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Scope of the Research – Writing Guide and Examples

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Scope of the Research

Scope of research refers to the range of topics, areas, and subjects that a research project intends to cover. It is the extent and limitations of the study, defining what is included and excluded in the research.

The scope of a research project depends on various factors, such as the research questions , objectives , methodology, and available resources. It is essential to define the scope of the research project clearly to avoid confusion and ensure that the study addresses the intended research questions.

How to Write Scope of the Research

Writing the scope of the research involves identifying the specific boundaries and limitations of the study. Here are some steps you can follow to write a clear and concise scope of the research:

• Identify the research question: Start by identifying the specific question that you want to answer through your research . This will help you focus your research and define the scope more clearly.
• Define the objectives: Once you have identified the research question, define the objectives of your study. What specific goals do you want to achieve through your research?
• Determine the population and sample: Identify the population or group of people that you will be studying, as well as the sample size and selection criteria. This will help you narrow down the scope of your research and ensure that your findings are applicable to the intended audience.
• Identify the variables: Determine the variables that will be measured or analyzed in your research. This could include demographic variables, independent variables , dependent variables , or any other relevant factors.
• Define the timeframe: Determine the timeframe for your study, including the start and end date, as well as any specific time intervals that will be measured.
• Determine the geographical scope: If your research is location-specific, define the geographical scope of your study. This could include specific regions, cities, or neighborhoods that you will be focusing on.
• Outline the limitations: Finally, outline any limitations or constraints of your research, such as time, resources, or access to data. This will help readers understand the scope and applicability of your research findings.

Examples of the Scope of the Research

Some Examples of the Scope of the Research are as follows:

Title : “Investigating the impact of artificial intelligence on job automation in the IT industry”

Scope of Research:

This study aims to explore the impact of artificial intelligence on job automation in the IT industry. The research will involve a qualitative analysis of job postings, identifying tasks that can be automated using AI. The study will also assess the potential implications of job automation on the workforce, including job displacement, job creation, and changes in job requirements.

Title : “Developing a machine learning model for predicting cyberattacks on corporate networks”

This study will develop a machine learning model for predicting cyberattacks on corporate networks. The research will involve collecting and analyzing network traffic data, identifying patterns and trends that are indicative of cyberattacks. The study aims to build an accurate and reliable predictive model that can help organizations identify and prevent cyberattacks before they occur.

Title: “Assessing the usability of a mobile app for managing personal finances”

This study will assess the usability of a mobile app for managing personal finances. The research will involve conducting a usability test with a group of participants, evaluating the app’s ease of use, efficiency, and user satisfaction. The study aims to identify areas of the app that need improvement, and to provide recommendations for enhancing its usability and user experience.

Title : “Exploring the effects of mindfulness meditation on stress reduction among college students”

This study aims to investigate the impact of mindfulness meditation on reducing stress levels among college students. The research will involve a randomized controlled trial with two groups: a treatment group that receives mindfulness meditation training and a control group that receives no intervention. The study will examine changes in stress levels, as measured by self-report questionnaires, before and after the intervention.

Title: “Investigating the impact of social media on body image dissatisfaction among young adults”

This study will explore the relationship between social media use and body image dissatisfaction among young adults. The research will involve a cross-sectional survey of participants aged 18-25, assessing their social media use, body image perceptions, and self-esteem. The study aims to identify any correlations between social media use and body image dissatisfaction, and to determine if certain social media platforms or types of content are particularly harmful.

When to Write Scope of the Research

Here is a guide on When to Write the Scope of the Research:

• Before starting your research project, it’s important to clearly define the scope of your study. This will help you stay focused on your research question and avoid getting sidetracked by irrelevant information.
• The scope of the research should be determined by the research question or problem statement. It should outline what you intend to investigate and what you will not be investigating.
• The scope should also take into consideration any limitations of the study, such as time, resources, or access to data. This will help you realistically plan and execute your research.
• Writing the scope of the research early in the research process can also help you refine your research question and identify any gaps in the existing literature that your study can address.
• It’s important to revisit the scope of the research throughout the research process to ensure that you stay on track and make any necessary adjustments.
• The scope of the research should be clearly communicated in the research proposal or study protocol to ensure that all stakeholders are aware of the research objectives and limitations.
• The scope of the research should also be reflected in the research design, methods, and analysis plan. This will ensure that the research is conducted in a systematic and rigorous manner that is aligned with the research objectives.
• The scope of the research should be written in a clear and concise manner, using language that is accessible to all stakeholders, including those who may not be familiar with the research topic or methodology.
• When writing the scope of the research, it’s important to be transparent about any assumptions or biases that may influence the research findings. This will help ensure that the research is conducted in an ethical and responsible manner.
• The scope of the research should be reviewed and approved by the research supervisor, committee members, or other relevant stakeholders. This will ensure that the research is feasible, relevant, and contributes to the field of study.
• Finally, the scope of the research should be clearly stated in the research report or dissertation to provide context for the research findings and conclusions. This will help readers understand the significance of the research and its contribution to the field of study.

Purpose of Scope of the Research

Purposes of Scope of the Research are as follows:

• Defines the boundaries and extent of the study.
• Determines the specific objectives and research questions to be addressed.
• Provides direction and focus for the research.
• Helps to identify the relevant theories, concepts, and variables to be studied.
• Enables the researcher to select the appropriate research methodology and techniques.
• Allows for the allocation of resources (time, money, personnel) to the research.
• Establishes the criteria for the selection of the sample and data collection methods.
• Facilitates the interpretation and generalization of the results.
• Ensures the ethical considerations and constraints are addressed.
• Provides a framework for the presentation and dissemination of the research findings.

Advantages of Scope of the Research

Here are some advantages of having a well-defined scope of research:

• Provides clarity and focus: Defining the scope of research helps to provide clarity and focus to the study. This ensures that the research stays on track and does not deviate from its intended purpose.
• Helps to manage resources: Knowing the scope of research allows researchers to allocate resources effectively. This includes managing time, budget, and personnel required to conduct the study.
• Improves the quality of research: A well-defined scope of research helps to ensure that the study is designed to achieve specific objectives. This helps to improve the quality of the research by reducing the likelihood of errors or bias.
• Facilitates communication: A clear scope of research enables researchers to communicate the goals and objectives of the study to stakeholders, such as funding agencies or participants. This facilitates understanding and enhances cooperation.
• Enables replication : A well-defined scope of research makes it easier to replicate the study in the future. This allows other researchers to validate the findings and build upon them, leading to the advancement of knowledge in the field.
• Increases the relevance of research: Defining the scope of research helps to ensure that the study is relevant to the problem or issue being investigated. This increases the likelihood that the findings will be useful and applicable to real-world situations.
• Reduces the risk of scope creep : Scope creep occurs when the research expands beyond the original scope, leading to an increase in the time, cost, and resources required to complete the study. A clear definition of the scope of research helps to reduce the risk of scope creep by establishing boundaries and limitations.
• Enhances the credibility of research: A well-defined scope of research helps to enhance the credibility of the study by ensuring that it is designed to achieve specific objectives and answer specific research questions. This makes it easier for others to assess the validity and reliability of the study.
• Provides a framework for decision-making : A clear scope of research provides a framework for decision-making throughout the research process. This includes decisions related to data collection, analysis, and interpretation.

Scope of the Research Vs Scope of the Project

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Scope and Delimitations – Explained & Example

• By DiscoverPhDs
• October 2, 2020

What Is Scope and Delimitation in Research?

The scope and delimitations of a thesis, dissertation or research paper define the topic and boundaries of the research problem to be investigated.

The scope details how in-depth your study is to explore the research question and the parameters in which it will operate in relation to the population and timeframe.

The delimitations of a study are the factors and variables not to be included in the investigation. In other words, they are the boundaries the researcher sets in terms of study duration, population size and type of participants, etc.

Difference Between Delimitations and Limitations

Delimitations refer to the boundaries of the research study, based on the researcher’s decision of what to include and what to exclude. They narrow your study to make it more manageable and relevant to what you are trying to prove.

Limitations relate to the validity and reliability of the study. They are characteristics of the research design or methodology that are out of your control but influence your research findings. Because of this, they determine the internal and external validity of your study and are considered potential weaknesses.

In other words, limitations are what the researcher cannot do (elements outside of their control) and delimitations are what the researcher will not do (elements outside of the boundaries they have set). Both are important because they help to put the research findings into context, and although they explain how the study is limited, they increase the credibility and validity of a research project.

Guidelines on How to Write a Scope

A good scope statement will answer the following six questions:

• Why – the general aims and objectives (purpose) of the research.
• What – the subject to be investigated, and the included variables.
• Where – the location or setting of the study, i.e. where the data will be gathered and to which entity the data will belong.
• When – the timeframe within which the data is to be collected.
• Who – the subject matter of the study and the population from which they will be selected. This population needs to be large enough to be able to make generalisations.
• How – how the research is to be conducted, including a description of the research design (e.g. whether it is experimental research, qualitative research or a case study), methodology, research tools and analysis techniques.

To make things as clear as possible, you should also state why specific variables were omitted from the research scope, and whether this was because it was a delimitation or a limitation. You should also explain why they could not be overcome with standard research methods backed up by scientific evidence.

How to Start Writing Your Study Scope

Use the below prompts as an effective way to start writing your scope:

• This study is to focus on…
• This study covers the…
• This study aims to…

Guidelines on How to Write Delimitations

Since the delimitation parameters are within the researcher’s control, readers need to know why they were set, what alternative options were available, and why these alternatives were rejected. For example, if you are collecting data that can be derived from three different but similar experiments, the reader needs to understand how and why you decided to select the one you have.

Your reasons should always be linked back to your research question, as all delimitations should result from trying to make your study more relevant to your scope. Therefore, the scope and delimitations are usually considered together when writing a paper.

