research methodology questions

• Privacy Policy

Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

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

Structure of Research Methodology

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

I. Introduction

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

II. Research Design

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

III. Data Collection Methods

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

IV. Data Analysis Methods

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

V. Ethical Considerations

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

VI. Limitations

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

VII. Conclusion

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

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

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

Qualitative Research Methodology

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

Mixed-Methods Research Methodology

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

Case Study Research Methodology

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

Action Research Methodology

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

Experimental Research Methodology

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

Survey Research Methodology

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

Grounded Theory Research Methodology

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

Research Methodology Example

An Example of Research Methodology could be the following:

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

Introduction:

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

Research Design:

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

Participants:

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

Intervention :

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

Data Collection:

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

Data Analysis:

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

Ethical Considerations:

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

Data Management:

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

Limitations:

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

Conclusion:

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

How to Write Research Methodology

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

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

When to Write Research Methodology

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

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

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

Applications of Research Methodology

Here are some of the applications of research methodology:

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

Purpose of Research Methodology

Research methodology serves several important purposes, including:

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

Advantages of Research Methodology

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

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

Research Methodology Vs Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Thesis Outline – Example, Template and Writing...

Conceptual Framework – Types, Methodology and...

Data Collection – Methods Types and Examples

Research Approach – Types Methods and Examples

APA Research Paper Format – Example, Sample and...

APA Table of Contents – Format and Example

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

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

199 Comments

Thank you for this simple yet comprehensive and easy to digest presentation. God Bless!

You’re most welcome, Leo. Best of luck with your research!

I found it very useful. many thanks

This is really directional. A make-easy research knowledge.

Thank you for this, I think will help my research proposal

Thanks for good interpretation,well understood.

Good morning sorry I want to the search topic

Thank u more

Thank you, your explanation is simple and very helpful.

Very educative a.nd exciting platform. A bigger thank you and I’ll like to always be with you

That’s the best analysis

So simple yet so insightful. Thank you.

This really easy to read as it is self-explanatory. Very much appreciated…

Thanks for this. It’s so helpful and explicit. For those elements highlighted in orange, they were good sources of referrals for concepts I didn’t understand. A million thanks for this.

Good morning, I have been reading your research lessons through out a period of times. They are important, impressive and clear. Want to subscribe and be and be active with you.

Thankyou So much Sir Derek…

Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on it so that we’ll continue to understand more.sorry that’s a suggestion.

Beautiful presentation. I love it.

please provide a research mehodology example for zoology

It’s very educative and well explained

Thanks for the concise and informative data.

This is really good for students to be safe and well understand that research is all about

Thank you so much Derek sir🖤🙏🤗

Very simple and reliable

This is really helpful. Thanks alot. God bless you.

very useful, Thank you very much..

thanks a lot its really useful

in a nutshell..thank you!

Thanks for updating my understanding on this aspect of my Thesis writing.

thank you so much my through this video am competently going to do a good job my thesis

Thanks a lot. Very simple to understand. I appreciate 🙏

Very simple but yet insightful Thank you

This has been an eye opening experience. Thank you grad coach team.

Very useful message for research scholars

Really very helpful thank you

yes you are right and i’m left

Research methodology with a simplest way i have never seen before this article.

wow thank u so much

Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on is so that we will continue to understand more.sorry that’s a suggestion.

Very precise and informative.

Thanks for simplifying these terms for us, really appreciate it.

Thanks this has really helped me. It is very easy to understand.

I found the notes and the presentation assisting and opening my understanding on research methodology

Good presentation

Im so glad you clarified my misconceptions. Im now ready to fry my onions. Thank you so much. God bless

Thank you a lot.

thanks for the easy way of learning and desirable presentation.

Thanks a lot. I am inspired

Well written

I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

Thank you so much for this!! God Bless

Thank you. Explicit explanation

Thank you, Derek and Kerryn, for making this simple to understand. I’m currently at the inception stage of my research.

Thnks a lot , this was very usefull on my assignment

excellent explanation

I’m currently working on my master’s thesis, thanks for this! I’m certain that I will use Qualitative methodology.

Thanks a lot for this concise piece, it was quite relieving and helpful. God bless you BIG…

I am currently doing my dissertation proposal and I am sure that I will do quantitative research. Thank you very much it was extremely helpful.

Very interesting and informative yet I would like to know about examples of Research Questions as well, if possible.

I’m about to submit a research presentation, I have come to understand from your simplification on understanding research methodology. My research will be mixed methodology, qualitative as well as quantitative. So aim and objective of mixed method would be both exploratory and confirmatory. Thanks you very much for your guidance.

OMG thanks for that, you’re a life saver. You covered all the points I needed. Thank you so much ❤️ ❤️ ❤️

Thank you immensely for this simple, easy to comprehend explanation of data collection methods. I have been stuck here for months 😩. Glad I found your piece. Super insightful.

I’m going to write synopsis which will be quantitative research method and I don’t know how to frame my topic, can I kindly get some ideas..

Thanks for this, I was really struggling.

This was really informative I was struggling but this helped me.

Thanks a lot for this information, simple and straightforward. I’m a last year student from the University of South Africa UNISA South Africa.

its very much informative and understandable. I have enlightened.

An interesting nice exploration of a topic.

Thank you. Accurate and simple🥰

This article was really helpful, it helped me understanding the basic concepts of the topic Research Methodology. The examples were very clear, and easy to understand. I would like to visit this website again. Thank you so much for such a great explanation of the subject.

Thanks dude

Thank you Doctor Derek for this wonderful piece, please help to provide your details for reference purpose. God bless.

Many compliments to you

Great work , thank you very much for the simple explanation

Thank you. I had to give a presentation on this topic. I have looked everywhere on the internet but this is the best and simple explanation.

thank you, its very informative.

Well explained. Now I know my research methodology will be qualitative and exploratory. Thank you so much, keep up the good work

Well explained, thank you very much.

This is good explanation, I have understood the different methods of research. Thanks a lot.

Great work…very well explanation

Thanks Derek. Kerryn was just fantastic!

Great to hear that, Hyacinth. Best of luck with your research!

Its a good templates very attractive and important to PhD students and lectuter

Thanks for the feedback, Matobela. Good luck with your research methodology.

Thank you. This is really helpful.

You’re very welcome, Elie. Good luck with your research methodology.

Well explained thanks

This is a very helpful site especially for young researchers at college. It provides sufficient information to guide students and equip them with the necessary foundation to ask any other questions aimed at deepening their understanding.

Thanks for the kind words, Edward. Good luck with your research!

Thank you. I have learned a lot.

Great to hear that, Ngwisa. Good luck with your research methodology!

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

Thanks for making it simple and clear. It greatly helped in understanding research methodology. Regards.

This is well simplified and straight to the point

Thank you Dr

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

Thanks a lot I am relieved of a heavy burden.keep up with the good work

I’m very much grateful Dr Derek. I’m planning to pursue one of the careers that really needs one to be very much eager to know. There’s a lot of research to do and everything, but since I’ve gotten this information I will use it to the best of my potential.

Thank you so much, words are not enough to explain how helpful this session has been for me!

Thanks this has thought me alot.

Very concise and helpful. Thanks a lot

Thank Derek. This is very helpful. Your step by step explanation has made it easier for me to understand different concepts. Now i can get on with my research.

I wish i had come across this sooner. So simple but yet insightful

really nice explanation thank you so much

I’m so grateful finding this site, it’s really helpful…….every term well explained and provide accurate understanding especially to student going into an in-depth research for the very first time, even though my lecturer already explained this topic to the class, I think I got the clear and efficient explanation here, much thanks to the author.

It is very helpful material

I would like to be assisted with my research topic : Literature Review and research methodologies. My topic is : what is the relationship between unemployment and economic growth?

Its really nice and good for us.

THANKS SO MUCH FOR EXPLANATION, ITS VERY CLEAR TO ME WHAT I WILL BE DOING FROM NOW .GREAT READS.

Short but sweet.Thank you

Informative article. Thanks for your detailed information.

I’m currently working on my Ph.D. thesis. Thanks a lot, Derek and Kerryn, Well-organized sequences, facilitate the readers’ following.

great article for someone who does not have any background can even understand

I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

concise and informative.

Thank you very much

How can we site this article is Harvard style?

Very well written piece that afforded better understanding of the concept. Thank you!

Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

Thank too much

Thank you very much for your comprehensive explanation about research methodology so I like to thank you again for giving us such great things.

Good very well explained.Thanks for sharing it.

Thank u sir, it is really a good guideline.

so helpful thank you very much.

Thanks for the video it was very explanatory and detailed, easy to comprehend and follow up. please, keep it up the good work

It was very helpful, a well-written document with precise information.

how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

Your explanation is easily understood. Thank you

Very help article. Now I can go my methodology chapter in my thesis with ease

I feel guided ,Thank you

This simplification is very helpful. It is simple but very educative, thanks ever so much

The write up is informative and educative. It is an academic intellectual representation that every good researcher can find useful. Thanks

Wow, this is wonderful long live.

Nice initiative

thank you the video was helpful to me.

Thank you very much for your simple and clear explanations I’m really satisfied by the way you did it By now, I think I can realize a very good article by following your fastidious indications May God bless you

Thanks very much, it was very concise and informational for a beginner like me to gain an insight into what i am about to undertake. I really appreciate.

very informative sir, it is amazing to understand the meaning of question hidden behind that, and simple language is used other than legislature to understand easily. stay happy.

This one is really amazing. All content in your youtube channel is a very helpful guide for doing research. Thanks, GradCoach.

research methodologies

Please send me more information concerning dissertation research.

Nice piece of knowledge shared….. #Thump_UP

This is amazing, it has said it all. Thanks to Gradcoach

This is wonderful,very elaborate and clear.I hope to reach out for your assistance in my research very soon.

This is the answer I am searching about…

realy thanks a lot

Thank you very much for this awesome, to the point and inclusive article.

Thank you very much I need validity and reliability explanation I have exams

Thank you for a well explained piece. This will help me going forward.

Very simple and well detailed Many thanks

This is so very simple yet so very effective and comprehensive. An Excellent piece of work.

I wish I saw this earlier on! Great insights for a beginner(researcher) like me. Thanks a mil!

Thank you very much, for such a simplified, clear and practical step by step both for academic students and general research work. Holistic, effective to use and easy to read step by step. One can easily apply the steps in practical terms and produce a quality document/up-to standard

Thanks for simplifying these terms for us, really appreciated.

Thanks for a great work. well understood .

This was very helpful. It was simple but profound and very easy to understand. Thank you so much!

Great and amazing research guidelines. Best site for learning research

hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

how does this really work?

perfect content, thanks a lot

As a researcher, I commend you for the detailed and simplified information on the topic in question. I would like to remain in touch for the sharing of research ideas on other topics. Thank you

Impressive. Thank you, Grad Coach 😍

Thank you Grad Coach for this piece of information. I have at least learned about the different types of research methodologies.

Very useful content with easy way

Thank you very much for the presentation. I am an MPH student with the Adventist University of Africa. I have successfully completed my theory and starting on my research this July. My topic is “Factors associated with Dental Caries in (one District) in Botswana. I need help on how to go about this quantitative research

I am so grateful to run across something that was sooo helpful. I have been on my doctorate journey for quite some time. Your breakdown on methodology helped me to refresh my intent. Thank you.

thanks so much for this good lecture. student from university of science and technology, Wudil. Kano Nigeria.

It’s profound easy to understand I appreciate

Thanks a lot for sharing superb information in a detailed but concise manner. It was really helpful and helped a lot in getting into my own research methodology.

Comment * thanks very much

This was sooo helpful for me thank you so much i didn’t even know what i had to write thank you!

You’re most welcome 🙂

Simple and good. Very much helpful. Thank you so much.

This is very good work. I have benefited.

Thank you so much for sharing

This is powerful thank you so much guys

I am nkasa lizwi doing my research proposal on honors with the university of Walter Sisulu Komani I m on part 3 now can you assist me.my topic is: transitional challenges faced by educators in intermediate phase in the Alfred Nzo District.

Appreciate the presentation. Very useful step-by-step guidelines to follow.

I appreciate sir

wow! This is super insightful for me. Thank you!

Indeed this material is very helpful! Kudos writers/authors.

I want to say thank you very much, I got a lot of info and knowledge. Be blessed.

I want present a seminar paper on Optimisation of Deep learning-based models on vulnerability detection in digital transactions.

Need assistance

Dear Sir, I want to be assisted on my research on Sanitation and Water management in emergencies areas.

I am deeply grateful for the knowledge gained. I will be getting in touch shortly as I want to be assisted in my ongoing research.

The information shared is informative, crisp and clear. Kudos Team! And thanks a lot!

hello i want to study

Hello!! Grad coach teams. I am extremely happy in your tutorial or consultation. i am really benefited all material and briefing. Thank you very much for your generous helps. Please keep it up. If you add in your briefing, references for further reading, it will be very nice.

All I have to say is, thank u gyz.

Good, l thanks

thank you, it is very useful

Trackbacks/Pingbacks

• What Is A Literature Review (In A Dissertation Or Thesis) - Grad Coach - […] the literature review is to inform the choice of methodology for your own research. As we’ve discussed on the Grad Coach blog,…
• Free Download: Research Proposal Template (With Examples) - Grad Coach - […] Research design (methodology) […]
• Dissertation vs Thesis: What's the difference? - Grad Coach - […] and thesis writing on a daily basis – everything from how to find a good research topic to which…

What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

An official website of the United States government

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

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

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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

What is Research Methodology? Definition, Types, and Examples

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

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

What is research methodology ?

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

Why is research methodology important?

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

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

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

Types of research methodology.

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

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

What are the types of sampling designs in research methodology?

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

• Probability sampling

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

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

What are data collection methods?

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

Qualitative research 5

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

Quantitative research 6

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

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

What are data analysis methods.

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

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

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

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

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

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

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

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

How to choose a research methodology?

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

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

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

How to write a research methodology .

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

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

Streamline Your Research Paper Writing Process with Paperpal

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

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

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

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

Frequently Asked Questions

Q1. What are the key components of research methodology?

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

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

Q2. Why is ethical consideration important in research methodology?

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

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

Q3. What is the difference between methodology and method?

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

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

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

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

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

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

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

Related Reads:

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

Language and Grammar Rules for Academic Writing

Climatic vs. climactic: difference and examples, you may also like, how to write an academic paragraph (step-by-step guide), maintaining academic integrity with paperpal’s generative ai writing..., research funding basics: what should a grant proposal..., how to write an abstract in research papers..., how to write dissertation acknowledgements, how to structure an essay, leveraging generative ai to enhance student understanding of..., how to write a good hook for essays,..., addressing peer review feedback and mastering manuscript revisions..., how paperpal can boost comprehension and foster interdisciplinary....

• Find My Rep

You are here

100 Questions (and Answers) About Research Methods

• Neil J. Salkind
• Description

"How do I know when my literature review is finished?"

"What is the difference between a sample and a population?"

"What is power and why is it important?"

In an increasingly data-driven world, it is more important than ever for students as well as professionals to better understand the process of research. This invaluable guide answers the essential questions that students ask about research methods in a concise and accessible way.

Sample Materials & Chapters

Question #16: Question #16: How Do I Know When My Literature Review Is Finished?

Question #32: How Can I Create a Good Research Hypothesis?

Question #40: What Is the Difference Between a Sample and a Population, and Why

Question #92: What Is Power, and Why Is It Important?

For instructors

Select a purchasing option.

• Electronic Order Options VitalSource Amazon Kindle Google Play eBooks.com Kobo

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

How To Write a Research Question

Academic writing and research require a distinct focus and direction. A well-designed research question gives purpose and clarity to your research. In addition, it helps your readers understand the issue you are trying to address and explore.

Every time you want to know more about a subject, you will pose a question. The same idea is used in research as well. You must pose a question in order to effectively address a research problem. That's why the research question is an integral part of the research process. Additionally, it offers the author writing and reading guidelines, be it qualitative research or quantitative research.

In your research paper , you must single out just one issue or problem. The specific issue or claim you wish to address should be included in your thesis statement in order to clarify your main argument.

A good research question must have the following characteristics.

• Should include only one problem in the research question
• Should be able to find the answer using primary data and secondary data sources
• Should be possible to resolve within the given time and other constraints
• Detailed and in-depth results should be achievable
• Should be relevant and realistic.
• It should relate to your chosen area of research

While a larger project, like a thesis, might have several research questions to address, each one should be directed at your main area of study. Of course, you can use different research designs and research methods (qualitative research or quantitative research) to address various research questions. However, they must all be pertinent to the study's objectives.

What is a Research Question?

A research question is an inquiry that the research attempts to answer. It is the heart of the systematic investigation. Research questions are the most important step in any research project. In essence, it initiates the research project and establishes the pace for the specific research A research question is:

• Clear : It provides enough detail that the audience understands its purpose without any additional explanation.
• Focused : It is so specific that it can be addressed within the time constraints of the writing task.
• Succinct: It is written in the shortest possible words.
• Complex : It is not possible to answer it with a "yes" or "no", but requires analysis and synthesis of ideas before somebody can create a solution.
• Argumental : Its potential answers are open for debate rather than accepted facts.

A good research question usually focuses on the research and determines the research design, methodology, and hypothesis. It guides all phases of inquiry, data collection, analysis, and reporting. You should gather valuable information by asking the right questions.

Why are Research Questions so important?

Regardless of whether it is a qualitative research or quantitative research project, research questions provide writers and their audience with a way to navigate the writing and research process. Writers can avoid "all-about" papers by asking straightforward and specific research questions that help them focus on their research and support a specific thesis.

Types of Research Questions

There are two types of research: Qualitative research and Quantitative research . There must be research questions for every type of research. Your research question will be based on the type of research you want to conduct and the type of data collection.

The first step in designing research involves identifying a gap and creating a focused research question.

Below is a list of common research questions that can be used in a dissertation. Keep in mind that these are merely illustrations of typical research questions used in dissertation projects. The real research questions themselves might be more difficult.

Example Research Questions

The following are a few examples of research questions and research problems to help you understand how research questions can be created for a particular research problem.

Steps to Write Research Questions

You can focus on the issue or research gaps you're attempting to solve by using the research questions as a direction.

If you're unsure how to go about writing a good research question, these are the steps to follow in the process:

• Select an interesting topic Always choose a topic that interests you. Because if your curiosity isn’t aroused by a subject, you’ll have a hard time conducting research around it. Alos, it’s better that you pick something that’s neither too narrow or too broad.
• Do preliminary research on the topic Search for relevant literature to gauge what problems have already been tackled by scholars. You can do that conveniently through repositories like Scispace , where you’ll find millions of papers in one place. Once you do find the papers you’re looking for, try our reading assistant, SciSpace Copilot to get simple explanations for the paper . You’ll be able to quickly understand the abstract, find the key takeaways, and the main arguments presented in the paper. This will give you a more contextual understanding of your subject and you’ll have an easier time identifying knowledge gaps in your discipline.

