Grad Coach

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

Free Webinar: Research Methodology 101

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?

learning objectives of research methodology

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 .

learning objectives of research methodology

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 ...

You Might Also Like:

Research ethics 101


Leo Balanlay

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

Derek Jansen

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


I found it very useful. many thanks

Solomon F. Joel

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

Upendo Mmbaga

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


Thanks for good interpretation,well understood.

Alhaji Alie Kanu

Good morning sorry I want to the search topic

Baraka Gombela

Thank u more


Thank you, your explanation is simple and very helpful.

Suleiman Abubakar

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

Daniel Mondela

That’s the best analysis


So simple yet so insightful. Thank you.

Wendy Lushaba

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.

Tabe Solomon Matebesi

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.

Hafiz Tahir

Thankyou So much Sir Derek…

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

James Olukoya

Beautiful presentation. I love it.


please provide a research mehodology example for zoology

Ogar , Praise

It’s very educative and well explained

Joseph Chan

Thanks for the concise and informative data.

Goja Terhemba John

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

Prakash thapa

Thank you so much Derek sir🖤🙏🤗


Very simple and reliable

Chizor Adisa

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


very useful, Thank you very much..

nakato justine

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

Adegboyega ADaeBAYO

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.

wogayehu tuji

wow thank u so much

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


Very precise and informative.

Javangwe Nyeketa

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

Mary Benard Mwanganya

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

Godfrey Martin Assenga

Good presentation

Nhubu Tawanda

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.

Ajala Tajudeen

Thanks a lot. I am inspired

Visor Likali

Well written

Pondris Patrick

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

Beulah Emmanuel

excellent explanation

Gino Raz

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…

Yonas Tesheme

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.

zahid t ahmad

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

Maisnam loyalakla

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.

Mila Milano

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.

Modie Maria Neswiswi

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

Mursel Amin

its very much informative and understandable. I have enlightened.

Mustapha Abubakar

An interesting nice exploration of a topic.


Thank you. Accurate and simple🥰

Sikandar Ali Shah

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.

omodara beatrice

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.

Ainembabazi Rose

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

Kamran Saeed

Great work…very well explanation

Hyacinth Chebe Ukwuani

Thanks Derek. Kerryn was just fantastic!

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

Matobela Joel Marabi

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.

Sakina Dalal

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!

Ngwisa Marie-claire NJOTU

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

Oluwafemi Taiwo

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


This is well simplified and straight to the point

Gabriel mugangavari

Thank you Dr

Dina Haj Ibrahim

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 – .


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

Ngaka Mokoena

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.

Pritam Pal

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


Thanks this has thought me alot.

kenechukwu ambrose

Very concise and helpful. Thanks a lot

Eunice Shatila Sinyemu 32070

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

yugine the

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

Lubabalo Ntshebe

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.

Ekokobe Aloysius



Short but sweet.Thank you

Shishir Pokharel

Informative article. Thanks for your detailed information.

Badr Alharbi

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

Hasan Chowdhury

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.

Ndileka Myoli

concise and informative.

Sureka Batagoda

Thank you very much

More Smith

How can we site this article is Harvard style?


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

Denis Eken Lomoro

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)”.

fatima sani

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.

Aqsa Iftijhar

Good very well explained.Thanks for sharing it.

Krishna Dhakal

Thank u sir, it is really a good guideline.


so helpful thank you very much.

Joelma M Monteiro

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.

orebotswe morokane

how do i reference this?


MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021,

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach.


Your explanation is easily understood. Thank you

Dr Christie

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

Alice W. Mbuthia

I feel guided ,Thank you

Joseph B. Smith

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

Dr. Ukpai Ukpai Eni

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

chimbini Joseph

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.

Adv Asad Ali

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.

Jonas Tan

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

mahmoud ali

research methodologies

Lucas Sinyangwe

Please send me more information concerning dissertation research.

Amamten Jr.

Nice piece of knowledge shared….. #Thump_UP

Hajara Salihu

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

Gerald Andrew Babu

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

Ahmed Saeed

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

Soraya Kolli

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.

Emmanuel Chukwuma

Very simple and well detailed Many thanks

Zeeshan Ali Khan

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

Molly Wasonga

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

Blessings Chigodo

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.

Joseph Kyereme

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

ankita bhatt

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?

princelow presley

perfect content, thanks a lot

George Nangpaak Duut

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.

Varinder singh Rana

Very useful content with easy way

Mbangu Jones Kashweeka

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

Carolyn Russell

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.

Indabawa Musbahu

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

Limpho Mphutlane

It’s profound easy to understand I appreciate

Mustafa Salimi

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.

Rabilu yau

Comment * thanks very much

Ari M. Hussein

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 🙂

Varsha Patnaik

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


This is very good work. I have benefited.

Dr Md Asraul Hoque

Thank you so much for sharing

Nkasa lizwi

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 topic is: transitional challenges faced by educators in intermediate phase in the Alfred Nzo District.

Atonisah Jonathan

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

Bello Suleiman

I appreciate sir


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

Emerita Guzman

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.

Akanji wasiu

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

Need assistance

Clement Lokwar

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

Peter Sone Kome

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!

Bipin pokhrel

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

Artak Ghonyan

thank you, it is very useful


  • 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…

Submit a Comment Cancel reply

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

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

  • Print Friendly

U.S. flag

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
  • CBE Life Sci Educ
  • v.21(3); Fall 2022

Writing and Using Learning Objectives

Rebecca b. orr.

† Division of Academic Affairs, Collin College, Plano, TX 75074

Melissa M. Csikari

‡ HHMI Science Education, BioInteractive, Chevy Chase, MD 20815

Scott Freeman

§ Department of Biology, University of Washington, Seattle, WA 98195

Michael C. Rodriguez

∥ Educational Psychology, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455

Learning objectives (LOs) are used to communicate the purpose of instruction. Done well, they convey the expectations that the instructor—and by extension, the academic field—has in terms of what students should know and be able to do after completing a course of study. As a result, they help students better understand course activities and increase student performance on assessments. LOs also serve as the foundation of course design, as they help structure classroom practices and define the focus of assessments. Understanding the research can improve and refine instructor and student use of LOs. This essay describes an online, evidence-based teaching guide published by CBE—Life Sciences Education ( LSE ) at . The guide contains condensed summaries of key research findings organized by recommendations for writing and using LOs, summaries of and links to research articles and other resources, and actionable advice in the form of a checklist for instructors. In addition to describing key features of the guide, we also identify areas that warrant further empirical studies.


Learning objectives (LOs) are statements that communicate the purpose of instruction to students, other instructors, and an academic field ( Mager, 1997 ; Rodriguez and Albano, 2017 ). They form the basis for developing high-quality assessments for formative and summative purposes. Once LOs and assessments are established, instructional activities can help students master the material. Aligning LOs with assessments and instructional practice is the essence of backward course design ( Fink, 2003 ).

Many terms in the literature describe statements about learning expectations. The terms “course objectives,” “course goals,” “learning objectives,” “learning outcomes,” and “learning goals” are often used interchangeably, creating confusion for instructors and students. To clarify and standardize usage, the term “objective” is defined as a declarative statement that identifies what students are expected to know and do . At the same time, “outcome” refers to the results measured at the end of a unit, course, or program. It is helpful to think of LOs as a tool instructors use for describing intended outcomes, regardless of the process for achieving the outcome ( Mager, 1997 ). The term “goal” is less useful. Although it is often used to express more general expectations, there is no consistent usage in the literature.

In this guide, “learning objective” is defined as a statement that communicates the purpose of instruction using an action verb and describes the expected performance and conditions under which the performance should occur. Examples include:

  • At the end of this lesson, students should be able to compare the processes of diffusion, osmosis, and facilitated diffusion, and provide biological examples that illustrate each process.
  • At the end of this lesson, students should be able to predict the relative rates at which given ions and molecules will cross a plasma membrane in the absence of membrane protein and explain their reasoning.

In terms of content and complexity, LOs should scaffold professional practice, requirements for a program, and individual course goals by communicating the specific content areas and skills considered important by the academic field ( Rodriguez and Albano, 2017 ). They also promote course articulation by supporting consistency when courses are taught by multiple instructors and furnishing valuable information about course alignment among institutions. As a result, LOs should serve as the basis of unit or module, course, and program design and can be declared in a nested hierarchy of levels. For clarity, we describe a hierarchy of LOs in Table 1 .

Levels of LOs ( Rodriguez and Albano, 2017 )

Type of LOScope and contextDescription
InstitutionalBroad, institution specific
ProgrammaticBroad, program specific
Course levelBroad, course specific, and student centered
InstructionalSpecific and descriptive, module or lesson specific, and student focused

a Hereafter, our use of the term “learning objectives” specifically refers to instructional LOs.

This article describes an evidence-based teaching guide that aggregates, summarizes, and provides actionable advice from research findings on LOs. It can be accessed at . The guide has several features intended to help instructors: a landing page that indicates starting points ( Figure 1 ), syntheses of observations from the literature, summaries of and links to selected papers ( Figure 2 ), and an instructor checklist that details recommendations and points to consider. The focus of our guide is to provide recommendations based on the literature for instructors to use when creating, revising, and using instructional LOs in their courses. The Effective Construction section provides evidence-based guidelines for writing effective LOs. The Instructor Use section contains research summaries about using LOs as a foundational element for successful course design, summaries of the research that supports recommended practices for aligning LOs with assessment and classroom instruction, and direction from experts for engaging with colleagues in improving instructor practice with LOs. The Student Use section includes a discussion on how students use LOs and how instructor guidance can improve student use of LOs, along with evidence on the impact of LO use coupled with pretests, transparent teaching methods, and summaries of LO-driven student outcomes in terms of exam scores, depth of learning, and affect (e.g., perception of utility and self-regulated learning). Some of the questions and considerations that serve to organize the guide are highlighted in the following sections.

An external file that holds a picture, illustration, etc.
Object name is cbe-21-fe3-g001.jpg

LO guide landing page, which provides readers with an overview of choice points.

An external file that holds a picture, illustration, etc.
Object name is cbe-21-fe3-g002.jpg

Screenshots representing summaries of and links to selected papers.


Writing LOs effectively is essential, as their wording should provide direction for developing instructional activities and guide the design of assessments. Effective LOs clearly communicate what students should know and be able to do and are written to be behavioral, measurable, and attainable ( Rodriguez and Albano, 2017 ). It is particularly important that each LO is written with enough information to ensure that other knowledgeable individuals can use the LO to measure a learner’s success and arrive at the same conclusions ( Mager, 1997 ). Clear, unambiguous wording encourages consistency across sections and optimizes student use of the stated LOs.

Effective LOs specify a visible performance—what students should be able to do with the content—and may also include conditions and the criteria for acceptable performance ( Mager, 1997 ). When constructing an LO, one should use an action verb to describe what students are expected to know and be able to do with the disciplinary knowledge and skills ( Figure 3 ). Bloom’s taxonomy of cognitive skills provides a useful framework for writing LOs that embody the intended complexity and the cognitive demands involved in mastering them ( Bloom, 1956 ; Anderson and Krathwohl, 2001 ). Assessment items and course activities can then be aligned with LOs using the Blooming Biology Tool described by Crowe et al. (2008) . However, LOs should not state the instructional method(s) planned to accomplish the objectives or be written so specifically as to be assessment tasks themselves ( Mager, 1997 ).

An external file that holds a picture, illustration, etc.
Object name is cbe-21-fe3-g003.jpg

Components of an LO.

Our Instructor Checklist provides specific recommendations for writing LOs, along with a link to examples of measurable action verbs associated with Bloom’s taxonomy.


Course designs and redesigns built around clear and measurable LOs result in measurable benefits to students (e.g., Armbruster et al. , 2009 , and other citations in the Course and Curriculum Design and Outcomes section of this guide). LOs are established as the initial step in backward design ( McTighe and Wiggins, 2012 ). They provide a framework for instructors to 1) design assessments that furnish evidence on the degree of student mastery of knowledge and skills and 2) select teaching and learning activities that are aligned with objectives ( Mager, 1997 ; Rodriguez and Albano, 2017) . Figure 4 depicts depicts integrated course course design, emphasizing the dynamic and reciprocal associations among LOs, assessment, and teaching practice.

An external file that holds a picture, illustration, etc.
Object name is cbe-21-fe3-g004.jpg

Components of integrated course design (after Fink, 2003 ).

Used in this way, LOs provide a structure for planning assessments and instruction while giving instructors the freedom to be creative and flexible ( Mager, 1997 ; Reynolds and Kearns, 2017 ). In essence, LOs respond to the question: “If you don’t know where you’re going, how will you know which road to take and how do you know when you get there?” ( Mager, 1997 , p. 14). When assessments are created, each assessment item or task must be specifically associated with at least one LO and measure student learning progress on that LO. The performance and conditions components of each LO should guide the type of assessment developed ( Mager, 1997 ). Data gathered from assessment results (feedback) can then inform future instruction. The Assessment section of our guide contains summaries of research reporting the results of aligning assessment with LOs and summaries of frameworks that associate assessment items with LOs.

The purpose of instruction is communicated to students most effectively when instructional activities are aligned with associated instructional and course-level LOs (e.g., Chasteen et al. , 2011 , and others within the Instructor Use section of this guide). The literature summarized in the Course and Curriculum Design section of the guide supports the hypothesis that student learning is strongly impacted by what instructors emphasize in the classroom. In the guide’s Student Buy-In and Metacognition section, we present strategies instructors have used to ensure that LOs are transparent and intentionally reinforced to students . When LOs are not reinforced in instruction, students may conclude that LOs are an administrative requirement rather than something developed for their benefit. The guide’s Instructor Checklist contains evidence-based suggestions for increasing student engagement through making LOs highly visible.

Using LOs as the foundation of course planning results in a more student-centered approach, shifting the focus from the content to be covered to the concepts and skills that the student should be able to demonstrate upon successfully completing the course (e.g., Reynolds and Kearns, 2017 , and others within the Active Learning section of this guide). Instead of designing memorization-driven courses that are “a mile wide and an inch deep,” instructors can use LOs to focus a course on the key concepts and skills that prepare students for future success in the field. Group problem solving, discussions, and other class activities that allow students to practice and demonstrate the competencies articulated in LOs can be prioritized over lectures that strive to cover all of the content. The guide’s Active Learning section contains a summary of the literature on the use of LOs to develop activities that promote student engagement, provide opportunities for students to practice performance, and allow instructors to gather feedback on learning progress. The evidence-based teaching guides on Group Work and Peer Instruction provide additional evidence and resources to support these efforts.


Momsen et al. (2010) examined Bloom’s level of assessment items and course goals from 50 faculty in 77 introductory biology courses for majors. The authors found that 93% of the assessment items were rated low-level Bloom’s, and 69% of the 250 course goals submitted were rated low-level Bloom’s ( Momsen et al. , 2010 ). A recent survey of 38 instructors of biology for nonmajors found similar results. Heil et al. (unpublished data) reported that 74% of the instructors surveyed write their own LOs, and 95% share their LOs with their students ( Heil et al. , unpublished data ). The action verbs used in 66% of these LOs were low-level Bloom’s cognitive skills, assessing knowledge and comprehension ( Heil et al. , unpublished data ). Further, an analysis of 1390 LOs from three best-selling biology textbooks for nonscience majors found that 89% were rated Bloom’s cognitive skill level 1 or level 2. Vision & Change competencies, as articulated in the BioSkills Guide ( Clemmons et al. , 2020 ), were only present in 17.7% of instructors’ LOs and 7% of the textbook LOs ( Heil et al. , unpublished data ). These data suggest that, in introductory biology for both majors and nonmajors, most instructors emphasize lower-order cognitive skills that are not aligned with teaching frameworks.

Researchers have documented effective strategies to improve instructors’ writing and use of LOs. The guide’s Engaging with Colleagues section contains summaries demonstrating that instructor engagement with the scholarship of teaching and learning can improve through professional development in collaborative groups—instructors can benefit by engaging in a collegial community of practice as they implement changes in their teaching practices (e.g., Richlin and Cox, 2004 , and others within the Engaging with Colleagues section of the guide). Collaboration among institutions can create common course-level LOs that promote horizontal and vertical course alignment, which can streamline articulation agreements and transfer pathways between institutions ( Kiser et al. , 2022 ). Departmental efforts to map LOs across program curricula can close gaps in programmatic efforts to convey field-expected criteria and develop student skills throughout a program ( Ezell et al. , 2019 ). The guide contains summaries of research-based recommendations that encourage departmental support for course redesign efforts (e.g., Pepper et al. , 2012 , and others within the Engaging with Colleagues section of the guide).


When instructors publish well-written LOs aligned with classroom instruction and assessments, they establish clear goalposts for students ( Mager, 1997 ). Using LOs to guide their studies, students should no longer have to ask “Do we have to know …?” or “Will this be on the test?” The Student Use section of the guide contains summaries of research on the impact of LOs from the student perspective.