How to Start Writing Your Study Delimitations

Use the below prompts as an effective way to start writing your study delimitations:

• This study does not cover…
• This study is limited to…
• The following has been excluded from this study…

Examples of Delimitation in Research

Examples of delimitations include:

• research objectives,
• research questions,
• research variables,
• target populations,
• statistical analysis techniques .

Examples of Limitations in Research

Examples of limitations include:

• Issues with sample and selection,
• Insufficient sample size, population traits or specific participants for statistical significance,
• Lack of previous research studies on the topic which has allowed for further analysis,
• Limitations in the technology/instruments used to collect your data,
• Limited financial resources and/or funding constraints.

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Setting Limits and Focusing Your Study: Exploring scope and delimitation

As a researcher, it can be easy to get lost in the vast expanse of information and data available. Thus, when starting a research project, one of the most important things to consider is the scope and delimitation of the study. Setting limits and focusing your study is essential to ensure that the research project is manageable, relevant, and able to produce useful results. In this article, we will explore the importance of setting limits and focusing your study through an in-depth analysis of scope and delimitation.

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Table of Contents

Scope and Delimitation – Definition and difference

Scope refers to the range of the research project and the study limitations set in place to define the boundaries of the project and delimitation refers to the specific aspects of the research project that the study will focus on.

In simpler words, scope is the breadth of your study, while delimitation is the depth of your study.

Scope and delimitation are both essential components of a research project, and they are often confused with one another. The scope defines the parameters of the study, while delimitation sets the boundaries within those parameters. The scope and delimitation of a study are usually established early on in the research process and guide the rest of the project.

Types of Scope and Delimitation

Significance of Scope and Delimitation

Setting limits and focusing your study through scope and delimitation is crucial for the following reasons:

• It allows researchers to define the research project’s boundaries, enabling them to focus on specific aspects of the project. This focus makes it easier to gather relevant data and avoid unnecessary information that might complicate the study’s results.
• Setting limits and focusing your study through scope and delimitation enables the researcher to stay within the parameters of the project’s resources.
• A well-defined scope and delimitation ensure that the research project can be completed within the available resources, such as time and budget, while still achieving the project’s objectives.

5 Steps to Setting Limits and Defining the Scope and Delimitation of Your Study

There are a few steps that you can take to set limits and focus your study.

1. Identify your research question or topic

The first step is to identify what you are interested in learning about. The research question should be specific, measurable, achievable, relevant, and time-bound (SMART). Once you have a research question or topic, you can start to narrow your focus.

2. Consider the key terms or concepts related to your topic

What are the important terms or concepts that you need to understand in order to answer your research question? Consider all available resources, such as time, budget, and data availability, when setting scope and delimitation.

The scope and delimitation should be established within the parameters of the available resources. Once you have identified the key terms or concepts, you can start to develop a glossary or list of definitions.

3. Consider the different perspectives on your topic

There are often different perspectives on any given topic. Get feedback on the proposed scope and delimitation. Advisors can provide guidance on the feasibility of the study and offer suggestions for improvement.

It is important to consider all of the different perspectives in order to get a well-rounded understanding of your topic.

4. Narrow your focus

Be specific and concise when setting scope and delimitation. The parameters of the study should be clearly defined to avoid ambiguity and ensure that the study is focused on relevant aspects of the research question.

This means deciding which aspects of your topic you will focus on and which aspects you will eliminate.

5. Develop the final research plan

Revisit and revise the scope and delimitation as needed. As the research project progresses, the scope and delimitation may need to be adjusted to ensure that the study remains focused on the research question and can produce useful results. This plan should include your research goals, methods, and timeline.

Examples of Scope and Delimitation

To better understand scope and delimitation, let us consider two examples of research questions and how scope and delimitation would apply to them.

Research question: What are the effects of social media on mental health?

Scope: The scope of the study will focus on the impact of social media on the mental health of young adults aged 18-24 in the United States.

Delimitation: The study will specifically examine the following aspects of social media: frequency of use, types of social media platforms used, and the impact of social media on self-esteem and body image.

Research question: What are the factors that influence employee job satisfaction in the healthcare industry?

Scope: The scope of the study will focus on employee job satisfaction in the healthcare industry in the United States.

Delimitation: The study will specifically examine the following factors that influence employee job satisfaction: salary, work-life balance, job security, and opportunities for career growth.

Setting limits and defining the scope and delimitation of a research study is essential to conducting effective research. By doing so, researchers can ensure that their study is focused, manageable, and feasible within the given time frame and resources. It can also help to identify areas that require further study, providing a foundation for future research.

So, the next time you embark on a research project, don’t forget to set clear limits and define the scope and delimitation of your study. It may seem like a tedious task, but it can ultimately lead to more meaningful and impactful research. And if you still can’t find a solution, reach out to Enago Academy using #AskEnago and tag @EnagoAcademy on Twitter , Facebook , and Quora .

Frequently Asked Questions

The scope in research refers to the boundaries and extent of a study, defining its specific objectives, target population, variables, methods, and limitations, which helps researchers focus and provide a clear understanding of what will be investigated.

Delimitation in research defines the specific boundaries and limitations of a study, such as geographical, temporal, or conceptual constraints, outlining what will be excluded or not within the scope of investigation, providing clarity and ensuring the study remains focused and manageable.

To write a scope; 1. Clearly define research objectives. 2. Identify specific research questions. 3. Determine the target population for the study. 4. Outline the variables to be investigated. 5. Establish limitations and constraints. 6. Set boundaries and extent of the investigation. 7. Ensure focus, clarity, and manageability. 8. Provide context for the research project.

To write delimitations; 1. Identify geographical boundaries or constraints. 2. Define the specific time period or timeframe of the study. 3. Specify the sample size or selection criteria. 4. Clarify any demographic limitations (e.g., age, gender, occupation). 5. Address any limitations related to data collection methods. 6. Consider limitations regarding the availability of resources or data. 7. Exclude specific variables or factors from the scope of the study. 8. Clearly state any conceptual boundaries or theoretical frameworks. 9. Acknowledge any potential biases or constraints in the research design. 10. Ensure that the delimitations provide a clear focus and scope for the study.

What is an example of delimitation of the study?

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How To Write Scope and Delimitation of a Research Paper (With Examples)

An effective research paper or thesis has a well-written Scope and Delimitation.  This portion specifies your study’s coverage and boundaries.

Not yet sure about how to write your research’s Scope and Delimitation? Fret not, as we’ll guide you through the entire writing process through this article.

Related: How To Write Significance of the Study (With Examples)

Table of Contents

What is the scope and delimitation of a research paper.

The “Scope and Delimitation” section states the concepts and variables your study covered. It tells readers which things you have included and excluded in your analysis.

This portion tells two things: 1

• The study’s “Scope” – concepts and variables you have explored in your research and;
• The study’s “Delimitation” – the “boundaries” of your study’s scope. It sets apart the things included in your analysis from those excluded.

For example, your scope might be the effectiveness of plant leaves in lowering blood sugar levels. You can “delimit” your study only to the effect of gabi leaves on the blood glucose of Swiss mice.

Where Should I Put the Scope and Delimitation?

This portion is in Chapter 1, usually after the “Background of the Study.”

Why Should I Write the Scope and Delimitation of My Research Paper?

There’s a lot to discover in a research paper or thesis. However, your resources and time dedicated to it are scarce. Thus, given these constraints, you have to narrow down your study. You do this in the Scope and Delimitation.

Suppose you’re studying the correlation between the quantity of organic fertilizer and plant growth . Experimenting with several types of plants is impossible because of several limitations. So, you’ve decided to use one plant type only. 

Informing your readers about this decision is a must. So, you have to state it in your Scope and Delimitation. It also acts as a “disclaimer” that your results are inapplicable to the entire plant kingdom.

What Is the Difference Between Delimitation and Limitation?

People often use the terms “Delimitation” and “Limitation” interchangeably. However, these words differ 2 .

Delimitation refers to factors you set to limit your analysis. It delineates those that are included in your research and those that are excluded. Remember, delimitations are within your control. 

Meanwhile, limitations are factors beyond your control that may affect your research’s results.  You can think of limitations as the “weaknesses” of your study. 

Let’s go back to our previous example. Due to some constraints, you’ve only decided to examine one plant type: dandelions. This is an example of a delimitation since it limits your analysis to dandelions only and not other plant types. Note that the number of plant types used is within your control. 

Meanwhile, your study cannot state that a higher quantity of organic fertilizer is the sole reason for plant growth. That’s because your research’s focus is only on correlation. Since this is already beyond your control, then this is a limitation. 

How To Write Scope and Delimitation: Step-by-Step Guide

To write your research’s Scope and Delimitation section, follow these steps:

1. Review Your Study’s Objectives and Problem Statement

Your study’s coverage relies on its objectives. Thus, you can only write this section if you know what you’re researching. Furthermore, ensure that you understand the problems you ought to answer. 

Once you understand the abovementioned things, you may start writing your study’s Scope and Delimitation.

2. State the Key Information To Explain Your Study’s Coverage and Boundaries

a. The Main Objective of the Research

This refers to the concept that you’re focusing on in your research. Some examples are the following:

• level of awareness or satisfaction of a particular group of people
• correlation between two variables
• effectiveness of a new product
• comparison between two methods/approaches
• lived experiences of several individuals

It’s helpful to consult your study’s Objectives or Statement of the Problem section to determine your research’s primary goal.

b. Independent and Dependent Variables Included

Your study’s independent variable is the variable that you manipulate. Meanwhile, the dependent variable is the variable whose result depends upon the independent variable. Both of these variables must be clear and specific when indicated. 

Suppose you study the relationship between social media usage and students’ language skills. These are the possible variables for the study:

• Independent Variable: Number of hours per day spent on using Facebook
• Dependent Variable: Grade 10 students’ scores in Quarterly Examination in English. 