     Also: ChatPDF vs. SciSpace Copilot: Unveiling the best tool for your research

• Consider your audience It is essential to understand your audience to develop focused research questions for essays or dissertations. When narrowing down your topic, you can identify aspects that might interest your audience.
• Ask questions Asking questions will give you a deeper understanding of the topic. Evaluate your question through the What, Why, When, How, and other open-ended questions assessment.
• Assess your question Once you have created a research question, assess its effectiveness to determine if it is useful for the purpose. Refine and revise the dissertation research question multiple times.

Additionally, use this list of questions as a guide when formulating your research question.

Are you able to answer a specific research question? After identifying a gap in research, it would be helpful to formulate the research question. And this will allow the research to solve a part of the problem. Is your research question clear and centered on the main topic? It is important that your research question should be specific and related to your central goal. Are you tackling a difficult research question? It is not possible to answer the research question with a simple yes or no. The problem requires in-depth analysis. It is often started with "How" and "Why."

Start your research Once you have completed your dissertation research questions, it is time to review the literature on similar topics to discover different perspectives.

Strong  Research Question Samples

Uncertain: How should social networking sites work on the hatred that flows through their platform?

Certain: What should social media sites like Twitter or Facebook do to address the harm they are causing?

This unclear question does not specify the social networking sites that are being used or what harm they might be causing. In addition, this question assumes that the "harm" has been proven and/or accepted. This version is more specific and identifies the sites (Twitter, Facebook), the type and extent of harm (privacy concerns), and who might be suffering from that harm (users). Effective research questions should not be ambiguous or interpreted.

Unfocused: What are the effects of global warming on the environment?

Focused: What are the most important effects of glacial melting in Antarctica on penguins' lives?

This broad research question cannot be addressed in a book, let alone a college-level paper. Focused research targets a specific effect of global heating (glacial  melting), an area (Antarctica), or a specific animal (penguins). The writer must also decide which effect will have the greatest impact on the animals affected. If in doubt, narrow down your research question to the most specific possible.

Too Simple: What are the U.S. doctors doing to treat diabetes?

Appropriately complex: Which factors, if any, are most likely to predict a person's risk of developing diabetes?

This simple version can be found online. It is easy to answer with a few facts. The second, more complicated version of this question is divided into two parts. It is thought-provoking and requires extensive investigation as well as evaluation by the author. So, ensure that a quick Google search should not answer your research question.

How to write a strong Research Question?

The foundation of all research is the research question. You should therefore spend as much time as necessary to refine your research question based on various data.

You can conduct your research more efficiently and analyze your results better if you have great research questions for your dissertation, research paper , or essay .

The following criteria can help you evaluate the strength and importance of your research question and can be used to determine the strength of your research question:

• Researchable
• It should only cover one issue.
• A subjective judgment should not be included in the question.
• It can be answered with data analysis and research.
• Specific and Practical
• It should not contain a plan of action, policy, or solution.
• It should be clearly defined
• Within research limits
• Complex and Arguable
• It shouldn't be difficult to answer.
• To find the truth, you need in-depth knowledge
• Allows for discussion and deliberation
• Original and Relevant
• It should be in your area of study
• Its results should be measurable
• It should be original

Conclusion - How to write Research Questions?

Research questions provide a clear guideline for research. One research question may be part of a larger project, such as a dissertation. However, each question should only focus on one topic.

Research questions must be answerable, practical, specific, and applicable to your field. The research type that you use to base your research questions on will determine the research topic. You can start by selecting an interesting topic and doing preliminary research. Then, you can begin asking questions, evaluating your questions, and start your research.

Now it's easier than ever to streamline your research workflow with SciSpace ResearchGPT . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, read, write and publish their research and fosters collaboration.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Prevent plagiarism. Run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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

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

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Other students also liked, writing strong research questions | criteria & examples.

• What Is a Research Design | Types, Guide & Examples
• Data Collection | Definition, Methods & Examples

More interesting articles

• Between-Subjects Design | Examples, Pros, & Cons
• Cluster Sampling | A Simple Step-by-Step Guide with Examples
• Confounding Variables | Definition, Examples & Controls
• Construct Validity | Definition, Types, & Examples
• Content Analysis | Guide, Methods & Examples
• Control Groups and Treatment Groups | Uses & Examples
• Control Variables | What Are They & Why Do They Matter?
• Correlation vs. Causation | Difference, Designs & Examples
• Correlational Research | When & How to Use
• Critical Discourse Analysis | Definition, Guide & Examples
• Cross-Sectional Study | Definition, Uses & Examples
• Descriptive Research | Definition, Types, Methods & Examples
• Ethical Considerations in Research | Types & Examples
• Explanatory and Response Variables | Definitions & Examples
• Explanatory Research | Definition, Guide, & Examples
• Exploratory Research | Definition, Guide, & Examples
• External Validity | Definition, Types, Threats & Examples
• Extraneous Variables | Examples, Types & Controls
• Guide to Experimental Design | Overview, Steps, & Examples
• How Do You Incorporate an Interview into a Dissertation? | Tips
• How to Do Thematic Analysis | Step-by-Step Guide & Examples
• How to Write a Literature Review | Guide, Examples, & Templates
• How to Write a Strong Hypothesis | Steps & Examples
• Inclusion and Exclusion Criteria | Examples & Definition
• Independent vs. Dependent Variables | Definition & Examples
• Inductive Reasoning | Types, Examples, Explanation
• Inductive vs. Deductive Research Approach | Steps & Examples
• Internal Validity in Research | Definition, Threats, & Examples
• Internal vs. External Validity | Understanding Differences & Threats
• Longitudinal Study | Definition, Approaches & Examples
• Mediator vs. Moderator Variables | Differences & Examples
• Mixed Methods Research | Definition, Guide & Examples
• Multistage Sampling | Introductory Guide & Examples
• Naturalistic Observation | Definition, Guide & Examples
• Operationalization | A Guide with Examples, Pros & Cons
• Population vs. Sample | Definitions, Differences & Examples
• Primary Research | Definition, Types, & Examples
• Qualitative vs. Quantitative Research | Differences, Examples & Methods
• Quasi-Experimental Design | Definition, Types & Examples
• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

"I thought AI Proofreading was useless but.."

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Definitions

Verbal Ability

• Interview Q

• Send your Feedback to [email protected]

Help Others, Please Share

Learn Latest Tutorials

Transact-SQL

Reinforcement Learning

R Programming

React Native

Python Design Patterns

Python Pillow

Python Turtle

Preparation

Interview Questions

Company Questions

Trending Technologies

Artificial Intelligence

Cloud Computing

Data Science

Machine Learning

B.Tech / MCA

Data Structures

Operating System

Computer Network

Compiler Design

Computer Organization

Discrete Mathematics

Ethical Hacking

Computer Graphics

Software Engineering

Web Technology

Cyber Security

C Programming

Control System

Data Mining

Data Warehouse

Research Methodology

• Introduction to Research Methodology
• Research Approaches
• Concepts of Theory and Empiricism
• Characteristics of scientific method
• Understanding the Language of Research
• 11 Steps in Research Process
• Research Design
• Different Research Designs
• Compare and Contrast the Main Types of Research Designs
• Cross-sectional research design
• Qualitative and Quantitative Research
• Descriptive Research VS Qualitative Research
• Experimental Research VS Quantitative Research
• Sampling Design
• Probability VS Non-Probability Sampling

40 MCQ on Research Methodology

• MCQ on research Process
• MCQ on Research Design
• 18 MCQ on Quantitative Research
• 30 MCQ on Qualitative Research
• 45 MCQ on Sampling Methods
• 20 MCQ on Principles And Planning For Research

Q1. Which of the following statement is correct? (A) Reliability ensures the validity (B) Validity ensures reliability (C) Reliability and validity are independent of each other (D) Reliability does not depend on objectivity

Answer:  (C)

Q2. Which of the following statements is correct? (A) Objectives of research are stated in first chapter of the thesis (B) Researcher must possess analytical ability (C) Variability is the source of problem (D) All the above

Answer:  (D)

Q3. The first step of research is: (A) Selecting a problem (B) Searching a problem (C) Finding a problem (D) Identifying a problem

Q4. Research can be conducted by a person who: (A) holds a postgraduate degree (B) has studied research methodology (C) possesses thinking and reasoning ability (D) is a hard worker

Answer: (B)

Q5. Research can be classified as: (A) Basic, Applied and Action Research (B) Philosophical, Historical, Survey and Experimental Research (C) Quantitative and Qualitative Research (D) All the above

Q6. To test null hypothesis, a researcher uses: (A) t test (B) ANOVA (C)  X 2 (D) factorial analysis

Answer:  (B)

Q7. Bibliography given in a research report: (A) shows vast knowledge of the researcher (B) helps those interested in further research (C) has no relevance to research (D) all the above

Q8. A research problem is feasible only when: (A) it has utility and relevance (B) it is researchable (C) it is new and adds something to knowledge (D) all the above

Q9. The study in which the investigators attempt to trace an effect is known as: (A) Survey Research (B) Summative Research (C) Historical Research (D) ‘Ex-post Facto’ Research

Answer: (D)

Q10. Generalized conclusion on the basis of a sample is technically known as: (A) Data analysis and interpretation (B) Parameter inference (C) Statistical inference (D) All of the above

Answer:  (A)

Q11. Fundamental research reflects the ability to: (A) Synthesize new ideals (B) Expound new principles (C) Evaluate the existing material concerning research (D) Study the existing literature regarding various topics

Q12. The main characteristic of scientific research is: (A) empirical (B) theoretical (C) experimental (D) all of the above

Q13. Authenticity of a research finding is its: (A) Originality (B) Validity (C) Objectivity (D) All of the above

Q14. Which technique is generally followed when the population is finite? (A) Area Sampling Technique (B) Purposive Sampling Technique (C) Systematic Sampling Technique (D) None of the above

Q15. Research problem is selected from the stand point of: (A) Researcher’s interest (B) Financial support (C) Social relevance (D) Availability of relevant literature

Q16. The research is always – (A) verifying the old knowledge (B) exploring new knowledge (C) filling the gap between knowledge (D) all of these

Q17. Research is (A) Searching again and again (B) Finding a solution to any problem (C) Working in a scientific way to search for the truth of any problem (D) None of the above

Q20. A common test in research demands much priority on (A) Reliability (B) Useability (C) Objectivity (D) All of the above

Q21. Which of the following is the first step in starting the research process? (A) Searching sources of information to locate the problem. (B) Survey of related literature (C) Identification of the problem (D) Searching for solutions to the problem

Answer: (C)

Q22. Which correlation coefficient best explains the relationship between creativity and intelligence? (A) 1.00 (B) 0.6 (C) 0.5 (D) 0.3

Q23. Manipulation is always a part of (A) Historical research (B) Fundamental research (C) Descriptive research (D) Experimental research

Explanation: In experimental research, researchers deliberately manipulate one or more independent variables to observe their effects on dependent variables. The goal is to establish cause-and-effect relationships and test hypotheses. This type of research often involves control groups and random assignment to ensure the validity of the findings. Manipulation is an essential aspect of experimental research to assess the impact of specific variables and draw conclusions about their influence on the outcome.

Q24. The research which is exploring new facts through the study of the past is called (A) Philosophical research (B) Historical research (C) Mythological research (D) Content analysis

Q25. A null hypothesis is (A) when there is no difference between the variables (B) the same as research hypothesis (C) subjective in nature (D) when there is difference between the variables

Q26. We use Factorial Analysis: (A) To know the relationship between two variables (B) To test the Hypothesis (C) To know the difference between two variables (D) To know the difference among the many variables

Explanation: Factorial analysis, specifically factorial analysis of variance (ANOVA), is used to investigate the effects of two or more independent variables on a dependent variable. It helps to determine whether there are significant differences or interactions among the independent variables and their combined effects on the dependent variable.

Q27. Which of the following is classified in the category of the developmental research? (A) Philosophical research (B) Action research (C) Descriptive research (D) All the above

Q28.  Action-research is: (A) An applied research (B) A research carried out to solve immediate problems (C) A longitudinal research (D) All the above

Explanation: Action research is an approach to research that encompasses all the options mentioned. It is an applied research method where researchers work collaboratively with practitioners or stakeholders to address immediate problems or issues in a real-world context. It is often conducted over a period of time, making it a longitudinal research approach. So, all the options (A) An applied research, (B) A research carried out to solve immediate problems, and (C) A longitudinal research are correct when describing action research.

Q29.  The basis on which assumptions are formulated: (A) Cultural background of the country (B) Universities (C) Specific characteristics of the castes (D) All of these

Q30. How can the objectivity of the research be enhanced? (A) Through its impartiality (B) Through its reliability (C) Through its validity (D) All of these

Q31.  A research problem is not feasible only when: (A) it is researchable (B) it is new and adds something to the knowledge (C) it consists of independent and dependent var i ables (D) it has utility and relevance

Explanation:  A research problem is considered feasible when it can be studied and investigated using appropriate research methods and resources. The presence of independent and dependent variables is not a factor that determines the feasibility of a research problem. Instead, it is an essential component of a well-defined research problem that helps in formulating research questions or hypotheses. Feasibility depends on whether the research problem can be addressed and answered within the constraints of available time, resources, and methods. Options (A), (B), and (D) are more relevant to the feasibility of a research problem.

Q32. The process not needed in experimental research is: (A) Observation (B) Manipulation and replication (C) Controlling (D) Reference collection

In experimental research, reference collection is not a part of the process.

Q33. When a research problem is related to heterogeneous population, the most suitable sampling method is: (A) Cluster Sampling (B) Stratified Sampling (C) Convenient Sampling (D) Lottery Method

Explanation: When a research problem involves a heterogeneous population, stratified sampling is the most suitable sampling method. Stratified sampling involves dividing the population into subgroups or strata based on certain characteristics or variables. Each stratum represents a relatively homogeneous subset of the population. Then, a random sample is taken from each stratum in proportion to its size or importance in the population. This method ensures that the sample is representative of the diversity present in the population and allows for more precise estimates of population parameters for each subgroup.

Q34.  Generalised conclusion on the basis of a sample is technically known as: (A) Data analysis and interpretation (B) Parameter inference (C) Statistical inference (D) All of the above

Explanation: Generalized conclusions based on a sample are achieved through statistical inference. It involves using sample data to make inferences or predictions about a larger population. Statistical inference helps researchers draw conclusions, estimate parameters, and test hypotheses about the population from which the sample was taken. It is a fundamental concept in statistics and plays a crucial role in various fields, including research, data analysis, and decision-making.

Q35. The experimental study is based on

(A) The manipulation of variables (B) Conceptual parameters (C) Replication of research (D) Survey of literature

Q36.  Which one is called non-probability sampling? (A) Cluster sampling (B) Quota sampling (C) Systematic sampling (D) Stratified random sampling

Q37.  Formulation of hypothesis may NOT be required in: (A) Survey method (B) Historical studies (C) Experimental studies (D) Normative studies

Q38. Field-work-based research is classified as: (A) Empirical (B) Historical (C) Experimental (D) Biographical

Q39. Which of the following sampling method is appropriate to study the prevalence of AIDS amongst male and female in India in 1976, 1986, 1996 and 2006? (A) Cluster sampling (B) Systematic sampling (C) Quota sampling (D) Stratified random sampling

Q40. The research that applies the laws at the time of field study to draw more and more clear ideas about the problem is: (A) Applied research (B) Action research (C) Experimental research (D) None of these

Answer: (A)

5 key questions to help you choose a research methodology

This simplified approach to choosing the right methodology uses five questions to guide researchers in determining whether to opt for secondary, qualitative or quantitative research and emphasizes the importance of aligning the chosen method with the target audience and research goals.

Plotting your next move

David Lyndon is head of operations at Reputation Leaders Ltd. He can be reached at [email protected] .

Here at our firm, our team was recently asked to recommend a research approach by a client without previous research experience. Our conversations revolved around five key questions and eventually turned into the accompanying flowchart. Use these five questions to simplify the research methodology decision-making:

• Has the same research already been done?
• How hard is it to reach my target respondents?
• What sort of answers do I need?
• Are my respondents in one specific location?
• How reactive does the research need to be as it continues?

Doubtless, each situation will need more thought than the simple diagram shown in the flowchart but I hope that common sense can fill in the gaps.

If you’re in the same situation as our stick figure in the flowchart – wanting to do some research but uncertain of the best methodology – work through the questions in the flowchart and see if the resulting strategy makes sense. (Perhaps it’s more than one!)

Principles should be clear  

This is a broad-brushstroke look at reasons to use different research methodologies and why you might choose one over the other but the principles should be clear.

The proper methodology is found at the intersection of who you want to talk to and what you want to know. The five questions, asked in the right way, can guide your decision-making. 

We’ve explained more about each methodology below and how the answers to the five questions can cause you to choose them.

Secondary research. Secondary research takes many forms but primarily consists of finding and reading what other researchers have already done. Using the internet, what used to take weeks can be completed in minutes. Academic reviews, social media searches and basic web searches can quickly tell you if someone else has already answered your questions or gathered the data to allow you to do so.

Choose this type of research if you know the work’s already been done. If you’re not sure, take the time to do some secondary research and find out. It may save you weeks of effort and thousands of dollars.

After this review, you can think about some primary research if there are still unanswered questions or if the available data is out of date.

Primary research.  Necessary when you need to get data that is not readily available, primary research is usually more demanding, more prolonged and more expensive than secondary research. However, it can be much more valuable. Primary research is often split into two types – qualitative and quantitative.

Qualitative research. Qualitative research is descriptive rather than definitive. Digging deeper into experiences, reasons and opinions, qualitative researchers use observation and conversation to understand the answers to questions like why and how.

In-depth one-on-one interviews

In-depth interviews allow researchers to dig deep into a topic with a few people. We often use these to reach experts who can bring their experience and observations to bear on complex subjects that only a few people understand.

We choose in-depth interviews when the people who can answer our questions are hard to reach. Because of their expertise, their time is valuable, so scheduling interviews and compensating these individuals takes effort and money.  

We often use in-depth interviews to talk to CEOs in specific industries, members of think tanks or political policy experts.

In-person focus groups

Focus groups are a tried and proven method if you need to explore a particular topic and discover ideas you’ve not yet considered. Get a small number of people (six-to-10) in the same room and have a guided conversation. The strength of focus groups is when different members connect and spark new avenues to explore. Multiple groups with different demographics or opinions round out the results.

