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  • Posted: Friday, 17 April 2020
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How To Write Chapter Three Of Your Research Project (Research Methodology)

Methodology In Research Paper

Chapter three of the research project or the research methodology is another significant part of the research project writing. In developing the chapter three of the research project, you state the purpose of research, research method you wish to adopt, the instruments to be used, where you will collect your data, types of data collection, and how you collected it.

This chapter explains the different methods to be used in the research project. Here you mention the procedures and strategies you will employ in the study such as research design, study design in research, research area (area of the study), the population of the study, etc.

You also tell the reader your research design methods, why you chose a particular method, method of analysis, how you planned to analyze your data. Your methodology should be written in a simple language such that other researchers can follow the method and arrive at the same conclusion or findings.

You can choose a survey design when you want to survey a particular location or behavior by administering instruments such as structured questionnaires, interviews, or experimental; if you intend manipulating some variables.

The purpose of chapter three (research methodology) is to give an experienced investigator enough information to replicate the study. Some supervisors do not understand this and require students to write what is in effect, a textbook.

A research design is used to structure the research and to show how all of the major parts of the research project, including the sample, measures, and methods of assignment, work together to address the central research questions in the study. The chapter three should begin with a paragraph reiterating the purpose of research.

It is very important that before choosing design methods, try and ask yourself the following questions:

Will I generate enough information that will help me to solve the research problem by adopting this method?

Method vs Methodology

I think the most appropriate in methods versus methodology is to think in terms of their inter-connectedness and relationship between both. You should not beging thinking so much about research methods without thinking of developing a research methodology.

Metodologia or methodology is the consideration of your research objectives and the most effective method  and approach to meet those objectives. That is to say that methodology in research paper is the first step in planning a research project work. 

Design Methodology: Methodological Approach                

Example of methodology in research paper, you are attempting to identify the influence of personality on a road accident, you may wish to look at different personality types, you may also look at accident records from the FRSC, you may also wish to look at the personality of drivers that are accident victims, once you adopt this method, you are already doing a survey, and that becomes your  metodologia or methodology .

Your methodology should aim to provide you with the information to allow you to come to some conclusions about the personalities that are susceptible to a road accident or those personality types that are likely to have a road accident. The following subjects may or may not be in the order required by a particular institution of higher education, but all of the subjects constitute a defensible in metodologia or methodology chapter.



A  methodology  is the rationale for the research approach, and the lens through which the analysis occurs. Said another way, a methodology describes the “general research strategy that outlines the way in which research is to be undertaken” The methodology should impact which method(s) for a research endeavor are selected in order to generate the compelling data.

Example Of Methodology In Research Paper :

  • Phenomenology: describes the “lived experience” of a particular phenomenon
  • Ethnography: explores the social world or culture, shared beliefs and behaviors
  • Participatory: views the participants as active researchers
  • Ethno methodology: examines how people use dialogue and body language to construct a world view
  • Grounding theory*: assumes a blank slate and uses an inductive approach to develop a new theory

A  method  is simply the tool used to answer your research questions — how, in short, you will go about collecting your data.

Methods Section Of Research Paper Example :

  • Contextual inquiry
  • Usability study
  • Diary study

If you are choosing among these, you might say “what method should I use?” and settle on one or more methods to answer your research question.


Research Design Definition: WRITING A RESEARCH DESIGN

A qualitative study does not have variables. A scientific study has variables, which are sometimes mentioned in Chapter 1 and defined in more depth in Chapter 3. Spell out the independent and dependent, variables. An unfortunate trend in some institutions is to repeat the research questions and/or hypotheses in both Chapter 1 and Chapter 3. Sometimes an operational statement of the research hypotheses in the null form is given to set the stage for later statistical inferences. In a quantitative study, state the level of significance that will be used to accept or reject the hypotheses.

Pilot Study

In a quantitative study, a survey instrument that the researcher designed needs a pilot study to validate the effectiveness of the instrument, and the value of the questions to elicit the right information to answer the primary research questions in. In a scientific study, a pilot study may precede the main observation to correct any problems with the instrumentation or other elements in the data collection technique. Describe the pilot study as it relates to the research design, development of the instrument, data collection procedures, or characteristics of the sample.


In a research study, the instrument used to collect data may be created by the researcher or based on an existing instrument. If the instrument is the researcher created, the process used to select the questions should be described and justified. If an existing instrument is used, the background of the instrument is described including who originated it, and what measures were used to validate it.