Researchers have shown that students support the use of LOs to design class activities and assessments. In the Guiding Learning section of the guide, we present evidence documenting how students use LOs and how instructors can train students to use them more effectively ( Brooks et al. , 2014 , and other citations within this section of the guide). However, several questions remain about the impact of LOs on students. For example, using LOs may improve students’ ability to self-regulate, which in turn may be particularly helpful in supporting the success of underprepared students ( Simon and Taylor, 2009 ; Osueke et al. , 2018 ). But this hypothesis remains untested.

There is evidence that transparency in course design improves the academic confidence and retention of underserved students ( Winkelmes et al. , 2016 ), and LOs make course expectations transparent to students. LOs are also reported to help students organize their time and effort and give students, particularly those from traditionally underserved groups, a better idea of areas in which they need help ( Minbiole, 2016 ). Additionally, LOs facilitate the construction of highly structured courses by providing scaffolding for assessment and classroom instruction. Highly structured course design has been demonstrated to improve all students’ academic performance. It significantly reduces achievement gaps (difference in final grades on a 4.0 scale) between disadvantaged and nondisadvantaged students ( Haak et al. , 2011 ). However, much more evidence is needed on how LOs impact underprepared and/or underresourced students:

  • Does the use of LOs lead to increased engagement with the content and/or instructor by underprepared and/or underserved students?
  • Does LO use have a disproportionate and positive impact on the ability of underprepared and/or underresourced students to self-direct their learning?
  • Is there a significant impact on underserved students’ academic performance and persistence with transparent LOs in place?

In general, how can instructors help students realize the benefits of well-written LOs? Research indicates that many students never receive instruction on using LOs ( Osueke et al. , 2018 ). However, when students receive explicit instruction on LO use, they benefit ( Osueke et al. , 2018 ). Examples include teaching students how to turn LOs into questions and how to answer and use those questions for self-assessment ( Osueke et al. , 2018 ). Using LOs for self-assessment allows students to take advantage of retrieval practice, a strategy that has a positive effect on learning and memory by helping students identify what they have and have not learned ( Bjork and Bjork, 2011 ; Brame and Biel, 2015 ). Some students, however, may avoid assessment strategies that identify what they do not understand or know because they find difficulty uncomfortable ( Orr and Foster, 2013 ; Dye and Stanton, 2017 ).

Brooks et al. (2014) reported that about one-third of students surveyed indicated that they had underestimated the depth of learning required to pass an assessment on the stated LOs. Further, students may have difficulty understanding the scope or expectations of stated LOs until after learning the content. Research on how instructors should train students to use LOs has been limited, and many of these open questions remain:

  • What are the best practices to help students use LOs in self-assessment strategies?
  • How can instructors motivate students to go outside their comfort zones for learning and use LOs in self-assessment strategies?
  • How can instructors help students better understand the performance, conditions, and criteria required by the LOs to demonstrate successful learning?
  • How might this differ for learners at different institutions, where academic preparedness and/or readiness levels may vary greatly?


The guide’s Pretesting section contains research findings building on the pretesting effect reported by Little and Bjork (2011) . Pretesting with questions based on LOs has been shown to better communicate course expectations to students, increase student motivation and morale by making learning progress more visible, and improve retention of information as measured by final test scores ( Beckman, 2008 ; Sana et al. , 2020 ). Operationalizing LOs as pretest questions may serve as an effective, evidence-based model for students to self-assess and prepare for assessment. The research supporting this strategy is very limited, however, prompting the following questions:

  • How broadly applicable—in terms of discipline and course setting—is the benefit of converting LOs to pretest questions?
  • Is the benefit of operationalizing LOs to create pretests sustained when converting higher-level Bloom’s LOs into pretest questions?
  • Does the practice of using LOs to create pretest questions narrow students’ focus such that the breadth/scope of their learning is overly limited/restricted? This is particularly concerning if students underestimate the depth of learning required by the stated LOs ( Brooks et al. , 2014 ).
  • Could this practice help instructors teach students to use LOs to self-assess with greater confidence and persistence?


The guide concludes with research summaries regarding the specific benefits to students associated with the use of LOs. Specifically, 1) alignment of LOs and assessment items is associated with higher exam scores (e.g., Armbruster et al. , 2009 , and others within the Outcomes section of the guide); 2) exam items designed to measure student mastery of LOs can support higher-level Bloom’s cognitive skills (e.g., Armbruster et al. , 2009 , and others within the Outcomes section of the guide); and 3) students adjust their learning approach based on course design and have been shown to employ a deeper approach to learning in courses in which assessment and class instruction are aligned with LOs ( Wang et al. , 2013 ).


It is difficult to find literature in which researchers measured the impact of LOs alone on student performance due to their almost-necessary conflation with approaches to assessment and classroom practices. We argue that measuring the impact of LOs independently of changes in classroom instruction or assessment would be inadvisable, considering the role that LOs play in integrated course design ( Figure 4 ). Consistent with this view, the guide includes summaries of research findings on course redesigns that focus on creating or refining well-defined, well-written LOs; aligning assessment and classroom practice with the LOs; and evaluating student use and/or outcomes ( Armbruster et al. , 2009 ; Chasteen et al. , 2011 ). We urge instructors to use LOs from this integrated perspective.


We encourage instructors to use LOs as the basis for course design, align LOs with assessment and instruction, and promote student success by sharing their LOs and providing practice with how best to use them. Instructor skill in using LOs is not static and can be improved and refined with collaborative professional development efforts. Our teaching guide ends with an Instructor Checklist of actions instructors can take to optimize their use of LOs ( ).


We thank Kristy Wilson for her guidance and support as consulting editor for this effort and Cynthia Brame and Adele Wolfson for their insightful feedback on this paper and the guide. This material is based upon work supported in part by the National Science Foundation under grant number DUE 201236 2. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors. They do not necessarily reflect the views of the National Science Foundation.

  • Anderson, L. W., Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (Complete ed.). New York, NY: Longman. [ Google Scholar ]
  • Armbruster, P., Patel, M., Johnson, E., Weiss, M. (2009). Active learning and student-centered pedagogy improve student attitudes and performance in introductory biology . CBE—Life Sciences Education , 3 ( 8 ), 203–213. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Beckman, W. S. (2008). Pre-testing as a method of conveying learning objectives . Journal of Aviation/Aerospace Education & Research , 17 ( 2 ), 61–70. [ Google Scholar ]
  • Bjork, E. L., Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning . In Gernsbacher, M. A., Pomerantz, J. (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (2nd ed., pp. 59–68). New York, NY: Worth Publishers. [ Google Scholar ]
  • Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals by a committee of college and university examiners. Handbook I: Cognitive domain . New York, NY: David McKay. [ Google Scholar ]
  • Brame, C. J., Biel, R. (2015). Test-enhanced learning: The potential for testing to promote greater learning in undergraduate science courses . CBE—Life Sciences Education , 14 , 1–12. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Brooks, S., Dobbins, K., Scott, J., Rawlinson, M., Norman, R. I. (2014). Learning about learning outcomes: The student perspective . Teaching in Higher Education , 19 ( 6 ), 721–733. [ Google Scholar ]
  • Chasteen, S. V., Perkins, K. K., Beale, P. D., Pollock, S. J., Wieman, C. E. (2011). A thoughtful approach to instruction: Course transformation for the rest of us . Journal of College Science Teaching , 40 , 24–30. [ Google Scholar ]
  • Clemmons, A. W., Timbrook, J., Herron, J. C., Crowe, A. J. (2020). BioSkills Guide: Development and national validation of a tool for interpreting the Vision and Change core competencies . CBE—Life Sciences Education , 19 ( 20 ), 1–19. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Crowe, A., Dirks, C., Wenderoth, M. P. (2008). Biology in Bloom: Implementing Bloom’s taxonomy to enhance student learning in biology . CBE—Life Sciences Education , 7 ( 4 ), 368–381. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Dye, K. M., Stanton, J. D. (2017). Metacognition in upper-division biology students: Awareness does not always lead to control . CBE—Life Sciences Education , 16 ( 2 ), 1–14. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Ezell, J. D., Lending, D., Dillon, T. W., May, J., Hurney, C. A., Fulcher, K. H. (2019). Developing measurable cross-departmental learning objectives for requirements elicitation in an information systems curriculum . Journal of Information Systems Education , 30 ( 1 ), 27–41. [ Google Scholar ]
  • Fink, L. D. (2003). A self-directed guide to designing courses for significant learning . San Francisco, CA: Dee Fink & Associates. [ Google Scholar ]
  • Haak, D. C., HilleRisLambers, J., Pitre, E., Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology . Science , 332 ( 6034 ), 1213–1216. [ PubMed ] [ Google Scholar ]
  • Heil, H., Gormally, C., Brickman, P. (in press). Low-level learning and expectations for non-science majors . Journal of College Science Teaching . [ Google Scholar ]
  • Kiser, S., Kayes, L. J., Baumgartner, E., Kruchten, A., Stavrianeas, S. (2022). Statewide curricular alignment & learning outcomes for introductory biology: Using Vision & Change as a vehicle for collaboration . American Biology Teacher , 84 ( 3 ), 130–136. [ Google Scholar ]
  • Little, J. L., Bjork, E. L. (2011). Pretesting with multiple-choice questions facilitates learning . Cognitive Science , 33 , 294–299. [ Google Scholar ]
  • Mager, R. F. (1962). Preparing instructional objectives: A critical tool in the development of effective instruction (1st ed.). Palo Alto, CA: Fearon Publishers. [ Google Scholar ]
  • Mager, R. F. (1997). Preparing instructional objectives: A critical tool in the development of effective instruction (3rd ed.). Atlanta, GA: Center for Effective Performance. [ Google Scholar ]
  • McTighe, J., Wiggins, G. (2012, March). Understanding by design framework . Alexandria, VA: Association for Supervision and Curriculum Development. [ Google Scholar ]
  • Minbiole, J. (2016). Improving course coherence & assessment rigor: “Understanding by Design” in a nonmajors biology course . American Biology Teacher , 78 ( 6 ), 463–470. [ Google Scholar ]
  • Momsen, J. L., Long, T. M., Wyse, S. A., Ebert-May, D. (2010). Just the facts? Introductory undergraduate biology courses focus on low-level cognitive skills . CBE—Life Sciences Education , 9 ( 4 ), 435–440. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Orr, R., Foster, S. (2013). Increasing student success using online quizzing in introductory (majors) biology . CBE—Life Sciences Education , 12 ( 3 ), 509–514. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Osueke, B., Mekonnen, B., Stanton, J. D. (2018). How undergraduate science students use learning objectives to study . Journal of Microbiology & Biology Education , 19 ( 2 ), 1–8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Pepper, R. E., Chasteen, S. V., Pollock, S. J., Perkins, K. K. (2012). Facilitating faculty conversations: Development of consensus learning goals . AIP Conference Proceedings , 1413 , 291. [ Google Scholar ]
  • Reynolds, H. L., Kearns, K. D. (2017). A planning tool for incorporating backward design, active learning, and authentic assessment in the college classroom . College Teaching , 65 ( 1 ), 17–27. [ Google Scholar ]
  • Richlin, L., Cox, M. D. (2004). Developing scholarly teaching and the scholarship of teaching and learning through faculty learning communities . New Directions for Teaching and Learning , 97 , 127–135. [ Google Scholar ]
  • Rodriguez, M. C., Albano, A. D. (2017). The college instructor’s guide to writing test items: Measuring student learning . New York, NY: Routledge. [ Google Scholar ]
  • Sana, F., Forrin, N. D., Sharma, M., Dubljevic, T., Ho, P., Jalil, E., Kim, J. A. (2020). Optimizing the efficacy of learning objectives through pretests . CBE—Life Sciences Education , 19 ( 3 ), 1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Simon, B., Taylor, J. (2009). What is the value of course-specific learning goals? Journal of College Science Teaching , 39 , 52–57. [ Google Scholar ]
  • Wang, X., Su, Y., Cheung, S., Wong, E., Kwong, T. (2013). An exploration of Biggs’ constructive alignment in course design and its impact on students’ learning approaches . Assessment & Evaluation in Higher Education , 38 , 477–491. [ Google Scholar ]
  • Winkelmes, M., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., Weevil, K. H. (2016). A teaching intervention that increases underserved college students’ success . Peer Review , 18 ( 1/2 ), 31–36. [ Google Scholar ]

Learning Objectives

After studying Chapter 2, you should know and understand the following key points:

Dependent variables are measures of behavior used to assess the effects of independent variables.

Scientific reporting is unbiased and objective; clear communication of concepts occurs when operational definitions are used.

Scientific instruments are accurate and precise; physical and psychological measurement should be valid and reliable.

A hypothesis is a tentative explanation for a phenomenon; testable hypotheses have clearly defined concepts (operational definitions), are not circular, and refer to concepts that can be observed.

Scientists adopt a skeptical attitude and are cautious about accepting explanations until sufficient empirical evidence is obtained.

Correlational relationships allow psychologists to predict behavior or events, but do not allow psychologists to infer what causes these relationships.

Psychologists understand the cause of a phenomenon when the three conditions for causal inference are met: covariation, time-order relationship, and elimination of plausible alternative causes.

The experimental method, in which researchers manipulate independent variables to determine their effect on dependent variables, establishes time-order and allows a clearer determination of covariation.

Plausible alternative causes for a relationship are eliminated if there are no confoundings in a study; a study free of confoundings has internal validity.

External validity refers to the extent to which a study's findings may be used to describe different populations, settings, and conditions.

Psychologists apply their knowledge and research methods to change people's lives for the better.

Successful scientific theories organize empirical knowledge, guide research by offering testable hypotheses, and survive rigorous testing.

Researchers evaluate theories by judging the theory's internal consistency, observing whether hypothesized outcomes occur when the theory is tested, and noting whether the theory makes precise predictions based on parsimonious explanations.

To learn more about the book this website supports, please visit its .
and .
is one of the many fine businesses of .
You must be a registered user to view the in this website.

If you already have a username and password, enter it below. If this is your first visit to this site, you can (it'll be on a card that came with your textbook) to register.
'); document.write(''); } // -->
( )
This form changes settings for this website only. To make changes to your user profile instead, please .
Send mail as:
'); } else { document.write(' '); } // -->
'); } else { document.write(' '); } document.write('
TA email: '); } else { document.write(' '); } // -->
Other email: '); } else { document.write(' '); } // -->
"Floating" navigation? '); } else if (floatNav == 2) { document.write(' '); } else { document.write(' '); } // -->
Drawer speed: '; theseOptions += (glideSpeed == 1) ? ' ' : ' ' ; theseOptions += (glideSpeed == 2) ? ' ' : ' ' ; theseOptions += (glideSpeed == 3) ? ' ' : ' ' ; theseOptions += (glideSpeed == 4) ? ' ' : ' ' ; theseOptions += (glideSpeed == 5) ? ' ' : ' ' ; theseOptions += (glideSpeed == 6) ? ' ' : ' ' ; document.write(theseOptions); // -->
1. (optional) Enter a note here:

2. (optional) Select some text on the page (or do this before you open the "Notes" drawer).
3.Highlighter Color:
Search for:
Search in:
Course-wide Content

News, Articles & Links


More Resources

Instructor Resources

Course-wide Content

Have a language expert improve your writing

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

  • Knowledge Base
  • Starting the research process
  • Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

  • Establish the scope and depth of your project
  • Contribute to your research design
  • Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

Prevent plagiarism. Run a free check.

Research objectives are important because they:

  • Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
  • Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
  • Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

  • Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
  • Measurable: Know how you’ll measure whether your objectives have been achieved.
  • Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
  • Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
  • Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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

learning objectives of research methodology

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


  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility


  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

  • Budget constraints or any specifics of grant funding
  • Your proposed timeline and duration
  • Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
  • Any inclusion and exclusion criteria
  • Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Cite this Scribbr article

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

Ryan, E. (2023, November 20). Research Objectives | Definition & Examples. Scribbr. Retrieved June 30, 2024, from

Is this article helpful?

Eoghan Ryan

Eoghan Ryan

Other students also liked, writing strong research questions | criteria & examples, how to write a problem statement | guide & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Course Information

Course objectives, student learning outcomes, assessment, course objectives.

This course is designed to enable students to:

  • identify and discuss the role and importance of research in the social sciences.
  • identify and discuss the issues and concepts salient to the research process.
  • identify and discuss the complex issues inherent in selecting a research problem, selecting an appropriate research design, and implementing a research project.
  • identify and discuss the concepts and procedures of sampling, data collection, analysis and reporting.

Student Learning Outcomes:

Students who successfully complete this course will be able to:

  • explain key research concepts and issues
  • read, comprehend, and explain research articles in their academic discipline.