Note how specific the variables stated above are. For the independent variable, we narrow it down to Facebook only. Since there are many ways to assess “language skills,” we zero in on the students’ English exam scores as our dependent variable. 

c. Subject of the Study

This refers to your study’s respondents or participants. 

In our previous example, the research participants are Grade 10 students. However, there are a lot of Grade 10 students in the Philippines. Thus, we have to select from a specific school only—for instance, Grade 10 students from a national high school in Manila. 

d. Timeframe and Location of the Study

Specify the month(s), quarter(s), or year(s) as the duration of your study. Also, indicate where you will gather the data required for your research. 

e. Brief Description of the Study’s Research Design and Methodology

You may also include whether your research is quantitative or qualitative, the sampling method (cluster, stratified, purposive) applied, and how you conducted the experiment.

Using our previous example, the Grade 10 students can be selected using stratified sampling. Afterward, the researchers may obtain their English quarterly exam scores from their respective teachers. You can add these things to your study’s Scope and Delimitation. 

3. Indicate Which Variables or Factors Are Not Covered by Your Research

Although you’ve already set your study’s coverage and boundaries in Step 2, you may also explicitly mention things you’ve excluded from your research. 

Returning to our previous example, you can state that your assessment will not include the vocabulary and oral aspects of the English proficiency skill. 

Examples of Scope and Delimitation of a Research Paper

1. scope and delimitation examples for quantitative research.

a. Example 1

Research Title

    A Study on the Relationship of the Extent of Facebook Usage on the English Proficiency Level of Grade 10 Students of Matagumpay High School

Scope and Delimitation

(Main Objective)

This study assessed the correlation between the respondents’ duration of Facebook usage and their English proficiency level. 

(Variables used)

The researchers used the number of hours per day of using Facebook and the activities usually performed on the platform to assess the respondents’ extent of Facebook usage. Meanwhile, the respondents’ English proficiency level is limited to their quarterly English exam scores. 

(Subject of the study)

A sample of fifty (50) Grade 10 students of Matagumpay High School served as the study’s respondents. 

(Timeframe and location)

This study was conducted during the Second Semester of the School Year 2018 – 2019 on the premises of Matagumpay High School in Metro Manila. 

(Methodology)

The respondents are selected by performing stratified random sampling to ensure that there will be ten respondents from five Grade 10 classes of the school mentioned above. The researchers administered a 20-item questionnaire to assess the extent of Facebook usage of the selected respondents. Meanwhile, the data for the respondents’ quarterly exam scores were acquired from their English teachers. The collected data are handled with the utmost confidentiality. Spearman’s Rank Order Correlation was applied to quantitatively assess the correlation between the variables.

(Exclusions)

This study didn’t assess other aspects of the respondents’ English proficiency, such as English vocabulary and oral skills. 

Note: The words inside the parentheses in the example above are guides only. They are not included in the actual text.

b. Example 2

  Level of Satisfaction of Grade 11 Students on the Implementation of the Online Learning Setup of Matagumpay High School for SY 2020 – 2021

This study aims to identify students’ satisfaction levels with implementing online learning setups during the height of the COVID-19 pandemic.

Students’ satisfaction was assessed according to teachers’ pedagogy, school policies, and learning materials used in the online learning setup. The respondents included sixty (60) Grade 11 students of Matagumpay High School who were randomly picked. The researchers conducted the study from October 2020 to February 2021. 

Online platforms such as email and social media applications were used to reach the respondents. The researchers administered a 15-item online questionnaire to measure the respondents’ satisfaction levels. Each response was assessed using a Likert Scale to provide a descriptive interpretation of their answers. A weighted mean was applied to determine the respondents’ general satisfaction. 

This study did not cover other factors related to the online learning setup, such as the learning platform used, the schedule of synchronous learning, and channels for information dissemination.

2. Scope and Delimitation Examples for Qualitative Research

  Lived Experiences of Public Utility Vehicle (PUV) Drivers of Antipolo City Amidst the Continuous June 2022 Oil Price Hikes

This research focused on the presentation and discussion of the lived experiences of PUV drivers during the constant oil price hike in June 2022.

The respondents involved are five (5) jeepney drivers from Antipolo City who agreed to be interviewed. The researchers assessed their experiences in terms of the following: (1) daily net income; (2) duration and extent of working; (3) alternative employment opportunity considerations; and (4) mental and emotional status. The respondents were interviewed daily at their stations on June 6 – 10, 2022. 

In-depth one-on-one interviews were used for data collection.  Afterward, the respondents’ first-hand experiences were drafted and annotated with the researchers’ insights. 

The researchers excluded some factors in determining the respondents’ experiences, such as physical and health conditions and current family relationship status. 

 A Study on the Perception of the Residents of Mayamot, Antipolo City on the Political and Socioeconomic Conditions During the Post-EDSA Period (1986 – 1996)

This research aims to discuss the perception of Filipinos regarding the political and socioeconomic economic conditions during the post-EDSA period, specifically during the years 1986 – 1996. 

Ten (10) residents of Mayamot, Antipolo City, who belonged to Generation X (currently 40 – 62 years old), were purposively selected as the study’s respondents. The researchers asked them about their perception of the following aspects during the period mentioned above (1) performance of national and local government; (2) bureaucracy and government services; (3) personal economic and financial status; and (4) wage purchasing power. 

The researchers conducted face-to-face interviews in the respondents’ residences during the second semester of AY 2018 – 2019. The responses were written and corroborated with the literature on the post-EDSA period. 

The following factors were not included in the research analysis: political conflicts and turmoils, the status of the legislative and judicial departments, and other macroeconomic indicators. 

Tips and Warnings

1. use the “5ws and 1h” as your guide in understanding your study’s coverage.

• Why did you write your study?  
• What variables are included?
• Who are your study’s subject
• Where did you conduct the study?
• When did your study start and end?
• How did you conduct the study?

2. Use key phrases when writing your research’s scope

• This study aims to … 
• This study primarily focuses on …
• This study deals with … 
• This study will cover …
• This study will be confined…

3. Use key phrases when writing factors beyond your research’s delimitations

• The researcher(s) decided to exclude …
• This study did not cover….
• This study excluded … 
• These variables/factors were excluded from the study…

4. Don’t forget to ask for help

Your research adviser can assist you in selecting specific concepts and variables suitable to your study. Make sure to consult him/her regularly. 

5. Make it brief

No need to make this section wordy. You’re good to go if you meet the “5Ws and 1Hs”. 

Frequently Asked Questions

1. what are scope and delimitation in tagalog.

In a Filipino research ( pananaliksik ), Scope and Delimitation is called “ Saklaw at Delimitasyon”. 

Here’s an example of Scope and Delimitation in Filipino:

Pamagat ng Pananaliksik

Epekto Ng Paggamit Ng Mga Digital Learning Tools Sa Pag-Aaral Ng Mga Mag-Aaral Ng Mataas Na Paaralan Ng Matagumpay Sa General Mathematics

Sakop at Delimitasyon ng Pag-aaral

Nakatuon ang pananaliksik na ito sa epekto ng paggamit ng mga digital learning aids sa pag-aaral ng mga mag-aaral.

Ang mga digital learning tools na kinonsidera sa pag-aaral na ito ay Google Classroom, Edmodo, Kahoot, at mga piling bidyo mula YouTube. Samantala, ang epekto sa pag-aaral ng mga mag-aaral ng mga nabanggit na digital learning tools ay natukoy sa pamamagitan ng kanilang (1) mga pananaw hinggil sa benepisyo nito sa kanilang pag-aaral sa General Mathematics at (2) kanilang average grade sa asignaturang ito.

Dalawampu’t-limang (25) mag-aaral mula sa Senior High School ng Mataas na Paaralan ng Matagumpay ang pinili para sa pananaliksik na ito. Sila ay na-interbyu at binigyan ng questionnaire noong Enero 2022 sa nasabing paaralan. Sinuri ang resulta ng pananaliksik sa pamamagitan ng mga instrumentong estadistikal na weighted mean at Analysis of Variance (ANOVA). Hindi saklaw ng pananaliksik na ito ang ibang mga aspeto hinggil sa epekto ng online learning aids sa pag-aaral gaya ng lebel ng pag-unawa sa aralin at kakayahang iugnay ito sa araw-araw na buhay. 

2. The Scope and Delimitation should consist of how many paragraphs?

Three or more paragraphs will suffice for your study’s Scope and Delimitation. Here’s our suggestion on what you should write for each paragraph:

Paragraph 1: Introduction (state research objective) Paragraph 2: Coverage and boundaries of the research (you may divide this section into 2-3 paragraphs) Paragraph 3 : Factors excluded from the study

• University of St. La Salle. Unit 3: Lesson 3 Setting the Scope and Limitation of a Qualitative Research [Ebook] (p. 12). Retrieved from https://www.studocu.com/ph/document/university-of-st-la-salle/senior-high-school/final-sg-pr1-11-12-unit-3-lesson-3-setting-the-scope-and-limitation-of-a-qualitative-research/24341582
• Theofanidis, D., & Fountouki, A. (2018). Limitations and Delimitations in the Research Process. Perioperative Nursing (GORNA), 7(3), 155–162. doi: 10.5281/zenodo.2552022

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

Browse all articles written by Jewel Kyle Fabula

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Academic Research in Education

• How to Find Books, Articles and eBooks
• Books, eBooks, & Multimedia
• Evaluating Information
• Deciding on a Topic
• Creating a Thesis Statement
• The Literature Review
• Scope of Research

Defining the Scope of your Project

What is scope.

• Choosing a Design
• Citing Sources & Avoiding Plagiarism
• Contact Library

Post-Grad Collective [PGC]. (2017, February 13). Thesis Writing-Narrow the Scope   [Video file]. Retrieved from https://www.youtube.com/watch?v=IlCO5yRB9No&feature=youtu.be

Learn to cite a YouTube Video! 