If a Likert scale is used, the scale should be described. If the study involves interviews, an interview protocol should be developed that will result in a consistent process of data collection across all interviews. Two types of questions are found in an interview protocol: the primary research questions, which are not asked of the participants, and the interview questions that are based on the primary research questions and are asked of the participants.

In a qualitative study, this is the section where most of the appendices are itemized, starting with letters of permission to conduct the study and letters of invitation to participate with the attached consent forms. Sample: this has to do with the number of your participants or subjects as the case may be. Analysis (how are you planning to analyze the results?)



This chapter deals effectively with the research methods to be adopted in conducting the research, and it is organized under the following sub-headings:

  • Research Design
  • Area of Study

The population of the Study

  • Sample and Sampling Techniques
  • Instruments for Data Collection

The validity of the Instrument

Reliability of the Instrument

  • Administration of the instruments
  • Scoring the instruments

Method of Data Collection

Method of Data Analysis

Research Design:

This has to do with the structure of the research instrument to be used in collecting data. It could be in sections depending on different variables that form the construct for the entire topic of the research problems. A reliable instrument with a wrong research design will adversely affect the reliability and generalization of the research. The choice of design suitable for each research is determined by many factors among which are: kind of research, research hypothesis, the scope of the research, and the sensitive nature of the research.

Area of Study:

Research Area; this has to do with the geographical environment of the study area where the places are located, the historical background when necessary and commercial activities of that geographical area. For example, the area of the study is Ebonyi State University. At the creation of Ebonyi State in 1996, the Abakaliki campus of the then ESUT was upgraded to Ebonyi State University College by Edict no. 5 of Ebonyi State, 1998 still affiliated to ESUT with Prof. Fidelis Ogah, former ESUT Deputy Vice-Chancellor as the first Rector. In 1997, the Faculty of Applied and Natural Sciences with 8 departments was added to the fledging University, and later in 1998 when the ESUT Pre-Science Programme was relocated to Nsukka, the EBSUC Pre-Degree School commenced lectures in both Science and Arts in replacement of the former. This study focused on the students of the Business Education department in Ebonyi state university.

The population is regarded in research work as the type of people and the group of people under investigation. It has to be specific or specified. For example educational study teachers in Lagos state. Once the population is chosen, the next thing is to choose the samples from the population.

According to Uma (2007), the population is referred to as the totality of items or object which the researcher is interested in. It can also be the total number of people in an area of study. Hence, the population of this study comprised of all the students in the department of Business Education, Ebonyi State University which is made up of year one to four totaling 482. The actual number for the study was ascertained using Yaro-Yamane's formula which stated thus:

n   =        N

N is the Population

1 is constant

e is the error margin

Then, n   =         482


= 214.35 approximately 214

Sample and sampling technique:

It may not be possible to reach out to the number of people that form the entire population for the study to either interview, observe, or serve them with copies of the questionnaire. To be realistic, the sample should be up to 20% of the total population. Two sampling techniques are popular among all the sampling techniques. These are random and stratified random sampling techniques. (A). in Random Sampling, the writers select any specific number from a place like a school, village, etc. (B). In Stratified Random Sampling, one has to indicate a specific number from a stratum which could be a group of people according to age, qualification, etc. or different groups from different locations and different considerations attached.

Instruments for Data Collection:

This is a device or different devices used in collecting data. Example: interview, questionnaire, checklist, etc. instrument is prepared in sets or subsections, each set should be an entity thus asking questions about a particular variable to be tested after collecting data. The type of instrument used will determine the responses expected. All questions should be well set so as to determine the reliability of the instrument.

This has to do with different measures in order to determine the validity and reliability of the research instrument. For example, presenting the drafted questionnaire to the supervisor for scrutiny. Giving the questionnaire to the supervisor for useful comments and corrections would help to validate the instrument.

The test-retest reliability method is one of the simplest ways of testing the stability and reliability of an instrument over time. The test-retest approach was adopted by the researcher in establishing the reliability of the instrument. In doing this 25 copies of the questionnaire were administered on twenty-five selected respondents. After two weeks another 25 copies of the same questionnaire were re-administered on the same group. Their responses on the two occasions were correlated using Parsons Product Moment Correlation. A co-efficient of 0.81 was gotten and this was high enough to consider the instrument reliable.

Administration of the instruments:

Here, the writer states whether he or she administers the test personally or through an assistant. He also indicates the rate of return of the copies of the questionnaire administered.

Scoring the instruments:

Here items on the questionnaire or any other device used must be assigned numerical values. For example, 4 points to strongly agree, 3 points to agree, 2 points to disagree, and 1 point to strongly disagree.

