36 ]. Consequently, this investigation not only advances the existing body of research on the relationship between aspartate and asparagine with schizophrenia, but also contributed to clinical treatment decisions for patients with schizophrenia.

Nevertheless, this study possesses certain limitations, as it solely relies on populations of European ancestry for both exposure and results. Consequently, it remains uncertain whether these findings can be replicated among non-European races, necessitating further investigation. In addition, in this study, whether the effects of aspartate and asparagine on schizophrenia vary by gender or age cannot be evaluated, and stratified MR analysis should be performed. Additional experimental research is imperative for a comprehensive understanding of the underlying biological mechanisms connecting aspartate and asparagine with schizophrenia.

In summary, our MR analysis found a negative correlation between asparagine and schizophrenia, indicating that asparagine reduces the risk of schizophrenia. However, there is no potential causal relationship between schizophrenia and asparagine. This study provides new ideas for the early detection of schizophrenia in the clinical setting and offers new insights into the etiology and pathogenesis of schizophrenia. Nonetheless, additional research is required to elucidate the potential mechanisms that underlie the association between aspartate and asparagine with schizophrenia.

Availability of data and materials

The datasets generated and analysed during the current study are available in the GWAS repository. https://gwas.mrcieu.ac.uk/datasets/met-a-388/ , https://gwas.mrcieu.ac.uk/datasets/met-a-638/ , https://gwas.mrcieu.ac.uk/datasets/ieu-b-42/ .

Charlson FJ, Ferrari AJ, Santomauro DF, Diminic S, Stockings E, Scott JG, McGrath JJ, Whiteford HA. Global epidemiology and burden of schizophrenia: findings from the global burden of disease study 2016. Schizophr Bull. 2018;44(6):1195–203.

Article   PubMed   PubMed Central   Google Scholar  

Rehm J, Shield KD. Global burden of disease and the impact of mental and addictive disorders. Curr Psychiatry Rep. 2019;21(2):10.

Article   PubMed   Google Scholar  

Vita A, Minelli A, Barlati S, Deste G, Giacopuzzi E, Valsecchi P, Turrina C, Gennarelli M. Treatment-resistant Schizophrenia: genetic and neuroimaging correlates. Front Pharmacol. 2019;10:402.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Adell A. Brain NMDA receptors in schizophrenia and depression. Biomolecules. 2020;10(6):947.

Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7.

Abdulbagi M, Wang L, Siddig O, Di B, Li B. D-Amino acids and D-amino acid-containing peptides: potential disease biomarkers and therapeutic targets? Biomolecules. 2021;11(11):1716.

Krashia P, Ledonne A, Nobili A, Cordella A, Errico F, Usiello A, D’Amelio M, Mercuri NB, Guatteo E, Carunchio I. Persistent elevation of D-Aspartate enhances NMDA receptor-mediated responses in mouse substantia Nigra pars compacta dopamine neurons. Neuropharmacology. 2016;103:69–78.

Article   CAS   PubMed   Google Scholar  

Kantrowitz JT, Epstein ML, Lee M, Lehrfeld N, Nolan KA, Shope C, Petkova E, Silipo G, Javitt DC. Improvement in mismatch negativity generation during d-serine treatment in schizophrenia: correlation with symptoms. Schizophr Res. 2018;191:70–9.

Elkis H, Buckley PF. Treatment-resistant Schizophrenia. Psychiatr Clin North Am. 2016;39(2):239–65.

Dunlop DS, Neidle A, McHale D, Dunlop DM, Lajtha A. The presence of free D-aspartic acid in rodents and man. Biochem Biophys Res Commun. 1986;141(1):27–32.

de Bartolomeis A, Vellucci L, Austin MC, De Simone G, Barone A. Rational and translational implications of D-Amino acids for treatment-resistant Schizophrenia: from neurobiology to the clinics. Biomolecules. 2022;12(7):909.

Taniguchi K, Sawamura H, Ikeda Y, Tsuji A, Kitagishi Y, Matsuda S. D-amino acids as a biomarker in schizophrenia. Diseases. 2022;10(1):9.

Singh SP, Singh V. Meta-analysis of the efficacy of adjunctive NMDA receptor modulators in chronic schizophrenia. CNS Drugs. 2011;25(10):859–85.