The scope of your project sets clear parameters for your research. 

A scope statement will give basic information about the depth and breadth of the project. It tells your reader exactly what you want to find out , how you will conduct your study, the reports and deliverables  that will be part of the outcome of the study, and the responsibilities of the researchers involved in the study. The extent of the scope will be a part of acknowledging any biases in the research project. 

Defining the scope of a project: 

• focuses your research goals
• clarifies the expectations for your research project
•  helps you determine potential biases in your research methodology by acknowledging the limits of your research study 
• identifies the limitations of your research 
• << Previous: The Literature Review
• Next: Choosing a Design >>
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Scope and Delimitations in Academic Research

Table of contents

• 1.1 Examples of Elements Included in the Scope
• 2.1 Examples of Delimitations in Research
• 3 Determining the Scope and Delimitation
• 4 Writing the Scope and Delimitations Section
• 5 Conclusion

Understanding the scope and delimitations of a study is crucial for defining its parameters and ensuring focused research efforts. What are delimitations in a research study? These components establish the boundaries within which the research will operate and clarify what the study aims to explore and achieve. This article delves into the significance of clearly defining the scope and every delimitation, how they guide the research focus, and their roles in shaping the research process. Additionally, it provides insights into determining these aspects and articulating them effectively in a research proposal or paper. Transitioning smoothly into the main discussion, let’s explore the importance of scope in research, guiding the focus.

The importance of Clearly Defining the Scope of the Study for Guiding Research Focus

The scope of research delineates its extent or range of inquiry, setting clear parameters for what the study will cover. It’s a foundational aspect that guides every step of the research process, from the formulation of research questions to the interpretation of results. Defining the scope helps in focusing the research efforts, ensuring that the study remains manageable and within realistic bounds.

Understanding the scope and limitation of the study allows researchers to allocate resources efficiently, ensuring that every aspect of the study receives adequate attention. It also helps in avoiding the common pitfall of overreaching, which can dilute the research’s impact and make findings less actionable. By setting a defined scope, researchers can more easily communicate their work’s relevance, limitations and delimitations in the research process to stakeholders, enhancing the credibility and applicability of their findings. Furthermore, a well-defined scope can facilitate a more targeted and effective literature review, laying a solid foundation for the research study.

When navigating the complexities of defining a study’s scope, researchers might seek external support to ensure their research is concise, well-structured, and impactful. A writing service , PapersOwl offers a spectrum tailored to meet academic research’s unique demands. Their expertise can be particularly beneficial in refining research proposals, ensuring the scope is clearly communicated and aligned with academic standards. Engaging with such a service allows researchers to benefit from professional insights, which can enhance the coherence and focus of their work. This collaboration can be instrumental in identifying the most relevant study areas and avoiding unnecessary diversions. With PapersOwl’s support, researchers can ensure their project’s scope is well-defined and compellingly presented, making a strong case for its significance and feasibility. This partnership can be a strategic step towards achieving a study’s specific objectives, ensuring it contributes valuable insights within its defined boundaries.

Examples of Elements Included in the Scope

Defining the scope of a research project is akin to drawing a map for a journey; it outlines the terrain to be explored and the boundaries within which the exploration will occur. This clarity is essential for guiding the research process, ensuring the investigation remains focused and relevant. The scope encompasses various elements, each contributing to the overall direction and integrity of the study. Let’s delve into some of these key elements:

• Research Objectives : The specific aim the study is designed to achieve.
• Geographical Coverage: The physical or virtual locations where the research is conducted.
• Time Frame: The period during which the study takes place, which could range from a few days to several years.
• Subject: The specific topics or issues the research intends to address.
• Population Being Studied: The group of individuals, organizations, or phenomena being investigated.

These components of the scope serve as critical navigational tools in the research journey. They ensure that the study remains grounded in its objectives, relevant to its intended audience or population, and manageable within its temporal and geographical constraints. By carefully defining these elements at the outset, researchers can avoid common pitfalls such as scope creep, where the study’s focus broadens uncontrollably, potentially diluting its impact and significance. A well-defined scope is instrumental in crafting a focused, coherent, and impactful research project.

Role of Delimitations in Qualitative Research

Delimitations in research examples specify the boundaries set by the investigator on what the study will not cover, distinguishing them from limitations, which are potential weaknesses in the study not controlled by the researcher. Delimitations are choices made to narrow the scope of a study, focusing on specific aspects while excluding others. In the intricate tapestry of research design, delimitations play a pivotal role in sharpening the focus and enhancing the clarity of a study. By explicitly stating what the research will not explore, delimitations help prevent the dispersion of the research efforts across too broad an area, thereby increasing the depth and specificity of the investigation. This strategic narrowing allows researchers to concentrate their inquiries on areas most likely to yield impactful insights, making efficient use of available resources and time.

One might wonder how to establish these boundaries effectively without compromising the potential breadth of discovery. Here, the expertise provided by platforms like PapersOwl, particularly their research paper help service, becomes invaluable. Their seasoned professionals can offer guidance on crafting a research design that is both focused and flexible, assisting in identifying and justifying delimitations that enhance the study’s relevance and feasibility. Through such collaboration, researchers can balance the scope and delimitation of the study, ensuring that it remains grounded in its objectives while open to unforeseen insights.

Furthermore, acknowledging delimitations in a research paper demonstrates a researcher’s critical understanding of their study’s context and constraints, enhancing the credibility of their work. It shows a mindful engagement with the research process, recognizing that by setting deliberate boundaries, the study can delve more deeply and meaningfully into its chosen area of inquiry. Thus, when thoughtfully articulated with support from research paper writing help, like that offered by PapersOwl, delimitation in research becomes a testament to the rigor and integrity of its effort.

Examples of Delimitations in Research

Delimitations in research are akin to the guardrails on a highway; they keep the investigation on track and prevent it from veering into less relevant or overly broad territories. Below are some examples of how researchers can apply delimitations to fine-tune their investigations:

• Restricting the Study to Certain Age Groups: Focusing on a specific demographic, such as teenagers or the elderly.
• Geographic Locations: Limiting the research to a particular country, city, or region.
• Specific Periods: Studying a phenomenon during a particular time frame, ignoring other periods.

Setting these research delimitations is not about narrowing the vision of the research, but rather about sharpening its focus. It allows for a more thorough and nuanced exploration of the chosen subjects, leading to more precise findings and general delimitation meaning in research. Delimitations highlight the researcher’s awareness of the study’s scope and commitment to conducting a focused, manageable investigation.

Determining the Scope and Delimitation

Identifying the scope and delimitations of your research involves understanding the research problem deeply and recognizing what is feasible within the constraints of time, resources, and data availability. Strategies for determining these include:

• Reviewing existing literature to identify gaps and opportunities.
• Consulting with experts or advisors to refine research questions.
• Considering data availability and methodological constraints.

Balancing the scope and delimitations involves ensuring the research is neither too broad, unmanageable, nor too narrow, limiting its significance. Crafting a research project that strikes the right balance between breadth and depth is a nuanced task. It requires a researcher to be acutely aware of where their study begins and ends, what it encompasses, and what it intentionally leaves out. This equilibrium is not found in isolation but through a diligent exploration of the field and an understanding of how to best position one’s work within it. A key step in this process is identifying and sourcing relevant literature and data, which can significantly influence the scope of research.

Leveraging resources such as PapersOwl’s guide on how to find sources for research papers can prove invaluable in this phase. This platform provides insights into locating credible and relevant information, ensuring that researchers build their work upon a solid foundation of existing knowledge. By understanding how to navigate the vast, effective ocean of available data, researchers can make informed decisions about the direction and limits of their study. This meticulous preparation is crucial for defining the scope and delimitations and justifying them within the context of the research proposal or paper. It demonstrates a researcher’s commitment to rigor and depth, showing that their choices are informed by a comprehensive understanding of the subject and its existing body of literature.

Writing the Scope and Delimitations Section

Articulating the scope and delimitations in a research paper or proposal is crucial for setting clear expectations. It should clearly define delimitations and what the study will and will not cover, providing a rationale for these choices. Effective wording and structure involve:

• Stating the research objectives and questions upfront.
• Describing the research methodology , data collection methods and analysis.
• Outlining the geographical coverage, time frame, and subject matter.
• Clearly stating the delimitations and the reasons behind them.

The presentation of the scope and delimitations within a research document not only guides the readers through the intentions of the research but also establishes a framework for evaluating the findings. It’s a critical section where transparency and precision are paramount, allowing the audience to grasp the extent of the study and the rationale behind its boundaries. This transparency is essential for the credibility of the research, as it demonstrates a conscious and deliberate effort to focus the investigation and acknowledges the existence of boundaries that the study does not cross.

To ensure clarity and impact, this section should seamlessly integrate with the overall narrative of the research proposal or paper. Researchers are advised to avoid jargon and overly technical language, making the research scope and delimitations accessible to a broader audience. This includes a layperson who may not have deep expertise in the field but an interest in the study’s outcomes. Additionally, it is beneficial to highlight how the defined study scope and delimitations contribute to addressing the research problem, filling knowledge gaps, or exploring uncharted territories.

Moreover, this part of the document offers an opportunity to discuss how the chosen delimitations enhance the study’s focus and depth. By justifying the exclusions, researchers can address potential critiques head-on, reinforcing the methodological choices and underscoring the study’s contribution to the field. This careful articulation ensures that the research is perceived as a well-thought-out endeavor, grounded in a strategic approach to inquiry.

The scope and delimitations of a study are foundational elements that guide the research process, setting clear boundaries and focusing efforts. By defining these aspects clearly, researchers can provide a clear roadmap for their investigation, ensuring that their work is both manageable and relevant. By consciously deciding what to exclude from the study, researchers can intensify their focus on the chosen subject, ensuring that the research efforts are concentrated where they are most needed and can be most effective. These self-imposed boundaries are critical for maintaining the study’s coherence and depth. This clarity not only aids in conducting the research but also in effectively communicating its implications, limits, and outcomes.