Table of Analysis


The researcher collected data using the questionnaire. Copies of the questionnaire were administered by the researcher on the respondents. All the respondents were expected to give maximum co-operation, as the information on the questionnaire is all on things that revolve around their study. Hence, enough time was taken to explain how to tick or indicate their opinion on the items stated in the research questionnaire.

In this study, the mean was used to analyze the data collected. A four (4) point Likert scale was used to analyze each of the questionnaire items.

The weighing was as follows:

VGE—————- Very Great Extent (4 points)

GE—————– Great Extent (3 points)

LE—————– Little Extent (2 points)

VLE—————- Very Little Extent (1 point)

SA—————– Strongly Agree (4 points)

A——————- Agree (3 points)

D—————— Disagree (2 points)

SD—————- Strongly Disagree (1 point)

The mean of the scale will then be determined by summing up the points and dividing their number as follows with the formula:

Where; x= mean

f= frequency

X= Nominal value of the option

∑= summation

N= Total Number

Therefore, the mean of the scale is 2.5.

This means that any item statement with a mean of 2.50 and above is considered agreed by the respondents and any item statement below 2.5 is considered disagreed.

EDITORS SOURCE: How To Write Chapter Three Of Your Research Project (Research Methodology)

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How To Write The Methodology Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021 (Updated April 2023)

So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper . But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .

Overview: The Methodology Chapter

  • The purpose  of the methodology chapter
  • Why you need to craft this chapter (really) well
  • How to write and structure the chapter
  • Methodology chapter example
  • Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical underpinnings of your research and outline the specific methodological choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your study and, just as importantly, why you did it this way.

Importantly, this chapter should comprehensively describe and justify all the methodological choices you made in your study. For example, the approach you took to your research (i.e., qualitative, quantitative or mixed), who  you collected data from (i.e., your sampling strategy), how you collected your data and, of course, how you analysed it. If that sounds a little intimidating, don’t worry – we’ll explain all these methodological choices in this post .

Free Webinar: Research Methodology 101

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these. Every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in a bit more detail later in this post, so stay tuned!

Need a helping hand?

parts of chapter 3 research methodology

How to write up the methodology chapter

First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (e.g., humanities, chemistry or engineering) as well as the university . So, be sure to always check the guidelines provided by your institution for clarity and, if possible, review past dissertations from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences.

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect. You don’t need a lot of detail here – just a brief outline will do.

The intro provides a roadmap to your methodology chapter

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover. 

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

The research philosophy is at the core of the methodology chapter

Methodological Choice #2 – Research Type

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other methodological choice, it’s essential to justify why you chose the research strategy you did.

Methodological Choice #4 – Time Horizon

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies.

Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . In the video below, we explain each of these in plain language.

For quantitative studies, you’ll almost always make use of descriptive statistics , and in many cases, you’ll also use inferential statistical techniques (e.g., correlation and regression analysis). In the video below, we unpack some of the core concepts involved in descriptive and inferential statistics.

In this section of your methodology chapter, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . Similarly, qualitative data will often require transcription and perhaps even translation. As always, remember to state both what you did and why you did it.

Section 3 – The Methodological Limitations

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.

Keep it simple

Methodology Chapter Example

In the video below, we walk you through an example of a high-quality research methodology chapter from a dissertation. We also unpack our free methodology chapter template so that you can see how best to structure your chapter.

Wrapping Up

And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

If you need a helping hand with your research methodology (or any other component of your research), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!

parts of chapter 3 research methodology

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  • What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

  • The type of research you conducted
  • How you collected and analyzed your data
  • Any tools or materials you used in the research
  • How you mitigated or avoided research biases
  • Why you chose these methods
  • Your methodology section should generally be written in the past tense .
  • Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
  • Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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  • Academic style
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  • Style consistency

See an example

parts of chapter 3 research methodology

Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

  • Aim to describe the characteristics of something?
  • Explore an under-researched topic?
  • Establish a causal relationship?

And what type of data did you need to achieve this aim?