Errico F, Napolitano F, Squillace M, Vitucci D, Blasi G, de Bartolomeis A, Bertolino A, D’Aniello A, Usiello A. Decreased levels of D-aspartate and NMDA in the prefrontal cortex and striatum of patients with schizophrenia. J Psychiatr Res. 2013;47(10):1432–7.

Rousseau J, Gagné V, Labuda M, Beaubois C, Sinnett D, Laverdière C, Moghrabi A, Sallan SE, Silverman LB, Neuberg D, et al. ATF5 polymorphisms influence ATF function and response to treatment in children with childhood acute lymphoblastic leukemia. Blood. 2011;118(22):5883–90.

Liu L, Zhao J, Chen Y, Feng R. Metabolomics strategy assisted by transcriptomics analysis to identify biomarkers associated with schizophrenia. Anal Chim Acta. 2020;1140:18–29.

Cao B, Wang D, Brietzke E, McIntyre RS, Pan Z, Cha D, Rosenblat JD, Zuckerman H, Liu Y, Xie Q, et al. Characterizing amino-acid biosignatures amongst individuals with schizophrenia: a case-control study. Amino Acids. 2018;50(8):1013–23.

Olthof BMJ, Gartside SE, Rees A. Puncta of neuronal nitric oxide synthase (nNOS) mediate NMDA receptor signaling in the Auditory Midbrain. J Neuroscience: Official J Soc Neurosci. 2019;39(5):876–87.

Article   CAS   Google Scholar  

Tortorella A, Monteleone P, Fabrazzo M, Viggiano A, De Luca L, Maj M. Plasma concentrations of amino acids in chronic schizophrenics treated with clozapine. Neuropsychobiology. 2001;44(4):167–71.

Rao ML, Strebel B, Gross G, Huber G. Serum amino acid profiles and dopamine in schizophrenic patients and healthy subjects: window to the brain? Amino Acids. 1992;2(1–2):111–8.

Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ (Clinical Res ed). 2005;330(7499):1076–9.

Article   Google Scholar  

Freuer D, Meisinger C. Causal link between thyroid function and schizophrenia: a two-sample mendelian randomization study. Eur J Epidemiol. 2023;38(10):1081–8.

Papiol S, Schmitt A, Maurus I, Rossner MJ, Schulze TG, Falkai P. Association between Physical Activity and Schizophrenia: results of a 2-Sample mendelian randomization analysis. JAMA Psychiat. 2021;78(4):441–4.

Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50.

Zhao JV, Kwok MK, Schooling CM. Effect of glutamate and aspartate on ischemic heart disease, blood pressure, and diabetes: a mendelian randomization study. Am J Clin Nutr. 2019;109(4):1197–206.

Zhou K, Zhu L, Chen N, Huang G, Feng G, Wu Q, Wei X, Gou X. Causal associations between schizophrenia and cancers risk: a mendelian randomization study. Front Oncol. 2023;13:1258015.

Zapata RC, Rosenthal SB, Fisch K, Dao K, Jain M, Osborn O. Metabolomic profiles associated with a mouse model of antipsychotic-induced food intake and weight gain. Sci Rep. 2020;10(1):18581.

Parksepp M, Leppik L, Koch K, Uppin K, Kangro R, Haring L, Vasar E, Zilmer M. Metabolomics approach revealed robust changes in amino acid and biogenic amine signatures in patients with schizophrenia in the early course of the disease. Sci Rep. 2020;10(1):13983.

Rao ML, Gross G, Strebel B, Bräunig P, Huber G, Klosterkötter J. Serum amino acids, central monoamines, and hormones in drug-naive, drug-free, and neuroleptic-treated schizophrenic patients and healthy subjects. Psychiatry Res. 1990;34(3):243–57.

Errico F, D’Argenio V, Sforazzini F, Iasevoli F, Squillace M, Guerri G, Napolitano F, Angrisano T, Di Maio A, Keller S, et al. A role for D-aspartate oxidase in schizophrenia and in schizophrenia-related symptoms induced by phencyclidine in mice. Transl Psychiatry. 2015;5(2):e512.

Nuzzo T, Sacchi S, Errico F, Keller S, Palumbo O, Florio E, Punzo D, Napolitano F, Copetti M, Carella M, et al. Decreased free d-aspartate levels are linked to enhanced d-aspartate oxidase activity in the dorsolateral prefrontal cortex of schizophrenia patients. NPJ Schizophr. 2017;3:16.