A well-moderated focus group is an excellent choice if you can gather these people in one place and want to dig into the why and how of a subject. There’s also a chance to do some small quantitative exercises and ask about who, what, where, when and how much, but the small number of responses means these results are guidelines at best.

Online focus groups

If you want the benefits of focus groups but can’t gather everyone in one place, don’t despair. Online focus groups have come of age. Either chat-based or video-based groups can work. Chat-based groups can even be better when discussing emotionally challenging topics to help respondents feel free to share without having to face other people.

These groups still aim to understand a subject’s why and how and need experienced moderators. Polls and ranking exercises can also add quantitative data but the small number of responses still means these results are guidelines at best.

Online communities

Unlike focus groups, online communities run asynchronously and last for days or weeks. Think of online communities like a temporary Facebook group with message boards, comments, polls, photos, videos and almost anything else you can imagine, guided by your research questions.

Because participants don’t all need to be online simultaneously, you can include many more people than a focus group. We’ve done communities as small as 12 people and as large as 200.

And with the extended time frame, you have time to think about what you want to ask and adjust as time goes on or dig into particular topics with subgroups. Online communities are one of the most agile forms of research. They are mainly used like focus groups to explore subjects qualitatively but can also be large enough to achieve quantitative results.

Quantitative research . You need to do quantitative research if you’re trying to prove a hypothesis or get statistics to drive PR and media headlines. This is all about the numbers, but unlike qualitative research, you only really get out what you put in. If you forget to include a question, there’s no chance to correct it. Quantitative researchers use predefined answer choices to answer who, what, when and where. This type of data then allows for analyses like segmentation, driver and principal components.

Basic online consumer surveys

If you want to know what general consumers in your market think and do, an online survey is quick, simple and cost-effective. You can ask a lot of single-choice or multiple-choice questions and gather hundreds of data points in a matter of hours or days.

Even better, if you’re reaching people across different languages, you can translate your survey into their language but get the answers back in your language. Truly global research is possible for everyone.

Whereas phone-based research used to be preferable, with internet penetration rising and well over 90% in places like the U.K. and the U.S., you can easily reach a representative sample. Good screeners, quotas and weighting strategies also minimize natural bias.

Advanced online consumer surveys

If simple answers to closed-end questions can’t meet your needs but you still need to reach many people across languages, geographies and the social spectrum, more advanced online survey options exist.

You can incorporate video, audio, interactive communications, gamification, message highlighting and other next-gen tools into a laptop or mobile-device survey. Find out what people look at in stores, what they hate about your planned advertisement or what draws their attention when they see your new website.

Setting up an online survey experience like this can be a lot of work but the results can be invaluable.

Online specialist surveys

Sometimes your target audience is smaller and more defined. Perhaps you’re trying to reach decision-makers in companies using AI or part-time workers who spend their spare time making YouTube videos. These niche audiences can usually be found in a specialist panel. They are more expensive to reach because the panel company must spend more to attract and engage them and you won’t be able to reach as many of them as general consumers. Still, you can get robust data from specialist groups worldwide through online surveys.

In other cases, you might have access to your audience yourself. They might be your customers or your employees. Perhaps there’s a hybrid approach, where you get some of your audience from a panel and some from your own database. You can set up an online survey and do quantitative research with this audience in these cases.

On-location surveys

Sometimes there’s no better way to reach your audience and find out what they think than to go to them in person. Whether you’re finding shoppers at the mall, workers in a factory or voters outside the polls, going and doing the research on-site is a guaranteed way to gather your data.

Interviewers can ask respondents to take a survey on a dedicated iPad, snap a QR code to take the survey on their own device or talk the respondent through the survey and record the answers.

This is slower and more time-intensive than reaching people online but it can be the best alternative when the target audience is difficult to reach and their usual location is known.

Navigate the maze of choices

Selecting the right research methodology is a crucial step in any research endeavor. The five key questions outlined in the flowchart serve as a valuable compass, helping you navigate through the maze of choices and ultimately guiding you to the methodology that best suits your objectives. While the decision-making process may often require additional consideration and nuance, the principles remain clear: your choice should be at the intersection of your target audience and your research goals. Whether you opt for secondary research to leverage existing knowledge, delve into qualitative research for deeper insights or harness the power of quantitative research for statistical validation, the method should align with your unique circumstances. Furthermore, the emergence of online tools and communities has expanded the horizons of research possibilities, offering flexibility and scalability. So, remember to weigh the options carefully, use these questions as your guide and embark on your research journey with confidence, knowing that the right methodology is within your reach. 

Study looks at how German citizens are using collective action to reclaim a sense of agency Related Categories: Consumers, Consumer Research, One-on-One (Depth) Interviews, Quantitative Research Consumers, Consumer Research, One-on-One (Depth) Interviews, Quantitative Research, Psychological/Emotion Research, Government, Political Research, Social Research

Real answers from real respondents on how to create better B2B surveys Related Categories: Consumers, Research Industry, Consumer Research, Survey Research Consumers, Research Industry, Consumer Research, Survey Research, Bus.-To-Bus. Research, Business-To-Business, Incentive Payment & Processing, Marketing Research-General, Respondent Cooperation/Satisfaction

In Case You Missed It...July/August 2024 Related Categories: Consumers, Research Industry, Consumer Research Consumers, Research Industry, Consumer Research, Psychological/Emotion Research, Shopper Insights, CX/UX-Customer/User Experience, Data Security

Survey Monitor July/August 2024 Related Categories: Consumers, Research Industry, Consumer Research Consumers, Research Industry, Consumer Research, Artificial Intelligence / AI, Employees, Entrepreneurs/Small Business, Health & Beauty Aids, Health Care Products-Natural, Political Research, Real Estate/Development, Research Employment, Shopper Insights

• Writing Center

Beginner’s Guide to Research

Click here to download a .pdf copy of our Beginner’s Guide to Research !

Last updated : July 18, 2024

Consider keeping a printed copy to have when writing and revising your resume!  If you have any additional questions, make an appointment or email us at [email protected] !

Most professors will require the use of academic (AKA peer-reviewed) sources for student writing. This is because these sources, written for academic audiences of specific fields, are helpful for developing your argument on many topics of interest in the academic realm, from history to biology. While popular sources like news articles also often discuss topics of interest within academic fields, peer-reviewed sources offer a depth of research and expertise that you cannot find in popular sources. Therefore, knowing how to (1) identify popular vs. academic sources, (2) differentiate between primary and secondary sources, and (3) find academic sources is a vital step in writing research. Below are definitions of the two ways scholars categorize types of sources based on when they were created (i.e. time and place) and how (i.e. methodology):

Popular vs. academic sources:

• Popular sources are publicly accessible periodicals–newspapers, magazines, and blogs–such as The Washington Post or The New Yorker . These sources are most often written for non-academic audiences, but can be helpful for finding general information and a variety of opinions on your topic.
• Academic sources , known also as peer reviewed or scholarly articles, are those that have undergone peer review before being published. Typically, these articles are written for other scholars in the field and are published in academic journals, like Feminist Studies or The American Journal of Psychology . Literature reviews, research projects, case studies, and notes from the field are common examples.

Primary vs. secondary sources:

• Primary sources are articles written by people directly involved in what they were writing about, including: News reports and photographs, diaries and novels, films and videos, speeches and autobiographies, as well as original research and statistics.
• Secondary sources , on the other hand, are second hand accounts written about a topic based on primary sources. Whether a journal article or other academic publication is considered a secondary source depends on how you use it.

How to Find Academic Sources

Finding appropriate academic sources from the hundreds of different journal publications can be daunting. Therefore, it is important to find databases –digital collections of articles–relevant to your topic to narrow your search. Albertson’s Library has access to several different databases, which can be located by clicking the “Articles and Databases” tab on the website’s homepage, and navigating to “Databases A-Z” to refine your search. Popular databases include: Academic Search Premier and Proquest Central (non-specific databases which include a wide variety of articles), JSTOR (humanities and social sciences, from literature to history), Web of Science (formal sciences and natural sciences such as biology and chemistry), and Google Scholar (a web search engine that searches scholarly literature and academic sources). If you are unable to access articles from other databases, make sure you’re signed in to Alberton’s Library through Boise State!

Performing a Database Search

Databases include many different types of sources besides academic journals, however, including book reviews and other periodicals. Using the search bar , you can limit search results to those containing specific keywords or phrases like “writing center” or “transfer theory.” Utilizing keywords in your search–names of key concepts, authors, or ideas–rather than questions is the most effective way to find articles in databases. When searching for a specific work by title, placing the title in quotation marks will ensure your search includes only results in that specific word order. In the example below, search terms including the author (“Virginia Woolf”) and subject (“feminism”) are entered into the popular database EBSCOhost:

Refining Your Search Results

Many databases have a bar on the left of the screen where you can further refine your results. For example, if you are only interested in finding complete scholarly articles, or peer-reviewed ones, you can toggle these different options to further limit your search. These options are located under the “Refine Results” bar in EBSCOhost, divided into different sections, with a display of currently selected search filters and filter options to refine your search based on your specific needs, as seen in the figure below:

Search results can also be limited by subject : If you search “Romeo and Juliet” on Academic Search Premier to find literary analysis articles for your English class, you’ll find a lot of other sources that include this search term, such as ones about theater production or ballets based on Shakespeare’s play. However, if you’re writing a literary paper on the text of the play itself, you might limit your search results to “fiction” to see only articles that discuss the play within the field of literature. Alternatively, for a theater class discussing the play, you might limit your search results to “drama.”

The Writing Center

430+ Research Methodology (RM) Solved MCQs

Done Reading?

• Question and answers in Research Methodology (RM),
• Research Methodology (RM) multiple choice questions and answers,
• Research Methodology (RM) Important MCQs,
• Solved MCQs for Research Methodology (RM),
• Research Methodology (RM) MCQs with answers PDF download

Research Question and Hypothesis

Ai generator.

Navigating the intricacies of research begins with crafting well-defined research questions and hypothesis statements. These essential components guide the entire research process, shaping investigations and analyses. In this comprehensive guide, explore the art of formulating research questions and hypothesis statements. Learn how to create focused, inquiry-driven questions and construct research hypothesis statements that capture the essence of your study. Unveil examples and invaluable tips to enhance your research endeavors.

What is an example of a Research Question and Hypothesis Statement?

Research Question: How does regular exercise impact the mental well-being of college students?

Hypothesis Statement: College students who engage in regular exercise experience improved mental well-being compared to those who do not exercise regularly.

In this example, the research question focuses on the relationship between exercise and mental well-being among college students. The hypothesis statement predicts a specific outcome, stating that there will be a positive impact on mental well-being for those who exercise regularly. The hypothesis guides the research process and provides a clear expectation for the study’s results.

100 Research Question and Hypothesis Statement Examples

Size: 366 KB

Quantitative Research Question and Hypothesis Statement Examples

In quantitative research, researchers aim to collect and analyze numerical data to answer specific research questions. A quantitative research question is designed to be measurable and testable, and it often involves examining the relationship between variables. The corresponding hypothesis statement predicts the expected outcome of the research based on previous knowledge or theories.

Psychology Research Question and Hypothesis Statement Examples

Psychology is the scientific study of human behavior and mental processes. Psychology research questions delve into various aspects of human behavior, cognition, emotion, and more. These questions are designed to gain a deeper understanding of psychological phenomena. Hypothesis statements for psychology hypothesis  research predict how certain factors or variables might influence human behavior or mental processes.

Testable Research Question and Hypothesis Statement Examples

Testable research questions are formulated in a way that allows them to be tested through empirical observation or experimentation. These questions are often used in scientific and experimental research to investigate cause-and-effect relationships between variables. The corresponding hypothesis statements propose an expected outcome based on the variables being studied and the conditions of the experiment.

Is the Hypothesis Statement and Research Question Statement the Same Thing?

The hypothesis statement and research question statement are closely related but not the same. Both play crucial roles in research, but they serve distinct purposes.

• Research Question Statement : A research question is a clear and concise inquiry that outlines the specific aspect of a topic you want to investigate. It is often expressed as an interrogative sentence and helps guide your research by focusing on a particular area of interest.
• Hypothesis Statement : A hypothesis is a testable statement that predicts the relationship between variables. It’s based on existing knowledge or theories and proposes an expected outcome of your research. Hypotheses are formulated for experimental research and provide a basis for collecting and analyzing data.

How Do You State a Research Question and Hypothesis?

Research question :.

• Identify the topic of interest.
• Specify the aspect you want to explore.
• Frame the question as a clear and concise interrogative sentence.
• Ensure the question is researchable and not too broad or too narrow.

Hypothesis Statement :

• Identify the variables involved (independent and dependent).
• Formulate a prediction about their relationship.
• Use clear language and avoid ambiguity.
• Write it as a declarative statement.

How Do You Write a Research Question and Hypothesis Statement? – A Step by Step Guide

• Identify the Topic : Choose a specific topic that interests you and is relevant to your field of study.
• Background Research : Gather information about existing research related to your topic. This helps you understand what’s already known and identify gaps or areas for exploration.
• Formulate the Research Question : Decide what aspect of the topic you want to investigate. Frame a clear, focused, and concise research question.
• Identify Variables : Determine the independent and dependent variables in your research question. The independent variable is what you manipulate, and the dependent variable is what you measure.
• Formulate the Hypothesis : Write a testable hypothesis that predicts the expected outcome based on the relationship between the variables.
• Consider Null Hypothesis : Formulate a null hypothesis that states no relationship exists between the variables. This provides a baseline for comparison.

Tips for Writing Research Question and Hypothesis

• Keep both the research question and hypothesis concise and specific.
• Ensure they are testable and can be investigated through research.
• Use clear language that accurately conveys your intentions.
• Base your hypothesis on existing knowledge or theories.
• Align the research question and hypothesis with the scope of your study.
• Revise and refine your statements based on feedback and further research.

Remember, both research questions and hypotheses play essential roles in guiding your research and framing the investigation’s purpose and expected outcomes.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Pardon Our Interruption

As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen:

• You've disabled JavaScript in your web browser.
• You're a power user moving through this website with super-human speed.
• You've disabled cookies in your web browser.
• A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article .

To regain access, please make sure that cookies and JavaScript are enabled before reloading the page.

Protect your data

This site uses cookies and related technologies for site operation, and analytics as described in our Privacy Policy . You may choose to consent to our use of these technologies, reject non-essential technologies, or further manage your preferences.

• Resume and Cover Letter
• Security Officer Skills: Add...

Security Officer Skills: Add to Improve Your Resume!

10 min read · Updated on June 03, 2024

Make sure your resume includes the Security Officer Skills employers want to see!

If you've devoted your career to protecting people and property, then you need a resume that highlights the Security Officer skills every hiring manager expects to see. 

But do you know which core competencies you need to add to that resume to help you get noticed by employers? 

Which key skills will they be looking for when your resume makes it to their desk?

We've created this Security Officer skills guide to help you find the answers to those and other important resume questions. In this guide, we'll offer some insight into Security Officer skills and explain why they're so vital for a successful job search. We'll also explore the core competencies you need to add to your resume and provide tips to help you maximize their impact.

What are Security Officer skills?

Security Officer skills include all the core competencies needed to perform the duties of a security professional. Whether in a capacity as a security guard or security manager, these professionals are dedicated to the protection of people and property. 

In that role, they engage in a variety of security-related duties, including 

On-site threat and risk assessments

Security plan creation and implementation

Premise monitoring and patrolling

Response to emergencies and other incidents

Competent Security Officers rely on a balanced mix of hard technical skills and soft interpersonal skills to achieve their mission. The skills that you select for your resume should directly correspond to the key qualifications that employers are looking for in new hires. Those skills should include job-related technical skills, people skills that you can use to interact with others, and any relevant transferable skills that can bolster your qualifications.

Related reading: What Are Skills? (With Examples and Tips on How to Improve Them)

Why are Security Officer skills important for your resume?

It's vital to understand the important role that your Security Officer skills play in your resume success. The right skills can help your resume make a powerful impression on any hiring manager who reviews your application. 

At the same time, including the wrong skills could cause your application to be rejected out of hand – no matter how qualified you might be. With that in mind, it's easy to see why successful job seekers place such a strong emphasis on including the best skills to highlight their qualifications.

Related reading: Make the Perfect First Impression With Your Resume

Which Security Officer skills do employers prioritize?

If you've ever believed that hiring managers just focus on random qualifications when they review resumes, think again. Employers will almost always have a set of key competencies that they're looking for when they skim candidate resumes. 

To understand which skills they expect to see, you need to know what they expect from the person they'll be hiring. What duties do they expect you to perform? 

As a Security Officer, you will need to:

Conduct routine patrols and threat assessments

Operate, monitor, and analyze surveillance cameras and other security tools

Follow best practices for premise and personnel security management

Manage on-site access points in accordance with policy

Maintain compliance with company policies and governmental regulations

Initiate rapid response to incidents, alarms, and emergency situations

Maintain regular patrol, surveillance, and incident report documentation

Be proficient in fire security, traffic control, crowd management protocols

Experienced in law enforcement collaboration

Clear and consistent communication with team and management

Professional interaction with visitors, at access points and during premise escorts

Key Security Officer skills for your resume

Reviewing the chief duties and responsibilities we listed above should help you identify the kinds of Security Officer skills you need to add to your resume. By including a balanced mix of these skills, you can demonstrate your experience and ability to fulfill the role's key duties. 

To help you further narrow that list of skills, let's examine some of the top hard and soft skills you should consider for your resume's core competencies section.

Hard skills for a Security Officer resume

1.       security procedures.

One of the most important Security Officer skills involves keen knowledge of security-related protocols, policies, and procedures. Your resume should reflect this expertise in everything from threat analysis to access management, patrolling, and emergency protocols.

2.       Physical fitness

The average Security Officer may not be required to engage in strenuous activity each day, but they should still maintain a level of fitness that enables them to perform their duties. There may be times when they need to chase intruders or detain suspects, so good physical condition is a must.

3.       Incident response

When incidents occur, the Security Officer must be prepared to respond in accordance with best practices and organizational protocols. To do that, they need a high level of confidence, familiarity with the organization's security processes, and strong interpersonal skills. In addition, they should be well-versed in emergency medical response procedures, including basic first aid.

4.       Policy and legal compliance

In addition to knowledge about their organization's policies, Security Officers need to understand the legal restraints governing things like detaining suspects, use of force, and privacy concerns. Interactions with visitors, customers, and potential suspects should always be conducted in accordance with policy and applicable laws.

5.       Proficiency with security technology

Security technology is in use throughout nearly every sector of the economy. Competent Security Officers will be familiar with technology tools like access control, surveillance cameras and systems, and various security management platforms.

6.       Law enforcement

As a Security Officer, you will need to maintain solid relationships with local law enforcement. When incidents occur, those law enforcement personnel will be among the first people you contact. You may need to request information or ask for them to dispatch officers to assist you. These basic skills will help you to understand how to coordinate with law enforcement to maintain a safe and secure environment.