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

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• Open access
• Published: 27 May 2024

Association between gut microbiota and anxiety disorders: a bidirectional two-sample mendelian randomization study

• Jianbing Li 1 ,
• Changhe Fan 1 ,
• Jiaqi Wang 2 ,
• Bulang Tang 2 ,
• Jiafan Cao 2 ,
• Xianzhe Hu 2 ,
• Xuan Zhao 2 &
• Caiqin Feng 1  

BMC Psychiatry volume  24 , Article number:  398 ( 2024 ) Cite this article

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There are many articles reporting that the component of intestinal microbiota implies a link to anxiety disorders (AD), and the brain-gut axis is also a hot topic in current research. However, the specific relevance between gut microbiota and AD is uncertain. We aimed to investigate causal relationship between gut microbiota and AD by using bidirectional Mendelian randomization (MR).

Genetic instrumental variable (IV) for the gut microbiota were obtained from a genome-wide association study (GWAS) involving 18,340 participants. Summary data for AD were derived from the GWAS and included 158,565 cases and 300,995 controls. We applied the inverse variance weighted (IVW) method as the main analysis. Cochran’s Q values was computed to evaluate the heterogeneity among IVs. Sensitivity analyses including intercept of MR-Egger method and MR-PRESSO analysis were used to test the horizontal pleiotropy.

We discovered 9 potential connections between bacterial traits on genus level and AD. Utilizing the IVW method, we identified 5 bacterial genera that exhibited a direct correlation with the risk of AD: genus Eubacteriumbrachygroup , genus Coprococcus3 , genus Enterorhabdus , genus Oxalobacter , genus Ruminiclostridium6 . Additionally, we found 4 bacterial genera that exhibited a negative association with AD: genus Blautia , genus Butyricicoccus , genus Erysipelotrichaceae-UCG003 and genus Parasutterella . The associations were confirmed by the sensitivity analyses.

Our study found a causal relation between parts of the gut microbiota and AD. Further randomized controlled trials are crucial to elucidate the positive effects of probiotics on AD and their particular protection systems.

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Introduction

Anxiety disorders (AD), being the prevailing mental disorders, have a substantial impact on individuals and society alike [ 1 ]. The core features of AD contain indiscriminate anxiety and fear or elusion of persistent and debilitating threats, resulting in substantial medical costs and a burdensome morbidity burden [ 1 , 2 ]. As one of the most popular mental illnesses among young individuals, AD are also the earliest-onset mental disorders [ 3 ]. Amidst the COVID-19 pandemic, there has been a significant surge in the occurrence of AD among children, adolescents, and young adults globally [ 4 ]. First-line treatments for AD include medication and psychotherapy [ 5 ]. However, medication treatments carry certain side effects and risks, such as dependence, cognitive impairment, and an increased risk of heart disease [ 6 ]. The majority of individuals suffering from AD lack access to efficacious treatment options, leaving them vulnerable to relapse [ 7 , 8 ].

Many studies have shown that the occurrence of AD is related to changes in intestinal flora [ 9 , 10 ]. In social anxiety disorder (SAD), there was an increase in the relative abundance of Anaeromassillibacillus and Gordonibacter genera, whereas healthy controls exhibited an enrichment of Parasuterella [ 11 ]. Another article found a reduction in Eubacterium rectale and Fecalibacterium , as well as an increase in Escherichia , Shigella , Fusobacterium , and Ruminococcus in patients with generalized anxiety disorder (GAD) [ 12 ]. In addition, there are numerous documents demonstrating an association between the gut microbiota and mental illness, and the modulation of the gut microbiota on the gut-brain axis has garnered significant attention, such as an elevation of Enterobacteriaceae and Desulfovibrio , and a reduction of Faecalibacterium in patients with AD [ 10 , 13 , 14 , 15 , 16 , 17 ]. In the aforementioned section, it was observed that the evidence exhibits complexities and disparities, as well as some contradictory results, potentially stemming from various confounding factors among different studies.

The previous studies examining the connection between gut microbiota and AD have predominantly relied on cross-sectional designs, which limits the ability to establish a causal relationship between these associations. Therefore, unraveling the causal mechanisms behind gut microbiota-derived AD not only enhances our understanding of their pathogenesis but also provides valuable guidance for implementing microbiota-directed interventions in clinical settings to address AD. Previous Mendelian randomization (MR) studies have primarily focused on investigating the causal relationship between oral microbiota abundance and AD, or between gut microbiota and other psychiatric disorders. A systematic MR study specifically examining the causal relationship between gut microbiota and AD is still lacking in the current literature. In light of this, it is imperative to unravel the causal link between the gut microbiota and AD.

MR is a statistical approach that infers a causal relationship with exposure to a result. It leverages genetic variations linked to the exposure as a proxy for the exposure itself, enabling the assessment of the association between the exposure and the outcome [ 18 ]. Due to the highly effective findings of large-scale genome-wide association study (GWAS) at the gut microbiota and disease level, MR analysis has been abroad used in many scenarios, such as between the oral microbiome and AD, relations between genetically determined metabolites and anxiety symptoms [ 19 , 20 ]. However, there are no specific studies on the causal relationship between gut microbiota and AD. In this research, we applied a bidirectional two-sample MR method to investigate causal relationship between the gut microbiota and AD.

Materials and methods

The assumptions and study design of mr.

MR is a methodology employed to assess causal associations between variables. In order to ensure the validity of MR analysis, 3 fundamental assumptions must be met: (i) the instrumental variable (IV) exhibits a strong link to the exposure factor, (ii) the IV remains unaffected by potential confounding factors., and (iii) the IV influences the result factor solely via the exposure factor [ 21 ]. By applying strict selection criteria, appropriate SNPs were selected as IV for conducting MR analysis on two independent samples. The main aim was to examine the causal relationship between gut microbiota and AD. Furthermore, this study adhered to the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) framework [ 22 ] (Fig.  1 ).

A flowchart illustrating the MR analysis process for the association between gut microbiota and AD

Data sources

The data on gut microbiota GWAS used in this study were obtained from an overall meta-analysis conducted by the MiBioGen consortium. The meta-analysis comprised a total of 18,340 individuals from 24 different groups. The alliance combines human whole-genome genotyping with fecal 16 S rRNA sequencing data to perform thorough research and analysis. The large-scale, multi-ethnic genome-wide meta-analysis provided valuable insights into the genetic influences on the gut microbiome composition [ 23 ]. The GWAS data on the gut microbiome can be integrated into MR studies to explore the causal relationship between genetic variations in the gut microbiome and phenotypic traits, providing valuable insights into the role of the microbiome in human health and disease.

As for the data on genetic variants linked to AD, they were sourced from the Medical Research Council Integrative Epidemiology Unit (MRC-IEU) consortium. The cases were defined as individuals who had sought medical attention for symptoms of nervousness, anxiety, or depression. The study population consisted of individuals of European descent, comprising both males and females, and the data were sourced from the year 2018. The dataset included a total of 158,565 cases and 300,995 controls. The diagnosis was based on self-report questionnaires. Detailed information regarding the data origins for this MR study can be found in Table  1 [ 24 , 25 ].

Selection of IV

The GWAS data of exposure contained a total of 5 taxonomic levels for 211 bacterial groups. The genus level is the smallest and most specific classification level. To accurately identify each pathogenic bacterial group, we focused our analysis only on the genus level, specifically examining 131 bacterial classifications. After excluding 12 unknown groups, a total of 119 bacterial genera were included in the study.

To fulfill the demands of MR studies, our initial step involved the SNPs that exhibited an intense association with the exposure factors. However, when employing a stringent threshold of ( P  < 5 × 10 − 8 ), we obtained a limited number of IVs. Consequently, we adjusted the threshold to ( P  < 1 × 10 − 5 ) to ensure the inclusion of more IVs, thereby enabling robust and reliable results. For the selection of IVs associated with AD in the reverse MR analysis, a heightened level of stringency was implemented by applying a P -value threshold of P  < 5 × 10 − 8 .

We utilized the F-statistic to further evaluate the instrument strength. The F-statistic was determined using the formula: F =  β 2 / SE 2 . This statistic provided an assessment of the overall instrument strength [ 26 ] (Fig.  2 ). An F-statistic exceeding 10 was considered indicative of an intense conjunction between the IV and the exposure. Besides P -value threshold, the F statistic in our analysis would provide additional information on the instrument strength beyond P -value.

Assumptions in MR studies: a brief overview

Statistical analysis

The primary methodology employed in MR analysis is the inverse variance weighting (IVW) method. This approach utilizes a meta-analysis technique to combine the Wald estimates connected to individual single nucleotide polymorphisms (SNPs), providing comprehensive estimate of the collective impact of gut microbiota on AD. A crucial assumption in MR is the absence of horizontal pleiotropy, where the IV has a direct impact on the outcome variable solely through the exposure factor, without any influence from through alternative pathways. When this assumption is satisfied, the IVW method can provide estimates that are consistent and estimates [ 27 ]. In cases where a causal relationship ( P  < 0.05) is established by the IVW method, two alternative approaches, namely MR-Egger and the weighted median approach, are utilized to supplement an enrich the IVW results. The MR-Egger method relaxes the assumption of a zero intercept, and it can estimate causal effects, even pleiotropy was presented in IVs. The intercept in the MR-Egger method can indicate the extent of horizontal pleiotropy [ 27 ]. These additional methods provide valuable insights and strengthen the overall analysis by considering potential biases and alternative causal pathways.