Errico F, Rossi S, Napolitano F, Catuogno V, Topo E, Fisone G, D’Aniello A, Centonze D, Usiello A. D-aspartate prevents corticostriatal long-term depression and attenuates schizophrenia-like symptoms induced by amphetamine and MK-801. J Neurosci. 2008;28(41):10404–14.

Nasyrova RF, Khasanova AK, Altynbekov KS, Asadullin AR, Markina EA, Gayduk AJ, Shipulin GA, Petrova MM, Shnayder NA. The role of D-Serine and D-aspartate in the pathogenesis and therapy of treatment-resistant schizophrenia. Nutrients. 2022;14(23):5142.

Hao D, Liu C. Deepening insights into food and medicine continuum within the context of pharmacophylogeny. Chin Herb Med. 2023;15(1):1–2.

PubMed   Google Scholar  

Zhang Y. Awareness and ability of paradigm shift are needed for research on dominant diseases of TCM. Chin Herb Med. 2023;15(4):475.

CAS   PubMed   PubMed Central   Google Scholar  

Chen J. Essential role of medicine and food homology in health and wellness. Chin Herb Med. 2023;15(3):347–8.

PubMed   PubMed Central   Google Scholar  

Download references

This work was supported by the National Natural Science Foundation of China (82271546, 82301725, 81971601); National Key Research and Development Program of China (2023YFC2506201); Key Project of Science and Technology Innovation 2030 of China (2021ZD0201800, 2021ZD0201805); China Postdoctoral Science Foundation (2023M732155); Fundamental Research Program of Shanxi Province (202203021211018, 202203021212028, 202203021212038). Research Project Supported by Shanxi Scholarship Council of China (2022 − 190); Scientific Research Plan of Shanxi Health Commission (2020081, 2020SYS03,2021RC24); Shanxi Provincial Administration of Traditional Chinese Medicine (2023ZYYC2034), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (2022L132); Shanxi Medical University School-level Doctoral Initiation Fund Project (XD2102); Youth Project of First Hospital of Shanxi Medical University (YQ2203); Doctor Fund Project of Shanxi Medical University in Shanxi Province (SD2216); Shanxi Science and Technology Innovation Talent Team (202304051001049); 136 Medical Rejuvenation Project of Shanxi Province, China; STI2030-Major Projects-2021ZD0200700. Key laboratory of Health Commission of Shanxi Province (2020SYS03);

Author information

Huang-Hui Liu and Yao Gao contributed equally to this work.

Authors and Affiliations

Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, NO.85 Jiefang Nan Road, Taiyuan, China

Huang-Hui Liu, Yao Gao, Dan Xu, Xin-Zhe Du, Si-Meng Wei, Jian-Zhen Hu, Yong Xu & Liu Sha

Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China

You can also search for this author in PubMed   Google Scholar

Contributions

Huang-Hui Liu and Yao Gao provided the concept and designed the study. Huang-Hui Liu and Yao Gao conducted the analyses and wrote the manuscript. Dan Xu, Huang-Hui Liu and Yao Gao participated in data collection. Xin-Zhe Du, Si-Meng Wei and Jian-Zhen Hu participated in the analysis of the data. Liu Sha, Yong Xu and Yao Gao revised and proof-read the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Yong Xu or Liu Sha .

Ethics declarations

Ethics approval and consent to participate.

Not applicable’ for that section.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Liu, HH., Gao, Y., Xu, D. et al. Asparagine reduces the risk of schizophrenia: a bidirectional two-sample mendelian randomization study of aspartate, asparagine and schizophrenia. BMC Psychiatry 24 , 299 (2024). https://doi.org/10.1186/s12888-024-05765-5

Download citation

Received : 20 February 2024

Accepted : 15 April 2024

Published : 19 April 2024

DOI : https://doi.org/10.1186/s12888-024-05765-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Schizophrenia
• Mendelian randomization

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Khoros AI and automation delivers unmatched efficiency for marketing and service excellence. Read more

AI & Automation

AI for every conversation, campaign, and customer

Khoros Communities

Self-service support, education, and collaboration

Khoros Service

Agent efficiency, automation, and operational insights

Khoros Social Media Management

Content management, publishing, and governance

Strategic Services

Our in-house experts in social media and community management for Khoros customers

Professional services

More than onboarding and implementation, this is where our partnership begins

Product coaching

Increase satisfaction and improve product adoption with complimentary training.