Learning Assessment:

  • SLO #1 is assessed via student-led discussions of the textbook in Modules 1 through 5.
  • SLO #2 is assessed via student led discussions of the 5 research designs presented in the Research Portfolio.
  • Course Objectives, Student Learning Outcomes, Assessment. Authored by : WIlliam Pelz. Provided by : Herkimer College. Project : Research Methods in Social Science - Achieving the Dream course. License : CC BY-SA: Attribution-ShareAlike

Handbook home

  • Search the Handbook
  • Undergraduate courses
  • Graduate courses
  • Research courses
  • Undergraduate subjects
  • Graduate subjects
  • Research subjects
  • Breadth Tracks
  • CAPS Login - Staff only
  • Research Methodology

Research Methodology (CLRS90001)

Graduate coursework Points: 12.5 On Campus (Hawthorn)

View full page

About this subject

  • Eligibility and requirements
  • Dates and times
  • Further information
  • Timetable (opens in new window)

Contact information

School of Melbourne Custom Programs

Currently enrolled students:

  • General information:
  • Email: [email protected]

Future students:

  • Further information:

The course focuses on social science research methods. Methods discussed include interview,content analysis, focus group discussions and surveys.

Intended learning outcomes

By the end of the subject students should be able to:

  • Demonstrate the ability to choose methods appropriate to research aims and objectives
  • Understand the limitations of particular research methods
  • Develop skills in qualitative and quantitative data analysis and presentation
  • Develop advanced critical thinking skills
  • Demonstrate enhanced writing skills

Generic skills

• Demonstrate the ability to choose methods appropriate to research aims and objectives • Understand the limitations of particular research methods • Develop skills in qualitative and quantitative data analysis and presentation • Develop advanced critical thinking skills • Demonstrate enhanced writing skills

Last updated: 3 November 2022

  • Privacy Policy

Research Method

Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Research Methodology


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


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 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.


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.


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

Research MethodologyResearch Methods
Research methodology refers to the philosophical and theoretical frameworks that guide the research process. refer to the techniques and procedures used to collect and analyze data.
It is concerned with the underlying principles and assumptions of research.It is concerned with the practical aspects of research.
It provides a rationale for why certain research methods are used.It determines the specific steps that will be taken to conduct research.
It is broader in scope and involves understanding the overall approach to research.It is narrower in scope and focuses on specific techniques and tools used in research.
It is concerned with identifying research questions, defining the research problem, and formulating hypotheses.It is concerned with collecting data, analyzing data, and interpreting results.
It is concerned with the validity and reliability of research.It is concerned with the accuracy and precision of data.
It is concerned with the ethical considerations of research.It is concerned with the practical considerations of research.

About the author

' src=

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Paper Title Page

Research Paper Title Page – Example and Making...

Research Design

Research Design – Types, Methods and Examples

Research Recommendations

Research Recommendations – Examples and Writing...

Research Topic

Research Topics – Ideas and Examples

Survey Instruments

Survey Instruments – List and Their Uses

APA Table of Contents

APA Table of Contents – Format and Example

Module 2: Research Methods in Learning and Behavior

Module Overview

Module 2 will cover the critical issue of how research is conducted in the experimental analysis of behavior. To do this, we will discuss the scientific method, research designs, the apparatus we use, how we collect data, and dependent measures used to show that learning has occurred. We also will break down the structure of a research article and make a case for the use of both humans and animals in learning and behavior research.

Module Outline

2.1. The Scientific Method

2.2. research designs used in the experimental analysis of behavior, 2.3. dependent measures, 2.4. animal and human research.

Module Learning Outcomes

  • Describe the steps in the scientific method and how this process is utilized in the experimental analysis of behavior.
  • Describe specific research designs, data collection methods, and apparatus used in the experimental analysis of behavior.
  • Understand the basic structure of a research article.
  • List and describe dependent measures used in learning experiments.
  • Explain why animals are used in learning research.
  • Describe safeguards to protect human beings in scientific research.

Section Learning Objectives

  • Define scientific method.
  • Outline and describe the steps of the scientific method, defining all key terms.
  • Define functional relationship and explain how it produces a contingency.
  • Explain the concept of a behavioral definition.
  • Distinguish between stimuli and responses and define related concepts.
  • Distinguish types of contiguity, and the term from contingency.
  • Describe the typical phases in learning research.

2.1.1. The Steps of The Scientific Method

In Module 1, we learned that psychology was the scientific study of behavior and mental processes. We will spend quite a lot of time on the behavior and mental processes part, but before we proceed, it is prudent to elaborate more on what makes psychology scientific. It is safe to say that most people not within our discipline or a sister science would be surprised to learn that psychology utilizes the scientific method at all.

So what is the scientific method? Simply, the scientific method is a systematic method for gathering knowledge about the world around us. The key word here is that it is systematic, meaning there is a set way to use it. What is that way? Well, depending on what source you look at it can include a varying number of steps. For our purposes, the following will be used:

Table 2.1: The Steps of the Scientific Method

0 Ask questions and be willing to wonder. To study the world around us you have to wonder about it. This inquisitive nature is the hallmark of or our ability to assess claims made by others and make objective judgments that are independent of emotion and anecdote and based on hard evidence and required to be a scientist.
1 Generate a research question or identify a problem to investigate. Through our wonderment about the world around us and why events occur as they do, we begin to ask questions that require further investigation to arrive at an answer. This investigation usually starts with a , which could include conducting a literature search through our university library or using a search engine such as Google Scholar to see what questions have been investigated already and what answers have been found, so that we can identify or holes in this body of work.
2 Attempt to explain the phenomena we wish to study. We now attempt to formulate an explanation of why the event occurs as it does. This systematic explanation of a phenomenon is a and our specific, testable prediction is the We will know if our theory is correct because we have formulated a hypothesis that we can now test.
3 Test the hypothesis. It goes without saying that if we cannot test our hypothesis, then we cannot show whether our prediction is correct or not. Our plan of action of how we will go about testing the hypothesis is called our . In the planning stage, we will select the appropriate research method to answer our question/test our hypothesis.
4 Interpret the results. With our research study done, we now examine the data to see if the pattern we predicted exists. We need to see if a cause-and-effect statement can be made, assuming our method allows for this inference. The statistics we use take on two forms. First, there are which provide a means of summarizing or describing data and presenting the data in a usable form. You likely have heard of the mean or average, median, and mode. Along with standard deviation and variance, these are ways to describe our data. Second, there are that allow for the analysis of two or more sets of numerical data to determine the of the results. These techniques include the -test, test, ANOVA, and regression., to name a few. Significance is an indication of how confident we are that our results are due to our manipulation or design and not chance. Typically, we set this significance at no higher than 5% due to chance.
5 Draw conclusions carefully. We need to accurately interpret our results and not overstate our findings. To do this, we need to be aware of our biases and avoid emotional reasoning so that they do not cloud our judgment. How so? In our effort to stop a child from engaging in self-injurious behavior that could cause substantial harm or even death, we might overstate the success of our treatment method.
6 Communicate our findings to the larger scientific community. Once we have decided on whether our hypothesis is correct or not, we need to share this information with others so that they might comment critically on our methodology, statistical analyses, and conclusions. Sharing also allows for or repeating the study to confirm its results. Communication is accomplished via scientific journals, conferences, or newsletters released by many of the organizations mentioned in Section 1.4.

2.1.2. Making Cause and Effect Statements in the Experimental Analysis of Behavior

As you have seen, scientists seek to make causal statements about what they are studying. In the study of learning and behavior, we call this a functional relationship. This occurs when we can say a target behavior has changed due to the use of a procedure/treatment/strategy and this relationship has been replicated at least one other time. A contingency is when one thing occurs due to another. Think of it as an if-then statement. If I do X then Y will happen. We can also say that when we experience Y that X preceded it. Concerning a functional relationship, if I introduce a treatment, then the animal responds as such or if that animal pushes the lever, then she receives a food pellet.

To help arrive at a functional relationship, we have to understand what we are studying. In science, we say we operationally define our variables. In the realm of learning, we call this a behavioral definition, or a precise, objective, unambiguous description of the behavior. The key is that we must state our behavioral definition with enough precision that anyone can read it and be able to accurately measure the behavior when it occurs.

2.1.3. Frequently Used Terms in the Experimental Analysis of Behavior

In the experimental analysis of behavior, we frequently talk about an animal or person experiencing a trial. Simply, a trial is one instance or attempt at learning. Each time a rat is placed in a maze this is considered one trial. We can then determine if learning is occurring using different dependent measures described in Section 2.3. If a child is asked to complete a math problem and then a second is introduced, and then a third, each practice problem represents a trial.

As you saw in Module 1, behaviorism is the science of stimuli and responses. What do these terms indicate? Stimuli are the environmental events that have the potential to trigger behavior, called a response . If your significant other does something nice for you and you say, ‘Thank you,’ the kind act is the stimulus which leads to your response of thanking him/her. Stimuli have to be sensed to bring about a response. This occurs through the five senses — vision, hearing, touch, smell, and taste. Stimuli can take on two forms. Appetitive stimuli are those that an organism desires and seeks out while aversive stimuli are readily avoided. An example of the former would be food or water and the latter is exemplified by extremes of temperature, shock, or a spanking by a parent.

As you will come to see in Module 6, we can make a stimulus more desirable or undesirable, called an establishing operation , or make it less desirable or undesirable, called an abolishing operation . Such techniques are called motivating operations . Food may be seen as more attractive, desirable, or pleasant if we are hungry but less desirable (or more undesirable) if we are full. A punishment such as taking away video games is more undesirable if the child likes to play games such as Call of Duty or Madden but is less undesirable (or maybe even has no impact) if they do not enjoy video games. Linked to the discussion above, food is an appetitive stimulus and could be an establishing operation if we are hungry. A valued video game also represents an establishing operation if we threaten its removal, and we will want to avoid such punishment, which makes the threat an aversive stimulus.

As noted earlier, the response is simply the behavior that is made and can take on many different forms. A dog may learn to salivate (response) to the sound of a bell (stimulus). A person may begin going to the gym if he or she seeks to gain tokens to purchase back up reinforcers (more on this in Module 7). A person may work harder in the future if they received a compliment from their boss today (either through email and visual or spoken or through hearing).

Another important concept is contiguity and occurs when two events are associated with one another because they occur together closely, whether in time called temporal contiguity or in space called spatial contiguity . In the case of time, we may come to associate thanking someone for saying ‘good job’ if we hear others doing this and the two verbal behaviors occur very close in time. Usually, the ‘Thank you’ (or other response) follows the praise within seconds. In the case of space, we may learn to use a spatula to flip our hamburgers on the grill if the spatula is placed next to the stove and not in another room. Do not confuse contiguity with contingency. Though the terms look the same they have very different meanings.

Finally, in learning research, we often distinguish two phases — baseline and treatment. Baseline Phase occurs before any strategy or strategies are put into effect. This phase will essentially be used to compare against the treatment phase. We are also trying to find out exactly how much of the target behavior the person or animal is engaging in. Treatment Phase occurs when the strategy or strategies are used, or you might say when the manipulation is implemented. Note that in behavior modification we also talk about what is called the maintenance phase. More on this in Module 7.

  • List the five main research methods used in psychology.
  • Describe observational research, listing its advantages and disadvantages.
  • Describe the case study approach to research, listing its advantages and disadvantages.
  • Describe survey research, listing its advantages and disadvantages.
  • Describe correlational research, listing its advantages and disadvantages.
  • Describe experimental research, listing its advantages and disadvantages.
  • Define key terms related to experiments.
  • Describe specific types of experimental designs used in learning research.
  • Describe the ways we gather data in learning research (or applied behavior analysis).
  • Outline the types of apparatus used in learning experiments.
  • Outline the parts of a research article and describe their function.

Step 3 called on the scientist to test his or her hypothesis. Psychology as a discipline uses five main research designs to do just that. These include observational research, case studies, surveys, correlational designs, and experiments.

2.2.1. Observational Research

In terms of naturalistic observation , the scientist studies human or animal behavior in its natural environment which could include the home, school, or a forest. The researcher counts, measures, and rates behavior in a systematic way and at times uses multiple judges to ensure accuracy in how the behavior is being measured. This is called inter-rater reliability . The advantage of this method is that you witness behavior as it occurs and it is not tainted by the experimenter. The disadvantage is that it could take a long time for the behavior to occur and if the researcher is detected then this may influence the behavior of those being observed. In the case of the latter, the behavior of the observed becomes artificial .

Laboratory observation involves observing people or animals in a laboratory setting. The researcher might want to know more about parent-child interactions and so brings a mother and her child into the lab to engage in preplanned tasks such as playing with toys, eating a meal, or the mother leaving the room for a short period of time. The advantage of this method over the naturalistic method is that the experimenter can use sophisticated equipment and videotape the session to examine it later. The problem is that since the subjects know the experimenter is watching them, their behavior could become artificial.

2.2.2. Case Studies

Psychology can also utilize a detailed description of one person or a small group based on careful observation. The advantage of this method is that you arrive at a rich description of the behavior being investigated, but the disadvantage is that what you are learning may be unrepresentative of the larger population and so lacks generalizability . Again, bear in mind that you are studying one person or a very small group. Can you possibly make conclusions about all people from just one or even five or ten? The other issue is that the case study is subject to the bias of the researcher in terms of what is included in the final write up and what is left out. Despite these limitations, case studies can lead us to novel ideas about the cause of a behavior and help us to study unusual conditions that occur too infrequently to study with large sample sizes and in a systematic way.

2.2.3. Surveys/Self-Report Data

A survey is a questionnaire consisting of at least one scale with a number of questions that assess a psychological construct of interest such as parenting style, depression, locus of control, attitudes, or sensation-seeking behavior. It may be administered by paper and pencil or computer. Surveys allow for the collection of large amounts of data quickly, but the actual survey could be tedious for the participant, and social desirability , or when a participant answers questions dishonestly so that he/she is seen in a more favorable light, could be an issue. For instance, if you are asking high school students about their sexual activity, they may not give genuine answers for fear that their parents will find out. Or if you wanted to know about prejudiced attitudes of a group of people, you could use the survey method. You could alternatively gather this information via an interview in a structured, semi-structured, or unstructured fashion. Important to survey research is that you have random sampling, or when everyone in the population has an equal chance of being included in the sample. This helps the survey to be representative of the population, and in terms of key demographic variables such as gender, age, ethnicity, race, education level, and religious orientation. Surveys are not frequently used in the experimental analysis of behavior.

2.2.4. Correlational Research

This research method examines the relationship between two variables or two groups of variables. A numerical measure of the strength of this relationship is derived, called the correlation coefficient , and can range from -1.00, which indicates a perfect inverse relationship meaning that as one variable goes up the other goes down, to 0 or no relationship at all, to +1.00 or a perfect relationship in which as one variable goes up or down so does the other. In terms of a negative correlation we might say that as a parent becomes more rigid, controlling, and cold, the attachment of the child to parent goes down. In contrast, as a parent becomes warmer, more loving, and provides structure, the child becomes more attached. The advantage of correlational research is that you can correlate anything. The disadvantage is also that you can correlate anything. Variables that do not have any relationship to one another could be viewed as related. Yes. This is both an advantage and a disadvantage. For instance, we might correlate instances of making peanut butter and jelly sandwiches with someone we are attracted to sitting near us at lunch. Are the two related? Not likely, unless you make a really good PB&J, but then the person is probably only interested in you for food and not companionship. The main issue here is that correlation does not allow you to make a causal statement.

2.2.5. Experiments

An experiment is a controlled test of a hypothesis in which a researcher manipulates one variable and measures its effect on another. A variable is anything that varies over time or from one situation to the next. Patience could be an example of a variable. Though we may be patient in one situation, we may have less if a second situation occurs close in time. The first could have lowered our ability to cope making an emotional reaction quicker to occur even if the two situations are about the same in terms of impact. Another variable is weight. Anyone who has tried to shed some pounds and weighs in daily knows just how much weight can vary from day to day, or even on the same day. In terms of experiments, the variable that is manipulated is called the independent variable (IV) and the one that is measured is called the dependent variable (DV) .

A common feature of experiments is to have a control group that does not receive the treatment, or is not manipulated, and an experimental group that does receive the treatment or manipulation. If the experiment includes random assignment, participants have an equal chance of being placed in the control or experimental group. The control group allows the researcher to make a comparison to the experimental group, making a causal statement possible, and stronger.

Within the experimental analysis of behavior (and applied behavior analysis), experimental procedures take on several different forms. In discussing each, understand that we will use the following notations:

A will represent the baseline phase and B will represent the treatment phase.