The weighted median method can return unbiased causal estimate when only 50% of SNPs are valid [ 28 ]. In this study, we employed a significance threshold of P  < 0.05 to determine statistical significance, and the assessment of causality was expressed through odds ratios (OR) and 95% confidence intervals (CI). In instances where causal relationships were established, unidentified taxa were excluded, and additional sensitivity analyses were performed to guarantee the stability of the consequences. The false discovery rate (FDR) is utilized to control for multiple testing and reduce the likelihood of false positive findings. All of the aforementioned analyses were performed utilizing the TwoSampleMR package (version 0.5.7) in R (version 4.3.0), providing a robust and standardized approach to MR analysis.

According to the criteria for IV selection, a total of 1,531 SNPs were identified and selected as IV associated with gut microbiota. The F-statistics for these IVs all exceed 10, suggesting that the estimated coefficients are improbable to be influenced by the bias caused by weak instruments. Supplementary Tables 1 and 2 provides detailed information about the selected IVs. None of the SNPs were involved in more than one of the association results in Fig.  3 .

The scatter plots depict the causal relationship between gut microbiota and AD

The majority of gut microbiota showed no significant correlation with AD. However, using the IVW method, we identified 9 bacterial features that were significantly associated with the risk of AD on genus level (Supplementary Table 3 ). We used 3 methods, IVW, weighted median and MR-Egger, and defined P  < 0.05 for IVW method screening as a positive result.

Among them, 4 bacterial genera are negatively correlated with AD, indicating that a higher genetically predicted a lower risk of for AD (Fig. 4 and Supplementary Table 4 ). They are: genus Blautia (OR = 0.9838, 95% CI, 0.9725–0.9952, P  = 0.0056), genus Butyricicoccus (OR = 0.9859, 95% CI, 0.9739–0.9981, P  = 0.0233), genus ErysipelotrichaceaeUCG003 (OR = 0.9914, 95% CI, 0.9833–0.9995, P  = 0.0381) and genus Parasutterella (OR = 0.9911, 95% CI, 0.9823–0.9999, P  = 0.0478). Supplementary Table 4 shows the completed data. In sensitivity analysis, MR-Egger, weighted median demonstrated consistent results, except for genus ErysipelotrichaceaeUCG003 , where the MR-Egger trend was in the contrary direction compared to IVW and weighted median.

The forest plot illustrates the connections between 9 bacterial genus traits and the likelihood of developing AD

Another 5 bacterial genera showed a positive correlation with AD, genus Eubacteriumbrachygroup (OR = 1.0068, 95% CI, 1.0010–1.0127, P  = 0.0225), genus Coprococcus3 (OR = 1.0164, 95% CI, 1.0046–1.0285, P  = 0.0065), genus Enterorhabdus (OR = 1.0117, 95% CI, 1.0027–1.0208, P  = 0.0108), genus Oxalobacter (OR = 1.0067, 95% CI, 1.0009–1.0125, P  = 0.0231) and genus Ruminiclostridium6 (OR = 1.0129, 95% CI, 1.0048–1.0212, P  = 0.0019) (Fig. 4 and Supplementary Table 4 ). In the MR-Egger method, the trends of genus Eubacteriumbrachygroup are different from those of the IVW and WM methods.

In horizontal pleiotropy analysis, we used the MR-Egger method and found P -value of the MR-intercept were all greater than 0.05. In addition, further MR PRESSO analysis was conducted, ruling out the existence of horizontal pleiotropy ( P  > 0.05) (Supplementary Tables 5 and 6 ). To assess the heterogeneity of gut microbiome IVs, we employed Cochran’s Q test statistics, which revealed no heterogeneity among the gut microbiome IVs ( P  > 0.05) (Supplementary Table 7 ).

Reverse MR analyses were conducted to examine the links between the 9 bacterial genera and AD. No significant statistical relationship was observed using the IVW method: genus Eubacteriumbrachygroup (OR = 1.4058, 95% CI, 0.4060–4.8674, P  = 0.5909), genus Blautia (OR = 0.9453, 95% CI, 0.5572–1.6038, P  = 0.8348), genus Butyricicoccus (OR = 0.9834, 95% CI, 0.5704–1.6952, P  = 0.9518), genus Coprococcus3 (OR = 0.8886, 95% CI, 0.5040–1.5667, P  = 0.6831), genus Enterorhabdus (OR = 1.0383, 95% CI, 0.4168–2.5868, P  = 0.9356), genus ErysipelotrichaceaeUCG003 (OR = 0.6593, 95% CI, 0.3556–1.2221, P  = 0.1858), genus Oxalobacter (OR = 1.2849, 95% CI, 0.4021–4.1051, P  = 0.6724), genus Parasutterella (OR = 0.7245, 95% CI, 0.3713–1.4136, P  = 0.3447), genus Ruminiclostridium6 (OR = 0.7095, 95% CI, 0.3825–1.3162, P  = 0.2764) (Supplementary Tables 8 and 9 ).

In the context of this study, we used two-sample MR studies to discover the link between AD and gut microbiota. Among the 9 bacterial genus we found, 4 bacteria were negatively correlated with AD and may have a positive effect on AD, and the other 5 bacteria were positively correlated with the occurrence of AD and may promote the development of AD.

Blautia stercoris MRx0006 has been shown to alleviate social dysfunctions, monotonous behaviors, and anxiety-like behaviors relevant to autism disorders in a mouse model. MRx0006 administration at the microbial level, as observed by Paromita Sen et al., resulted in a reduction in the abundance of Alistipes putredinis, which likely underlie the observed increase in expressions of oxytocin, arginine vasopressin, and their receptors, ultimately leading to improved behavioral outcomes [ 29 ]. Butyricicoccus was also inversely associated with AD in a cross-sectional study, which is consistent with our findings [ 12 ]. Approximately 70% of individuals with autism spectrum disorder (ASD) exhibit comorbid symptoms of anxiety, and the findings from a published article confirming the decreased relative abundance of ErysipelotrichaceaeUCG003 in ASD patients further support our research results indicating a negative correlation between ErysipelotrichaceaeUCG003 and AD [ 30 ]. In a study examining SAD, the control group exhibited higher levels of the positive bacteria Parasutterella compared to the anxiety group. The term “psychobiotics” has been coined to refer to these microbes that are associated with improved mood [ 11 ]. However, in a study by Yi Zhang et al., a psychological stress model was established in C57BL/6J mice, followed by fecal microbiota transplantation using samples from stressed (S) and non-stressed (NS) mice. The results showed an increased abundance of Parasutterella in S mice and mechanistic analysis suggested its potential involvement in negative regulation of metabolism. Despite this controversial finding, our study utilized MR to reveal a negative association between Parasutterella and anxiety disorders. However, further experimental investigations are required to elucidate the underlying molecular mechanisms [ 31 ].

Five bacterial genera positively linked to anxiety may indicate that they exacerbate anxiety, but they were less reported. In a study in which consuming prebiotics altered the microbiota of healthy adults, the prebiotics reduced Eubacteriumbrachygroup but did not significantly change biomarkers of stress or mental health symptoms [ 32 ]. In previous studies on AD cases, it has been found that individuals with AD have lower levels of Coprococcus [ 33 ]. However, in our study, we observed an increasing trend in Coprococcus3 , despite belonging to the same genus. This suggests that even within the same genus, the impact of different genus may vary. In contrast to our findings, Enterorhabdus exhibited a declining pattern in a mouse model of anxiety and depression induced by social defeat [ 34 ]. This observation highlights the influence of various factors on alterations in gut microbiota, which may diverge across different species.

Nevertheless, it is crucial to acknowledge that our study has certain limitations. First, the results of this analysis are limited to European populations and may not be generalizable to other populations. Secondly, we observed that the adjusted P -values remained relatively large after multiple test adjustment. The reduced statistical power resulting from the limited sample size may also constrain our ability to detect significant associations between variables. Finally, proving the direct impact of sample types on the outcomes is challenging. However, the selection of sample types is often constrained by the availability of suitable genetic instruments and relevant data sources. The dataset we utilized does not provide specific information on the dietary habits of the individuals or their other medical conditions. Therefore, further examination and validation are needed in the future.

In summary, utilizing large-scale GWAS analysis, MR studies have disclosed a causal relationship between gut microbiota and AD. Among these, 4 bacterial genera exhibited a negative correlation, while 5 bacteria genera showed a positive correlation with AD. However, further exploration of the mechanisms linking gut microbiota to AD requires the establishment of larger GWAS databases. Several gut bacteria have been identified to reduce the occurrence of anxiety, offering promising prospects for the treatment and precaution of AD. Subsequent research should prioritize the exploration of the underlying mechanisms and the development of targeted interventions based on these findings.

Data availability

The raw data analyzed during the current study were available in public databases including IEU database(ukb-b-6991) and MiBioGen database(https://mibiogen.gcc.rug.nl). The code and data related to this study are available from the corresponding author upon reasonable request.

Abbreviations

• Anxiety disorders
• Mendelian randomization

Instrumental variable(s)

Genome-wide association study

Medical Research Council Integrative Epidemiology Unit

Inverse variance weighting

Social anxiety disorder

Generalized anxiety disorder

Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization

Single nucleotide polymorphism(s)

Odds ratios

Confidence intervals

Autism spectrum disorder

Major depressive disorder

Penninx BW, Pine DS, Holmes EA, et al. Anxiety disorders[J] Lancet. 2021;397:914–27.

PubMed   PubMed Central   Google Scholar  

Bandelow B, Michaelis S. Epidemiology of anxiety disorders in the 21st century[J]. Dialogues Clin Neurosci. 2015;17:327–35.

Article   PubMed   PubMed Central   Google Scholar  

Warner EN, Ammerman RT, Glauser TA, et al. Developmental epidemiology of pediatric anxiety disorders[J]. Child Adolesc Psychiatr Clin N Am. 2023;32:511–30.