Upcoming events

Join us for webinars and in-person events

Insights, tips, news, and more from our team to yours

Customer stories

Case studies with successful customers to see how they did it

Resource Center

Guides, tipsheets, ebooks, on-demand webinars, & more

Integrations

Integrations to connect with your customers, wherever they are

Tech integrations

Developer information

Technical overviews and links to developer documentation

• Join our community

EXPERT INSIGHTS

Apr-19-2024

The 2024 social media demographics guide

Khoros Staff

Editor's Note: This post was originally created in 2018 and has since been updated to reflect the latest data available.

According to Statista , 61.4% of the world’s population — a whopping 4.95 billion people — use social media.

That’s a lot of social media demographic research to sort through when you want to zero in on understanding audience characteristics of specific platforms — and we know the last thing a social media marketer has is time to spare. That’s why we’ve done all the heavy lifting for you.

Our updated 2024 Social Media Demographics Guide surfaces the demographic data you need to inform a smart strategy, like age, gender, and income — plus device usage and site behavior in one easy-to-read infographic.

View the 2024 Social Media Demographics Guide to discover more about what makes the audiences of Facebook, Instagram, Twitter, LinkedIn, YouTube, Snapchat, and TikTok unique or bookmark it now to reference anytime.

General social media demographics

Before we jump into platform-specific demographics, let’s cover some high-level insights about social media in general.

Social media usage over time

There are currently 4.95 billion social media users and 5.3 billion total internet users, meaning 93.4% of people who use the internet also use social media. What’s more astounding is the rate that social media usage has grown and is expected to continue growing. The number of social media users has grown by 79.1% since 2017 , when there were only 2.73 billion social media users. By the end of 2024, Statista predicts there will be 5.17 billion social media users which would be 5.7% growth compared to 2023. By 2027, the number of social media users is expected to reach 5.85 billion, with an annual growth rate between 3.7-5.7% each year until then.

(Source: Statista )

Social media usage by age

According to new data from eMarketer , most U.S. social media users are between the ages of 27 and 42 and fall under the Millennial generation. With 68.5 million Millennials using social media in the U.S., this group accounts for nearly one-third (30.3%) of all U.S. social media users. The next closest age group by usage is Gen Z (ages 11-26), with 56.4 million social media users, followed by Gen X (ages 43-58), with 51.8 million users. Baby Boomers between the ages of 59 and 77 are the age group with the lowest social media usage, with only 36.9 million users.

(Source: Oberlo )

Although Millennials are the age group that uses social media the most, eMarketer predicts their usage will remain relatively flat through 2027, while the number of Gen Z users is expected to grow significantly. The data also suggests that the number of Gen X and Baby Boomers who use social media will decrease over time.

(Source: eMarketer )

Daily time on social media

On average, people spend 2 hours and 24 minutes on social media each day. Combined, it’s estimated that users will have spent 4 trillion hours on social media in 2023. Not all social media platforms are equally engaging, as Statista found people spent more time on TikTok than anywhere else. On average, social media users in the U.S. spent 53.8 minutes on TikTok, with the next closest being YouTube at 48.7 minutes per day. After that, there was a steep drop off to 34.1 minutes for Twitter/X and other platforms before reaching last place, Reddit at only 24.1 minutes per day.

Despite TikTok having the most time on average per day, DataReportal found that YouTube has the highest average session duration at 7 minutes and 29 seconds. This could be because users are watching longer-form content on YouTube compared to the shorter content that TikTok is known for.

(Source: DataReportal via Exploding Topics )

Facebook demographics

Given that Facebook is the number one platform for adults , understanding its audience is crucial for devising the social media strategy for your business.

2024 Facebook demographics data

Active monthly users

Facebook has 3.03 billion active monthly users

Active daily users

Facebook has 2.085 billion daily active users

4.6% of Facebook’s users are between the ages of 13-17

22.6% of Facebook’s users are between the ages of 18-24

29.4% of Facebook’s users are between the ages of 25-34

19.1% of Facebook’s users are between the ages of 35-44

11.4% of Facebook’s users are between the ages of 45-54

7.2% of Facebook’s users are between the ages of 55-64

5.7% of Facebook’s users are 65+

43.7% of Facebook users are female

56.3% of Facebook users are male

On average, U.S. Facebook users spend 30.9 minutes a day on the platform

98.5% of users access Facebook via mobile devices

81.8% of users access Facebook via mobile devices only

16.7% of users access Facebook via mobile devices and computers

1.5% of users access Facebook via laptop or desktop only

Businesses and shopping

19% of U.S. users search for products on Facebook before shopping

The global advertising audience of Facebook is 2.249 billion

90% of social media marketers use Facebook to promote their business.