7.       Surveillance and reporting

In addition to knowing how to use surveillance technology, you should also possess well-developed surveillance skills to help you monitor your surroundings, identify security risks, and craft effective responses. You'll also need to be familiar with the right protocols for creating reports and maintaining timely documentation.

Soft skills for a Security Officer resume

1.       communication.

Excellent written and verbal communication skills are among the most vital Security Officer skills. Security guards and similar personnel need to be able to communicate information and ideas to many different types of people, including their team, clients, superiors, and members of the public.

Related reading : 11 Best Communication Skills for Your Resume (With Examples)

2.       Critical thinking

For a Security Officer, critical thinking skills empower their ability to solve problems. These skills include the ability to quickly analyze a situation, draw the right conclusions based on available evidence, and respond in a decisive and effective way.

3.       Situational awareness

Because the Security Officer role requires you to quickly respond to potential threats and other problems, situational awareness skills are essential for success. These skills include keen observation abilities, vigilance, and continual awareness of your surroundings.

4.       Attention to detail

It's also important to be detail-oriented. As a Security Officer, you need to be focused on the details of the job. The ability to read body language, remember faces, and identify questionable behavior is crucial for detecting potential risks before incidents occur. These skills are also important for accurate incident reporting and interactions with law enforcement.

5.       Professionalism

Security Officers need to possess a high level of professionalism. In many cases, they may be the first person that customers encounter when they visit an office or business establishment. They should always conduct themselves in an ethical manner, showing respect and empathy even as they enforce policy and maintain a safe and secure environment.

6.       Conflict resolution

When any type of disagreement or incident occurs, Security Officers need to know how to defuse the situation. Their calm and confident use of vital conflict resolution skills can help to de-escalate potentially volatile situations without force and reduce tensions that might pose a threat to people and property.

7.       Collaboration

The ability to work closely with others is essential for security personnel. Security projects are always team efforts and experienced Security Officers are able to collaborate closely with their clients, team members, and local law enforcement to identify risks, create effective solutions, and maintain operational efficiency at all times.

How to add Security Officer skills to your resume

The key to any successful resume lies in your ability to create a targeted resume that is tailored to the specific job you're hoping to get. Below are some tips designed to assist you as you create a tailored resume that helps you stand out from the competition:

Related reading : How to Write a Targeted Resume That Lands You an Interview

Find vital keywords in the job description

Many of the Security Officer skills you'll need to include in your resume can be found in the role's job description. Review the job posting to identify specific skill-related terms that the employer has included as qualifications. 

Those terms are keywords that you need to add to your resume – using those exact words. If the company is using an applicant tracking system to screen candidate submissions, the ATS will be scanning for those keywords.

Related reading : How to Make an ATS-Friendly Resume - Tips for ATS 2024

Add skill keywords in your resume headline

The first place to include Security Officer skills is in your resume headline. Just create a single line of text right below your contact information that highlights the job you're seeking and your experience or specialty. 

For example:

Dedicated Security Officer with 5 Years of Experience in On-Site Risk Mitigation and Event Management

Include keywords in your resume profile

Your resume profile can also be a great place to highlight one or two skills. Just create a paragraph of between three and five sentences highlighting your experience, skills, qualifications, and a quantifiable achievement that reinforces your value. 

Solutions-oriented Security Officer with 5 years of experience securing property and lives. Skilled in access point monitoring and management, regular patrols, and incident response. Experienced professional with deep knowledge of industry best practices, legal compliance, and law enforcement collaboration. Supervised 10-person team credited with 33% reduction in security incidents over a two-year period.

Related reading: Resume Profile Explained (with Examples)

Focus on relevant skills in your skills section

Obviously, you need to create a list of these skills to add to the core competencies section of your resume. Start with the skill keywords you found in the job description and add as many other relevant skills as it takes to create a list of between nine and twelve hard and soft skills. You should use a bullet point list of skills formatted into two or three columns.

Add skills to your work experience achievement statements

To truly drive home a message of value, use some of these skills in your work experience achievement statements. These bullet point achievements should focus on tangible results that your skills helped you achieve. 

• Conducted routine security audits that identified and resolved more than 40 potential access vulnerabilities
• Revised monitoring procedures for four facilities at ABC Corp., resulting in a 35% reduction in trespassing and other access breaches
• Implemented digital biometrics access system that reduced check-in and check-out processing times by 75%, with 95% reduction in unauthorized access to facilities

Separate yourself from the crowd with the right Security Officer skills

Adding the best Security Officer skills to your resume can be one of the best ways to demonstrate your fitness for the job. By following the tips and recommendations in this guide, you should be well on your way to impressing your next hiring manager – which could be just what you need to land your next great interview.

Get your free resume review from our team of experts today. They have the experience you need to make sure that your resume includes the Security Officer skills employers expect to see!

Recommended reading:

7 Best Problem-Solving Skills for Your Resume + Examples

17 Resume Tips to Get Seen and Hired Faster

11 Key Things to Put on Your Resume

Related Articles:

7 Signs Your Resume is Making You Look Old

Why a Simple Resume Layout is a Successful Resume

Software Developer Top Needed Skills

See how your resume stacks up.

Career Advice Newsletter

Our experts gather the best career & resume tips weekly. Delivered weekly, always free.

Thanks! Career advice is on its way.

Share this article:

Let's stay in touch.

Subscribe today to get job tips and career advice that will come in handy.

Your information is secure. Please read our privacy policy for more information.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 12 July 2024

The nature of the last universal common ancestor and its impact on the early Earth system

• Edmund R. R. Moody   ORCID: orcid.org/0000-0002-8785-5006 1 ,
• Sandra Álvarez-Carretero   ORCID: orcid.org/0000-0002-9508-6286 1 ,
• Tara A. Mahendrarajah   ORCID: orcid.org/0000-0001-7032-6581 2 ,
• James W. Clark 3 ,
• Holly C. Betts 1 ,
• Nina Dombrowski   ORCID: orcid.org/0000-0003-1917-2577 2 ,
• Lénárd L. Szánthó   ORCID: orcid.org/0000-0003-3363-2488 4 , 5 , 6 ,
• Richard A. Boyle 7 ,
• Stuart Daines 7 ,
• Xi Chen   ORCID: orcid.org/0000-0001-7098-607X 8 ,
• Nick Lane   ORCID: orcid.org/0000-0002-5433-3973 9 ,
• Ziheng Yang   ORCID: orcid.org/0000-0003-3351-7981 9 ,
• Graham A. Shields   ORCID: orcid.org/0000-0002-7828-3966 8 ,
• Gergely J. Szöllősi 5 , 6 , 10 ,
• Anja Spang   ORCID: orcid.org/0000-0002-6518-8556 2 , 11 ,
• Davide Pisani   ORCID: orcid.org/0000-0003-0949-6682 1 , 12 ,
• Tom A. Williams   ORCID: orcid.org/0000-0003-1072-0223 12 ,
• Timothy M. Lenton   ORCID: orcid.org/0000-0002-6725-7498 7 &
• Philip C. J. Donoghue   ORCID: orcid.org/0000-0003-3116-7463 1  

Nature Ecology & Evolution ( 2024 ) Cite this article

57k Accesses

924 Altmetric

Metrics details

• Microbial genetics
• Molecular evolution
• Phylogenetics

The nature of the last universal common ancestor (LUCA), its age and its impact on the Earth system have been the subject of vigorous debate across diverse disciplines, often based on disparate data and methods. Age estimates for LUCA are usually based on the fossil record, varying with every reinterpretation. The nature of LUCA’s metabolism has proven equally contentious, with some attributing all core metabolisms to LUCA, whereas others reconstruct a simpler life form dependent on geochemistry. Here we infer that LUCA lived ~4.2 Ga (4.09–4.33 Ga) through divergence time analysis of pre-LUCA gene duplicates, calibrated using microbial fossils and isotope records under a new cross-bracing implementation. Phylogenetic reconciliation suggests that LUCA had a genome of at least 2.5 Mb (2.49–2.99 Mb), encoding around 2,600 proteins, comparable to modern prokaryotes. Our results suggest LUCA was a prokaryote-grade anaerobic acetogen that possessed an early immune system. Although LUCA is sometimes perceived as living in isolation, we infer LUCA to have been part of an established ecological system. The metabolism of LUCA would have provided a niche for other microbial community members and hydrogen recycling by atmospheric photochemistry could have supported a modestly productive early ecosystem.

Similar content being viewed by others

Co‐evolution of early Earth environments and microbial life

The curious consistency of carbon biosignatures over billions of years of Earth-life coevolution

Phanerozoic radiation of ammonia oxidizing bacteria

The common ancestry of all extant cellular life is evidenced by the universal genetic code, machinery for protein synthesis, shared chirality of the almost-universal set of 20 amino acids and use of ATP as a common energy currency 1 . The last universal common ancestor (LUCA) is the node on the tree of life from which the fundamental prokaryotic domains (Archaea and Bacteria) diverge. As such, our understanding of LUCA impacts our understanding of the early evolution of life on Earth. Was LUCA a simple or complex organism? What kind of environment did it inhabit and when? Previous estimates of LUCA are in conflict either due to conceptual disagreement about what LUCA is 2 or as a result of different methodological approaches and data 3 , 4 , 5 , 6 , 7 , 8 , 9 . Published analyses differ in their inferences of LUCA’s genome, from conservative estimates of 80 orthologous proteins 10 up to 1,529 different potential gene families 4 . Interpretations range from little beyond an information-processing and metabolic core 6 through to a prokaryote-grade organism with much of the gene repertoire of modern Archaea and Bacteria 8 , recently reviewed in ref. 7 . Here we use molecular clock methodology, horizontal gene-transfer-aware phylogenetic reconciliation and existing biogeochemical models to address questions about LUCA’s age, gene content, metabolism and impact on the early Earth system.

Estimating the age of LUCA

Life’s evolutionary timescale is typically calibrated to the oldest fossil occurrences. However, the veracity of fossil discoveries from the early Archaean period has been contested 11 , 12 . Relaxed Bayesian node-calibrated molecular clock approaches provide a means of integrating the sparse fossil and geochemical record of early life with the information provided by molecular data; however, constraining LUCA’s age is challenging due to limited prokaryote fossil calibrations and the uncertainty in their placement on the phylogeny. Molecular clock estimates of LUCA 13 , 14 , 15 have relied on conserved universal single-copy marker genes within phylogenies for which LUCA represented the root. Dating the root of a tree is difficult because errors propagate from the tips to the root of the dated phylogeny and information is not available to estimate the rate of evolution for the branch incident on the root node. Therefore, we analysed genes that duplicated before LUCA with two (or more) copies in LUCA’s genome 16 . The root in these gene trees represents this duplication preceding LUCA, whereas LUCA is represented by two descendant nodes. Use of these universal paralogues also has the advantage that the same calibrations can be applied at least twice. After duplication, the same species divergences are represented on both sides of the gene tree 17 , 18 and thus can be assumed to have the same age. This considerably reduces the uncertainty when genetic distance (branch length) is resolved into absolute time and rate. When a shared node is assigned a fossil calibration, such cross-bracing also serves to double the number of calibrations on the phylogeny, improving divergence time estimates. We calibrated our molecular clock analyses using 13 calibrations (see ‘Fossil calibrations’ in Supplementary Information ). The calibration on the root of the tree of life is of particular importance. Some previous studies have placed a younger maximum constraint on the age of LUCA based on the assumption that life could not have survived Late Heavy Bombardment (LHB) (~3.7–3.9 billion years ago (Ga)) 19 . However, the LHB hypothesis is extrapolated and scaled from the Moon’s impact record, the interpretation of which has been questioned in terms of the intensity, duration and even the veracity of an LHB episode 20 , 21 , 22 , 23 . Thus, the LHB hypothesis should not be considered a credible maximum constraint on the age of LUCA. We used soft-uniform bounds, with the maximum-age bound based on the time of the Moon-forming impact (4,510 million years ago (Ma) ± 10 Myr), which would have effectively sterilized Earth’s precursors, Tellus and Theia 13 . Our minimum bound on the age of LUCA is based on low δ 98 Mo isotope values indicative of Mn oxidation compatible with oxygenic photosynthesis and, therefore, total-group Oxyphotobacteria in the Mozaan Group, Pongola Supergroup, South Africa 24 , 25 , dated minimally to 2,954 Ma ± 9 Myr (ref. 26 ).

Our estimates for the age of LUCA are inferred with a concatenated and a partitioned dataset, both consisting of five pre-LUCA paralogues: catalytic and non-catalytic subunits from ATP synthases, elongation factor Tu and G, signal recognition protein and signal recognition particle receptor, tyrosyl-tRNA and tryptophanyl-tRNA synthetases, and leucyl- and valyl-tRNA synthetases 27 . Marginal densities (commonly referred to as effective priors) fall within calibration densities (that is, user-specified priors) when topologically adjacent calibrations do not overlap temporally, but may differ when they overlap, to ensure the relative age relationships between ancestor-descendant nodes. We consider the marginal densities a reasonable interpretation of the calibration evidence given the phylogeny; we are not attempting to test the hypothesis that the fossil record is an accurate temporal archive of evolutionary history because it is not 28 . The duplicated LUCA node age estimates we obtained under the autocorrelated rates (geometric Brownian motion (GBM)) 29 , 30 and independent-rates log-normal (ILN) 31 , 32 relaxed-clock models with our partitioned dataset (GBM, 4.18–4.33 Ga; ILN, 4.09–4.32 Ga; Fig. 1 ) fall within our composite age estimate for LUCA ranging from 3.94 Ga to 4.52 Ga, comparable to previous studies 13 , 18 , 33 . Dating analyses based on single genes, or concatenations that excluded each gene in turn, returned compatible timescales (Extended Data Figs. 1 and 2 and ‘Additional methods’ in Methods ).

Our results suggest that LUCA lived around 4.2 Ga, with a 95% confidence interval spanning 4.09–4.33 Ga under the ILN relaxed-clock model (orange) and 4.18–4.33 Ga under the GBM relaxed-clock model (teal). Under a cross-bracing approach, nodes corresponding to the same species divergences (that is, mirrored nodes) have the same posterior time densities. This figure shows the corresponding posterior time densities of the mirrored nodes for the last universal, archaeal, bacterial and eukaryotic common ancestors (LUCA, LACA, LBCA and LECA, respectively); the last common ancestor of the mitochondrial lineage (Mito-LECA); and the last plastid-bearing common ancestor (LPCA). Purple stars indicate nodes calibrated with fossils. Arc, Archaea; Bac, Bacteria; Euk, Eukarya.

LUCA’s physiology

To estimate the physiology of LUCA, we first inferred an updated microbial phylogeny from 57 phylogenetic marker genes (see ‘Universal marker genes’ in Methods ) on 700 genomes, comprising 350 Archaea and 350 Bacteria 15 . This tree was in good agreement with recent phylogenies of the archaeal and bacterial domains of life 34 , 35 . For example, the TACK 36 and Asgard clades of Archaea 37 , 38 , 39 and Gracilicutes within Bacteria 40 , 41 were recovered as monophyletic. However, the analysis was equivocal as to the phylogenetic placement of the Patescibacteria (CPR) 42 and DPANN 43 , which are two small-genome lineages that have been difficult to place in trees. Approximately unbiased 44 tests could not distinguish the placement of these clades, neither at the root of their respective domains nor in derived positions, with CPR sister to Chloroflexota (as reported recently in refs. 35 , 41 , 45 ) and DPANN sister to Euryarchaeota. To account for this phylogenetic uncertainty, we performed LUCA reconstructions on two trees: our maximum likelihood (ML) tree (topology 1; Extended Data Fig. 3 ) and a tree in which CPR were placed as the sister of Chloroflexota, with DPANN sister to all other Archaea (topology 2; Extended Data Fig. 4 ). In both cases, the gene families mapped to LUCA were very similar (correlation of LUCA presence probabilities (PP), r  = 0.6720275, P  < 2.2 × 10 − 16 ). We discuss the results on the tree with topology 2 and discuss the residual differences in Supplementary Information , ‘Topology 1’ (Supplementary Data 1 ).

We used the probabilistic gene- and species-tree reconciliation algorithm ALE 46 to infer the evolution of gene family trees for each sampled entry in the KEGG Orthology (KO) database 47 on our species tree. ALE infers the history of gene duplications, transfers and losses based on a comparison between a distribution of bootstrapped gene trees and the reference species tree, allowing us to estimate the probability that the gene family was present at a node in the tree 35 , 48 , 49 . This reconciliation approach has several advantages for drawing inferences about LUCA. Most gene families have experienced gene transfer since the time of LUCA 50 , 51 and so explicitly modelling transfers enables us to include many more gene families in the analysis than has been possible using previous approaches. As the analysis is probabilistic, we can also account for uncertainty in gene family origins and evolutionary history by averaging over different scenarios using the reconciliation model. Using this approach, we estimated the probability that each KEGG gene family (KO) was present in LUCA and then used the resulting probabilities to construct a hypothetical model of LUCA’s gene content, metabolic potential (Fig. 2 ) and environmental context (Fig. 3 ). Using the KEGG annotation is beneficial because it allows us to connect our inferences to curated functional annotations; however, it has the drawback that some widespread gene families that were likely present in LUCA are divided into multiple KO families that individually appear to be restricted to particular taxonomic groups and inferred to have arisen later. To account for this limitation, we also performed an analysis of COG (Clusters of Orthologous Genes) 52 gene families, which correspond to more coarse-grained functional annotations (Supplementary Data 2 ).

In black: enzymes and metabolic pathways inferred to be present in LUCA with at least PP = 0.75, with sampling in both prokaryotic domains. In grey: those inferred in our least-stringent threshold of PP = 0.50. The analysis supports the presence of a complete WLP and an almost complete TCA cycle across multiple confidence thresholds. Metabolic maps derived from KEGG 47 database through iPath 109 . GPI, glycosylphosphatidylinositol; DDT, 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane.

a , A representation of LUCA based on our ancestral gene content reconstruction. Gene names in black have been inferred to be present in LUCA under the most-stringent threshold (PP = 0.75, sampled in both domains); those in grey are present at the least-stringent threshold (PP = 0.50, without a requirement for presence in both domains). b , LUCA in the context of the tree of life. Branches on the tree of life that have left sampled descendants today are coloured black, those that have left no sampled descendants are in grey. As the common ancestor of extant cellular life, LUCA is the oldest node that can be reconstructed using phylogenetic methods. It would have shared the early Earth with other lineages (highlighted in teal) that have left no descendants among sampled cellular life today. However, these lineages may have left a trace in modern organisms by transferring genes into the sampled tree of life (red lines) before their extinction. c , LUCA’s chemoautotrophic metabolism probably relied on gas exchange with the immediate environment to achieve organic carbon (C org ) fixation via acetogenesis and it may also have run the metabolism in reverse. d , LUCA within the context of an early ecosystem. The CO 2 and H 2 that fuelled LUCA’s plausibly acetogenic metabolism could have come from both geochemical and biotic inputs. The organic matter and acetate that LUCA produced could have created a niche for other metabolisms, including ones that recycled CO 2 and H 2 (as in modern sediments). e , LUCA in an Earth system context. Acetogenic LUCA could have been a key part of both surface and deep (chemo)autotrophic ecosystems, powered by H 2 . If methanogens were also present, hydrogen would be released as CH 4 to the atmosphere, converted to H 2 by photochemistry and thus recycled back to the surface ecosystem, boosting its productivity. Ferm., fermentation.