  • A-B design — This is by far the most basic of all designs used in behavior modification and includes just one rotation from baseline to treatment phase and from that we see if the behavior changed in the predicted manner. The issue with this design is that no functional relationship can be established since there is no replication. It is possible that the change occurred not due to the treatment that was used, but due to an extraneous variable , or an unseen and unaccounted for factor on the results and specifically our DV.
  • A-B-A-B Reversal Design — In this design, the baseline and treatment phases are implemented twice. After the first treatment phase occurs, the individual(s) is/are taken back to baseline and then the treatment phase is implemented again. Replication is built into this design, allowing for a causal statement, but it may not be possible or ethical to take the person back to baseline after a treatment has been introduced, and one that likely is working well. What if you developed a successful treatment to reduce self-injurious behavior in children or to increase feelings of self-worth? You would want to know if the decrease in this behavior or increase in the positive thoughts was due to your treatment and not extraneous behaviors, but can you take the person back to baseline? Is it ethical to remove a treatment for something potentially harmful to the person? Now let’s say a teacher developed a new way to teach fractions to a fourth-grade class. Was it the educational paradigm or maybe additional help a child received from his/her parents or a tutor that accounts for improvement in performance? Well, we need to take the child back to baseline and see if the strategy works again, but can we? How can the child forget what has been learned already? ABAB Reversal Designs work well at establishing functional relationships if you can take the person back to baseline but are problematic if you cannot. An example of them working well includes establishing a system, such as a token economy (more on this later), to ensure your son does his chores, having success with it, and then taking it away. If the child stops doing chores and only restarts when the token economy is put back into place, then your system works. Note that with time the behavior of doing chores would occur on its own and the token economy would be fazed out.
  • Multiple-baseline designs — This design can take on three different forms. In an across-subjects design, there is a baseline and treatment phase for two or more subjects for the same target behavior. For example, an applied behavior analyst is testing a new intervention to reduce disruptions in the classroom. The intervention involves a combination of antecedent manipulations, prompts, social support, differential reinforcement, and time-outs. He uses the intervention on six problematic students in a 6th period math class. Secondly, the across-settings design has a baseline and treatment phase for two or more settings in the same person for which the same behavior is measured. What if this same specialist now tests the intervention with one student but across her other five classes which include social studies, gym, science, English, and shop. Finally, in an across-behaviors design , there is a baseline and treatment phase for two or more different behaviors the same participant makes. The intervention continues to show promise and now the ABA specialist wants to see if it can help the same student but with his problem with procrastination and inability to organize.
  • Changing-Criterion Design — In this design, the performance criteria changes as the subject achieves specific goals. The individual may go from having to workout at the gym 2 days a week to 3 days, then 4 days, and then finally 5 days. Once the goal of 2 days a week is met, the criterion changes to 3 days a week. In a learning study, a rat may have to press the lever 5 times to receive a food pellet and then once this is occurring regularly, the schedule changes to 10 times to receive the same food pellet. We are asking the rat to make more behaviors for the same consequence. The changing-criterion design has an A-B design but rules out extraneous variables since the person or animal continues meeting the changing criterion/new goals using the same treatment plan or experimental manipulation. Hence successfully moving from one goal to the next must be due to the strategies that were selected.

2.2.6. Ways We Gather Data

When we record, we need to decide what method we will use. Several strategies are possible to include continuous, product or outcome, and interval. First, in continuous recording, we watch a person or animal continuously throughout an observation period , or time when observations will be made, and all occurrences of the behavior are recorded. This technique allows you to record both frequency and duration. The frequency is reported as a rate, or the number of responses that occur per minute. Duration is the total time the behavior takes from start to finish. You can also record the intensity using a rating scale in which 1 is low intensity and 5 is high intensity. Finally, latency can be recorded by noting how long it took the person to engage in the desirable behavior, or to discontinue a problem behavior, from when the demand was uttered. You can also use real-time recording in which you write down the time when the behavior starts and when it ends, and then do this each time the behavior occurs. You can look at the number of start-stops to get the frequency and then average out the time each start-stop lasted to get the duration. For instance:

learning objectives of research methodology

Next is product or outcome recording . This technique can be used when there is a tangible outcome you are interested in, such as looking at how well a student has improved his long division skills by examining his homework assignment or a test. Or you might see if your friend’s plan to keep a cleaner house is working by inspecting his or her house randomly once a week. This will allow you to know if an experimental teaching technique works. It is an indirect assessment method meaning that the observer does not need to be present. You can also examine many types of behaviors. But because the observer is not present, you are not sure if the person did the work himself or herself. It may be that answers were looked up online, cheating occurred as in the case of a test, or someone else did the homework for the student such as a sibling, parent, or friend. Also, you have to make sure you are examining the result/outcome of the behavior and not the behavior itself.

Finally, interval recording occurs when you take the observation period and divide it up into shorter periods of time. The person or animal is observed, and the target behavior recorded based on whether it occurs during the entire interval, called whole interval recording, or some part of the interval, called partial interval recording. With the latter, you are not interested in the dimensions of duration and frequency. We also say the interval recording is continuous if each subsequent interval follows immediately after the current one. Let’s say you are studying students in a classroom. Your observation period is the 50 minutes the student is in his home economics class and you divide it up into ten, 5-minute intervals. If using whole, then the behavior must occur during the entire 5-minute interval. If using partial, it only must occur sometime during the 5-minute interval. You can also use what is called time sample recording in which you divide the observation period into intervals of time but then observe and record during part of each interval (the sample). There are periods of time in between the observation periods in which no observation and recording occur. As such, the recording is discontinuous. This is a useful method since the observer does not have to observe the entire interval and the level of behavior is reported as the percentage of intervals in which the behavior occurred. Also, more than one behavior can be observed.

2.2.7. The Apparatus We Use

What we need to understand next in relation to learning research is what types of apparatus’ are used. As you might expect, the maze is the primary tool and has been so for over 100 years. Through the use of mazes, we can determine general principles about learning that apply to not only animals such as rats, but to human beings too. The standard or classic maze is built on a large platform with vertical walls and a transparent ceiling. The rat begins at a start point or box and moves through the maze until it reaches the end or goal box. There may be a reward at the end such as food or water to encourage the rat to learn the maze. Through the use of such a maze, we can determine how many trials it takes for the rat to reach the goal box without making a mistake. As you will see, in Section 2.3, we can also determine how long it took the rat to run the maze.

An alternative to this design is what is called the T-maze which obtains its name from its characteristic T-structure. The rat begins in a start box and proceeds up the corridor until it reaches a decision point – go left or right. We might discover if rats have a side preference or how fast they can learn if food-deprived the night before. One arm would have a food pellet while the other would not. It is also a great way to distinguish place and response learning (Blodgett & McCutchan, 1947). Some forms of the T-maze have multiple T-junctions in which the rat can make the correct decision and continues in the maze or makes a wrong decision. The rat can use cues in the environment to learn how to correctly navigate the maze and once learned, the rat will make few errors and run through it very quickly (Gentry, Brown, & Lee, 1948; Stone & Nyswander, 1927).

Similar to the T-maze is what is called the Y-maze . Starting in one arm, the rat moves forward and then has to choose one of two arms. The turns are not as sharp as in a T-maze making learning a bit easier.  There is also a radial arm maze (Olton, 1987; Olton, Collison, & Werz, 1977) in which a rat starts in the center and can choose to enter any of 8, 12, or 16 spokes radiating out from this central location. It is a great test of short-term memory as the rat has to recall which arms have been visited and which have not. The rat successfully completes the maze when all arms have been visited.

One final maze is worth mentioning. The Morris water maze (Morris, 1984) is an apparatus that includes a large round tub of opaque water. There are two hidden platforms 1-2 cm under the water’s surface. The rat begins on a start platform and swims around until the other platform is located and it stands on it. It utilizes external cues placed outside the maze to find the end platform and run time is the typical dependent measure that is used.

To learn more about rat mazes, please visit:

Check this Out

Do you want to increase how fast rats learn their way through a multiple T-maze? Research has shown that you can do this by playing Mozart. Rats were exposed in utero plus 60 days to either a complex piece of music in the form of a sonata from Mozart, minimalist music, white noise, or silence. They were then tested over 5 days with 3 trials per day in a multiple T-maze. Results showed that rats exposed to Mozart completed the maze quicker and made fewer errors than the rats in the other conditions. The authors state that exposure to complex music facilitates spatial-temporal learning in rats and this matches results found in humans (Rauscher, Robinson, & Jens, 1998). Another line of research found that when rats were stressed they performed worse in water maze learning tasks than their non-stressed counterparts (Holscher, 1999).

So when you are studying for your quizzes or exams in this class (or other classes), play Mozart and minimize stress. These actions could result in a higher grade.

Outside of mazes, learning researchers may also utilize a Skinner Box . This is a small chamber used to conduct operant conditioning experiments with animals such as rats or pigeons. Inside the chamber, there is a lever for rats to push or a key for pigeons to peck which results in the delivery of food or water. The behavior of pushing or pecking is recorded through electronic equipment which allows for the behavior to be counted or quantified. This device is also called an operant conditioning chamber .

Finally, Edward Thorndike (1898) used a puzzle box to arrive at his law of effect or the idea that an organism will be more likely to repeat a behavior if it produced a satisfying effect in the past than if the effect was negative. This later became the foundation upon which operant conditioning was built. In his experiments, a hungry cat was placed in a box with a plate of fish outside the box. It was close enough that the cat could see and smell it but could not touch it. To get to the food, the cat had to figure out how to escape the box or which mechanism would help it to escape. Once free, the cat would take a bite, be placed back into the box, and then had to work to get out again. Thorndike discovered that the cat was able to get out quicker each time which demonstrated learning.

2.2.8. The Scientific Research Article

In scientific research, it is common practice to communicate the findings of our investigation. By reporting what we found in our study, other researchers can critique our methodology and address our limitations. Publishing allows psychology to grow its knowledge base about human behavior. We can also see where gaps still exist. We move it into the public domain so others can read and comment on it. Scientists can also replicate what we did and possibly extend our work if it is published.

As noted earlier, there are several ways to communicate our findings. We can do so at conferences in the form of posters or oral presentations, through newsletters from APA itself or one of its many divisions or other organizations, or through research journals and specifically scientific research articles. Published journal articles represent a form of communication between scientists and in them, the researchers describe how their work relates to previous research, how it replicates and/or extends this work, and what their work might mean theoretically.

Research articles begin with an abstract or a 150-250-word summary of the entire article. The purpose is to describe the experiment and allows the reader to decide whether he or she wants to read it further. The abstract provides a statement of purpose, overview of the methods, main results, and a brief statement of what these results mean. Keywords are also given that allow for students and other researchers alike to find the article when doing a search.

The abstract is followed by four major sections – Introduction, Method, Results, and Discussion. First, the introduction is designed to provide a summary of the current literature as it relates to the topic. It helps the reader to see how the researcher arrived at their hypothesis and the design of the study. Essentially, it gives the logic behind the decisions that were made.

Next, is the method section. Since replication is a required element of science, we must have a way to share information on our design and sample with readers. This is the essence of the method section and covers three major aspects of a study — the participants, materials or apparatus, and procedure. The reader needs to know who was in the study so that limitations related to the generalizability of the findings can be identified and investigated in the future. The researcher will also state the operational/behavioral definition, describe any groups that were used, identify random sampling or assignment procedures, and provide information about how a scale was scored or if a specific piece of apparatus was used, etc. Think of the method section as a cookbook. The participants are the ingredients, the materials or apparatus are whatever tools are needed, and the procedure is the instructions for how to bake the cake.

Third, is the results section. In this section, the researcher states the outcome of the experiment and whether it was statistically significant or not. The researchers can also present tables and figures. It is here we will find both descriptive and inferential statistics.

Finally, the discussion section starts by restating the main findings and hypothesis of the study. Next, is an interpretation of the findings and what their significance might be. Finally, the strengths and limitations of the study are stated which will allow the researcher to propose future directions or for other researchers to identify potential areas of exploration for their work. Whether you are writing a research paper for a class, preparing an article for publication, or reading a research article, the structure and function of a research article is the same. Understanding this will help you when reading articles in learning and behavior but also note, this same structure is used across disciplines.

  • List typical dependent measures used in learning experiments.
  • Describe the use of errors as a dependent measure.
  • Describe the use of frequency as a dependent measure.
  • Describe the use of intensity as a dependent measure.
  • Describe the use of duration/run time/speed as a dependent measure.
  • Describe the use of latency as a dependent measure.
  • Describe the use of topography as a dependent measure.
  • Describe the use of rate as a dependent measure.
  • Describe the use of fluency as a dependent measure.

As we have learned, experiments include dependent and independent variables. The independent variable is the manipulation we are making while the dependent variable is what is being measured to see the effect of the manipulation. So, what types of DVs might we use in the experimental analysis of behavior or applied behavior analysis? We will cover the following: errors, frequency, intensity, duration, latency, topography, rate, and fluency.

2.3.1. Errors

A very simple measure of learning is to assess the number of errors made. If an animal running a maze has learned the maze, he/she should make fewer errors or mistakes with each trial, compared to say the first trial when many errors were made. The same goes for a child learning how to do multiplication. There will be numerous errors at start and then fewer to none later.

2.3.2. Frequency

Frequency is a measure of how often a behavior occurs. If we want to run more often, we may increase the number of days we run each week from 3 to 5. In terms of behavior modification, I once had a student who wished to decrease the number of times he used expletives throughout the day.

2.3.3. Intensity

Intensity is a measure of how strong the response is. For instance, a person on a treadmill may increase the intensity from 5 mph to 6 mph meaning the belt moves quicker and so the runner will have to move faster to keep up. We might tell children in a classroom to use their inside voices or to speak softer as opposed to their playground voices when they can yell.

2.3.4. Duration/Run Time/Speed

Duration is a measure of how long the behavior lasts. A runner may run more often (frequency), faster (intensity), or may run longer (duration). In the case of the latter, the runner may wish to build endurance and run for increasingly longer periods of time. A parent may wish to decrease the amount of time a child plays video games or is on his/her phone before bed. For rats in a maze, the first few attempts will likely take longer to reach the goal box than later attempts once the path needed to follow is learned. In other words, duration, or run time, will go down which demonstrates learning.

2.3.5. Latency

Latency represents the time it takes for a behavior to follow from the presentation of a stimulus. For instance, if a parent tells a child to take out the trash and he does so 5 minutes later, then the latency for the behavior of walking the trash outside is 5 minutes.

2.3.6. Topography

Topography represents the physical form a behavior takes. For instance, if a child is being disruptive, in what way is this occurring? Could it be the child is talking out of turn, being aggressive with other students, fidgeting in his/her seat, etc? In the case of rats and pushing levers, the mere act of pushing may not be of interest, but which paw is used or how much pressure is applied to the lever?

2.3.7. Rate

Rate is a measure of the change in response over time, or how often a behavior occurs. We may wish the rat to push the lever more times per minute to earn food reinforcement. Initially, the rat was required to push the lever 20 times per minute and now the experimenter requires 35 times per minute to receive a food pellet. In humans, a measure of rate would be words typed per minute. I may start at 20 words per minute but with practice (representing learning) I could type 60 words per minute or more.

2.3.8. Fluency

Though I may type fast, do I type accurately? This is where fluency comes in. Think about a foreign language. If you are fluent you speak it well. So, fluency is a measure of the number of correct responses made per minute. I may make 20 errors per minute of typing but with practice, I not only get quicker (up to 60 words per minute) but more accurate and reduce mistakes measure to 5 errors per minute. A student taking a semester of Spanish may measure learning by how many verbs he can correctly conjugate in a minute. Initially, he could only conjugate 8 verbs per minute but by the end of the semester can conjugate 24.

  • Defend the use of animals in research.
  • Describe safeguards to protect human research subjects.

2.4.1. Animal Models of Behavior

Learning research frequently uses animal models. According to , animals are used “…when there is a need to find out what happens in the whole, living body, which is far more complex than the sum of its parts. It is difficult, and in most cases simply not yet possible, to replace the use of living animals in research with alternative methods.” They cite four main reasons to use animals. First, to advance scientific understanding such as how living things work to apply that knowledge for the benefit of both humans and animals. They state, “Many basic cell processes are the same in all animals, and the bodies of animals are like humans in the way that they perform many vital functions such as breathing, digestion, movement, sight, hearing, and reproduction.”

Second, animals can serve as models to study disease. For example, “Dogs suffer from cancer, diabetes, cataracts, ulcers and bleeding disorders such as hemophilia, which make them natural candidates for research into these disorders. Cats suffer from some of the same visual impairments as humans.” Therefore, animal models help us to understand how diseases affect the body and how our immune system responds.

Third, animals can be used to develop and test potential treatments for these diseases. As the website says, “Data from animal studies is essential before new therapeutic techniques and surgical procedures can be tested on human patients.”