Most followed accounts

Cristiano Ronaldo: 163 million followers

Mr. Bean: 136 million followers

Shakira: 122 million followers

Instagram demographics

Instagram is the Meta-owned photo and video sharing app that continues to grow its user base, with 2 billion people using Instagram every month (up from 800 million in 2018).

2024 Instagram demographics data

Instagram has 2 billion monthly active users

Instagram has 500 million daily active users

8% of Instagram’s users are between the ages of 13-17

30.8% of Instagram’s users are between the ages of 18-24

30.3% of Instagram’s users are between the ages of 25-34

15.7% of Instagram’s users are between the ages of 35-44

8.4% of Instagram’s users are between the ages of 45-54

4.3% of Instagram’s users are between the ages of 55-64

2.6% of Instagram’s users are 65+

48.2% of Instagram users are female

51.8% of Instagram users are male

On average, U.S. Instagram users spend 33.1 minutes per day on the platform

90% of Instagram users follow a business

2 out of 3 people say Instagram enables interaction with brands

83% of Instagram users say they discover new products and services on Instagram

Cristiano Ronaldo: 613 million followers

Lionel Messi: 494 million followers

Selena Gomez: 429 million followers

Pinterest demographics

Pinterest is a visual search engine that pioneered online shopping through social media. On Pinterest, people are 90% more likely to say they’re ‘always shopping’ than on other platforms. Additionally, shoppers on Pinterest spend 80% more monthly than on other platforms. Why? Pinterest claims it’s because they take the best of shopping offline and bring it online, with strong visual connections between products and what users can do with those products. There’s a lot marketers can learn from the platform, but it all starts with gaining a better understanding of the audience.

2024 Pinterest demographics data

Pinterest has 465 million monthly active users

27% of Pinterest’s users are between the ages of 18-24

30.9% of Pinterest’s users are between the ages of 25-34

15.8% of Pinterest’s users are between the ages of 35-44

10.4% of Pinterest’s users are between the ages of 45-54

8.7% of Pinterest’s users are between the ages of 55-64

4.3% of Pinterest’s users are 65+

Pinterest is one of the most gendered social media channels, which may inform which brands target this audience and how they do so.

76.2% of Pinterest users are female

17.2% of Pinterest users are male

6.6% of Pinterest users did not specify their gender

On average, U.S. Pinterest users spend 14.2 minutes per day on the platform

85% of users access Pinterest via the mobile app

Business and Shopping

More than 25% of time spent on Pinterest is spent shopping

85% of users have bought something based on pins from brands

X (formerly Twitter) demographics

X (formerly known as Twitter) allows users to reach practically any person or business simply by tagging them in a Tweet. That’s why Twitter is such a popular platform for customer service — allowing users to air complaints in real time and for customer service teams to react quickly.

2024 X demographics data

X has 666 million monthly active users

X has 245 million monetizable daily active users

28.35% of X’s users are between the ages of 18-24

29.63% of X’s users are between the ages of 25-34

17.96% of X’s users are between the ages of 35-44

11.63% of X’s users are between the ages of 45-54

7.61% of X’s users are between the ages of 55-64

4.83% of X’s users are 65+

Like Pinterest, X is highly gendered, although this channel skews the other direction.

23.28% of X users are female

66.72% of X users are male

On average, U.S. X users spend 34.1 minutes per day on the platform

Elon Musk: 156.9 million followers

Barack Obama: 132 million followers

Justin Bieber: 111.7 million followers

Business and shopping

82% of B2B content marketers use X

79% of X users follow brands on the platform

X drives 40% higher ROI than other social media channels

LinkedIn demographics

LinkedIn is a professional networking site and the top social media platform for B2B marketing . As a result of its focus on business, it’s a great way for companies to drive leads, share news, and keep up with others in their industry.