Genome size and cellular features

By using modern prokaryotic genomes as training data, we used a predictive model to estimate the genome size and the number of protein families encoded by LUCA based on the relationship between the number of KEGG gene families and the total number of proteins encoded by modern prokaryote genomes (Extended Data Figs. 5 and 6 ). On the basis of the PPs for KEGG KO gene families, we identified a conservative subset of 399 KOs that were likely to be present in LUCA, with PPs ≥0.75, and found in both Archaea and Bacteria (Supplementary Data 1 ); these families form the basis of our metabolic reconstruction. However, by integrating over the inferred PPs of all KO gene families, including those with low probabilities, we also estimate LUCA’s genome size. Our predictive model estimates a genome size of 2.75 Mb (2.49–2.99 Mb) encoding 2,657 (2,451–2,855) proteins ( Methods ). Although we can estimate the number of genes in LUCA’s genome, it is more difficult to identify the specific gene families that might have already been present in LUCA based on the genomes of modern Archaea and Bacteria. It is likely that the modern version of the pathways would be considered incomplete based on LUCA’s gene content through subsequent evolutionary changes. We should therefore expect reconstructions of metabolic pathways to be incomplete due to this phylogenetic noise and other limitations of the analysis pipeline. For example, when looking at genes and pathways that can uncontroversially be mapped to LUCA, such as the ribosome and aminoacyl-tRNA synthetases for implementing the genetic code, we find that we map many (but not all) of the key components to LUCA (see ‘Notes’ in Supplementary Information ). We interpret this to mean that our reconstruction is probably incomplete but our interpretation of LUCA’s metabolism relies on our inference of pathways, not individual genes.

The inferred gene content of LUCA suggests it was an anaerobe as we do not find support for the presence of terminal oxidases (Supplementary Data 1 ). Instead we identified almost all genes encoding proteins of the archaeal (and most of the bacterial) versions of the Wood–Ljungdahl pathway (WLP) (PP > 0.7), indicating that LUCA had the potential for acetogenic growth and/or carbon fixation 53 , 54 , 55 (Supplementary Data 3 ). LUCA encoded some NiFe hydrogenase subunits ( K06281 , PP = 0.90; K14126 , PP = 0.92), which may have enabled growth on hydrogen (see ‘Notes’ in Supplementary Information ). Complexes involved in methanogenesis such as methyl-coenzyme M reductase and tetrahydromethanopterin S-methyltransferase were inferred to be absent, suggesting that LUCA was unlikely to function as a modern methanogen. We found strong support for some components of the TCA cycle (including subunits of oxoglutarate/2-oxoacid ferredoxin oxidoreductase ( K00175 and K00176 ), succinate dehydrogenase ( K00239 ) and homocitrate synthase ( K02594 )), although some steps are missing. LUCA was probably capable of gluconeogenesis/glycolysis in that we find support for most subunits of enzymes involved in these pathways (Supplementary Data 1 and 3 ). Considering the presence of the WLP, this may indicate that LUCA had the ability to grow organoheterotrophically and potentially also autotrophically. Gluconeogenesis would have been important in linking carbon fixation to nucleotide biosynthesis via the pentose phosphate pathway, most enzymes of which seem to be present in LUCA (see ‘Notes’ in Supplementary Information ). We found no evidence that LUCA was photosynthetic, with low PPs for almost all components of oxygenic and anoxygenic photosystems (Supplementary Data 3 ).

We find strong support for the presence of ATP synthase, specifically, the A ( K02117 , PP = 0.98) and B ( K02118 , PP = 0.94) subunit components of the hydrophilic V/A1 subunit, and the I (subunit a, K02123 , PP = 0.99) and K (subunit c, K02124 , PP = 0.82) subunits of the transmembrane V/A0 subunit. In addition, if we relax the sampling threshold, we also infer the presence of the F1-type β-subunit ( K02112 , PP = 0.94). This is consistent with many previous studies that have mapped ATP synthase subunits to LUCA 6 , 17 , 18 , 56 , 57 .

We obtain moderate support for the presence of pathways for assimilatory nitrate (ferredoxin-nitrate reductase, K00367 , PP = 0.69; ferredoxin-nitrite reductase, K00367 , PP = 0.53) and sulfate reduction (sulfate adenylyltransferase, K00957 , PP = 0.80, and K00958 , PP = 0.73; sulfite reductase, K00392 , PP = 0.82; phosphoadenosine phosphosulfate reductase, K00390 , PP = 0.56), probably to fuel amino acid biosynthesis, for which we inferred the presence of 37 partially complete pathways.

We found support for the presence of 19 class 1 CRISPR–Cas effector protein families in the genome of LUCA, including types I and III (cas3, K07012 , PP = 0.80, and K07475 , PP = 0.74; cas10, K07016 , PP = 0.96, and K19076 , PP = 0.67; and cas7, K07061 , PP = 0.90, K09002 , PP = 0.84, K19075 , PP = 0.97, K19115 , PP = 0.98, and K19140 , PP = 0.80). The absence of Cas1 and Cas2 may suggest LUCA encoded an early Cas system with the means to deliver an RNA-based immune response by cutting (Cas6/Cas3) and binding (CSM/Cas10) RNA, but lacking the full immune-system-site CRISPR. This supports the idea that the effector stage of CRISPR–Cas immunity evolved from RNA sensing for signal transduction, based on the similarities in RNA binding modules of the proteins 58 . This is consistent with the idea that cellular life was already involved in an arms race with viruses at the time of LUCA 59 , 60 . Our results indicate that an early Cas system was an ancestral immune system of extant cellular life.

Altogether, our metabolic reconstructions suggest that LUCA was a relatively complex organism, similar to extant Archaea and Bacteria 6 , 7 . On the basis of ancient duplications of the Sec and ATP synthase genes before LUCA, along with high PPs for key components of those systems, membrane-bound ATP synthase subunits, genes involved in peptidoglycan synthesis ( mraY , K01000 ; murC , K01924 ) and the cytoskeletal actin-like protein, MreB ( K03569 ) (Supplementary Data 3 ), it is highly likely that LUCA possessed the core cellular apparatus of modern prokaryotic life. This might include the basic constituents of a phospholipid membrane, although our analysis did not conclusively establish its composition. In particular, we recovered the following enzymes involved in the synthesis of ether and ester lipids, (alkyldihydroxyacetonephosphate synthase, glycerol 3-phosphate and glycerol 1-phosphate) and components of the mevalonate pathway (mevalonate 5-phosphate dehydratase (PP = 0.84), hydroxymethylglutaryl-CoA reductase (PP = 0.52), mevalonate kinase (PP = 0.51) and hydroxymethylglutaryl-CoA synthase (PP = 0.51)).

Compared with previous estimates of LUCA’s gene content, we find 81 overlapping COG gene families with the consensus dataset of ref. 7 and 69 overlapping KOs with the dataset of ref. 6 . Key points of agreement between previous studies include the presence of signal recognition particle protein, ffh (COG0541, K03106 ) 7 used in the targeting and delivery of proteins for the plasma membrane, a high number of aminoacyl-tRNA synthetases for amino acid synthesis and glycolysis/gluconeogenesis enzymes.

Ref. 6 inferred LUCA to be a thermophilic anaerobic autotroph using the WLP for carbon fixation based on the presence of a single enzyme (CODH), and similarly suggested that LUCA was capable of nitrogen fixation using a nitrogenase. Our reconstruction agrees with ref. 6 that LUCA was an anaerobic autotroph using the WLP for carbon fixation, but we infer the presence of a much more complete WLP than that previously obtained. We did not find strong evidence for nitrogenase or nitrogen fixation, and the reconstruction was not definitive with respect to the optimal growth environment of LUCA.

We used a probabilistic approach to reconstruct LUCA—that is, we estimated the probability with which each gene family was present in LUCA based on a model of how gene families evolve along an overarching species tree. This approach differs from analyses of phylogenetic presence–absence profiles 3 , 4 , 9 or those that used filtering criteria (such as broadly distributed or highly vertically evolving families) to define a high-confidence subset of modern genes that might have been present in LUCA. Our reconstruction maps many more genes to LUCA—albeit each with lower probability—than previous analyses 8 and yields an estimate of LUCA’s genome size that is within the range of modern prokaryotes. The result is an incomplete picture of a cellular organism that was prokaryote grade rather than progenotic 2 and that, similarly to prokaryotes today, probably existed as part of an ecosystem. As the common ancestor of sampled, extant prokaryotic life, LUCA is the oldest node on the species tree that we can reconstruct via phylogenomics but, as Fig. 3 illustrates, it was already the product of a highly innovative period in evolutionary history during which most of the core components of cells were established. By definition, we cannot reconstruct LUCA’s contemporaries using phylogenomics but we can propose hypotheses about their physiologies based on the reconstructed LUCA whose features immediately suggest the potential for interactions with other prokaryotic metabolisms.

LUCA’s environment, ecosystem and Earth system context

The inference that LUCA used the WLP helps constrain the environment and ecology in which it could have lived. Modern acetogens can grow autotrophically on H 2 (and CO 2 ) or heterotrophically on a wide range of alternative electron donors including alcohols, sugars and carboxylic acids 55 . This metabolic flexibility is key to their modern ecological success. Acetogenesis, whether autotrophic or heterotrophic, has a low energy yield and growth efficiency (although use of the reductive acetyl-CoA pathway for both energy production and biosynthesis reduces the energy cost of biosynthesis). This would be consistent with an energy-limited early biosphere 61 .

If LUCA functioned as an organoheterotrophic acetogen, it was necessarily part of an ecosystem containing autotrophs providing a source of organic compounds (because the abiotic source flux of organic molecules was minimal on the early Earth). Alternatively, if LUCA functioned as a chemoautotrophic acetogen it could (in principle) have lived independently off an abiotic source of H 2 (and CO 2 ). However, it is implausible that LUCA would have existed in isolation as the by-products of its chemoautotrophic metabolism would have created a niche for a consortium of other metabolisms (as in modern sediments) (Fig. 3d ). This would include the potential for LUCA itself to grow as an organoheterotroph.

A chemoautotrophic acetogenic LUCA could have occupied two major potential habitats (Fig. 3e ): the first is the deep ocean where hydrothermal vents and serpentinization of sea-floor provided a source of H 2 (ref. 62 ). Consistent with this, we find support for the presence of reverse gyrase (PP = 0.97), a hallmark enzyme of hyperthermophilic prokaryotes 6 , 63 , 64 , 65 , which would not be expected if early life existed at the ocean surface (although the evolution of reverse gyrase is complex 63 ; see ‘Reverse gyrase’ in Supplementary Information ). The second habitat is the ocean surface where the atmosphere would have provided a source of H 2 derived from volcanoes and metamorphism. Indeed, we detected the presence of spore photoproduct lyase (COG1533, K03716 , PP = 0.88) that in extant organisms repairs methylene-bridged thymine dimers occurring in spore DNA as a result of damage induced through ultraviolet (UV) radiation 66 , 67 . However, this gene family also occurs in modern taxa that neither form endospores nor dwell in environments where they are likely to accrue UV damage to their DNA and so is not an exclusive hallmark of environments exposed to UV. Previous studies often favoured a deep-ocean environment for LUCA as early life would have been better protected there from an episode of LHB. However, if the LHB was less intense than initially proposed 20 , 22 , or just a sampling artefact 21 , these arguments weaken. Another possibility may be that LUCA inhabited a shallow hydrothermal vent or a hot spring.

Hydrogen fluxes in these ecosystems could have been several times higher on the early Earth (with its greater internal heat source) than today. Volcanism today produces ~1 × 10 12  mol H 2  yr −1 and serpentinization produces ~0.4 × 10 12  mol H 2  yr − 1 . With the present H 2 flux and the known scaling of the H 2 escape rate to space, an abiotic atmospheric concentration of H 2 of ~150 ppmv is predicted 68 . Chemoautotrophic acetogens would have locally drawn down the concentration of H 2 (in either surface or deep niche) but their low growth efficiency would ensure H 2 (and CO 2 ) remained available. This and the organic matter and acetate produced would have created niches for other metabolisms, including methanogenesis (Fig. 3d ).

On the basis of thermodynamic considerations, CH 4 and CO 2 are expected to be the eventual metabolic end products of the resulting ecosystem, with a small fraction of the initial hydrogen consumption buried as organic matter. The resulting flux of CH 4 to the atmosphere would fuel photochemical H 2 regeneration and associated productivity in the surface ocean (Fig. 3e ). Existing models suggest the resulting global H 2 recycling system is highly effective, such that the supply flux of H 2 to the surface could have exceeded the volcanic input of H 2 to the atmosphere by at least an order of magnitude, in turn implying that the productivity of such a biosphere was boosted by a comparable factor 69 . Photochemical recycling to CO would also have supported a surface niche for organisms consuming CO (ref. 69 ).

In deep-ocean habitats, there could be some localized recycling of electrons (Fig. 3d ) but a quantitative loss of highly insoluble H 2 and CH 4 to the atmosphere and minimal return after photochemical conversion of CH 4 to H 2 means global recycling to depth would be minimal (Fig. 3e ). Hence the surface environment for LUCA could have become dominant (albeit recycling of the resulting organic matter could be spread through ocean depth; ‘Deep heterotrophic ecosystem’ in Fig. 3e ). The global net primary productivity of an early chemoautotrophic biosphere including acetogenic LUCA and methanogens could have been of order ~1 × 10 12 to 7 × 10 12  mol C yr − 1 (~3 orders of magnitude less than today) 69 .

The nutrient supply (for example, N) required to support such a biosphere would need to balance that lost in the burial flux of organic matter. Earth surface redox balance dictates that hydrogen loss to space and burial of electrons/hydrogen must together balance input of electrons/hydrogen. Considering contemporary H 2 inputs, and the above estimate of net primary productivity, this suggests a maximum burial flux in the order of ~10 12  mol C yr − 1 , which, with contemporary stoichiometry (C:N ratio of ~7) could demand >10 11  mol N yr − 1 . Lightning would have provided a source of nitrite and nitrate 70 , consistent with LUCA’s inferred pathways of nitrite and (possibly) nitrate reduction. However, it would only have been of the order 3 × 10 9  mol N yr − 1 (ref. 71 ). Instead, in a global hydrogen-recycling system, HCN from photochemistry higher in the atmosphere, deposited and hydrolysed to ammonia in water, would have increased available nitrogen supply by orders of magnitude toward ~3 × 10 12  mol N yr − 1 (refs. 71 , 72 ). This HCN pathway is consistent with the anomalously light nitrogen isotopic composition of the earliest plausible biogenic matter of 3.8–3.7 Ga (ref. 73 ), although that considerably postdates our inferred age of LUCA. These considerations suggest that the proposed LUCA biosphere (Fig. 3e ) would have been energy or hydrogen limited not nitrogen limited.

Conclusions

By treating gene presence probabilistically, our reconstruction maps many more genes (2,657) to LUCA than previous analyses and results in an estimate of LUCA’s genome size (2.75 Mb) that is within the range of modern prokaryotes. The result is a picture of a cellular organism that was prokaryote grade rather than progenotic 2 and that probably existed as a component of an ecosystem, using the WLP for acetogenic growth and carbon fixation. We cannot use phylogenetics to reconstruct other members of this early ecosystem but we can infer their physiologies based on the metabolic inputs and outputs of LUCA. How evolution proceeded from the origin of life to early communities at the time of LUCA remains an open question, but the inferred age of LUCA (~4.2 Ga) compared with the origin of the Earth and Moon suggests that the process required a surprisingly short interval of geologic time.

Universal marker genes

A list of 298 markers were identified by creating a non-redundant list of markers used in previous studies on archaeal and bacterial phylogenies 10 , 35 , 38 , 74 , 75 , 76 , 77 , 78 , 79 . These markers were mapped to the corresponding COG, arCOG and TIGRFAM profile to identify which profile is best suited to extract proteins from taxa of interest. To evaluate whether the markers cover all archaeal and bacterial diversity, proteins from a set of 574 archaeal and 3,020 bacterial genomes were searched against the COG, arCOG and TIGRFAM databases using hmmsearch (v.3.1b2; settings, hmmsearch–tblout output–domtblout–notextw) 52 , 80 , 81 , 82 . Only hits with an e-value less than or equal to 1 × 10 −5 were investigated further and for each protein the best hit was determined based on the e-value (expect value) and bit-score. Results from all database searches were merged based on the protein identifiers and the table was subsetted to only include hits against the 298 markers of interest. On the basis of this table we calculated whether the markers occurred in Archaea, Bacteria or both Archaea and Bacteria. Markers were only included if they were present in at least 50% of taxa and contained less than 10% of duplications, leaving a set of 265 markers. Sequences for each marker were aligned using MAFFT L-INS-i v.7.407 (ref. 83 ) for markers with less than 1,000 sequences or MAFFT 84 for those with more than 1,000 sequences (setting, –reorder) 84 and sequences were trimmed using BMGE 85 , set for amino acids, a BLOcks SUbstitution Matrix 30 similarity matrix, with a entropy score of 0.5 (v.1.12; settings, -t AA -m BLOSUM30 -h 0.5). Single gene trees were generated with IQ-TREE 2 (ref. 86 ), using the LG substitution matrix, with ten-profile mixture models, four CPUs, with 1,000 ultrafast bootstraps optimized by nearest neighbour interchange written to a file retaining branch lengths (v.2.1.2; settings, -m LG + C10 + F + R -nt 4 -wbtl -bb 1,000 -bnni). These single gene trees were investigated for archaeal and bacterial monophyly and the presence of paralogues. Markers that failed these tests were not included in further analyses, leaving a set of 59 markers (3 arCOGs, 46 COGs and 10 TIGRFAMs) suited for phylogenies containing both Archaea and Bacteria (Supplementary Data 4 ).