Finally, animals help protect the safety of people, other animals, and our environment. Before a new medicine can go to market, it must be tested to ensure that the benefits outweigh the harmful effects. Legally and ethically, we have to move away from in vitro testing of tissues and isolated organs to suitable animal models and then testing in humans.

In conducting research with animals, three principles are followed. First, when possible, animals should be replaced with alternative techniques such as cell cultures, tissue engineering, and computer modeling. Second, the number of animals used in research should be reduced to a minimum. We can do this by “re-examining the findings of studies already conducted (e.g. by systematic reviews), by improving animal models, and by use of good experimental design.” Finally, we should refine the way experiments are conducted to reduce any suffering the animals may experience as much as possible. This can include better housing and improving animal welfare. Outside of the obvious benefit to the animals, the quality of research findings can also increase due to reduced stress in the animals. This framework is called the 3Rs.

Please visit:

One way to guarantee these principles are followed is through what is called the Institutional Animal Care and Use Committee (IACUC). The IACUC is responsible for the oversight and review of the humane care and use of animals; upholds standards set forth in laws, policies, and guidance; inspects animal housing facilities; approves protocols for use of animals in research, teaching, or education; addresses animal welfare concerns of the public; and reports to the appropriate bodies within a university, accrediting organizations, or government agencies. At times, projects may have to be suspended if found to be noncompliant with the regulations and policies of that institution.

  • For more on the IACUC within the National Institutes of Health, please visit:
  • For another article on the use of animals in research, please check out the following published in the National Academies Press –
  • The following is an article published on the ethics of animal research and discusses the 3Rs in more detail –
  • And finally, here is a great article published by the Washington State University IACUC on the use of animals in research and teaching at WSU –

2.4.2. Human Models of Behavior

Throughout this module, we have seen that it is important for researchers to understand the methods they are using. Equally important, they must understand and appreciate ethical standards in research. As we have seen already in Section 2.3.1, such standards exist for the use of animals in research. The American Psychological Association (APA) identifies high standards of ethics and conduct as one of its four main guiding principles or missions and as it relates to humans. To read about the other three, please visit . Studies such as Milgram’s obedience study, Zimbardo’s Stanford prison study, and others, have necessitated standards for the use of humans in research. The standards can be broken down in terms of when they should occur during the process of a person participating in the study. Before participating. First, researchers must obtain informed consent or when the person agrees to participate because they are told what will happen to them. They are given information about any risks they face, or potential harm that could come to them, whether physical or psychological. They are also told about confidentiality or the person’s right not to be identified. Since most research is conducted with students taking introductory psychology courses, they have to be given the right to do something other than a research study to likely earn required credits for the class. This is called an alternative activity and could take the form of reading and summarizing a research article. The amount of time taken to do this should not exceed the amount of time the student would be expected to participate in a study. While participating. Participants are afforded the ability to withdraw or the person’s right to exit the study if any discomfort is experienced. After participating . Once their participation is over, participants should be debriefed or when the true purpose of the study is revealed and they are told where to go if they need assistance and how to reach the researcher if they have questions. So, can researchers deceive participants, or intentionally withhold the true purpose of the study from them? According to the APA, a minimal amount of deception is allowed.

Human research must be approved by an Institutional Review Board or IRB. It is the IRB that will determine whether the researcher is providing enough information for the participant to give consent that is truly informed, if debriefing is adequate, and if any deception is allowed or not. According to the Food and Drug Administration (FDA), “The purpose of IRB review is to assure, both in advance and by periodic review, that appropriate steps are taken to protect the rights and welfare of humans participating as subjects in the research. To accomplish this purpose, IRBs use a group process to review research protocols and related materials (e.g., informed consent documents and investigator brochures) to ensure the protection of the rights and welfare of human subjects of research.”

If you would like to learn more about how to use ethics in your research, please read:

To learn more about IRBs, please visit:

 Module Recap

That’s it. In Module 2 we discussed the process of research used when studying learning and behavior. We learned about the scientific method and its steps which are universally used in all sciences and social sciences. Our breakdown consisted of six steps but be advised that other authors could combine steps or separate some of the ones in this module. Still, the overall spirit is the same. In the experimental analysis of behavior, we do talk about making a causal statement in the form of an If-Then statement, or respectfully we discuss functional relationships and contingencies. We also define our terms clearly, objectively, and precisely through a behavioral definition. In terms of research designs, psychology uses five main ones and our investigation of learning and behavior focuses on three of those designs, with experiment and observation being the main two. Methods by which we collect data, the apparatus we use, and later, who our participants/subjects are, were discussed. The structure of a research article was outlined which is consistent across disciplines and we covered some typical dependent variables or measures used in the study of learning and behavior. These include errors, frequency, intensity, duration, latency, topography, rate, and fluency.

Armed with this information we begin to explore the experimental analysis of behavior by investigating elicited behaviors and more in Module 3. From this, we will move to a discussion of respondent and then operant conditioning and finally observational learning. Before closing out with complementary cognitive processes we will engage in an exercise to see how the three models complement one another and are not competing with each other.

2nd edition

Creative Commons License

Share This Book

  • Increase Font Size

learning objectives of research methodology

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

What is Research Methodology? Definition, Types, and Examples

learning objectives of research methodology

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.
  • Types of research methodology. Eduvoice website. Accessed August 16, 2023.
  • The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023.
  • Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023.
  • What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023.
  • What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023.
  • Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023.
  • Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023.
  • What is research methodology? Research and writing guides. Accessed August 14, 2023.
  • Ethical considerations. Business research methodology website. Accessed August 17, 2023.

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 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..., what is the importance of a concept paper..., how to write the first draft of a..., mla works cited page: format, template & examples, how to ace grant writing for research funding..., powerful academic phrases to improve your essay writing .

Starting Point Logo

  • Learning Objectives

Undergraduate Research in Biology

What is a Learning Objective?

Bloom (1956) suggests a six-stage hierarchy of cognitive competencies :

  • Knowledge - Students can collect and restate information.
  • Comprehension - Students can interpret and understand information.
  • Application - Students can apply information to solve problems.
  • Analysis - Students can organize and analyze information.
  • Synthesis - Students can create information from information.
  • Evaluation - Students can compare and assess information and ideas.

In an update to Bloom, Anderson and Krathwohl (2001) argue that students should be able to:

Undergraduate research experiences that engage students in the scientific method require and develop skills that can be mapped to Bloom's taxonomy and require students to do the tasks that Anderson and Krathwohl suggest they should be able to. And rather than offering isolated experiences with each of the steps of the scientific method, their relationships to one another become transparent, furthering one's understanding of what it means to "do science." Before going headfirst into an undergraduate research experience, you want to consider how directly and deeply you want students engaged in each step of the research process. This will help you determine where you place an undergraduate research experience in your curriculum or course, or if you do undergraduate research outside of the classroom instead (perhaps as part of a summer research experience).

As an example, if your key learning objectives are related to synthesis and evaluation, you may want the culminating project in your class to be a research paper and, if time is limited, you may want to supply students with the background literature and data for the project rather ask them to collect it themselves. If you have an opportunity to supervise the project as an independent study, though, you may have time to work on each of the six competencies more intensely and can involve your student just as seriously in tasks like reviewing the literature and collecting data as in evaluating evidence.

Krathwohl et al (1964) suggests a hierarchy of affective competencies , and you may consider forming some affective learning objectives as well. These competencies are:

  • Receiving - Students can notice and tolerate ideas.
  • Responding - Students can respond to ideas by investing in them in some way.
  • Valuing - Students can demonstrate to others that they value some ideas.
  • Organizing - Students can connect that value to existing ones.
  • Characterizing - Students' actions are consistent with the internalized values.

Developing Learning and Content Objectives

Here are some tips for writing cognitive learning objectives from the Higher Education Academy.

« Previous Page       Next Page »

  • Campus Living Laboratory
  • ConcepTests
  • Conceptual Models
  • Cooperative Learning
  • Earth History Approach
  • Experience-Based Environmental Projects
  • First Day of Class
  • Gallery Walks
  • Indoor Labs
  • Interactive Lecture Demonstrations
  • Interactive Lectures
  • Investigative Case Based Learning
  • Just in Time Teaching
  • Mathematical and Statistical Models
  • Peer Review
  • Role Playing
  • Service Learning
  • Socratic Questioning
  • Spreadsheets Across the Curriculum
  • Studio Teaching in the Geosciences
  • Teaching Urban Students
  • Teaching with Data
  • Teaching with GIS
  • Teaching with Google Earth
  • to see 28 more...
  • Teaching with Visualizations
  • Undergraduate Research
  • What is Undergraduate Research?
  • Why Use Undergraduate Research Experiences?
  • How To Engage Undergraduates in Research
  • Forms of Undergraduate Research Experiences
  • Determine Project Needs
  • Set Expectations
  • Structure the Critical Elements
  • Provide the Right Support
  • Assess the Experience
  • Further the Undergraduate Research Experience
  • References and Resources
  • Using an Earth System Approach


  • About this Site
  • Accessibility

Citing and Terms of Use

Material on this page is offered under a Creative Commons license unless otherwise noted below.

Show terms of use for text on this page »

Show terms of use for media on this page »

Undergraduate Research in Biology

  • None found in this page
  • Initial Publication Date: July 30, 2010
  • Short URL: What's This?

What's Your Question?

  • History & Geography
  • Science & Technology
  • Business & Finance
  • Pets & Animals

Crafting an Effective Research Proposal: Learning from Noteworthy PDF Examples

Research proposals are essential documents that outline the objectives, methodology, and significance of a research project. They serve as blueprints for researchers, guiding them through the process of conducting their investigations. While there are various formats and templates available, PDF examples of research proposals can be particularly beneficial in understanding the structure and content required for a successful proposal. In this article, we will explore some noteworthy PDF examples of research proposals and discuss what makes them effective.


The introduction section of a research proposal sets the stage for the study by providing background information on the topic and stating the research problem or question. A well-crafted introduction should capture the reader’s interest and clearly articulate the significance of the proposed research.

One example of an effective introduction in a research proposal is a study on climate change’s impact on coastal communities. The introduction outlines key statistics related to rising sea levels and emphasizes the vulnerability of coastal areas to environmental changes. It also highlights gaps in existing literature and explains how the proposed study aims to address these gaps.

Literature Review

The literature review section demonstrates that you have thoroughly researched existing studies related to your topic and have identified a gap that your research will fill. It showcases your ability to critically analyze previous work while highlighting its relevance to your own study.

An exemplary PDF example of a literature review within a research proposal is one that explores mental health interventions among college students. This section summarizes various studies on mental health issues faced by college students, including stress, anxiety, and depression. It then highlights gaps in current intervention strategies and proposes new approaches based on emerging evidence.


The methodology section describes how you will conduct your research, including details about data collection methods, sample selection criteria, and data analysis techniques. This section should demonstrate your ability to design a rigorous study that will yield reliable results.

A notable PDF example showcases a research proposal investigating the effects of a new teaching method on student performance in mathematics. The methodology section outlines the study’s design, including the selection of schools and participants, data collection through pre- and post-tests, and statistical analysis methods. It also discusses potential limitations and ethical considerations.

Significance and Expected Outcomes

The significance and expected outcomes section explains the potential impact of your research and how it contributes to existing knowledge in the field. It should highlight the practical implications of your findings and explain how they can be applied to real-world situations.

An informative PDF example of this section could be a research proposal on renewable energy sources. It discusses the significance of transitioning from fossil fuels to renewable energy for environmental sustainability. The proposal outlines expected outcomes such as reduced greenhouse gas emissions, increased energy efficiency, and long-term cost savings.

In conclusion, examining PDF examples of research proposals can provide valuable insights into crafting an effective proposal. By studying well-structured introductions, comprehensive literature reviews, detailed methodologies, and impactful significance sections, researchers can learn from successful proposals in their fields. These examples serve as guideposts for developing their own research proposals that are compelling, rigorous, and contribute meaningfully to their respective disciplines.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.


learning objectives of research methodology

Sample Learning Outcomes

Course outcome samples.

Carnegie Melon University, Eberly Center - learning objective samples

This website provides a collection of sample learning objectives across various disciplines, serving as a practical resource for educators seeking inspiration and guidance in creating clear and effective learning outcomes. These examples cover a wide range of subjects and demonstrate how to formulate specific, measurable, and achievable objectives that align with instructional goals. This resource is a valuable tool for instructors looking to enhance their course design and ensure their teaching aligns with intended learning outcomes.

Co-Curricular Outcome Samples


Study Site Homepage

  • Request new password
  • Create a new account

Research Methodology: Best Practices for Rigorous, Credible, and Impactful Research

Student resources, learning objectives.

By the end of this chapter, you will be able to do the following:

2.1 Explain why you should care about ethical research.

2.2 Compare differences between two research philosophies: utilitarian and deontological.

2.3 Follow ethical standards in planning the purpose and study.

2.4 Execute ethical research that considers the rights of participants.

2.5 Consider special ethical requirements when conducting research in field settings.

2.6 Follow ethical standards in reporting your results.

2.7 Implement ethical standards when conducting research with online participants.

2.8 Enforce research ethics to prevent misconduct.

2.9 Apply your own ethical beliefs when considering ethical challenges and dilemmas.

TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices

  • Open access
  • Published: 26 June 2024
  • Volume 21 , article number  121 , ( 2024 )

Cite this article

You have full access to this open access article

learning objectives of research methodology

  • Abdussalam Elhanashi 1 ,
  • Pierpaolo Dini 1 ,
  • Sergio Saponara 1 &
  • Qinghe Zheng 2  

136 Accesses

Explore all metrics

Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field.

Similar content being viewed by others

learning objectives of research methodology

Stroke detection in the brain using MRI and deep learning models

learning objectives of research methodology

Edge intelligence-assisted smart healthcare solution for health pandemic: a federated environment approach

learning objectives of research methodology

A comparative study of federated learning methods for COVID-19 detection

Avoid common mistakes on your manuscript.

1 Introduction

Stroke is a leading cause of death and disability worldwide, making early detection and diagnosis crucial for improving patient outcomes. Rapid and accurate identification of stroke symptoms, along with timely diagnostic testing, are essential for initiating appropriate treatment and minimizing long-term consequences. This introduction will provide an overview of stroke detection and diagnosis, including the signs and symptoms of stroke, diagnostic tools and techniques, and the importance of early intervention [ 1 , 2 , 3 , 4 ]. Stroke, also known as a cerebrovascular accident, occurs when the blood supply to the brain is interrupted, leading to damage or death of brain cells. There are two main types of strokes: ischemic stroke, caused by a blockage in a blood vessel supplying the brain, and hemorrhagic stroke, caused by bleeding into the brain. The common signs and symptoms of stroke can be remembered using the acronym FAST: face drooping, arm weakness, speech difficulty, time to call emergency services. Other symptoms include sudden numbness or weakness in the face, arm, or leg, especially on one side of the body; sudden confusion, trouble speaking or understanding speech; sudden trouble seeing in one or both eyes; sudden trouble walking, dizziness, loss of balance or coordination; and sudden severe headache with no known cause [ 5 ]. When a patient presents with symptoms suggestive of a stroke, healthcare providers must act quickly to confirm the diagnosis and determine the type of stroke in order to initiate appropriate treatment. Several diagnostic tools and techniques are used in the evaluation of stroke patients. These include imaging studies such as computed tomography (CT) scans, magnetic resonance imaging (MRI), and angiography to visualize the brain to assess electrical activity in the brain; and blood tests to evaluate for potential causes of stroke such as high cholesterol, clotting disorders, or infection [ 6 ]. Early intervention is critical in the management of stroke as it can help minimize brain damage and improve patient outcomes. The “time is brain” concept emphasizes the importance of rapid assessment and treatment to preserve brain function. For ischemic strokes, timely administration of thrombolytic therapy (such as tissue plasminogen activator) or endovascular clot retrieval can help restore blood flow to the affected area of the brain. In cases of hemorrhagic stroke, prompt neurosurgical intervention would be necessary to control bleeding and reduce pressure on the brain. Despite advancements in stroke diagnosis, current diagnostic tools, such as CT and MRI, often fail to detect minor strokes and differentiate between ischemic and hemorrhagic strokes in the acute phase. There is also a lack of portable, rapid, and cost-effective diagnostic devices for use in pre-hospital settings, where early detection is crucial. Existing biomarkers lack specificity and sensitivity, limiting their clinical utility. The role of telemedicine in stroke diagnosis is underexplored, especially in remote areas with limited access to advanced medical facilities. It is necessary to bridge these gaps and improve diagnostic accuracy and timeliness, ultimately enhancing patient outcomes and reducing the burden on healthcare systems. Therefore, accurate and timely diagnosis is essential for guiding appropriate interventions and improving patient prognosis [ 7 , 8 ]. In recent years, the rapid advancement of artificial intelligence (AI) and deep learning technologies has revolutionized various industries, including healthcare. These cutting-edge technologies have shown great promise in transforming the way medical diagnostics are conducted and in enhancing the delivery of e-healthcare services. By leveraging AI and deep learning, healthcare professionals can harness the power of data-driven insights to improve diagnostic accuracy, optimize treatment plans, and provide personalized care to patients. This introduction will explore the utilization of AI and deep learning in diagnostics and e-healthcare, highlighting their potential benefits and implications [ 9 ]. The integration of AI in healthcare has significantly impacted the way medical professionals approach diagnosis and treatment. AI algorithms have demonstrated the ability to analyze complex medical data, such as imaging scans, genetic information, and patient records, with remarkable speed and accuracy. This has led to the development of advanced diagnostic tools that can assist clinicians in detecting and predicting various medical conditions, ranging from cancer and cardiovascular diseases to neurological disorders [ 10 ].