2024 LinkedIn demographics data

LinkedIn has 310 million monthly active users

16.2% of LinkedIn’s users login to the platform daily

21.7% of LinkedIn’s users are between the ages of 18-24

60% of LinkedIn’s users are between the ages of 25-34

15.4% of LinkedIn’s users are between the ages of 35-54

2.9% of LinkedIn’s users are 55+

43.7% of LinkedIn users are female

56.3% of LinkedIn users are male

On average, LinkedIn users spend just over 7 minutes per day on the platform

58.5% of LinkedIn traffic is through desktop devices

41.5% of LinkedIn traffic is through mobile devices

More than 61 million companies are on LinkedIn

96% of B2B marketers use LinkedIn for organic social marketing

Marketers see up to 2x higher conversion rates on LinkedIn compared to other social media platforms

Most Followed Accounts

Bill Gates: 34.9 million followers

Richard Branson: 18.7 million followers

Jeff Weiner: 10.4 million followers

YouTube demographics

YouTube is as utilitarian (think “how to change a spare tire”) as it is entertaining (think funny pet videos). With 2.5 billion monthly active users, YouTube offers expansive opportunities for businesses to share and market information.

2024 YouTube demographics data

YouTube has 2.491 billion monthly active users

YouTube has 122 million daily active users

15.5% of YouTube users are between the ages of 18-24

21.3% of YouTube users are between the ages of 25-34

17.5% of YouTube users are between the ages of 35-44

12.5% of YouTube users are between the ages of 45-54

9.2% of YouTube users are between the ages of 55-64

9.2% of YouTube users are between the ages of 65+

45.6% of YouTube users are female

54.4% of YouTube users are male

On average, U.S. YouTube users spend 48.7 minutes per day on the platform

70% of viewers have made a purchase after seeing a brand on YouTube

54% of marketers use YouTube

T-Series: 254 million subscribers

MrBeast: 217 million subscribers

Cocomelon: 168 million subscribers

Snapchat Demographics

Snapchat has become popular among teens and young adults under 35, making it a great platform for marketers to reach Gen Z. Interestingly, Gen Z spends less time looking at content on Snapchat but shows higher advertising recall than other generations. After watching two seconds or less of an advertiser video, 59% of Gen Z was able to recall it. Outside of Gen Z Snapchat statistics, here’s some other information about the platform’s demographics.

2024 Snapchat demographics data

Snapchat has 750 million monthly active users

Snapchat has 406 million daily active users

19.7% of Snapchat users are between the ages of 13-17

38.1% of Snapchat users are between the ages of 18-24

23.4% of Snapchat users are between the ages of 25-34

14% of Snapchat users are between the ages of 35-49

3.8% of Snapchat users are 50+

51% of Snapchat users are female

48.2% of Snapchat users are male

On average, U.S. Snapchat users spend 30 minutes per day on the platform

Snapchat users hold $4.4 trillion in global spending power

Snapchat users are 2x more likely to share their purchases with their network

Kylie Jenner: 37 million followers

Kim Kardashian: 27.2 million followers

Khloe Kardashian: 15 million followers

TikTok demographics

TikTok’s explosive growth in recent years has marked its place as a major player in the social media world (even though it doesn’t call itself a social media platform ). Like Snapchat, younger audiences dominate TikTok’s user base with 37.3% of users being between 18-24. It’s also a great platform for brands, with spending reaching 2.5 billion globally. Here’s some other information about TikTok’s user demographics to bear in mind when creating marketing strategies.

2024 TikTok demographics data

TikTok has 1.218 billion monthly active users

TikTok has 45.1 million daily active users

37.3% of TikTok users are between the ages of 18-24

32.9% of TikTok users are between the ages of 25-34

15.7% of TikTok users are between the ages of 35-44

8.3% of TikTok users are between the ages of 45-54

5.8% of TikTok users are 55+

49.2% of TikTok users are female

50.8% of TikTok users are male

On average, U.S. TikTok users spend 53.8 minutes per day on the platform

Khabane lame: 162 million followers

Charli D’Amelio: 151.6 million followers

Bella Poarch: 93.6 million followers

Consumer spending on TikTok has surpassed $2.5 billion globally

58.2% of TikTok users said they used the platform for shopping inspiration

49% of TikTok users say the platform helped them make purchasing decisions

55% of TikTok users made a purchase after seeing a brand or product on the platform

Which social media networks should your business prioritize?

Of course, knowing who’s using each social media platform is one thing, and engaging those people is a completely different thing. If your business needs help organizing and managing your social media activity, request a demo of our social media management software to see how you can understand your audience and seamlessly manage efforts across social media channels.

RELATED RESOURCES

ONLINE COMMUNITIES

4 brands who drive real value with their online communities

Learn how four leading brands leveraged their online communities to create lasting value for themselves and their customers.

How brands can stand out on social media

Discover how your brand can build awareness and stand out on social media using these tips with real-world examples.