Marker gene sequence selection

To limit selecting distant paralogues and false positives, we used a bidirectional or reciprocal approach to identify the sequences corresponding to the 59 single-copy markers. In the first inspection (query 1), the 350 archaeal and 350 bacterial reference genomes were queried against all arCOG HMM (hidden Markov model) profiles (All_Arcogs_2018.hmm), all COG HMM profiles (NCBI_COGs_Oct2020.hmm) and all TIGRFAM HMM profiles (TIGRFAMs_15.0_HMM.LIB) using a custom script built on hmmsearch: hmmsearchTable <genomes.faa> <database.hmm> -E 1 × 10 −5 >HMMscan_Output_e5 (HMMER v.3.3.2) 87 . HMM profiles corresponding to the 59 single-copy marker genes (Supplementary Data 4 ) were extracted from each query and the best-hit sequences were identified based on the e-value and bit-score. We used the same custom hmmsearchTable script and conditions (see above) in the second inspection (query 2) to query the best-hit sequences identified above against the full COG HMM database (NCBI_COGs_Oct2020.hmm). Results were parsed and the COG family assigned in query 2 was compared with the COG family assigned to sequences based on the marker gene identity (Supplementary Data 4 ). Sequence hits were validated using the matching COG identifier, resulting in 353 mismatches (that is, COG family in query 1 does not match COG family in query 2) that were removed from the working set of marker gene sequences. These sequences were aligned using MAFFT L-INS-i 83 and then trimmed using BMGE 85 with a BLOSUM30 matrix. Individual gene trees were inferred under ML using IQ-TREE 2 (ref. 86 ) with model fitting, including both the default homologous substitution models and the following complex heterogeneous substitution models (LG substitution matrices with 10–60-profile mixture models, with empirical base frequencies and a discrete gamma model with four categories accounting for rate heterogeneity across sites): LG + C60 + F + G, LG + C50 + F + G, LG + C40 + F + G, LG + C30 + F + G, LG + C20 + F + G and LG + C10 + F + G, with 10,000 ultrafast bootstraps and 10 independent runs to avoid local optima. These 59 gene trees were manually inspected and curated over multiple rounds. Any horizontal gene transfer events, paralogous genes or sequences that violated domain monophyly were removed and two genes (arCOG01561, tuf ; COG0442, ProS ) were dropped at this stage due to the high number of transfer events, resulting in 57 single-copy orthologues for further tree inference.

Species-tree inference

These 57 orthologous sequences were concatenated and ML trees were inferred after three independent runs with IQ-TREE 2 (ref. 86 ) using the same model fitting and bootstrap settings as described above. The tree with the highest log-likelihood of the three runs was chosen as the ML species tree (topology 1). To test the effect of removing the CPR bacteria, we removed all CPR bacteria from the alignment before inferring a species tree (same parameters as above). We also performed approximately unbiased 44 tree topology tests (with IQ-TREE 2 (ref. 86 ), using LG + C20 + F + G) when testing the significance of constraining the species-tree topology (ML tree; Supplementary Fig. 1 ) to have a DPANN clade as sister to all other Archaea (same parameters as above but with a minimally constrained topology with monophyletic Archaea and DPANN sister to other Archaea present in a polytomy (Supplementary Fig. 2 )) and testing a constraint of CPR to be sister to Chloroflexi (Supplementary Fig. 3 ), and a combination of both the DPANN and CPR constraints (topology 2); these were tested against the ML topology, both using the normal 20 amino acid alignments and also with Susko–Roger recoding 88 .

Gene families

For the 700 representative species 15 , gene family clustering was performed using EGGNOGMAPPER v.2 (ref. 89 ), with the following parameters: using the DIAMOND 90 search, a query cover of 50% and an e-value threshold of 0.0000001. Gene families were collated using their KEGG 47 identifier, resulting in 9,365 gene families. These gene families were then aligned using MAFFT 84 v.7.5 with default settings and trimmed using BMGE 85 (with the same settings as above). Five independent sets of ML trees were then inferred using IQ-TREE 2 (ref. 86 ), using LG + F + G, with 1,000 ultrafast bootstrap replicates. We also performed a COG-based clustering analysis in which COGs were assigned based on the modal COG identifier annotated for each KEGG gene family based on the results from EGGNOGMAPPER v.2 (ref. 89 ). These gene families were aligned, trimmed and one set of gene trees (with 1,000 ultrafast bootstrap replicates) was inferred using the same parameters as described above for the KEGG gene families.

Reconciliations

The five sets of bootstrap distributions were converted into ALE files, using ALEobserve, and reconciled against topology 1 and topology 2 using ALEml_undated 91 with the fraction missing for each genome included (where available). Gene family root origination rates were optimized for each COG functional category as previously described 35 and families were categorized into four different groups based on the probability of being present in the LUCA node in the tree. The most-stringent category was that with sampling above 1% in both domains and a PP ≥ 0.75, another category was with PP ≥ 0.75 with no sampling requirement, another with PP ≥ 0.5 with the sampling requirement; the least stringent was PP ≥ 0.5 with no sampling requirement. We used the median probability at the root from across the five runs to avoid potential biases from failed runs in the mean and to account for variation across bootstrap distributions (see Supplementary Fig. 4 for distributions of the inferred ratio of duplications, transfers and losses for all gene families across all tips in the species tree; see Supplementary Data 5 for the inferred duplications, transfers and losses ratios for LUCA, the last bacterial common ancestor and the last archaeal common ancestor).

Metabolic pathway analysis

Metabolic pathways for gene families mapped to the LUCA node were inferred using the KEGG 47 website GUI and metabolic completeness for individual modules was estimated with Anvi’o 92 (anvi-estimate-metabolism), with pathwise completeness.

Additional testing

We tested for the effects of model complexity on reconciliation by using posterior mean site frequency LG + C20 + F + G across three independent runs in comparison with 3 LG + F + G independent runs. We also performed a 10% subsampling of the species trees and gene family alignments across two independent runs for two different subsamples, one with and one without the presence of Asgard archaea. We also tested the likelihood of the gene families under a bacterial root (between Terrabacteria and Gracilicutes) using reconciliations of the gene families under a species-tree topology rooted as such.

Fossil calibrations

On the basis of well-established geological events and the fossil record, we modelled 13 uniform densities to constrain the maximum and minimum ages of various nodes in our phylogeny. We constrained the bounds of the uniform densities to be either hard (no tail probability is allowed after the age constraint) or soft (a 2.5% tail probability is allowed after the age constraint) depending on the interpretation of the fossil record ( Supplementary Information ). Nodes that refer to the same duplication event are identified by MCMCtree as cross-braced (that is, one is chosen as the ‘driver’ node, the rest are ‘mirrored’ nodes). In other words, the sampling during the Markov chain Monte Carlo (MCMC) for cross-braced nodes is not independent: the same posterior time density is inferred for matching mirror–driver nodes (see ‘Additional methods’ for details on our cross-bracing approach).

Timetree inference analyses

Timetree inference with the program MCMCtree (PAML v.4.10.7 (ref. 93 )) proceeded under both the GBM and ILN relaxed-clock models. We specified a vague rate prior with the shape parameter equal to 2 and the scale parameter equal to 2.5: Γ(2, 2.5). This gamma distribution is meant to account for the uncertainty on our estimate for the mean evolutionary rate, ~0.81 substitutions per site per time unit, which we calculated by dividing the tree height of our best-scoring ML tree ( Supplementary Information ) into the estimated mean root age of our phylogeny (that is, 4.520 Ga, time unit = 10 9 years; see ‘Fossil calibrations’ in Supplementary Information for justifications on used calibrations). Given that we are estimating very deep divergences, the molecular clock may be seriously violated. Therefore, we applied a very diffuse gamma prior on the rate variation parameter ( σ 2 ), Γ(1, 10), so that it is centred around σ 2  = 0.1. To incorporate our uncertainty regarding the tree shape, we specified a uniform kernel density for the birth–death sampling process by setting the birth and death processes to 1, λ  (per-lineage birth rate) =  μ  (per-lineage death rate) = 1, and the sampling frequency to ρ  (sampling fraction) = 0.1. Our main analysis consisted of inferring the timetree for the partitioned dataset under both the GBM and the ILN relaxed-clock models in which nodes that correspond to the same divergences are cross-braced (that is, hereby referred to as cross-bracing A). In addition, we ran 10 additional inference analyses to benchmark the effect that partitioning, cross-bracing and relaxed-clock models can have on species divergence time estimation: (1) GBM + concatenated alignment + cross-bracing A, (2) GBM + concatenated alignment + cross-bracing B (only nodes that correspond to the same divergences for which there are fossil constraints are cross-braced), (3) GBM + concatenated alignment + without cross-bracing, (4) GBM + partitioned alignment + cross-bracing B, (5) GBM + partitioned alignment + without cross-bracing, (6) ILN + concatenated alignment + cross-bracing A, (7) ILN + concatenated alignment + cross-bracing B, (8) ILN + concatenated alignment + without cross-bracing, (9) ILN + partitioned alignment + cross-bracing B, and (10) ILN + partitioned alignment + without cross-bracing. Lastly, we used (1) individual gene alignments, (2) a leave-one-out strategy (rate prior changed for alignments without ATP and Leu , Γ(2, 2.2), and without Tyr , Γ(2, 2.3), but was Γ(2, 2.5) for the rest; see ‘Additional methods’), and (3) a more complex substitution model 94 to assess their impact on timetree inference. Refer to ‘Additional methods’ for details on how we parsed the dataset we used for timetree inference analyses, ran PAML programs CODEML and MCMCtree to approximate the likelihood calculation 95 , and carried out the MCMC diagnostics for the results obtained under each of the previously mentioned scenarios.

We simulated 100 samples of ‘KEGG genomes’ based on the probabilities of each of the (7,467) gene families being present in LUCA using the random.rand function in numpy 96 . The mean number of KEGG gene families was 1,298.25, the 95% HPD (highest posterior density) minimum was 1,255 and the maximum was 1,340. To infer the relationship between the number of KEGG KO gene families encoded by a genome, the number of proteins and the genome size, we used LOESS (locally estimated scatter-plot smoothing) regression to estimate the relationship between the number of KOs and (1) the number of protein-coding genes and (2) the genome size for the 700 prokaryotic genomes used in the LUCA reconstruction. To ensure that our inference of genome size is robust to uncertainty in the number of paralogues that can be expected to have been present in LUCA, we used the presence of probability for each of these KEGG KO gene families rather than the estimated copy number. We used the predict function to estimate the protein-coding genes and genome size of LUCA using these models and the simulated gene contents encoded with 95% confidence intervals.

Additional methods

Cross-bracing approach implemented in mcmctree.

The PAML program MCMCtree was implemented to allow for the analysis of duplicated genes or proteins so that some nodes in the tree corresponding to the same speciation events in different paralogues share the same age. We used the tree topology depicted in Supplementary Fig. 5 to explain how users can label driver or mirror nodes (more on these terms below) so that the program identifies them as sharing the same speciation events. The tree topology shown in Supplementary Fig. 5 can be written in Newick format as:

(((A1,A2),A3),((B1,B2),B3));

In this example, A and B are paralogues and the corresponding tips labelled as A1–A3 and B1–B3 represent different species. Node r represents a duplication event, whereas other nodes are speciation events. If we want to constrain the same speciation events to have the same age (that is, Supplementary Fig. 5 , see labels a and b (that is, A1–A2 ancestor and B1–B2 ancestor, respectively) and labels v and b (that is, A1–A2–A3 ancestor and B1–B2–B3 ancestor, respectively), we use node labels in the format #1, #2, and so on to identify such nodes:

(((A1, A2) #1, A3) #2, ((B1, B2) [#1 B{0.2, 0.4}], B3) #2) 'B(0.9,1.1)';

Node a and node b are assigned the same label (#1) and so they share the same age ( t ): t a  =  t b . Similarly, node u and node v have the same age: t u  =  t v . The former nodes are further constrained by a soft-bound calibration based on the fossil record or geological evidence: 0.2 <  t a  =  t b  < 0.4. The latter, however, does not have fossil constraints and thus the only restriction imposed is that both t u and t v are equal. Finally, there is another soft-bound calibration on the root age: 0.9 <  t r  < 1.1.

Among the nodes on the tree with the same label (for example, those nodes labelled with #1 and those with #2 in our example), one is chosen as the driver node, whereas the others are mirror nodes. If calibration information is provided on one of the shared nodes (for example, nodes a and b in Supplementary Fig. 5 ), the same information therefore applies to all shared nodes. If calibration information is provided on multiple shared nodes, that information has to be the same (for example, you could not constrain node a with a different calibration used to constrain node b in Supplementary Fig. 5 ). The time prior (or the prior on all node ages on the tree) is constructed by using a density at the root of the tree, which is specified by the user (for example, 'B(0.9,1.1)' in our example, which has a minimum of 0.9 and a maximum of 1.1). The ages of all non-calibrated nodes are given by the uniform density. This time prior is similar to that used by ref. 29 . The parameters in the birth–death sampling process ( λ , μ , ρ ; specified using the option BDparas in the control file that executes MCMCtree) are ignored. It is noteworthy that more than two nodes can have the same label but one node cannot have two or more labels. In addition, the prior on rates does not distinguish between speciation and duplication events. The implemented cross-bracing approach can only be enabled if option duplication = 1 is included in the control file. By default, this option is set to 0 and users are not required to include it in the control file (that is, the default option is duplication = 0 ).

Timetree inference

Data parsing.

Eight paralogues were initially selected based on previous work showing a likely duplication event before LUCA: the amino- and carboxy-terminal regions from carbamoyl phosphate synthetase, aspartate and ornithine transcarbamoylases, histidine biosynthesis genes A and F , catalytic and non-catalytic subunits from ATP synthase ( ATP ), elongation factor Tu and G ( EF ), signal recognition protein and signal recognition particle receptor ( SRP ), tyrosyl-tRNA and tryptophanyl-tRNA synthetases ( Tyr ), and leucyl- and valyl-tRNA synthetases ( Leu ) 27 . Gene families were identified using BLASTp 97 . Sequences were downloaded from NCBI 98 , aligned with MUSCLE 99 and trimmed with TrimAl 100 (-strict). Individual gene trees were inferred under the LG + C20 + F + G substitution model implemented in IQ-TREE 2 (ref. 86 ). These trees were manually inspected and curated to remove non-homologous sequences, horizontal gene transfers, exceptionally short or long sequences and extremely long branches. Recent paralogues or taxa of inconsistent and/or uncertain placement inferred with RogueNaRok 101 were also removed. Independent verification of an archaeal or bacterial deep split was achieved using minimal ancestor deviation 102 . This filtering process resulted in the five pairs of paralogous gene families 27 ( ATP , EF , SRP , Tyr and Leu ) that we used to estimate the origination time of LUCA. The alignment used for timetree inference consisted of 246 species, with the majority of taxa having at least two copies (for some eukaryotes, they may be represented by plastid, mitochondrial and nuclear sequences).

To assess the impact that partitioning can have on divergence time estimates, we ran our inference analyses with both a concatenated and a partitioned alignment (that is, gene partitioning scheme). We used PAML v.4.10.7 (programs CODEML and MCMCtree) for all divergence time estimation analyses. Given that a fixed tree topology is required for timetree inference with MCMCtree, we inferred the best-scoring ML tree with IQ-TREE 2 under the LG + C20 + F + G4 (ref. 103 ) model following our previous phylogenetic analyses. We then modified the resulting inferred tree topology following consensus views of species-level relationships 34 , 35 , 104 , which we calibrated with the available fossil calibrations (see below). In addition, we ran three sensitivity tests: timetree inference (1) with each gene alignment separately, (2) under a leave-one-out strategy in which each gene alignment was iteratively removed from the concatenated dataset (for example, remove gene ATP but keep genes EF , Leu , SRP and Tyr concatenated in a unique alignment block; apply the same procedure for each gene family), and (3) using the vector of branch lengths, the gradient vector and the Hessian matrix estimated under a complex substitution model (bsinBV method described in ref. 94 ) with the concatenated dataset used for our core analyses. Four of the gene alignments generated for the leave-one-out strategy had gap-only sequences, these were removed when re-inferring the branch lengths under the LG + C20 + F + G4 model (that is, without ATP , 241 species; without EF , 236 species; without Leu , 243 species; without Tyr , 244 species). We used these trees to set the rate prior used for timetree inference for those alignments not including ATP , EF , Leu or Tyr , respectively. The β value (scale parameter) for the rate prior used when analysing alignments without ATP , Leu and Tyr changed minimally but we updated the corresponding rate priors accordingly (see above). When not including SRP , the alignment did not have any sequences removed (that is, 246 species). All alignments were analysed with the same rate prior, Γ(2, 2.5), except for the three previously mentioned alignments.

Approximating the likelihood calculation during timetree inference using PAML programs

Before timetree inference, we ran the CODEML program to infer the branch lengths of the fixed tree topology, the gradient (first derivative of the likelihood function) and the Hessian matrix (second derivative of the likelihood function); the vectors and matrix are required to approximate the likelihood function in the dating program MCMCtree 95 , an approach that substantially reduces computational time 105 . Given that CODEML does not implement the CAT (Bayesian mixture model for across-site heterogeneity) model, we ran our analyses under the closest available substitution model: LG + F + G4 (model = 3). We calculated the aforementioned vectors and matrix for each of the five gene alignments (that is, required for the partitioned alignment), for the concatenated alignment and for the concatenated alignments used for the leave-one-out strategy; the resulting values are written out in an output file called rst2. We appended the rst2 files generated for each of the five individual alignments in the same order the alignment blocks appear in the partitioned alignment file (for example, the first alignment block corresponds to the ATP gene alignment, and thus the first rst2 block will be the one generated when analysing the ATP gene alignment with CODEML). We named this file in_5parts.BV. There is only one rst2 output file for the concatenated alignments, which we renamed in.BV (main concatenated alignment and concatenated alignments under leave-one-out strategy). When analysing each gene alignment separately, we renamed the rst2 files generated for each gene alignment as in.BV.