The main contributions of this research are as the following:

The research introduces a novel approach to addressing the urgent need for timely stroke diagnosis based on facial paralysis acquired from the images by presenting a real-time stroke detection system. The proposed approach automates the process of stroke detection, potentially reducing the time required for diagnosis and treatment initiation.

This study emphasizes the importance of comprehensive datasets comprising both stroke and non-stroke faces of individuals for effective model training. By utilizing the YOLOv8 models, the study leverages their advanced architectural improvements to enhance real-time processing capabilities. Extensive training on diverse datasets enables the model to discern intricate patterns and features associated with strokes and non-stroke cases, thereby improving its accuracy and responsiveness in real-time applications. The improvements in YOLOv8, such as optimized network structures and enhanced computational efficiency, are crucial for meeting the real-time requirements of stroke detection systems.

This research adopts a federated learning technique, allowing the model to learn from data distributed across various clients without compromising patient privacy. By decentralizing the training process, sensitive patient information remains localized, addressing privacy concerns while maintaining model performance.

Implementation of the proposed methodology on NVIDIA platforms showcases the practical feasibility of real-time stroke detection. By harnessing advanced GPU capabilities, the system demonstrates remarkable speed and accuracy, revolutionizing stroke diagnosis and treatment with its efficient analysis.

The rest of the paper is organized as follows: Sect.  2 presents the related work; Sect.  3 presents the proposed algorithm and design methodology; Sect.  4 explores the experimental results and discussion. Section  5 describes real-time implementation on edge NVIDIA platforms. Finally, conclusions are drawn in Sect.  6 .

2 Related work

Imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of stroke. CT scans are commonly used to rule out hemorrhagic stroke, while MRI is more sensitive in detecting ischemic stroke, aiding in the differentiation from non-stroke conditions. Deep learning methodologies have demonstrated substantial efficacy in the fields of image and video recognition, revolutionizing the way that computers interpret visual data [ 11 , 12 , 13 ]. Deep learning has revolutionized the fields of classification and modulation, offering robust performance gains over traditional methods [ 14 ]. Advanced imaging techniques such as perfusion imaging and diffusion-weighted imaging have shown promise in enhancing the accuracy of stroke diagnosis [ 15 ].

Utilizing deep learning for medical images has revolutionized diagnostic accuracy and patient care. Deep learning, particularly through convolutional neural networks (CNNs), has shown exceptional promise in various medical imaging applications, including ophthalmology, respiratory imaging, and breast disease. Research has documented instances of medical conditions such diabetic retinopathy, lung nodules, and breast cancer being diagnosed through the use of medical imaging techniques, including retinal fundus photography, CT scans, and mammograms.. Despite these advancements, challenges persist, such as the need for robust quantitative imaging biomarkers, concerns about data quality and bias, and the black-box nature of deep learning models. Efforts are underway to address these challenges and enhance the clinical impact of deep learning in medical imaging [ 16 , 17 , 18 ].

Deep learning models have the capacity to forecast patient outcomes, tailor treatment strategies, and streamline administrative operations within telemedicine frameworks. By incorporating deep learning into telemedicine services, healthcare professionals are enabled to deliver care that is both more efficient and economical, effectively overcoming the challenges posed by geographic limitations. This approach ensures that patients receive high-quality care tailored to their specific needs, regardless of where they are located, making healthcare more accessible and personalized [ 19 , 20 , 21 ]. Real-time deep learning has emerged as a powerful tool for enhancing diagnostic accuracy and efficiency. Deep learning models can analyze complex medical data such as imaging scans, genetic information, and patient records to aid in the identification of diseases and conditions. Real-time processing capabilities enable the analysis of incoming data streams, facilitating rapid decision-making and timely interventions [ 22 , 23 , 24 ].

Biomarkers have garnered significant attention as potential tools for differentiating between stroke and non-stroke conditions. For instance, elevated levels of specific biomarkers such as brain natriuretic peptide (BNP) and D-dimer have been associated with an increased risk of stroke, serving as valuable indicators in the diagnostic process. Furthermore, ongoing research is exploring the utility of novel biomarkers in discriminating between different stroke subtypes and non-stroke etiologies [ 25 ]. Several clinical scales and scores have been developed to aid in the rapid assessment and differentiation of stroke from non-stroke conditions. The National Institutes of Health Stroke Scale (NIHSS) is widely used to quantify stroke severity and guide treatment decisions, while the Cincinnati Prehospital Stroke Scale (CPSS) enables prehospital providers to identify potential stroke cases with high specificity [ 26 ]. These tools contribute to streamlining the diagnostic process and facilitating intervention. The emergence of telemedicine has revolutionized the delivery of acute stroke care, allowing for remote assessment and diagnosis. TeleStroke networks leverage audiovisual communication to connect stroke specialists with healthcare facilities, enabling timely evaluation of patients presenting with stroke symptoms [ 27 ]. This approach has proven instrumental in extending expert guidance to underserved areas, ultimately improving access to accurate diagnosis and treatment.

Advancements in machine learning and deep learning have paved the way for innovative diagnostic tools in the realm of stroke care. AI algorithms trained on large datasets can analyze medical images and clinical data to differentiate between stroke and non-stroke conditions with high accuracy [ 28 ]. Moreover, AI-based decision support systems hold potential for enhancing the efficiency and precision of stroke diagnosis, heralding a new era of personalized medicine. Exploration of genomic and proteomic profiles has unveiled valuable insights into the pathophysiology of stroke and non-stroke conditions. Genetic variations have been linked to an increased susceptibility to certain types of strokes, underscoring the potential utility of genetic testing in risk stratification and differential diagnosis. Furthermore, proteomic profiling offers a window into the molecular signatures associated with different disease states, presenting opportunities for developing novel diagnostic biomarkers [ 29 ].

3 Proposed algorithm design methodology

In the experimental setup for real-time stroke detection, a combination of advanced technologies of deep learning is utilized to improve accuracy and maintain privacy. We examined several architectures of YOLOv8 models, which is a cutting-edge neural network architecture targeted for real-time object detection. The proposed models are utilized to identify stroke based on the facial paralysis and non-stroke condition from the faces of the individuals in the images. In this research, federated learning, a decentralized training method, is employed to train the model collaboratively across multiple clients while ensuring the confidentiality of sensitive proposed data. The setup involves a central server overseeing the training process and coordinating interactions with three different clients, each providing valuable data to enhance the model’s performance. To optimize the system’s capabilities, NVIDIA platforms are utilized to assess the deployed model’s inference. We expedite the assessment of proposed models, facilitating real-time stroke detection and diagnosis. This ensures that the system can efficiently process the recognition of the faces of individuals, promptly identifying signs of stroke and enabling timely medical intervention. Figure  1 illustrates the proposed framework for utilizing real-time deep learning models for stroke and non-stroke detection.

figure 1

The proposed system for utilizing real-time deep learning model and federated learning for stroke and non-stroke detection

3.1 Dataset collection and labeling

The data used in this research consists of two different categories of images. One group includes individuals diagnosed with acute stroke, while the other group of individuals without such a diagnosis. In total, the dataset comprises 3745 images, offering a substantial pool for analysis. Table 1 provides the main description for the proposed dataset of stroke/non-stroke.

To enhance the efficacy of the model, various data augmentation methods were implemented. These techniques serve to expand the dataset by generating modified versions of the existing images. Specifically, augmentation involved operations such as flipping, rotating, and scaling the images. By applying these transformations, the dataset becomes more diverse and resilient, mimicking a broader range of real-world scenarios. By augmenting the dataset in this manner, the model’s accuracy is expected to improve. The varied representations provided by augmented data can help the model generalize better to unseen instances, thereby enhancing its performance when applied to classify images of individuals with or without acute stroke. In essence, data augmentation enriches the dataset, enabling the model to learn from a wider spectrum of image variations and complexities. Figure  2 illustrates the distribution for the two classes for the proposed dataset. These visual aids are invaluable for evaluating model performance, detecting class imbalances or dependencies, and guiding decisions related to model refinement and data handling strategies. Class frequency pertains to the distribution of data points among different categories or classes within a dataset. When considering normalized height and width space for stroke and non-stroke datasets, it indicates the number of data points falling within specific height and width ranges. Typically, labels denote the assigned categories or classes for each data point, distinguishing between stroke and non-stroke instances in relevant datasets. A height-width graph typically illustrates data point distributions based on their height and width values, often presented as a scatter plot with height on the x -axis and width on the y -axis. Alternatively, an x – y graph can visualize the data, particularly if additional features beyond height and width are involved, with each data point representing a point in a multi-dimensional space where each axis corresponds to a different feature.

figure 2

Visualization of class distribution in the proposed dataset, with the upper bar graph showing a higher number of instances for ‘nostroke’ than ‘stroke’, and the lower scatter plots displaying the density and spread of feature coordinates ( x , y ) and bounding box dimensions (width, height) for each class

3.2 The proposed object detection architecture

The YOLOv8 architectures mark the forefront of the YOLO series, specializing in the real-time detection of individual faces. YOLOv8 architecture excels in real-time object detection due to its faster speed, increased accuracy, and anchor-free design. This model leverages computer vision, neural networks, deep learning, and image processing techniques to deliver outstanding object recognition capabilities. YOLOv8’s multi-scale prediction and enhanced backbone network further boost its object detection performance. Its ability to handle diverse datasets and ease of training make it a versatile tool for various applications. With faster and more precise results, YOLOv8 stands out as a powerful solution in the fields of artificial intelligence and computer vision. It represents the pinnacle of progress in the YOLO object detector lineage, offering unparalleled precision and speed. YOLOv8 models build upon the legacy of their forerunners by integrating groundbreaking features and enhancements, making it the go-to choose for a broad spectrum of object detection challenges across different settings. It leverages advanced backbone and neck architectures to improve feature extraction significantly, thus boosting the overall efficiency of object detection. With the introduction of an anchor-free split Ultralytics head, it achieves remarkable accuracy improvements and streamlines the detection workflow, outperforming conventional anchor-based methods. YOLOv8 strikes a perfect harmony between speed and accuracy, making it ideal for real-time detection needs in various application areas. It provides a wide range of pre-trained models, facilitating the selection of the most suitable model for specific requirements.

As the most compact variant in the YOLOv8 series, YOLOv8 is engineered for a wide array of detection tasks, from simple object identification to more complex challenges such as instance segmentation, detection of key points, orientation of objects, and categorization. The architecture is a refined version of the CSPDarknet53, incorporating 53 convolutional layers and cross-stage partial connections to bolster the flow of information between layers. The YOLOv8 head consists of several convolutional layers leading to fully connected layers, tasked with predicting bounding boxes, abjectness scores, and class probabilities for identified objects. A distinct feature of YOLOv8 is its self-attention mechanism within the head, enabling the model to focus on specific parts of an image and adjust feature significance according to the relevance of the task. Remarkably adept at detecting objects across multiple scales, it uses a feature pyramid network to identify objects of diverse sizes and scales within an image effectively. This multi-layered pyramid facilitates the detection of both large and small objects, underscoring YOLOv8’s adaptability and thoroughness in object detection. The architecture and capabilities of YOLOv8 are visually depicted in Fig.  3 .

figure 3

The YOLOv8 architecture incorporates a modified CSPDarknet53 as its core framework. It introduces the C2f module as a replacement for the CSPLayer found in YOLOv5, enhancing its structure. To speed up the computation process, it employs a spatial pyramid pooling fast (SPPF) layer, which consolidates features into a uniform-size map [ 15 ]

In the architecture’s initial setup, the 6 × 6 convolution in the stem is replaced with a 3 × 3 convolution, altering the primary building block, and substituting C2f for C3 as shown in Fig.  4 . A summary of the module is provided in the accompanying image, indicating “f” as the feature count, “e” as the expansion rate, and CBS representing a structure that includes a Convolution, Batch Normalization, and a SiLU operation. In the C2f configuration, outputs from all Bottleneck stages (which consist of two 3 × 3 convolutions connected by residuals and are elaborately termed) are merged. Conversely, C3 utilizes only the output from the final Bottleneck stage. This Bottleneck component mirrors the one found in YOLOv5, with the exception that the initial convolution’s kernel size has been altered from 1 × 1 to 3 × 3. This change demonstrates that YOLOv8 is gradually returning to the use of the ResNet block.

figure 4

The C2f layer for YOLOv8 architecture

Within the network’s neck section, feature concatenation occurs directly, bypassing the need for uniform channel dimensions. This approach serves to diminish both the parameter count and the overall tensor sizes, streamlining the network’s complexity.

Table 2 presents a comparison among different versions of YOLOv8: YOLOv8x, YOLOv8l, YOLOv8m, YOLOv8s, and YOLOv8n, which vary in complexity and performance.

All YOLOv8 versions are designed to process images with an input size of 640 pixels, maintaining consistency in image resolution across models. The complexity and capacity to learn from data are indicated by the number of parameters (Params), measured in millions (M), and the computational workload during inference is represented by floating-point operations per second (FLOPs), measured in billions (B). YOLOv8x, the most complex model, has 68.2 million parameters and requires 257.8 billion FLOPs, which has the highest capacity for learning and computational complexity. Conversely, YOLOv8n is the simplest with only 3.2 million parameters and 8.7 billion FLOPs, indicating it is the least complex and has the lowest computational demand. This gradient of complexity and performance from YOLOv8n to YOLOv8x allows for a range of applications, from lightweight deployments to scenarios requiring high accuracy and computational resources.

3.3 Federated learning

In this research, federated learning (FL) was employed to train the proposed model. FL is a decentralized machine learning method enabling multiple clients to jointly train a model without sharing raw data. Our objective was to emulate a FL setup with 3 clients operating on a single machine, where both the server and all 3 clients reside, sharing CPU, GPU, and memory resources. With 3 clients, we maintain 3 instances of FlowerClient in memory. Executing this on a single machine can strain available memory resources, even if only a subset of clients engages in a FL round. Leveraging the FLOWER platform, clients and the server with overlapping data contribute to model training. Notably, FLOWER boasts an efficient communication protocol, transmitting only model updates, not raw data, thereby significantly reducing communication overhead. This feature renders it ideal for scenarios with limited bandwidth or high-latency connections. Moreover, FLOWER ensures that raw data remains on local devices, with only model updates, in the form of weight differentials, shared with the central server, safeguarding sensitive information against data breaches and unauthorized access.

Our proposed approach implements an FL system for object detection utilizing the YOLOv8n model, comprising 3 clients and a server. Each client is equipped to train a YOLOvn8 on local data, employing the Ultralytics library, with the ( DetectionTrainer ) class managing training specifics. Each client possesses its dataset located in the “data/clients/{ idx } directory, periodically training its local model and computing metrics such as mAP, recall, precision, and loss. Subsequently, during the FL process, the client transmits its model parameters to the server. The server orchestrates the FL process within the Flower framework, aggregating model updates from the 3 clients utilizing the Federated Averaging ( FedAvg ) strategy provided by the flwr library. The FL process iterates for a specified number of epochs, with the server engaging in communication with the 3 clients. Following each round, the server aggregates model updates, evaluates the federated model, and logs the results in the “results” directory. Its primary responsibility entails overseeing and coordinating the training process, supervising the participation of the 3 clients in the FL process. These clients, structurally similar, differ mainly in the data they possess, each holding data pertaining to distinct classes of stroke and non-stroke cases. The following four phases outline the steps in a distributed deep learning workflow: from receiving and updating models to local training, evaluation, and secure transmission of improved data back to the server, ensuring a refined and robust predictive performance:

Receive updated model : Clients establish a secure connection with the server, ensuring data integrity and privacy, through which the server transmits the latest version of the global machine learning model, utilizing efficient communication protocols to minimize latency.