Increasing brand engagement on social media

Get expert tips to increase brand engagement with your audience on social media and check out real-world examples from leading brands.

SOCIAL MEDIA

How to drive conversions, leads, and sales on social media

Maximize the value of your brand's social media presence by learning how to use it to drive conversions, leads, and sales.

CUSTOMER ENGAGEMENT

How to set customer service goals (+9 example goals)

Learn how to set customer service goals to increase your customers’ satisfaction including 9 example goals you can get started with right away.

Apr-15-2024

CUSTOMER SERVICE

8 essential customer service channels for your business

Customer support is now available across various customer service channels. Learn more about the most essential channels for your business here.

5 ways service leaders can automate to improve agent efficiency, limit attrition, and save costs

This comprehensive whitepaper with original research shows how a contact center can automate to improve agent efficiency, limit attrition, and save costs.

Apr-12-2024

A month in review: March social media trends and updates

Read all about the latest trends and industry updates that our Strategic Services team uncovered throughout March.

Apr-10-2024

Community vendor evaluation scorecard

Assess and score community vendors against the areas that matter most with our community vendor evaluation scorecard.

Questions to ask when evaluating community vendors

Navigate the complexities of community vendor evaluation with our checklist, designed to cover every critical aspect of the selection process.

6 steps to identify the right tech partner for customer service excellence

Discover expert tips to find the perfect tech partner and enhance your customer service.

Apr-04-2024

The strategic advantage of outsourcing customer service (and how to do it)

Explore 5 tips for outsourcing customer care. Elevate your service standards by mastering true partnership and transforming your customer experience approach.

Apr-01-2024

Sign up for our newsletter

Would you like to learn more about khoros.

Discover the advantages of our solution in action. Request a demo now!

Mobile Navigation

Moderna and OpenAI partner to accelerate the development of life-saving treatments.

More resources

• View product

Moderna partners with OpenAI to deploy ChatGPT Enterprise to thousands of employees across the company. Now every function is empowered with AI, creating novel use cases and GPTs that accelerate and expand the impact of every team.

Moderna has been at the intersection of science, technology, and health for more than 10 years. Moderna’s mission is to deliver the greatest possible impact to people through mRNA medicines—with the COVID-19 vaccine being their most well-known breakthrough. 

The company has partnered with OpenAI since early 2023. Now, ChatGPT Enterprise is evolving how Moderna operates across each function.

Moderna is using its platform for developing mRNA medicines to bring up to 15 new products to market in the next 5 years—from a vaccine against RSV to individualized cancer treatments. In order to achieve its ambitions, Moderna has adopted a people-centric, technology-forward approach, constantly testing new technology and innovation that can increase human capacity and clinical performance.

We believe very profoundly at Moderna that ChatGPT and what OpenAI is doing is going to change the world. We’re looking at every business process—from legal, to research, to manufacturing, to commercial—and thinking about how to redesign them with AI.

Moderna brings AI to everyone

Moderna adopted generative AI the same way Moderna adopts other technology: with the mindset of using the power of digital to maximize its positive impact on patients. To allow AI to flourish, they knew they needed to start with the user and invest in laying a strong foundation for change.

Moderna’s objective was to achieve 100% adoption and proficiency of generative AI by all its people with access to digital solutions in six months. “We believe in collective intelligence when it comes to paradigm changes,” said Miller, “it’s everyone together, everyone with a voice and nobody left behind.” For this, Moderna assigned a team of dedicated experts to drive a bespoke transformation program. Their approach combined individual, collective and structural change management initiatives.   

Individual change management initiatives included in-depth research and listening programs, as well as trainings hosted in person, online and with dedicated AI learning companions. “Using AI to teach AI was key to our success”, Miller points out. Collective change management initiatives included an AI prompt contest to identify the top 100 AI power users who were then structured as a cohort of internal Generative AI Champions. Moderna’s culture of learning led to local office hours in every business line and geography, and scaled through an internal forum on AI, which now has 2,000 active weekly participants. Lastly, structural change management initiatives included engaging Moderna’s CEO and executive committee members to foster AI culture through leadership meetings and town halls as well as incentive programs and sponsored events with internal and external experts.  

 This work led to an early win with the launch of an internal AI chatbot tool, mChat, at the beginning of 2023. Built on OpenAI’s API, mChat was a success, adopted by more than 80% of employees across the company, building a solid foundation for the adoption of ChatGPT Enterprise.  