Article   PubMed   Google Scholar  

Fortuna LR, Brown IC, Lewis Woods GG, et al. The impact of COVID-19 on anxiety disorders in Youth: coping with stress, worry, and recovering from a Pandemic[J]. Child Adolesc Psychiatr Clin N Am. 2023;32:531–42.

Wehry AM, Beesdo-Baum K, Hennelly MM, et al. Assessment and treatment of anxiety disorders in children and adolescents[J]. Curr Psychiatry Rep. 2015;17:52.

Szuhany KL, Simon NM. Anxiety disorders: a review[J]. JAMA. 2022;328:2431–45.

Article   CAS   PubMed   Google Scholar  

Uher R. The global impact of anxiety disorders[J]. Lancet Psychiatry. 2023;10:239–40.

Scholten W, Ten Have M, Van Geel C, et al. Recurrence of anxiety disorders and its predictors in the general population[J]. Psychol Med. 2023;53:1334–42.

Yang B, Wei J, Ju P, et al. Effects of regulating intestinal microbiota on anxiety symptoms: a systematic review[J]. Gen Psychiatr. 2019;32:e100056.

Simpson CA, Diaz-Arteche C, Eliby D, et al. The gut microbiota in anxiety and depression - a systematic review[J]. Clin Psychol Rev. 2021;83:101943.

Butler MI, Bastiaanssen TFS, Long-Smith C, et al. The gut microbiome in social anxiety disorder: evidence of altered composition and function[J]. Translational Psychiatry. 2023;13:95.

Jiang HY, Zhang X, Yu ZH, et al. Altered gut microbiota profile in patients with generalized anxiety disorder[J]. J Psychiatr Res. 2018;104:130–6.

Socała K, Doboszewska U, Szopa A, et al. The role of microbiota-gut-brain axis in neuropsychiatric and neurological disorders[J]. Pharmacol Res. 2021;172:105840.

Nikolova VL, Smith MRB, Hall LJ, et al. Perturbations in gut microbiota composition in psychiatric disorders: a review and meta-analysis[J]. JAMA Psychiatry. 2021;78:1343–54.

Generoso JS, Giridharan VV, Lee J, et al. The role of the microbiota-gut-brain axis in neuropsychiatric disorders[J]. Braz J Psychiatry. 2021;43:293–305.

Mörkl S, Butler MI, Holl A, et al. Probiotics and the microbiota-gut-brain axis: focus on psychiatry[J]. Curr Nutr Rep. 2020;9:171–82.

Needham BD, Funabashi M, Adame MD, et al. A gut-derived metabolite alters brain activity and anxiety behaviour in mice[J]. Nature. 2022;602:647–53.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization[J] JAMA. 2017;318:1925–6.

Li C, Chen Y, Wen Y, et al. A genetic association study reveals the relationship between the oral microbiome and anxiety and depression symptoms[J]. Front Psychiatry. 2022;13:960756.

Xiao G, He Q, Liu L, et al. Causality of genetically determined metabolites on anxiety disorders: a two-sample mendelian randomization study[J]. J Transl Med. 2022;20:475.

Xie L, Zhao H, Chen W. Relationship between gut microbiota and thyroid function: a two-sample mendelian randomization study[J]. Front Endocrinol (Lausanne). 2023;14:1240752.

Skrivankova VW, Richmond RC, Woolf BR, et al. Strengthening the reporting of Observational studies in Epidemiology using mendelian randomization: the STROBE-MR Statement[J]. JAMA. 2021;326:1614–21.

Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition[J]. Nat Genet. 2021;53:156–65.

Lyall DM, Inskip HM, Mackay D, et al. Low birth weight and features of neuroticism and mood disorder in 83 545 participants of the UK Biobank cohort[J]. BJPsych Open. 2016;2:38–44.

Smith DJ, Nicholl BI, Cullen B, et al. Prevalence and characteristics of probable major depression and bipolar disorder within UK biobank: cross-sectional study of 172,751 participants[J]. PLoS ONE. 2013;8:e75362.

Burgess S, Thompson SG. Avoiding bias from weak instruments in mendelian randomization studies[J]. Int J Epidemiol. 2011;40:755–64.

Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol. 2013;37:658–65.

Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator[J]. Genet Epidemiol. 2016;40:304–14.

Sen P, Sherwin E, Sandhu K, et al. The live biotherapeutic Blautia stercoris MRx0006 attenuates social deficits, repetitive behaviour, and anxiety-like behaviour in a mouse model relevant to autism[J]. Brain Behav Immun. 2022;106:115–26.

Chen Y-C, Lin H-Y, Chien Y, et al. Altered gut microbiota correlates with behavioral problems but not gastrointestinal symptoms in individuals with autism[J]. Brain Behav Immun. 2022;106:161–78.

Zhang Y, Zhang J, Wu J, et al. Implications of gut microbiota dysbiosis and fecal metabolite changes in psychologically stressed mice[J]. Front Microbiol. 2023;14:1124454.

Mysonhimer AR, Cannavale CN, Bailey MA, et al. Prebiotic consumption alters Microbiota but not biological markers of stress and inflammation or mental health symptoms in healthy adults: a randomized, controlled, crossover trial[J]. J Nutr. 2023;153:1283–96.

Chen YH, Bai J, Wu D, et al. Association between fecal microbiota and generalized anxiety disorder: severity and early treatment response[J]. J Affect Disord. 2019;259:56–66.

Zou R, Tian P, Xu M, et al. Psychobiotics as a novel strategy for alleviating anxiety and depression[J]. J Funct Foods. 2021;86:104718.

Article   Google Scholar  

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Acknowledgements

We express our gratitude to the hospital action teams, staff, and participants from the participating hospitals for their valuable support in data collection. Additionally, we extend our appreciation to our collaborators for their assistance throughout the process.

Program of Guangzhou Science and Technology Program Project (No. 202102010115) and Guangdong Yiyang Healthcare Charity Foundation (No. JZ2022001-3).

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Jianbing Li, Changhe Fan & Caiqin Feng

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CQF designed the research framework. JBL is responsible for data and analysis methods determination as well as manuscript writing. CHF assisted in conducting the literature review. JQW was responsible for manuscript writing. BLT and JFC performed the data statistical analysis. XZH and XZ were responsible for critical revisions.

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Li, J., Fan, C., Wang, J. et al. Association between gut microbiota and anxiety disorders: a bidirectional two-sample mendelian randomization study. BMC Psychiatry 24 , 398 (2024). https://doi.org/10.1186/s12888-024-05824-x

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Received : 23 December 2023

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• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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An Author Correction to this article was published on 07 May 2024

This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

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07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

Henrekson, M. & Johansson, D. Gazelles as job creators: A survey and interpretation of the evidence. Small Bus. Econ. 35 , 227–244 (2010).

Article   Google Scholar  

Davila, A., Foster, G., He, X. & Shimizu, C. The rise and fall of startups: Creation and destruction of revenue and jobs by young companies. Aust. J. Manag. 40 , 6–35 (2015).

Which vaccine saved the most lives in 2021?: Covid-19. The Economist (Online) (2022). noteName - AstraZeneca; Pfizer Inc; BioNTech SE; Copyright - Copyright The Economist Newspaper NA, Inc. Jul 14, 2022; Last updated - 2022-11-29.

Oltermann, P. Pfizer/biontech tax windfall brings mainz an early christmas present (2021). noteName - Pfizer Inc; BioNTech SE; Copyright - Copyright Guardian News & Media Limited Dec 27, 2021; Last updated - 2021-12-28.

Grant, K. A., Croteau, M. & Aziz, O. The survival rate of startups funded by angel investors. I-INC WHITE PAPER SER.: MAR 2019 , 1–21 (2019).

Google Scholar  

Top 20 reasons start-ups fail - cb insights version (2019). noteCopyright - Copyright Newstex Oct 21, 2019; Last updated - 2022-10-25.

Hochberg, Y. V., Ljungqvist, A. & Lu, Y. Whom you know matters: Venture capital networks and investment performance. J. Financ. 62 , 251–301 (2007).

Fracassi, C., Garmaise, M. J., Kogan, S. & Natividad, G. Business microloans for us subprime borrowers. J. Financ. Quantitative Ana. 51 , 55–83 (2016).

Davila, A., Foster, G. & Gupta, M. Venture capital financing and the growth of startup firms. J. Bus. Ventur. 18 , 689–708 (2003).

Nann, S. et al. Comparing the structure of virtual entrepreneur networks with business effectiveness. Proc. Soc. Behav. Sci. 2 , 6483–6496 (2010).

Guzman, J. & Stern, S. Where is silicon valley?. Science 347 , 606–609 (2015).

Article   ADS   CAS   PubMed   Google Scholar  

Aldrich, H. E. & Wiedenmayer, G. From traits to rates: An ecological perspective on organizational foundings. 61–97 (2019).

Gartner, W. B. Who is an entrepreneur? is the wrong question. Am. J. Small Bus. 12 , 11–32 (1988).

Thornton, P. H. The sociology of entrepreneurship. Ann. Rev. Sociol. 25 , 19–46 (1999).

Eikelboom, M. E., Gelderman, C. & Semeijn, J. Sustainable innovation in public procurement: The decisive role of the individual. J. Public Procure. 18 , 190–201 (2018).

Kerr, S. P. et al. Personality traits of entrepreneurs: A review of recent literature. Found. Trends Entrep. 14 , 279–356 (2018).

Hamilton, B. H., Papageorge, N. W. & Pande, N. The right stuff? Personality and entrepreneurship. Quant. Econ. 10 , 643–691 (2019).

Salmony, F. U. & Kanbach, D. K. Personality trait differences across types of entrepreneurs: A systematic literature review. RMS 16 , 713–749 (2022).