Local model training : Clients preprocess their local datasets, addressing outliers, missing values, and performing feature scaling as required to enhance data quality. Leveraging their computational resources, clients undertake model training locally, employing fine-tuning such as mini-batch gradient descent or federated learning to effectively handle the datasets

Model evaluation : After completing the model training phase, clients proceed to conduct thorough evaluations on the trained model, employing diverse metrics including accuracy, precision, recall, and F 1 score, customized to suit their particular application requirements. To ensure robustness and reliability, clients meticulously analyze the model’s predictions on their local datasets, conducting detailed error analysis to pinpoint instances of incorrect predictions and iteratively enhance the training process, thereby refining the model’s predictive capabilities and overall performance.

Send updated weights : Upon completion of local model training and evaluation, clients securely transmit the updated model weights back to the server for aggregation, ensuring data confidentiality. The server aggregates the received model updates from multiple clients using techniques such as federated averaging while synthesizing a new global model. Subsequently, the server conducts additional validation checks on the aggregated model to verify its integrity and stability before deploying it for further inference or subsequent rounds of training.

3.4 Training

The training procedure for YOLOv8 models entails optimizing several hyperparameters to achieve effective object detection. Table 3 illustrates the hyperparameters for tuning YOLOv8 models. The image size, set to 640 pixels, determines the dimensions of input images, crucial for feature extraction and localization. Batch size, specified as 32 defines the number of samples processed before updating the model’s parameters, affecting computational efficiency and convergence stability. With 30 epochs, the training duration is partitioned into iterations over the entire dataset, refining the model’s performance iteratively. Mosaic, at 0.8, incorporates a data augmentation technique blending multiple images to enhance generalization and robustness. Mixup, assigned a value of 0.2, further diversifies the dataset by linearly interpolating between pairs of images and their labels, augmenting training data diversity. The learning rate, set at 10 –5 , regulates the step size in updating model parameters, influencing convergence speed and optimization quality. Utilizing the AdamW optimizer, the training algorithm adjusts model weights to minimize the defined loss function effectively. Lastly, the cache parameter, set as False, controls whether to cache datasets in memory, affecting training speed and memory consumption. Overall, fine-tuning these hyperparameters orchestrates the training process to yield a YOLOv8 model optimized for object detection tasks. All the experiments have been conducted using AWS EC2 G4 instances equipped with 8 NVIDIA T4 GPUs, featuring 96 CPUs and a network bandwidth of 100 Gbps. Table 3 shows the hyperparameters for YOLOv8 training and tuning.

4 Experiment results and discussion

4.1 evaluation matrices.

To evaluate the proposed models, we meticulously performed assessments focusing on key metrics: recall, precision, F 1 score, and mean Average Precision (mAP). Recall, the measure of a model’s ability to identify all relevant instances, is crucial for understanding its sensitivity. Precision, on the other hand, evaluates how many of the identified instances are relevant, highlighting the model’s accuracy. The F 1 score harmonizes recall and precision, providing a single metric to assess a model’s balance between sensitivity and accuracy. Lastly, mAP offers a comprehensive evaluation of the model’s performance across different thresholds, encapsulating its ability to rank instances correctly. For each metric, equations play a pivotal role in quantification. Recall is calculated as the ratio of true positives to the sum of true positives and false negatives, precision as the ratio of true positives to the sum of true positives and false positives, F 1 score as the harmonic mean of precision and recall, and mAP as the average of the precision scores at different thresholds, offering a nuanced view of the model’s performance. mAP evaluates the balance between precision and recall, providing a comprehensive view of an algorithm’s effectiveness across various thresholds. Refer to Eqs. ( 1 – 5 ) for details, where \( k \) represents the number of queries and \(AP_ i \) denotes the average precision for a specific query \( i \). These metrics collectively offer a robust framework for evaluating the proposed models, ensuring a comprehensive analysis of their effectiveness:

4.2 Results of the proposed YOLOv8 models

Figure  5 illustrates the performance for the five YOLOv8 models across four metrics which include precision, recall, F 1 score, and mean average precision (mAP). All models demonstrate a rapid improvement in the first few epochs, indicating fast learning in the early stages. For YOLOv8l and YOLOv8m, precision, recall, and F 1 scores plateau near the maximum value of 1.0, which illustrates that these models are very accurate and maintain a strong balance between precision and recall. Their mAP, a measure of precision across different recall levels, also levels off high, indicating these models are consistently reliable across both classes. The YOLOv8n figures show a more dramatic learning curve with significant improvements in all metrics until stabilizing. However, the values for all metrics are lower than in the other models, especially the mAP, which illustrates this model has a lower overall object detection performance. For YOLOv8s, the figure indicates a similar trend to YOLOv8l and YOLOv8m, with all metrics reaching and maintaining high values, implying it is quite effective and stable. Lastly, YOLOv8x displays a high performance with all metrics, closely resembling the trends seen in YOLOv8l and YOLOv8m, signifying a robust model with high precision and recall.

figure 5

The performance of YOLOv8 models verses other YOLO architectures, which illustrates recall, precision, mAP, and F 1: a YOLOv8n, b YOLOv8l, c YOLOv8m, d YOLOv8s, e YOLOv8x, f YOLOv5n, g YOLOv7, h YOLOv7x

Overall, except for YOLOv8n which appears slightly less effective, the models quickly achieve and maintain high precision, recall, and F 1 scores, with mAP also indicating strong predictive power across different thresholds. YOLOv8n is a simplified model prioritizing speed and efficiency, with fewer parameters and computational needs, leading to lower accuracy compared to more complex models such as YOLOv8x, YOLOv8l, YOLOv8m, and YOLOv8s. These larger models, with more parameters and higher computational demands, are more capable of complex pattern recognition, resulting in better performance on key metrics such as precision and recall. While YOLOv8n is beneficial for fast processing in resource-constrained environments, the larger models are preferable for tasks requiring high accuracy where resources are abundant.

In this research, we have compared the proposed architectures (YOLOv8 models) with YOLOv5n, YOLOv7, and YOLOv7x, all trained for the same number of epochs (30) to ensure a fair comparison. The experimental results indicated that YOLOv5n performed more effectively than both YOLOv7 and YOLOv7x. However, the YOLOv8 models demonstrated superior performance over the previously mentioned architectures, excelling in multiple key metrics including mean Average Precision (mAP), Recall, Precision, and F 1 score, see Fig.  5 . This enhanced performance underscores the advancements in the YOLOv8 models, making them a suitable choice for stroke and non-stroke application.

4.3 Results of federated learning

Federated learning is a machine learning approach where a model is trained across multiple decentralized edge devices (clients) without sharing data, improving privacy, and reducing data centralization. The server coordinates the process, aggregating the client models to form a global model. Here is a detailed analysis of each performance metric across the server and clients: Mean Average Precision (mAP): the first figure shows the mAP metric, which is a measure of precision across recall levels and is commonly used in ranking tasks, object detection, and information retrieval. It appears that the server and all clients quickly improve performance within the first five rounds, indicating a rapid learning rate in the early stages of training. After around five rounds, the server and client 0 show stable performance, while client 1 and client 2 have some variability but generally maintain a high mAP. This could imply that client 1 and client 2 are encountering more complex and variable data than client 0, leading to slight fluctuations in performance. Precision: the second figure measures precision, the ratio of true positive predictions to the total number of positive predictions. The precision for all entities climbs sharply within the initial rounds and then plateaus, indicating that the number of false positives did not significantly increase as more instances were classified as positives, which is good. The server maintains the highest precision, which shows that the aggregated model is accurate in its predictions compared to individual clients. Recall: the third figure represents recall, which is the ratio of true positive predictions to the total number of actual positives. Recall also increases rapidly at the beginning, which illustrates that the model’s has the ability to identify relevant instances with initial training rounds. The server shows the highest recall, indicating it is better at detecting true positives than the clients. Across all metrics, the performance of the server is consistently equal to and better than the clients, which is expected in federated learning because the server model benefits from the aggregated updates from all clients. Key observations: rapid learning: all metrics improve significantly in the initial rounds of training, indicating the clients’ updates are meaningful and improve the global model quickly. Stabilization: after the initial improvement, all metrics tend to stabilize, with the server typically showing the least variance, which indicates the aggregated model’s robustness. Client variability: there is some variability in the clients’ performance metrics, particularly in mAP and precision. This could be due to differences in local data distributions, that some clients have data that is not as well represented in the global model. Convergence: all clients seem to converge towards the server’s performance, especially in recall, which shows that over time, client-specific models are becoming more aligned with the aggregated server model, which is the desired outcome in federated learning for consistency and fairness. Figure  6 shows the performance which includes mean Average Precision (mAP), precision, and recall scores across multiple rounds of training for 3 clients and server in a federated learning system.

figure 6

The performance evolution which include a mean Average Precision (mAP), b precision, and c recall scores across multiple rounds of training for 3 clients and server in a federated learning system

5 Real-time edge execution techniques

NVIDIA’s Jetson platform has a series of powerful and efficient AI computing devices designed to bring deep learning to the edge. The series encompasses a range of hardware tailored to various performance needs and power constraints, making AI accessible across a broad spectrum of applications, from robotics to embedded systems. At the entry-level, the Jetson Nano stands out as a compact yet capable device, providing a cost-effective solution for projects that require AI but are constrained by power and space. Scaling up, the Jetson Xavier AGX presents a significant leap in performance with its advanced GPU architecture and AI accelerators, aimed at more demanding tasks that require higher computational throughput. At the pinnacle is the Jetson Orin, the latest addition to the series, which represents the cutting-edge in edge AI performance. It offers breakthrough capabilities for autonomous machines, delivering the highest performance and energy efficiency for AI, robotics, and other compute-intensive tasks. Each of these platforms is engineered to provide flexibility, scalability, and ease-of-integration, all crucial for innovators looking to pioneer the next generation of intelligent machines. The graph compares the NVIDIA Jetson Nano, Jetson AGX Xavier, and Jetson Orin platforms, focusing on CUDA cores and AI performance (TOPS). The bar plot shows a significant increase in CUDA cores across the models, indicating enhanced parallel computing capability. The line plot illustrates AI performance, where Jetson Orin demonstrates a substantial leap, reaching up to 200 TOPS, compared to Jetson Nano’s 0.5 TOPS and Jetson AGX Xavier’s 32 TOPS. This progression underscores the evolution in processing power and AI capabilities, with Jetson Orin offering vastly superior performance for demanding AI applications and computational tasks. Figure  7 presents a comparative analysis of NVIDIA Jetson Platforms, displaying the count of CUDA cores and the AI performance capacity (quantified in TOPS—tera operations per second) among three variants: Jetson Nano, Jetson Xavier AGX, and Jetson Orin AGX.

figure 7

A comparative analysis of NVIDIA Jetson Platforms, which illustrates number of CUDA cores and AI performance (measured in TOPS—tera operations per second) across three models: Jetson Nano, Jetson Xavier AGX, and Jetson Orin AGX

Figure  8 illustrates a real-time detection frames per second (FPS) comparison among different YOLOv8 models running on three NVIDIA Jetson platforms: Nano, Xavier AGX, and Orin. Across all models, the Jetson Xavier AGX and Jetson Orin outperform the Jetson Nano in terms of FPS, which illustrates that they have more processing power and are more efficient in handling these tasks. We compared the performance of the proposed approach on NVIDIA devices with and without the utilization of CUDA. This comparison underscores the critical importance of using CUDA and GPU acceleration for executing YOLOv8 models to meet real-time requirements. CUDA significantly enhances the speed, enabling the YOLOv8 models to process data at a much faster rate. By leveraging the parallel processing power of GPUs, CUDA reduces the execution time, making it possible to achieve real-time object detection and analysis. From Fig.  8 , it is evident that YOLOv8n achieves the highest FPS on all three platforms, indicating it is the fastest model for real-time detection. This aligns with its structure of being a streamlined model that prioritizes speed and efficiency. As a result, despite having lower accuracy metrics, YOLOv8n is highly suitable for applications where real-time processing is crucial. In contrast, YOLOv8x, which is the most computationally intensive model, exhibits the lowest FPS across all platforms. This illustrates that while YOLOv8x has the best accuracy metrics, its complexity makes it less suitable for scenarios requiring high-speed object detection. The other models, YOLOv8l, YOLOv8m, and YOLOv8s, show a gradation of FPS performance, with the more complex models (e.g., YOLOv8l) being slower than the simpler ones (e.g., YOLOv8s). The performance on the Jetson Xavier AGX and Jetson Orin is significantly better than on the Jetson Nano, which is the least powerful of the three hardware platforms.

figure 8

The frames per second (FPS) performance for different YOLO models (YOLOv8n, YOLOv8l, YOLOv8m, YOLOv8s, YOLOv8x) across three NVIDIA Jetson devices (Nano, Xavier, Orin) with and without CUDA acceleration

The graph highlights the trade-off between accuracy and speed among the YOLOv8 models and underscores the importance of choosing the right model and hardware platform based on the specific requirements of the application. Real-time detection of strokes using the proposed deep learning object detection models is crucial for timely medical intervention and improved patient outcomes. The proposed models, in particular YOLOv8n architecture, are designed to quickly and accurately identify stroke indicators from facial features. By processing data in real time, the models can provide immediate feedback to healthcare professionals, enabling rapid decision-making. This rapid analysis is essential in emergency situations where every second counts. The ability to differentiate between stroke and non-stroke cases on the spot helps in prioritizing treatment, reducing the risk of long-term damage, and potentially saving lives.

Power consumption measurement has been carried across the three NVIDIA platforms. The Jetson Nano shows remarkable energy efficiency with consistently low power usage across all YOLO models, making it an ideal candidate for power-sensitive applications. In contrast, the Jetson Orin, while being the most power-hungry, presumably offers superior computational performance, a trade-off that might be justified for demanding tasks where processing speed is crucial. The Jetson Xavier AGX stands in between a moderate balance between power and performance. Notably, the progression from the YOLOv8l to YOLOv8m and further models exhibits a trend of increasing power requirements, which could indicate more computationally intensive processes and greater exploitation of the hardware’s capabilities. This increasing trend is most pronounced with the Jetson Orin, which shows a substantial leap in power consumption for the latest models, signifying its capability to unleash the full potential of advanced YOLO models, albeit at a greater energy cost. This data is vital for selecting the appropriate hardware for proposed models where both the performance and the power budget are to be optimized. Figure  9 illustrates the power consumption across various YOLO models when deployed on NVIDIA’s Jetson hardware platforms: Nano, Xavier AGX, and Orin.

figure 9

Comparison of power consumption across NVIDIA Jetson platforms (Nano, Xavier AGX, Orin) running different YOLOv8 models, demonstrating the varying energy requirements of each model on the respective hardware

The temperature data provided for different YOLOv8 models across NVIDIA Jetson platforms Nano, Xavier AGX, and Orin show some interesting trends: Starting with the Jetson Nano, the temperatures for the YOLO models are higher compared to the other two platforms, with YOLOv8x reaching the highest temperature at 54 °C. This demonstrates that while the Nano is power-efficient, as seen in the previous analysis, it does not dissipate heat as effectively. This is due to a less robust cooling system and lower thermal capacity.

However, there is a clear downward trend in temperature from YOLOv8x to YOLOv8n, with the latter running at a cooler 40 °C. This indicates that the newer models are more efficient. The Jetson Xavier AGX shows a consistently cooler operational temperature across all YOLO models when compared to the Jetson Nano, which shows better thermal management. The temperatures range from 43 °C for YOLOv8x to 37 °C for YOLOv8n. The lower temperatures could also imply that the Xavier AGX is more capable of handling the computational demands of the YOLO models efficiently, thereby generating less heat. The Jetson Orin records temperatures similar to the Xavier AGX but is marginally cooler across all models except for YOLOv8s, where it equals the AGX at 39 °C. This could be due to the efficient power usage that translates into less heat output. YOLOv8n runs coolest on the Orin at 36 °C. Tables 4 , 5 and 6 provide operation temperature for the executed YOLOv8 model on NVIDIA platforms.

6 Conclusion

This research presented an innovative, real-time stroke detection system by utilization deep learning and federated learning to offer a solution that is both efficient and privacy conscious. By combining the advanced analytical capabilities of deep learning, particularly through the use of YOLOv8 models, with the privacy-preserving features of federated learning, we have developed a system that stands to significantly improve the timeliness and accuracy of stroke detection. This approach mitigates the limitations of traditional stroke detection methods, such as the reliance on manual interpretation which is slow and error-prone, and addresses the challenges of requiring extensive, diverse datasets and navigating privacy concerns. YOLOv8 models have demonstrated promising results in terms of mean Average Precision (mAP), recall, and precision. However, the YOLOv8n model appears to be slightly less effective. Designed to prioritize speed and efficiency, YOLOv8n is a simplified version with fewer parameters and reduced computational requirements. As a consequence, it achieves lower accuracy compared to the other models. The utilization of NVIDIA platforms for their superior GPU capabilities has enabled real-time processing and analysis, ensuring that the proposed system can function effectively in a clinical setting. The YOLOv8n model has been recognized for its superior real-time detection performance compared to earlier versions. This improvement is mainly because of its lightweight architecture. YOLOv8 models, while excelling in real-time detection on NVIDIA devices, demand high power consumption. This requirement limits their practicality for low-cost embedded devices, highlighting a significant challenge in balancing computational efficiency and performance in resource-constrained environments. Further optimization is needed to make these models viable for broader applications. The implications of this research are profound, offering a pathway to enhancing patient outcomes by enabling healthcare professionals to make quicker, more informed decisions. Further to our exploration, we will convert the proposed approach to TensorFlow Lite to optimize the architecture. This will enhance our results for real-time detection and reduce the demand for power consumption. In addition to that, we will continue further research and experimentation with the most recent object detection models, such as YOLOv9, which has been released recently.