90% of companies want to do GenAI, but only 10% of them are successful, and the reason they fail is because they haven’t built the mechanisms of actually transforming the workforce to adopt new technology and new capabilities.

Building momentum with ChatGPT Enterprise

With the launch of ChatGPT Enterprise, Moderna had a decision to make: continue developing mChat as an all-purpose AI tool, or give employees access to ChatGPT Enterprise?

“As a science-based company, we research everything,” said Brice Challamel, Head of AI Products and Platforms at Moderna. Challamel’s team did extensive user testing comparing mChat, Copilot, and ChatGPT Enterprise. “We found out that the net promoter score of ChatGPT Enterprise was through the roof. This was by far the company-favorite solution, and the one we decided to double down on,” Challamel said.  

Once employees had a way to create their own GPTs easily, the only limit was their imaginations. “We were never here to fill a bucket, but to light a fire,” Challamel said. “We saw the fire spread, with hundreds of use cases creating positive value across teams. We knew we were on to something revolutionary for the company.”

The company’s results are beyond expectations. Within two months of the ChatGPT Enterprise adoption: 

• Moderna had 750 GPTs across the company
• 40% of weekly active users created GPTs 
• Each user has 120 ChatGPT Enterprise conversations per week on average

Augmenting clinical trial development with GPTs

One of the many solutions Moderna has built and is continuing to develop and validate with ChatGPT Enterprise is a GPT pilot called Dose ID. Dose ID has the potential to review and analyze clinical data and is able to integrate and visualize large datasets. Dose ID is intended for use as a data-analysis assistant to the clinical study team, helping to augment the team’s clinical judgment and decision-making.

 “Dose ID has provided supportive rationale for why we have picked a specific dose over other doses. It has allowed us to create customized data visualizations and it has also helped the study team members converse with the GPT to further analyze the data from multiple different angles,” said Meklit Workneh, Director of Clinical Development at Moderna. 

Dose ID uses ChatGPT Enterprise’s advanced data analysis feature to automate the analysis and verify the optimal vaccine dose selected by the clinical study team, by applying standard dose selection criteria and principles. Dose ID provides a rationale, references its sources, and generates informative charts illustrating the key findings. This allows for a detailed review, led by humans and with AI input, prioritizing safety and optimizing the vaccine profile prior to further development in late-stage clinical trials. 

“The Dose ID GPT has the potential to boost the amount of work we’re able to do as a team. We can comprehensively evaluate these extremely large amounts of data, and do it in a very efficient, safe, and accurate way, while helping to ensure security and privacy,” added Workneh.

Improving compliance and telling the company’s story

Moderna’s legal team boasts 100% adoption of ChatGPT Enterprise. “It lets us focus our time and attention on those matters that are truly driving an impact for patients,” said Shannon Klinger, Moderna’s Chief Legal Officer. 

Now, with the Contract Companion GPT, any function can get a clear, readable summary of a contract. The Policy Bot GPT helps employees get quick answers about internal policies without needing to search through hundreds of documents. 

Moderna’s corporate brand team has also found many ways to take advantage of ChatGPT Enterprise. They have a GPT that helps prepare slides for quarterly earnings calls, and another GPT that helps convert biotech terminology into approachable language for investor communications. 

“Sometimes we’re so in our own world, and AI helps the brand think beyond that,” explained Kate Cronin, Chief Brand Officer of Moderna. “What would my mother want to know about Moderna, versus a regulator, versus a doctor? How do we tell our story in an effective way across different audiences? That’s where I think there’s a huge opportunity.”

A team of a few thousand can perform like a team of 100,000

With an ambitious plan to launch multiple products in the next few years, Moderna sees AI as a key component to their success—and their ability to stay lean as a business while setting new benchmarks in innovation. 

“If we had to do it the old biopharmaceutical ways, we might need a hundred thousand people today,” said Bancel. “We really believe we can maximize our impact on patients with a few thousand people, using technology and AI to scale the company.” 

Moderna has been well positioned to leverage generative AI having spent the last decade building a robust tech stack and data platform. The company fosters a culture of learning and curiosity, attracting employees that excel in adopting new technologies and building AI-first solutions.

By making business processes at Moderna more efficient and accurate, the use of AI ultimately translates to better outcomes for patients. “I’m really thankful for the entire OpenAI team, and the time and engagement they have with our team, so that together we can save more lives,” Bancel said.