MCMC diagnostics

All the chains that we ran with MCMCtree for each type of analysis underwent a protocol of MCMC diagnostics consisting of the following steps: (1) flagging and removal of problematic chains; (2) generating convergence plots before and after chain filtering; (3) using the samples collected by those chains that passed the filters (that is, assumed to have converged to the same target distribution) to summarize the results; (4) assessing chain efficiency and convergence by calculating statistics such as R-hat, tail-ESS and bulk-ESS (in-house wrapper function calling Rstan functions, Rstan v.2.21.7; https://mc-stan.org/rstan/ ); and (5) generating the timetrees for each type of analysis with confidence intervals and high-posterior densities to show the uncertainty surrounding the estimated divergence times. Tail-ESS is a diagnostic tool that we used to assess the sampling efficiency in the tails of the posterior distributions of all estimated divergence times, which corresponds to the minimum of the effective sample sizes for quantiles 2.5% and 97.5%. To assess the sampling efficiency in the bulk of the posterior distributions of all estimated divergence, we used bulk-ESS, which uses rank-normalized draws. Note that if tail-ESS and bulk-ESS values are larger than 100, the chains are assumed to have been efficient and reliable parameter estimates (that is, divergence times in our case). R-hat is a convergence diagnostic measure that we used to compare between- and within-chain divergence time estimates to assess chain mixing. If R-hat values are larger than 1.05, between- and within-chain estimates do not agree and thus mixing has been poor. Lastly, we assessed the impact that truncation may have on the estimated divergence times by running MCMCtree when sampling from the prior (that is, the same settings specified above but without using sequence data, which set the prior distribution to be the target distribution during the MCMC). To summarize the samples collected during this analysis, we carried out the same MCMC diagnostics procedure previously mentioned. Supplementary Fig. 6 shows our calibration densities (commonly referred to as user-specified priors, see justifications for used calibrations above) versus the marginal densities (also known as effective priors) that MCMCtree infers when building the joint prior (that is, a prior built without sequence data that considers age constraints specified by the user, the birth–death with sampling process to infer the time densities for the uncalibrated nodes, the rate priors, and so on). We provide all our results for these quality-control checks in our GitHub repository ( https://github.com/sabifo4/LUCA-divtimes ) and in Extended Data Fig. 1 , Supplementary Figs. 7 – 10 and Supplementary Data 6 . Data, figures and tables used and/or generated following a step-by-step tutorial are detailed in the GitHub repository for each inference analysis.

Additional sensitivity analyses

We compared the divergence times we estimated with the concatenated dataset under the calibration strategy cross-bracing A with those inferred (1) for each gene, (2) for gene alignments analysed under a leave-one-out strategy, and (3) for the main concatenated dataset but when using the vector of branch lengths, the gradient vector and the Hessian matrix estimated under a more complex substitution model 94 . The results are summarized in Extended Data Fig. 2 and Supplementary Data 7 and 8 . The same pattern regarding the calibration densities and marginal densities when the tree topology was pruned (that is, see above for details on the leave-one-out strategy) was observed, and thus no additional figures have been generated. As for our main analyses, the results for these additional sensitivity analyses can be found on our GitHub repository ( https://github.com/sabifo4/LUCA-divtimes ).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data required to interpret, verify and extend the research in this article can be found at our figshare repository at https://doi.org/10.6084/m9.figshare.24428659 (ref. 106 ) for the reconciliation and phylogenomic analyses and GitHub at https://github.com/sabifo4/LUCA-divtimes (ref. 107 ) for the molecular clock analyses. Additional data are available at the University of Bristol data repository, data.bris, at https://doi.org/10.5523/bris.405xnm7ei36d2cj65nrirg3ip (ref. 108 ).

Code availability

All code relating to the dating analysis can be found on GitHub at https://github.com/sabifo4/LUCA-divtimes (ref. 107 ), and other custom scripts can be found in our figshare repository at https://doi.org/10.6084/m9.figshare.24428659 (ref. 106 ).

Theobald, D. L. A formal test of the theory of universal common ancestry. Nature 465 , 219–222 (2010).

Article   CAS   PubMed   Google Scholar  

Woese, C. R. & Fox, G. E. The concept of cellular evolution. J. Mol. Evol. 10 , 1–6 (1977).

Mirkin, B. G., Fenner, T. I., Galperin, M. Y. & Koonin, E. V. Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes. BMC Evol. Biol. 3 , 2 (2003).

Article   PubMed   PubMed Central   Google Scholar  

Ouzounis, C. A., Kunin, V., Darzentas, N. & Goldovsky, L. A minimal estimate for the gene content of the last universal common ancestor—exobiology from a terrestrial perspective. Res. Microbiol. 157 , 57–68 (2006).

Gogarten, J. P. & Deamer, D. Is LUCA a thermophilic progenote? Nat. Microbiol 1 , 16229 (2016).

Weiss, M. C. et al. The physiology and habitat of the last universal common ancestor. Nat. Microbiol 1 , 16116 (2016).

Crapitto, A. J., Campbell, A., Harris, A. J. & Goldman, A. D. A consensus view of the proteome of the last universal common ancestor. Ecol. Evol. 12 , e8930 (2022).

Kyrpides, N., Overbeek, R. & Ouzounis, C. Universal protein families and the functional content of the last universal common ancestor. J. Mol. Evol. 49 , 413–423 (1999).

Koonin, E. V. Comparative genomics, minimal gene-sets and the last universal common ancestor. Nat. Rev. Microbiol. 1 , 127–136 (2003).

Harris, J. K., Kelley, S. T., Spiegelman, G. B. & Pace, N. R. The genetic core of the universal ancestor. Genome Res. 13 , 407–412 (2003).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Javaux, E. J. Challenges in evidencing the earliest traces of life. Nature 572 , 451–460 (2019).

Lepot, K. Signatures of early microbial life from the Archean (4 to 2.5 Ga) eon. Earth Sci. Rev. 209 , 103296 (2020).

Article   CAS   Google Scholar  

Betts, H. C. et al. Integrated genomic and fossil evidence illuminates life’s early evolution and eukaryote origin. Nat. Ecol. Evol. 2 , 1556–1562 (2018).

Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat. Commun. 10 , 5477 (2019).

Moody, E. R. R. et al. An estimate of the deepest branches of the tree of life from ancient vertically evolving genes. eLife 11 , e66695 (2022).

Schwartz, R. M. & Dayhoff, M. O. Origins of prokaryotes, eukaryotes, mitochondria, and chloroplasts. Science 199 , 395–403 (1978).

Shih, P. M. & Matzke, N. J. Primary endosymbiosis events date to the later Proterozoic 994 with cross-calibrated phylogenetic dating of duplicated ATPase proteins. Proc. Natl Acad. Sci. USA 110 , 996 (2013).

Article   Google Scholar  

Mahendrarajah, T. A. et al. ATP synthase evolution on a cross-braced dated tree of life. Nat. Commun. 14 , 7456 (2023).

Bottke, W. F. & Norman, M. D. The Late Heavy Bombardment. Annu. Rev. Earth Planet. Sci. 45 , 619–647 (2017).

Reimink, J. et al. Quantifying the effect of late bombardment on terrestrial zircons. Earth Planet. Sci. Lett. 604 , 118007 (2023).

Boehnke, P. & Harrison, T. M. Illusory Late Heavy Bombardments. Proc. Natl Acad. Sci. USA 113 , 10802–10806 (2016).

Ryder, G. Mass flux in the ancient Earth–Moon system and benign implications for the origin of life on Earth. J. Geophys. Res. 107 , 6-1–6-13 (2002).

Google Scholar  

Hartmann, W. K. History of the terminal cataclysm paradigm: epistemology of a planetary bombardment that never (?) happened. Geosciences 9 , 285 (2019).

Planavsky, N. J. et al. Evidence for oxygenic photosynthesis half a billion years before the great oxidation event. Nat. Geosci. 7 , 283–286 (2014).

Ossa, F. O. et al. Limited oxygen production in the Mesoarchean ocean. Proc. Natl Acad. Sci. USA 116 , 6647–6652 (2019).

Mukasa, S. B., Wilson, A. H. & Young, K. R. Geochronological constraints on the magmatic and tectonic development of the Pongola Supergroup (Central Region), South Africa. Precambrian Res. 224 , 268–286 (2013).

Zhaxybayeva, O., Lapierre, P. & Gogarten, J. P. Ancient gene duplications and the root(s) of the tree of life. Protoplasma 227 , 53–64 (2005).

Article   PubMed   Google Scholar  

Donoghue, P. C. J. & Yang, Z. The evolution of methods for establishing evolutionary timescales. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371 , 3006–3010 (2016).

Thorne, J. L., Kishino, H. & Painter, I. S. Estimating the rate of evolution of the rate of molecular evolution. Mol. Biol. Evol. 15 , 1647–1657 (1998).

Yang, Z. & Rannala, B. Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds. Mol. Biol. Evol. 23 , 212–226 (2006).

Rannala, B. & Yang, Z. Inferring speciation times under an episodic molecular clock. Syst. Biol. 56 , 453–466 (2007).

Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol. 27 , 1877–1885 (2010).

Craig, J. M., Kumar, S. & Hedges, S. B. The origin of eukaryotes and rise in complexity were synchronous with the rise in oxygen. Front. Bioinform. 3 , 1233281 (2023).

Aouad, M. et al. A divide-and-conquer phylogenomic approach based on character supermatrices resolves early steps in the evolution of the Archaea. BMC Ecol. Evol. 22 , 1 (2022).

Coleman, G. A. et al. A rooted phylogeny resolves early bacterial evolution. Science 372 , eabe0511 (2021).

Guy, L. & Ettema, T. J. G. The archaeal ‘TACK’ superphylum and the origin of eukaryotes. Trends Microbiol. 19 , 580–587 (2011).

Spang, A. et al. Complex Archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521 , 173–179 (2015).

Zaremba-Niedzwiedzka, K. et al. Asgard Archaea illuminate the origin of eukaryotic cellular complexity. Nature 541 , 353–358 (2017).

Eme, L. et al. Inference and reconstruction of the heimdallarchaeial ancestry of eukaryotes. Nature 618 , 992–999 (2023).

Raymann, K., Brochier-Armanet, C. & Gribaldo, S. The two-domain tree of life is linked to a new root for the Archaea. Proc. Natl Acad. Sci. USA 112 , 6670–6675 (2015).

Megrian, D., Taib, N., Jaffe, A. L., Banfield, J. F. & Gribaldo, S. Ancient origin and constrained evolution of the division and cell wall gene cluster in Bacteria. Nat. Microbiol. 7 , 2114–2127 (2022).

Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523 , 208–211 (2015).

Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499 , 431–437 (2013).

Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51 , 492–508 (2002).

Taib, N. et al. Genome-wide analysis of the Firmicutes illuminates the diderm/monoderm transition. Nat. Ecol. Evol. 4 , 1661–1672 (2020).

Szöllõsi, G. J., Rosikiewicz, W., Boussau, B., Tannier, E. & Daubin, V. Efficient exploration of the space of reconciled gene trees. Syst. Biol. 62 , 901–912 (2013).

Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 , 27–30 (2000).

Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl Acad. Sci. USA 114 , E4602–E4611 (2017).

Dharamshi, J. E. et al. Gene gain facilitated endosymbiotic evolution of Chlamydiae. Nat. Microbiol. 8 , 40–54 (2023).

Doolittle, W. F. Phylogenetic classification and the universal tree. Science 284 , 2124–2128 (1999).

Dagan, T. & Martin, W. The tree of one percent. Genome Biol. 7 , 118 (2006).

Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinf. 4 , 41 (2003).

Ragsdale, S. W. & Pierce, E. Acetogenesis and the Wood–Ljungdahl pathway of CO 2 fixation. Biochim. Biophys. Acta 1784 , 1873–1898 (2008).

Schuchmann, K. & Müller, V. Autotrophy at the thermodynamic limit of life: a model for energy conservation in acetogenic bacteria. Nat. Rev. Microbiol. 12 , 809–821 (2014).

Schuchmann, K. & Müller, V. Energetics and application of heterotrophy in acetogenic bacteria. Appl. Environ. Microbiol. 82 , 4056–4069 (2016).

Iwabe, N., Kuma, K., Hasegawa, M., Osawa, S. & Miyata, T. Evolutionary relationship of archaebacteria, eubacteria, and eukaryotes inferred from phylogenetic trees of duplicated genes. Proc. Natl Acad. Sci. USA 86 , 9355–9359 (1989).

Gogarten, J. P. et al. Evolution of the vacuolar H + -ATPase: implications for the origin of eukaryotes. Proc. Natl Acad. Sci. USA 86 , 6661–6665 (1989).

Koonin, E. V. & Makarova, K. S. Origins and evolution of CRISPR–Cas systems. Phil. Trans. R. Soc. Lond. B Biol. Sci. 374 , 20180087 (2019).

Krupovic, M., Dolja, V. V. & Koonin, E. V. The LUCA and its complex virome. Nat. Rev. Microbiol. 18 , 661–670 (2020).

Koonin, E. V., Dolja, V. V. & Krupovic, M. The logic of virus evolution. Cell Host Microbe 30 , 917–929 (2022).

Lever, M. A. Acetogenesis in the energy-starved deep biosphere—a paradox? Front. Microbiol. 2 , 284 (2011).

PubMed   Google Scholar  

Martin, W. & Russell, M. J. On the origin of biochemistry at an alkaline hydrothermal vent. Phil. Trans. R. Soc. Lond. B Biol. Sci. 362 , 1887–1925 (2007).

Catchpole, R. J. & Forterre, P. The evolution of reverse gyrase suggests a nonhyperthermophilic last universal common ancestor. Mol. Biol. Evol. 36 , 2737–2747 (2019).

Groussin, M., Boussau, B., Charles, S., Blanquart, S. & Gouy, M. The molecular signal for the adaptation to cold temperature during early life on Earth. Biol. Lett. 9 , 20130608 (2013).

Boussau, B., Blanquart, S., Necsulea, A., Lartillot, N. & Gouy, M. Parallel adaptations to high temperatures in the Archaean eon. Nature 456 , 942–945 (2008).

Chandor, A. et al. Dinucleotide spore photoproduct, a minimal substrate of the DNA repair spore photoproduct lyase enzyme from Bacillus subtilis. J. Biol. Chem. 281 , 26922–26931 (2006).

Chandra, T. et al. Spore photoproduct lyase catalyzes specific repair of the 5R but not the 5S spore photoproduct. J. Am. Chem. Soc. 131 , 2420–2421 (2009).

Kasting, J. F. The evolution of the prebiotic atmosphere. Orig. Life 14 , 75–82 (1984).

Kharecha, P. A. A Coupled Atmosphere–Ecosystem Model of the Early Archean Biosphere . PhD thesis, Pennsylvania State Univ. (2005).

Barth, P. et al. Isotopic constraints on lightning as a source of fixed nitrogen in Earth’s early biosphere. Nat. Geosci. 16 , 478–484 (2023).

Tian, F., Kasting, J. F. & Zahnle, K. Revisiting HCN formation in Earth’s early atmosphere. Earth Planet. Sci. Lett. 308 , 417–423 (2011).

Zahnle, K. J. Photochemistry of methane and the formation of hydrocyanic acid (HCN) in the Earth’s early atmosphere. J. Geophys. Res. 91 , 2819–2834 (1986).

Stüeken, E. E., Boocock, T., Szilas, K., Mikhail, S. & Gardiner, N. J. Reconstructing nitrogen sources to Earth’s earliest biosphere at 3.7 Ga. Front. Earth Sci. 9 , 675726 (2021).

Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311 , 1283–1287 (2006).

Yutin, N., Makarova, K. S., Mekhedov, S. L., Wolf, Y. I. & Koonin, E. V. The deep archaeal roots of eukaryotes. Mol. Biol. Evol. 25 , 1619–1630 (2008).

Petitjean, C., Deschamps, P., López-García, P. & Moreira, D. Rooting the domain Archaea by phylogenomic analysis supports the foundation of the new kingdom Proteoarchaeota. Genome Biol. Evol. 7 , 191–204 (2014).

Williams, T. A., Cox, C. J., Foster, P. G., Szöllősi, G. J. & Embley, T. M. Phylogenomics provides robust support for a two-domains tree of life. Nat. Ecol. Evol. 4 , 138–147 (2020).

Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6 , 946–959 (2021).

Parks, D. H. et al. Selection of representative genomes for 24,706 bacterial and archaeal species clusters provide a complete genome-based taxonomy. Preprint at bioRxiv https://doi.org/10.1101/771964 (2019).

Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39 , W29–W37 (2011).

Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Archaeal Clusters of Orthologous Genes (arCOGs): an update and application for analysis of shared features between Thermococcales, Methanococcales, and Methanobacteriales. Life 5 , 818–840 (2015).

Haft, D. H., Selengut, J. D. & White, O. The TIGRFAMs database of protein families. Nucleic Acids Res. 31 , 371–373 (2003).

Katoh, K., Kuma, K.-I., Toh, H. & Miyata, T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33 , 511–518 (2005).

Katoh, K., Misawa, K., Kuma, K.-I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30 , 3059–3066 (2002).

Criscuolo, A. & Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol. Biol. 10 , 210 (2010).

Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37 , 1530–1534 (2020).

Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7 , e1002195 (2011).

Susko, E. & Roger, A. J. On reduced amino acid alphabets for phylogenetic inference. Mol. Biol. Evol. 24 , 2139–2150 (2007).

Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38 , 5825–5829 (2021).

Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18 , 366–368 (2021).

Szöllősi, G. J., Davín, A. A., Tannier, E., Daubin, V. & Boussau, B. Genome-scale phylogenetic analysis finds extensive gene transfer among fungi. Phil. Trans. R. Soc. Lond. B Biol. Sci. 370 , 20140335 (2015).

Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6 , 3–6 (2021).

Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24 , 1586–1591 (2007).

Wang, S. & Luo, H. Dating the bacterial tree of life based on ancient symbiosis. Preprint at bioRxiv https://doi.org/10.1101/2023.06.18.545440 (2023).

dos Reis, M. & Yang, Z. Approximate likelihood calculation on a phylogeny for Bayesian estimation of divergence times. Mol. Biol. Evol. 28 , 2161–2172 (2011).

Harris et al. Array programming with NumPy. Nature 585 , 357–362 (2020).

Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215 , 403–410 (1990).

Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 39 , D38–D51 (2011).

Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32 , 1792–1797 (2004).

Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25 , 1972–1973 (2009).

Aberer, A. J., Krompaß, D. & Stamatakis, A. RogueNaRok: An efficient and exact algorithm for rogue taxon identification. Exelixis-RRDR-2011–10 (Heidelberg Institute for Theoretical Studies, 2011).

Tria, F. D. K., Landan, G. & Dagan, T. Phylogenetic rooting using minimal ancestor deviation. Nat. Ecol. Evol. 1 , 193 (2017).

Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35 , 518–522 (2018).

Burki, F., Roger, A. J., Brown, M. W. & Simpson, A. G. B. The new tree of eukaryotes. Trends Ecol. Evol. 35 , 43–55 (2020).

Battistuzzi, F. U., Billing-Ross, P., Paliwal, A. & Kumar, S. Fast and slow implementations of relaxed-clock methods show similar patterns of accuracy in estimating divergence times. Mol. Biol. Evol. 28 , 2439–2442 (2011).

Moody, E. R. R. The nature of the last universal common ancestor and its impact on the early Earth system. figshare https://doi.org/10.6084/m9.figshare.24428659 (2024).