Freiberg, B. & Matz, S. C. Founder personality and entrepreneurial outcomes: A large-scale field study of technology startups. Proc. Natl. Acad. Sci. 120 , e2215829120 (2023).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kern, M. L., McCarthy, P. X., Chakrabarty, D. & Rizoiu, M.-A. Social media-predicted personality traits and values can help match people to their ideal jobs. Proc. Natl. Acad. Sci. 116 , 26459–26464 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Dalle, J.-M., Den Besten, M. & Menon, C. Using crunchbase for economic and managerial research. (2017).

Block, J. & Sandner, P. What is the effect of the financial crisis on venture capital financing? Empirical evidence from us internet start-ups. Ventur. Cap. 11 , 295–309 (2009).

Antretter, T., Blohm, I. & Grichnik, D. Predicting startup survival from digital traces: Towards a procedure for early stage investors (2018).

Dworak, D. Analysis of founder background as a predictor for start-up success in achieving successive fundraising rounds. (2022).

Hsu, D. H. Venture capitalists and cooperative start-up commercialization strategy. Manage. Sci. 52 , 204–219 (2006).

Blank, S. Why the lean start-up changes everything (2018).

Kaplan, S. N. & Lerner, J. It ain’t broke: The past, present, and future of venture capital. J. Appl. Corp. Financ. 22 , 36–47 (2010).

Hallen, B. L. & Eisenhardt, K. M. Catalyzing strategies and efficient tie formation: How entrepreneurial firms obtain investment ties. Acad. Manag. J. 55 , 35–70 (2012).

Gompers, P. A. & Lerner, J. The Venture Capital Cycle (MIT Press, 2004).

Shane, S. & Venkataraman, S. The promise of entrepreneurship as a field of research. Acad. Manag. Rev. 25 , 217–226 (2000).

Zahra, S. A. & Wright, M. Understanding the social role of entrepreneurship. J. Manage. Stud. 53 , 610–629 (2016).

Bonaventura, M. et al. Predicting success in the worldwide start-up network. Sci. Rep. 10 , 1–6 (2020).

Schwartz, H. A. et al. Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS ONE 8 , e73791 (2013).

Plank, B. & Hovy, D. Personality traits on twitter-or-how to get 1,500 personality tests in a week. In Proceedings of the 6th workshop on computational approaches to subjectivity, sentiment and social media analysis , pp 92–98 (2015).

Arnoux, P.-H. et al. 25 tweets to know you: A new model to predict personality with social media. In booktitleEleventh international AAAI conference on web and social media (2017).

Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2 , 313–345 (2007).

Article   PubMed   PubMed Central   Google Scholar  

Youyou, W., Kosinski, M. & Stillwell, D. Computer-based personality judgments are more accurate than those made by humans. Proc. Natl. Acad. Sci. 112 , 1036–1040 (2015).

Soldz, S. & Vaillant, G. E. The big five personality traits and the life course: A 45-year longitudinal study. J. Res. Pers. 33 , 208–232 (1999).

Damian, R. I., Spengler, M., Sutu, A. & Roberts, B. W. Sixteen going on sixty-six: A longitudinal study of personality stability and change across 50 years. J. Pers. Soc. Psychol. 117 , 674 (2019).

Article   PubMed   Google Scholar  

Rantanen, J., Metsäpelto, R.-L., Feldt, T., Pulkkinen, L. & Kokko, K. Long-term stability in the big five personality traits in adulthood. Scand. J. Psychol. 48 , 511–518 (2007).

Roberts, B. W., Caspi, A. & Moffitt, T. E. The kids are alright: Growth and stability in personality development from adolescence to adulthood. J. Pers. Soc. Psychol. 81 , 670 (2001).

Article   CAS   PubMed   Google Scholar  

Cobb-Clark, D. A. & Schurer, S. The stability of big-five personality traits. Econ. Lett. 115 , 11–15 (2012).

Graham, P. Do Things that Don’t Scale (Paul Graham, 2013).

McCarthy, P. X., Kern, M. L., Gong, X., Parker, M. & Rizoiu, M.-A. Occupation-personality fit is associated with higher employee engagement and happiness. (2022).

Pratt, A. C. Advertising and creativity, a governance approach: A case study of creative agencies in London. Environ. Plan A 38 , 1883–1899 (2006).

Klotz, A. C., Hmieleski, K. M., Bradley, B. H. & Busenitz, L. W. New venture teams: A review of the literature and roadmap for future research. J. Manag. 40 , 226–255 (2014).

Duggan, M., Ellison, N. B., Lampe, C., Lenhart, A. & Madden, M. Demographics of key social networking platforms. Pew Res. Center 9 (2015).

Fisch, C. & Block, J. H. How does entrepreneurial failure change an entrepreneur’s digital identity? Evidence from twitter data. J. Bus. Ventur. 36 , 106015 (2021).

Brush, C., Edelman, L. F., Manolova, T. & Welter, F. A gendered look at entrepreneurship ecosystems. Small Bus. Econ. 53 , 393–408 (2019).

Kanze, D., Huang, L., Conley, M. A. & Higgins, E. T. We ask men to win and women not to lose: Closing the gender gap in startup funding. Acad. Manag. J. 61 , 586–614 (2018).

Fan, J. S. Startup biases. UC Davis Law Review (2022).

AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9 , 1–10 (2018).

Article   CAS   Google Scholar  

Żbikowski, K. & Antosiuk, P. A machine learning, bias-free approach for predicting business success using crunchbase data. Inf. Process. Manag. 58 , 102555 (2021).

Corea, F., Bertinetti, G. & Cervellati, E. M. Hacking the venture industry: An early-stage startups investment framework for data-driven investors. Mach. Learn. Appl. 5 , 100062 (2021).

Chapman, G. & Hottenrott, H. Founder personality and start-up subsidies. Founder Personality and Start-up Subsidies (2021).

Antoncic, B., Bratkovicregar, T., Singh, G. & DeNoble, A. F. The big five personality-entrepreneurship relationship: Evidence from slovenia. J. Small Bus. Manage. 53 , 819–841 (2015).

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Potential impact of gut microbiota on pemphigus: a two-sample Mendelian randomization study

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Scaglione GL, Fania L, De Paolis E et al (2020) Evaluation of cutaneous, oral and intestinal microbiota in patients affected by pemphigus and bullous pemphigoid: a pilot study. Exp Mol Pathol 112:104331. https://doi.org/10.1016/j.yexmp.2019.104331

Chen C, Wang P, Zhang RD et al (2022) Mendelian randomization as a tool to gain insights into the mosaic causes of autoimmune diseases. AutoimmunRev 21(12):103210. https://doi.org/10.1016/j.autrev.2022.103210

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Burgess S, Dudbridge F, Thompson SG (2016) Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med 35(11):1880–1906. https://doi.org/10.1002/sim.6835

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The author expresses profound gratitude to the researchers and participants of the initial genome-wide association studies for their meticulous collection and stewardship of extensive data sets.

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Fats and Cholesterol

When it comes to dietary fat, what matters most is the type of fat you eat. Contrary to past dietary advice promoting low-fat diets , newer research shows that healthy fats are necessary and beneficial for health.

• When food manufacturers reduce fat, they often replace it with carbohydrates from sugar, refined grains, or other starches. Our bodies digest these refined carbohydrates and starches very quickly, affecting blood sugar and insulin levels and possibly resulting in weight gain and disease. ( 1-3 )
• Findings from the Nurses’ Health Study ( 4 ) and the Health Professionals Follow-up Study ( 5 ) show that no link between the overall percentage of calories from fat and any important health outcome, including cancer, heart disease, and weight gain.

Rather than adopting a low-fat diet, it’s more important to focus on eating beneficial “good” fats and avoiding harmful “bad” fats. Fat is an important part of a healthy diet. Choose foods with “good” unsaturated fats, limit foods high in saturated fat, and avoid “bad” trans fat.

• “Good” unsaturated fats — Monounsaturated and polyunsaturated fats — lower disease risk. Foods high in good fats include vegetable oils (such as olive, canola, sunflower, soy, and corn), nuts, seeds, and fish.
• “Bad” fats — trans fats — increase disease risk, even when eaten in small quantities. Foods containing trans fats are primarily in processed foods made with trans fat from partially hydrogenated oil. Fortunately, trans fats have been eliminated from many of these foods.
• Saturated fats , while not as harmful as trans fats, by comparison with unsaturated fats negatively impact health and are best consumed in moderation. Foods containing large amounts of saturated fat include red meat, butter, cheese, and ice cream. Some plant-based fats like coconut oil and palm oil are also rich in saturated fat.
• When you cut back on foods like red meat and butter, replace them with fish, beans, nuts, and healthy oils instead of refined carbohydrates.

Read more about healthy fats in this “Ask the Expert” with HSPH’s Dr. Walter Willett and Amy Myrdal Miller, M.S., R.D., formerly of The Culinary Institute of America

1. Siri-Tarino, P.W., et al., Saturated fatty acids and risk of coronary heart disease: modulation by replacement nutrients. Curr Atheroscler Rep, 2010. 12(6): p. 384-90.

2. Hu, F.B., Are refined carbohydrates worse than saturated fat? Am J Clin Nutr, 2010. 91(6): p. 1541-2.

3. Jakobsen, M.U., et al., Intake of carbohydrates compared with intake of saturated fatty acids and risk of myocardial infarction: importance of the glycemic index. Am J Clin Nutr, 2010. 91(6): p. 1764-8.

4. Hu, F.B., et al., Dietary fat intake and the risk of coronary heart disease in women. N Engl J Med, 1997. 337(21): p. 1491-9.

5. Ascherio, A., et al., Dietary fat and risk of coronary heart disease in men: cohort follow up study in the United States. BMJ, 1996. 313(7049): p. 84-90.

6. Hu, F.B., J.E. Manson, and W.C. Willett, Types of dietary fat and risk of coronary heart disease: a critical review. J Am Coll Nutr, 2001. 20(1): p. 5-19.

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