Data availability

No datasets were generated or analyzed during the current study.

Powers, W.J., Rabinstein, A.A., Ackerson, T., et al.: Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 50 (12), e344–e418 (2019)

Article   Google Scholar  

Basu, A.P.: Early intervention after perinatal stroke: opportunities and challenges. Dev. Med. Child Neurol. 56 (6), 516–521 (2014)

Hayes, S.H., Carroll, S.R.: Early intervention care in the acute stroke patient. Arch. Phys. Med. Rehabil. 67 (5), 319–321 (1986)

Google Scholar  

Díez-Tejedor, E., Fuentes, B.: Acute care in stroke: the importance of early intervention to achieve better brain protection. Cerebrovasc. Dis. 17 (Suppl. 1), 130–137 (2003)

Jauch, E.C., Saver, J.L., Adams, H.P., Jr., et al.: Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 44 (3), 870–947 (2013)

Merino, J.G., Warach, S.: Imaging of acute stroke. Nat. Rev. Neurol. 6 (10), 560–571 (2010)

Adeoye, O., Hornung, R.: Practical considerations in acute stroke management: update to the 2018 American Heart Association/American Stroke Association guidelines for the early management of acute ischemic stroke [published correction in Stroke. 2019 Jan;50(1):e18]. Stroke 49 (3), e123–e125 (2018)

Albers, G.W., Marks, M.P., Kemp, S., et al.: Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging [published correction appears in N Engl J Med. 2018 Mar 22;378(12):1161]. N. Engl. J. Med. 378 (8), 708–718 (2018)

Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 , 115–118 (2017)

McKinney, S.M., et al.: International evaluation of an AI system for breast cancer screening. Nature 577 , 89–94 (2020)

Elhanashi, A., Lowe, D., Saponara, S., Moshfeghi, Y.: Deep learning techniques to identify and classify COVID-19 abnormalities on chest X-ray images. In: Proceedings of SPIE 12102, Real-Time Image Processing and Deep Learning, p. 1210204 (2022)

Saponara, S., Elhanashi, A., Gagliardi, A.: Reconstruct fingerprint images using deep learning and sparse autoencoder algorithms. In: Proceedings of SPIE 11736, Real-Time Image Processing and Deep Learning (2021)

Elhanashi, A., Saponara, S., Dini, P., et al.: An integrated and real-time social distancing, mask detection, and facial temperature video measurement system for pandemic monitoring. J. Real-Time Image Proc. 20 , 95 (2023).

Zheng, Q., Zhao, P., Wang, H., Elhanashi, A., Saponara, S.: Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation. IEEE Commun. Lett. 26 (6), 1298–1302 (2022).

Campbell, B.C.V., De Silva, D.A., Macleod, M.R., et al.: Ischaemic stroke. Nat. Rev. Dis. Primers. 5 , 70 (2019).

Elhanashi, A., Saponara, S., Zheng, Q.: Classification and localization of multi-type abnormalities on chest X-rays images. IEEE Access 11 , 83264–83277 (2023).

Aggarwal, R., Sounderajah, V., Martin, G., et al.: Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. npj Digit. Med. 4 , 65 (2021).

Varoquaux, G., Cheplygina, V.: Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digit. Med. 5 , 48 (2022).

Schünke, L.C., Mello, B., da Costa, C.A., Antunes, R.S., Rigo, S.J., Ramos, G.O., Righi, R.D.R., Scherer, J.N., Donida, B.: A rapid review of machine learning approaches for telemedicine in the scope of COVID-19. Artif. Intell. Med. 129 , 102312 (2022).

El-Sherif, D.M., Abouzid, M., Elzarif, M.T., Ahmed, A.A., Albakri, A., Alshehri, M.M.: Telehealth and artificial intelligence insights into healthcare during the COVID-19 pandemic. Healthcare (Basel) 10 (2), 385 (2022).

Christopoulou, S.C.: Machine learning models and technologies for evidence-based telehealth and smart care: a review. BioMedInformatics 4 , 754–779 (2024).

Haleem, M.S., Ekuban, A., Antonini, A., Pagliara, S., Pecchia, L., Allocca, C.: Deep-learning-driven techniques for real-time multimodal health and physical data synthesis. Electronics 12 , 1989 (2023).

Elhanashi, A., Dini, P., Saponara, S., Zheng, Q.: Integration of deep learning into the IoT: a survey of techniques and challenges for real-world applications. Electronics 12 , 4925 (2023).

Salehin, I., et al.: Real-time medical image classification with ML framework and dedicated CNN–LSTM architecture. J. Sens. (2023).

Johnson, C.O., et al.: Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18 (5), 439–458 (2019)

Katz, B.S., McMullan, J.T., Sucharew, H., Adeoye, O., Broderick, J.P.: Design and validation of a prehospital scale to predict stroke severity: Cincinnati Prehospital Stroke Severity Scale. Stroke 46 (6), 1508–1512 (2015).

Meyer, B.C., Raman, R., Hemmen, T., Obler, R., Zivin, J.A., Rao, R., Thomas, R.G., Lyden, P.D.: Efficacy of site-independent telemedicine in the STRokE DOC trial: a randomised, blinded, prospective study. Lancet Neurol. 7 (9), 787–795 (2008).

Zhao, J., et al.: Impact of the COVID-19 epidemic on stroke care and potential solutions. Stroke 51 (7), 1996–2001 (2020)

Hunt, L.C., et al.: Integrated genomic and proteomic analyses identify stimulus-dependent molecular changes associated with distinct modes of skeletal muscle atrophy. Cell Rep. 37 (6), 109971 (2021)

Download references


This work has been partially supported by the Forelab Dipartimento di Eccellenza project and by the spoke 6 of CN1 on HPC, Big Data and quantum of the PNRR, both by MUR

Open access funding provided by Università di Pisa within the CRUI-CARE Agreement.

Author information

Authors and affiliations.

Ingegneria Informazione, University of Pisa, Pisa, Italy

Abdussalam Elhanashi, Pierpaolo Dini & Sergio Saponara

School of Intelligent Engineering, Shandong Management University, Jinan, 250357, Shandong, China

Qinghe Zheng

You can also search for this author in PubMed   Google Scholar


AE carried out the experiments, and wrote the main manuscript text with support from PD and QZ. SS supervised the project.

Corresponding author

Correspondence to Abdussalam Elhanashi .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

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

Rights and permissions

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

Reprints and permissions

About this article

Elhanashi, A., Dini, P., Saponara, S. et al. TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices. J Real-Time Image Proc 21 , 121 (2024).

Download citation

Received : 25 March 2024

Accepted : 15 June 2024

Published : 26 June 2024


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

  • Stroke detection
  • Federated learning
  • Deep learning
  • NVIDIA platforms
  • Find a journal
  • Publish with us
  • Track your research


  • Author Services


You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

  • Active Journals
  • Find a Journal
  • Proceedings Series
  • For Authors
  • For Reviewers
  • For Editors
  • For Librarians
  • For Publishers
  • For Societies
  • For Conference Organizers
  • Open Access Policy
  • Institutional Open Access Program
  • Special Issues Guidelines
  • Editorial Process
  • Research and Publication Ethics
  • Article Processing Charges
  • Testimonials
  • SciProfiles
  • Encyclopedia


Article Menu

learning objectives of research methodology

  • Subscribe SciFeed
  • Recommended Articles
  • Author Biographies
  • Google Scholar
  • on Google Scholar
  • Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Dual-objective reinforcement learning-based adaptive traffic signal control for decarbonization and efficiency optimization.

learning objectives of research methodology

Share and Cite

Zhang, G.; Chang, F.; Huang, H.; Zhou, Z. Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization. Mathematics 2024 , 12 , 2056.

Zhang G, Chang F, Huang H, Zhou Z. Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization. Mathematics . 2024; 12(13):2056.

Zhang, Gongquan, Fangrong Chang, Helai Huang, and Zilong Zhou. 2024. "Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization" Mathematics 12, no. 13: 2056.

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.


Subscribe to receive issue release notifications and newsletters from MDPI journals


  1. Aims and objectives of Research methodology

    learning objectives of research methodology

  2. 21 Research Objectives Examples (Copy and Paste)

    learning objectives of research methodology

  3. PPT

    learning objectives of research methodology

  4. Relationship between the objectives and research methodology

    learning objectives of research methodology

  5. Research Objectives

    learning objectives of research methodology

  6. Aims and objectives of Research methodology

    learning objectives of research methodology


  1. Research Questions, Hypotheses, Objectives: A Lecture in URDU ( اُردو میں لیکچر)

  2. Tips for Writing Research Objectives, Research Questions and Research Hypotheses from Model

  3. Meaning, Objectives and Motivation in Research

  4. Research Seminar by Dr. Phon Sophat

  5. Metho 4: Good Research Qualities / Research Process / Research Methods Vs Research Methodology

  6. Introduction to Research Methodology ---- Full Course #shorts


  1. PDF Research Methods Learning Objectives

    The following learning objectives have been prepared to assist you in your preparation for the master's comprehensive examination in the area of research methods. A review of content related to these learning objectives should provide you with the foundation required for a successful mastery of the content. 1. Students should understand a ...

  2. PDF J380 Introduction to Research Methods Course Description and Objectives

    4. Propose a research study and justify the theory as well as the methodological decisions, including sampling and measurement. 5. Understand the importance of research ethics and integrate research ethics into the research process. 6. Be able to assess and critique a published journal article that uses one of the primary research methods in ...


    21. Learning Objectives (Continued) • Compare and contrast empirical, interpretive, and critical research methodologies. • Compare and contrast quantitative, qualitative, and mixed methods methodologies. esearch methodology with the research question• Understand the conventions for writing the research methodology section of a paper The ...

  4. How to Write Well-Defined Learning Objectives

    Well-defined learning objectives outline the desired outcome for learners, which will help specify the instructional method. For example, if we want the learners to demonstrate correct intubation procedure in a normal adult 100% of the time, we need the instructional method to involve some sort of hands-on experience so that learners can ...

  5. What Is Research Methodology? Definition + Examples

    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:

  6. Writing and Using Learning Objectives

    Learning objectives (LOs) are used to communicate the purpose of instruction. Done well, they convey the expectations that the instructor—and by extension, the academic field—has in terms of what students should know and be able to do after completing a course of study. As a result, they help students better understand course activities and ...

  7. Methodologies of Research on Learning (Overview Article)

    Definition. "Good methodology is essential to good science". (Simon and Kaplan 1989, p. 20). The term "methodology" refers to the theoretical analysis of research methods in a discipline that are generally considered appropriate for the inquiry of relevant or important issues. It may refer to a set of methods or procedures or to the ...

  8. PDF research method fm

    Research methodology 1 CHAPTER ONE INTRODUCTION TO RESEARCH 1.1 Learning Objectives After completing this chapter, the student should be able to: 1. Define research in general and health systems research in particular 2. Enumerate the characteristics of research 3. Identify the different types of research 4.

  9. Learning Objectives

    Learning Objectives. Define knowledge, social science, research, and research methods. Summarize why understanding research methods is important. Evaluate and describe each of the major steps taken to conduct research, as well as the importance of each step. Develop research questions that would describe, associate, and predict variables.

  10. Research Methods in Psychology: Learning Objectives

    Learning Objectives (See related pages) After studying Chapter 2, you should know and understand the following key points: Scientific and Everyday Approaches to Knowledge. The scientific method is empirical and requires systematic, controlled observation. To achieve control in a research situation, researchers manipulate independent variables ...

  11. Research Objectives

    Example: Research aim. To examine contributory factors to muscle retention in a group of elderly people. Example: Research objectives. To assess the relationship between sedentary habits and muscle atrophy among the participants. To determine the impact of dietary factors, particularly protein consumption, on the muscular health of the ...

  12. Course Objectives, Student Learning Outcomes, Assessment

    COURSE OBJECTIVES. This course is designed to enable students to: identify and discuss the role and importance of research in the social sciences. identify and discuss the issues and concepts salient to the research process. identify and discuss the complex issues inherent in selecting a research problem, selecting an appropriate research ...

  13. Research Methodology (CLRS90001)

    Intended learning outcomes. By the end of the subject students should be able to: Demonstrate the ability to choose methods appropriate to research aims and objectives; Understand the limitations of particular research methods; Develop skills in qualitative and quantitative data analysis and presentation; Develop advanced critical thinking skills

  14. Writing Effective Research Aims and Objectives

    This step in your research journey is usually the first written method used to convey your research idea to your tutor. Therefore, aims and objectives should clearly convey your topic, academic foundation, and research design. In order to write effective research aims and objectives, researchers should consider all aspects of their proposed work.

  15. 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: ... Explain how the research methodology addresses the research question(s) and objectives; Research Methodology Types. Types of Research Methodology are as ...

  16. Module 2: Research Methods in Learning and Behavior

    2.2. Research Designs Used in the Experimental Analysis of Behavior . Section Learning Objectives. List the five main research methods used in psychology. Describe observational research, listing its advantages and disadvantages. Describe the case study approach to research, listing its advantages and disadvantages.

  17. Learning Objectives

    Learning Objectives. By the end of this chapter, you will be able to do the following: 13.1 Assess the main challenges involved in interviewing key informants and plan questions regarding each research stage (i.e., research design, data collection, reporting of results). 13.2 Formulate the benefits of using Computer-Aided Text Analysis (CATA ...

  18. Learning Objectives

    By the end of this chapter, you will be able to do the following:7.1 Argue why you should care about experiments and how experiments provide evidence regarding causality.7.2 Defend the usefulness of experimental vignette methodology (EVM) and describe the two main types of EVM.7.3 Recommend how to conduct studies using experimental vignette methodology, including how to maximize internal and ...

  19. What is Research Methodology? Definition, Types, and Examples

    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 ...

  20. Objectives, methodologies and research issues of learning analytics

    The objective of learning analytics is to provide helpful information to optimize or improve learning designs, learning outcomes and learning environments based on the analysis results (Greller & Drachsler, 2012 ). In recent years, learning analytics has become an important issue in education, in particular, in the field of technology-enhanced ...

  21. (PDF) Teaching and Learning Research Methodologies in Education: A

    ing objectives, research competencies, teaching methods, and teaching-learning activities, as well as the resources used and the assessment strategies employed; focused on instruc- Educ. Sci ...

  22. Learning Objectives

    What is a Learning Objective? Bloom (1956) suggests a six-stage hierarchy of cognitive competencies: Knowledge - Students can collect and restate information. Comprehension - Students can interpret and understand information. Application - Students can apply information to solve problems. Analysis - Students can organize and analyze information.

  23. Crafting an Effective Research Proposal: Learning from Noteworthy PDF

    A notable PDF example showcases a research proposal investigating the effects of a new teaching method on student performance in mathematics. The methodology section outlines the study's design, including the selection of schools and participants, data collection through pre- and post-tests, and statistical analysis methods.

  24. Sample Learning Outcomes

    These examples cover a wide range of subjects and demonstrate how to formulate specific, measurable, and achievable objectives that align with instructional goals. This resource is a valuable tool for instructors looking to enhance their course design and ensure their teaching aligns with intended learning outcomes. Co-Curricular Outcome Samples

  25. Learning Objectives

    By the end of this chapter, you will be able to do the following:2.1 Explain why you should care about ethical research.2.2 Compare differences between two research philosophies: utilitarian and deontological.2.3 Follow ethical standards in planning the purpose and study.2.4 Execute ethical research that considers the rights of participants.2.5 Consider special ethical requirements when ...

  26. TeleStroke: real-time stroke detection with federated learning and

    The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone ...

  27. Crowdfunding performance prediction using feature‐selection‐based

    It is a new attempt to evaluate the relative performance of different machine learning algorithms for crowdfunding prediction. Objectives. This study aims to identify the key factors of crowdfunding, and find the different performance and usage of machine learning algorithms for crowdfunding prediction. Method

  28. Mathematics

    A novel network structure combining Convolutional Neural Networks and Long Short-Term Memory Networks is designed to map the intersection traffic state to a Q-value, accelerating the learning process. The reward mechanism involves a multi-objective optimization function, employing the entropy weight method to balance the weights among dual goals.