Álvarez-Carretero, S. The nature of the last universal common ancestor and its impact on the early Earth system—timetree inference analyses. Zenodo https://doi.org/10.5281/zenodo.11260523 (2024).

Moody, E. R. R. et al. The nature of the Last Universal Common Ancestor and its impact on the early Earth system. Nat. Ecol. Evol. https://doi.org/10.5523/bris.405xnm7ei36d2cj65nrirg3ip (2024).

Darzi, Y., Letunic, I., Bork, P. & Yamada, T. iPath3.0: interactive pathways explorer v3. Nucleic Acids Res. 46 , W510–W513 (2018).

Download references

Acknowledgements

Our research is funded by the John Templeton Foundation (62220 to P.C.J.D., N.L., T.M.L., D.P., G.A.S., T.A.W. and Z.Y.; the opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation), Biotechnology and Biological Sciences Research Council (BB/T012773/1 to P.C.J.D. and Z.Y.; BB/T012951/1 to Z.Y.), by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (947317 ASymbEL to A.S.; 714774, GENECLOCKS to G.J.S.), Leverhulme Trust (RF-2022-167 to P.C.J.D.), Gordon and Betty Moore Foundation (GBMF9741 to T.A.W., D.P., P.C.J.D., A.S. and G.J.S.; GBMF9346 to A.S.), Royal Society (University Research Fellowship (URF) to T.A.W.), the Simons Foundation (735929LPI to A.S.) and the University of Bristol (University Research Fellowship (URF) to D.P.).

Author information

Authors and affiliations.

Bristol Palaeobiology Group, School of Earth Sciences, University of Bristol, Bristol, UK

Edmund R. R. Moody, Sandra Álvarez-Carretero, Holly C. Betts, Davide Pisani & Philip C. J. Donoghue

Department of Marine Microbiology and Biogeochemistry, NIOZ, Royal Netherlands Institute for Sea Research, Den Burg, The Netherlands

Tara A. Mahendrarajah, Nina Dombrowski & Anja Spang

Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, UK

James W. Clark

Department of Biological Physics, Eötvös University, Budapest, Hungary

Lénárd L. Szánthó

MTA-ELTE ‘Lendulet’ Evolutionary Genomics Research Group, Budapest, Hungary

Lénárd L. Szánthó & Gergely J. Szöllősi

Institute of Evolution, HUN-REN Center for Ecological Research, Budapest, Hungary

Global Systems Institute, University of Exeter, Exeter, UK

Richard A. Boyle, Stuart Daines & Timothy M. Lenton

Department of Earth Sciences, University College London, London, UK

Xi Chen & Graham A. Shields

Department of Genetics, Evolution and Environment, University College London, London, UK

Nick Lane & Ziheng Yang

Model-Based Evolutionary Genomics Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan

Gergely J. Szöllősi

Department of Evolutionary & Population Biology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, The Netherlands

Bristol Palaeobiology Group, School of Biological Sciences, University of Bristol, Bristol, UK

Davide Pisani & Tom A. Williams

You can also search for this author in PubMed   Google Scholar

Contributions

The project was conceived and designed by P.C.J.D., T.M.L., D.P., G.J.S., A.S. and T.A.W. Dating analyses were performed by H.C.B., J.W.C., S.Á.-C., P.J.C.D. and E.R.R.M. T.A.M., N.D. and E.R.R.M. performed single-copy orthologue analysis for species-tree inference. L.L.S., G.J.S., T.A.W. and E.R.R.M. performed reconciliation analysis. E.R.R.M. performed homologous gene family annotation, sequence, alignment, gene tree inference and sensitivity tests. E.R.R.M., A.S. and T.A.W. performed metabolic analysis and interpretation. T.M.L., S.D. and R.A.B. provided biogeochemical interpretation. E.R.R.M., T.M.L., A.S., T.A.W., D.P. and P.J.C.D. drafted the article to which all authors (including X.C., N.L., Z.Y. and G.A.S.) contributed.

Corresponding authors

Correspondence to Edmund R. R. Moody , Davide Pisani , Tom A. Williams , Timothy M. Lenton or Philip C. J. Donoghue .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Ecology & Evolution thanks Aaron Goldman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended data fig. 1 comparison of the mean divergence times and confidence intervals estimated for the two duplicates of luca under each timetree inference analysis..

Black dots refer to estimated mean divergence times for analyses without cross-bracing, stars are used to identify those under cross-bracing and triangles for estimated upper and lower confidence intervals. Straight lines are used to link mean divergence time estimates across the various inference analyses we carried out, while dashed lines are used to link the estimated confidence intervals. The node label for the driver node is “248”, while it is “368” for the mirror node, as shown in the title of each graph. Coloured stars and triangles are used to identify which LUCA time estimates were inferred under the same cross-braced analysis for the driver-mirror nodes (that is, equal time and CI estimates). Black dots and triangles are used to identify those inferred when cross-bracing was not enabled (that is, different time and CI estimates). -Abbreviations. “GBM”: Geometric Brownian motion relaxed-clock model; “ILN”: Independent-rate log-normal relaxed-clock model; “conc, cb” dots/triangles: results under cross-bracing A when the concatenated dataset was analysed under GBM (red) and ILN (blue); “conc, fosscb”: results under cross-bracing B when the concatenated dataset was analysed under GBM (orange) and ILN (cyan); “part, cb” dots/triangles: results under cross-bracing A when the partitioned dataset was analysed under GBM (pink) and ILN (purple); “part, fosscb”: results under cross-bracing B when the concatenated dataset was analysed under GBM (light green) and ILN (grey); black dots and triangles: results when cross-bracing was not enabled for both concatenated and partitioned datasets.

Extended Data Fig. 2 Comparison of the posterior time estimates and confidence intervals for the two duplicates of LUCA inferred under the main calibration strategy cross-bracing A with the concatenated dataset and with the datasets for the three additional sensitivity analyses.

Dots refer to estimated mean divergence times and triangles to estimated 2.5% and 97.5% quantiles. Straight lines are used to link the mean divergence times estimated in the same analysis under the two different relaxed-clock models (GBM and ILN). Labels in the x axis are informative about the clock model under which the analysis ran and the type of analysis we carried (see abbreviations below). Coloured dots are used to identify which time estimates were inferred when using the same dataset and strategy under GBM and ILN, while triangles refer to the corresponding upper and lower quantiles for the 95% confidence interval. -Abbreviations. “GBM”: Geometric Brownian motion relaxed-clock model; “ILN”: Independent-rate log-normal relaxed-clock model; “main-conc”: results obtained with the concatenated dataset analysed in our main analyses under cross-bracing A; “ATP/EF/Leu/SRP/Tyr”: results obtained when using each gene alignment separately; “noATP/noEF/noLeu/noSRP/noTyr”: results obtained when using concatenated alignments without the gene alignment mentioned in the label as per the “leave-one-out” strategy; “main-bsinbv”: results obtained with the concatenated dataset analysed in our main analyses when using branch lengths, Hessian, and gradient calculated under a more complex substitution model to infer divergence times.

Extended Data Fig. 3 Maximum Likelihood species tree.

The Maximum Likelihood tree inferred across three independent runs, under the best fitting model (according to BIC: LG + F + G + C60) from a concatenation of 57 orthologous proteins, support values are from 10,000 ultrafast bootstraps. Referred to as topology I in the main text. Tips coloured according to taxonomy: Euryarchaeota (teal), DPANN (purple), Asgardarchaeota (cyan), TACK (blue), Gracilicutes (orange), Terrabacteria (red), DST (brown), CPR (green).

Extended Data Fig. 4 Maximum Likelihood tree for focal reconciliation analysis.

Maximum Likelihood tree (topology II in the main text), where DPANN is constrained to be sister to all other Archaea, and CPR is sister to Chloroflexi. Tips coloured according to taxonomy: Euryarchaeota (teal), DPANN (purple), Asgardarchaeota (cyan), TACK (blue), Gracilicutes (orange), Terrabacteria (red), DST (brown), CPR (green). AU topology test, P = 0.517, this is a one-sided statistical test.

Extended Data Fig. 5 The relationship between the number of KO gene families encoded on a genome and its size.

LOESS regression of the number of KOs per sampled genome against the genome size in megabases. We used the inferred relationship for modern prokaryotes to estimate LUCA’s genome size based on reconstructed KO gene family content, as described in the main text. Shaded area represents the 95% confidence interval.

Extended Data Fig. 6 The relationship between the number of KO gene families encoded on a genome and the total number of protein-coding genes.

LOESS regression of the number of KOs per sampled genome against the number of proteins encoded for per sampled genome. We used the inferred relationship for modern prokaryotes to estimate the total number of protein-coding genes encoded by LUCA based on reconstructed KO gene family content, as described in the main text. Shaded area represents the 95% confidence interval.

Supplementary information

Supplementary information.

Supplementary Notes and Figs. 1–10.

Reporting Summary

Peer review file, supplementary data 1.

This table contains the results of the reconciliations for each gene family. KEGG_ko is the KEGG orthology ID; arc_domain_prop is the proportion of the sampled Archaea; bac_domain_prop is the proportion of the sampled bacteria; gene refers to gene name, description and enzyme code; map refers to the different KEGG maps of which this KEGG gene family is a component; pathway is a text description of the metabolic pathways of which these genes are a component; alignment_length refers to the length of the alignment in amino acids; highest_COG_cat refers to the number of sequences placed in the most frequent COG category; difference_1st_and_2nd is the difference between the most frequent COG category and the second most frequent COG category; categories is the number of different COG categories assigned to this KEGG gene family; COG_freq is the proportion of the sequences placed in the most frequent COG category; COG_cat is the most frequent COG functional category; Archaea is the number of archaeal sequences sampled in the gene family; Bacteria is the number of bacterial sequences sampled in the gene family; alternative_COGs is the number of alternative COG gene families assigned across this KEGG orthologous gene family; COG_perc is the proportion of the most frequent COG ID assigned to this KEGG gene family; COG is the COG ID of the most frequenty COG assigned to this gene family; COG_NAME is the description of the most frequent COG ID assigned to this gene family; COG_TAG is the symbol associated with the most frequent COG gene familiy; sequences is the total number of sequences assigned to this gene family; Arc_prop is the proportion of Archaea that make up this gene family; Bac_prop is the proportion of Bacteria that make up this gene family; constrained_median is the median probability (PP) that this gene was present in LUCA from our reconciliation under the focal constrained tree search across the 5 independent bootstrap distribution reconciliations; ML_median is the median PP of the gene family being present in LUCA with gene tree bootstrap distributions against the ML species-tree topology across the 15 independent bootstrap distribution reconciliations; MEAN_OF_MEDIANS is the mean value across the constrained and ML PP results; RANGE_OF_MEDIANS is the range of the PPs for the constrained and ML topology PPs for LUCA; Probable_and_sampling_threshold_met is our most stringent category of gene families inferred in LUCA with 0.75 + PP and a sampling requirement of 1% met in both Archaea and Bacteria; Possible_and_sampling_threshold_met is a threshold of 0.50 + PP and sampling both domains; probable is simply 0.75 + PP; and possible is 0.50 + PP.

Supplementary Data 2

PP for COGs. This table contains the results for the reconciliations of COG-based gene family clustering against the constrained focal species-tree topology. Columns are named similarly to Supplementary Data 1 but each row is a different COG family. The column Modal_KEGG_ko refers to the most frequent KEGG gene family in which a given COG is found; sequences_in_modal_KEGG refers to the number of sequences in the most frequent KEGG gene family.

Supplementary Data 3

Module completeness. Estimated pathway completeness for KEGG metabolic modules (with a completeness greater than zero in at least one confidence threshold) using Anvi’o’s stepwise pathway completeness 48 . Module_name is the name of the module; module_category is the broader category into which the module falls; module_subcategory is a more specific category; possible_anvio includes the gene families with a median PP ≥ 0.50; probable_anvio related to gene families PP ≥ 0.75; and _ws refers to the sampling requirement being met (presence in at least 1% of the sampled Archaea and Bacteria).

Supplementary Data 4

Marker gene metadata for all markers checked during marker gene curation, including the initial 59 single-copy marker genes used in species-tree inference (see Methods ). Data include marker gene set provenance, marker gene name, marker gene description, presence in different marker gene sets 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , and presence in Archaea and Bacteria. When available, marker genes are matched with their arCOG, TIGR, and COG ID and their respective occurrence across different taxonomic sets is quantified.

Supplementary Data 5

The ratio of duplications, transfers and losses in relation to the total number of copies for the deep ancestral nodes: the LUCA, archaeal (LACA) and bacterial (LBCA) common ancestors, and the average (mean) and 95th percentile.

Supplementary Data 6

Spreadsheet containing a list of the estimated divergence times for all timetree inferences carried out and the corresponding results of the MCMC diagnostics. Tabs Divtimes_GBM-allnodes and Divtimes_ILN-allnodes represent a list of the estimated divergence times (Ma) for all nodes under the 12 inference analyses we ran under GBM and ILN, respectively. Tabs Divtimes_GBM-highlighted and Divtimes_ILN-highlighted represent a list of the estimated divergence times (Ma) for selected nodes ordered according to their mirrored nodes under the 12 inference analyses we ran under GBM and ILN, respectively. Each of the tabs MCMCdiagn_prior, MCMCdiagn_postGBM and MCMCdiagn_postILN contains the statistical results of the MCMC diagnostics we ran for each inference analysis. Note that, despite the analyses carried out when sampling from the prior could have only been done three times (that is, data are not used, and thus only once under each calibration strategy was enough), we repeated them with each dataset regardless. In other words, results for (1) ‘concatenated + cross-bracing A’ and ‘partitioned + cross-bracing A’; (2) ‘concatenated + without cross-bracing’ and ‘partitioned + without cross-bracing’; and (3) ‘concatenated + cross-bracing B’ and ‘partitioned + cross-bracing B’ would be equivalent, respectively. For tabs 1–4, part represents partitioned dataset; conc, concatenated dataset; cb, cross-bracing A; notcb, without cross-bracing; fosscb, cross-bracing B; mean_t, mean posterior time estimate; 2.5%q, 2.5% quantile of the posterior time density for a given node; and 97.5%q, 97.5% quantile of the posterior time density for a given node. For tabs 5–7, med. num. samples collected per chain represents median of the total amount of samples collected per chain; min. num. samples collected per chain, minimum number of samples collected per chain; max. num. samples collected per chain, minimum number of samples collected per chain; num. samples used to calculate stats, number of samples collected by all chains that passed the filters that were used to calculate the tail-ESS, bulk-ESS and R-hat values. For tail-ESS, we report the median, minimum, and maximum tail-ESS values; all larger than 100 as required for assuming reliable parameter estimates. For bulk-ESS, we report the median, minimum and maximum bulk-ESS values; all larger than 100 as required for assuming reliable parameter estimates. For R-hat, minimum and maximum values reported, all smaller than 1.05 as required to assume good mixing.

Supplementary Data 7

Spreadsheet containing a list of the posterior time estimates for LUCA obtained under the main calibration strategy cross-bracing A with the concatenated dataset and with the datasets for the three additional sensitivity analyses. The first column ‘label’ contains the node number for both the driver and mirror nodes for LUCA (the latter includes the term -dup in the label). Columns mean_t, 2.5%q, and 97.5%q refer to the estimated mean divergence times, and the 2.5%/97.5% quantiles of the posterior time density for the corresponding node. Main-conc, refers to results obtained with the concatenated dataset analysed in our main analyses under cross-bracing A; ATP/EF/Leu/SRP/Tyr, results obtained when using each gene alignment separately; noATP/noEF/noLeu/noSRP/noTyr, results obtained when using concatenated alignments without the gene alignment mentioned in the label as per the leave-one-out strategy; main-bsinbv, results obtained with the concatenated dataset analysed in our main analyses when using branch lengths, Hessian and gradient calculated under a more complex substitution model to infer divergence times.

Supplementary Data 8

Spreadsheet containing a list of the estimated divergence times for all timetree inferences carried out for the sensitivity analyses and the corresponding results for the MCMC diagnostics. Tabs Divtimes_GBM-allnodes and Divtimes_ILN-allnodes represent a list of the estimated divergence times (Ma) for all nodes under the 11 inference analyses we ran under GBM and ILN when testing the impact on divergence times estimation when (1) analysing each gene alignment individually, (2) following a leave-one-out strategy, and (3) using the branch lengths, Hessian and gradient estimated under a more complex model for timetree inference (bsinBV approach). Tabs Divtimes_GBM-highlighted and Divtimes_ILN-highlighted represent a list of the estimated divergence times (Ma) for selected nodes ordered according to their mirrored nodes we ran under GBM and ILN for the sensitivity analyses (we also included the results with the main concatenated dataset for reference). Each of tabs MCMCdiagn_prior, MCMCdiagn_postGBM and MCMCdiagn_postILN contains the statistical results of the MCMC diagnostics we ran for the sensitivity analyses. Note that, despite the analyses carried out when sampling from the prior could have only been done once for each different tree topology (that is, data are not used, only topological changes may affect the resulting marginal densities), we ran them with each dataset regardless as part of our pipeline. For tabs 1–4, main-conc represents results obtained with the concatenated dataset analysed in our main analyses under cross-bracing A; ATP/EF/Leu/SRP/Tyr, results obtained when using each gene alignment separately; noATP/noEF/noLeu/noSRP/noTyr, results obtained when using concatenated alignments without the gene alignment mentioned in the label as per the leave-one-out strategy; main-bsinbv, results obtained with the concatenated dataset analysed in our main analyses when using branch lengths, Hessian and gradient calculated under a more complex substitution model to infer divergence times; mean_t, mean posterior time estimate; 2.5%q, 2.5% quantile of the posterior time density for a given node; and 97.5%q, 97.5% quantile of the posterior time density for a given node. For tabs 5–7, med. num. samples collected per chain represents the median of the total amount of samples collected per chain; min. num. samples collected per chain, minimum number of samples collected per chain; max. num. samples collected per chain, minimum number of samples collected per chain; num. samples used to calculate stats, number of samples collected by all chains that passed the filters that were used to calculate the tail-ESS, bulk-ESS and R-hat values. For tail-ESS, we report the median, minimum and maximum tail-ESS values; all larger than 100 as required for assuming reliable parameter estimates. For bulk-ESS, we report the median, minimum and maximum bulk-ESS values; all larger than 100 as required for assuming reliable parameter estimates. For R-hat, minimum and maximum values are reported, all smaller than 1.05 as required to assume good mixing.

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

Moody, E.R.R., Álvarez-Carretero, S., Mahendrarajah, T.A. et al. The nature of the last universal common ancestor and its impact on the early Earth system. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02461-1

Download citation

Received : 19 January 2024

Accepted : 04 June 2024

Published : 12 July 2024

DOI : https://doi.org/10.1038/s41559-024-02461-1

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.