  • Quantitative data , qualitative data , or a mix of both?
  • Primary data collected yourself, or secondary data collected by someone else?
  • Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

  • Why is this the best way to answer your research question?
  • Is this a standard methodology in your field, or does it require justification?
  • Were there any ethical considerations involved in your choices?
  • What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

  • How did you design the questionnaire?
  • What form did your questions take (e.g., multiple choice, Likert scale )?
  • Were your surveys conducted in-person or virtually?
  • What sampling method did you use to select participants?
  • What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

  • How did you design the experiment ?
  • How did you recruit participants?
  • How did you manipulate and measure the variables ?
  • What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

  • Where did you source the material?
  • How was the data originally produced?
  • What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

  • Information bias
  • Omitted variable bias
  • Regression to the mean
  • Survivorship bias
  • Undercoverage bias
  • Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

  • How did you find and select participants?
  • How many participants took part?
  • What form did the interviews take ( structured , semi-structured , or unstructured )?
  • How long were the interviews?
  • How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

  • What group or community did you observe? How long did you spend there?
  • How did you gain access to this group? What role did you play in the community?
  • How long did you spend conducting the research? Where was it located?
  • How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

  • What type of materials did you analyze?
  • How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

  • The Hawthorne effect
  • Observer bias
  • The placebo effect
  • Response bias and Nonresponse bias
  • The Pygmalion effect
  • Recall bias
  • Social desirability bias
  • Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

  • How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
  • Which software you used (e.g., SPSS, Stata or R)
  • Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

  • Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
  • Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
  • Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

  • Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
  • Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
  • Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

  • Show that you followed established practice for your type of research
  • Discuss how you decided on your approach by evaluating existing research
  • Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles


  • Cluster sampling
  • Stratified sampling
  • Thematic analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias

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

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

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

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

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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

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

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

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

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Completing Your Qualitative Dissertation: A Roadmap from Beginning to End

  • By: Linda Dale Bloomberg & Marie Volpe
  • Publisher: SAGE Publications, Inc.
  • Publication year: 2008
  • Online pub date: January 01, 2012
  • Discipline: Anthropology
  • Methods: Dissertation , Data visualization , Literature review
  • DOI: https:// doi. org/10.4135/9781452226613
  • Keywords: attitudes , inquiry , knowledge , proposals , students , tradition Show all Show less
  • Print ISBN: 9781412956512
  • Online ISBN: 9781452226613
  • Buy the book icon link

Subject index

Fills an important gap in qualitative research literature by specifically addressing the fast-growing practice of qualitative master's studies and doctoral dissertations in colleges and universities throughout the world. Many students struggle with turning qualitative research projects into a master's thesis or doctoral dissertation because the research itself is inherently messy. To address this challenge, authors Linda Dale Bloomberg and Marie Volpe have distilled decades of experience into a first-of-its-kind, highly practical reference for graduate students.

Front Matter

  • List of Tables
  • List of Figures
  • List of Appendices
  • Acknowledgments
  • About the Authors
  • Part I | Taking Charge of Yourself and Your Work
  • Chapter 1 | Introduction to Your Study
  • Chapter 2 | Developing and Presenting the Literature Review
  • Chapter 3 | Presenting Methodology and Research Approach
  • Chapter 4 | Analyzing Data and Reporting Findings
  • Chapter 5 | Analyzing and Interpreting Findings
  • Chapter 6 | Drawing Sound Conclusions and Presenting Actionable Recommendations
  • Part III | Nearing Completion: Some Final Considerations

Back Matter

  • Author Index
  • Early Praise for Completing Your Qualitative Dissertation

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CHAPTER 3 - RESEARCH METHODOLOGY: Data collection method and Research tools

Profile image of Spyros Langkos

As it is indicated in the title, this chapter includes the research methodology of the dissertation. In more details, in this part the author outlines the research strategy, the research method, the research approach, the methods of data collection, the selection of the sample, the research process, the type of data analysis, the ethical considerations and the research limitations of the project. The research held with respect to this dissertation was an applied one, but not new. Rather, numerous pieces of previous academic research exist regarding the role of DMOs in promoting and managing tourist destinations, not only for Athens in specific, but also for other tourist destinations in Greece and other places of the world. As such, the proposed research took the form of a new research but on an existing research subject. In order to satisfy the objectives of the dissertation, a qualitative research was held. The main characteristic of qualitative research is that it is mostly appropriate for small samples, while its outcomes are not measurable and quantifiable (see table 3.1). Its basic advantage, which also constitutes its basic difference with quantitative research, is that it offers a complete description and analysis of a research subject, without limiting the scope of the research and the nature of participant’s responses (Collis & Hussey, 2003).

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parts of chapter 3 research methodology

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business ethics and leadership

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As the tourism sector is continually evolving, touristic destinations and service providers should give close and thoughtful attention to customers' satisfaction, particularly during the Covid-19 pandemic period. Tourism for Greece represents one of the most valuable pillars of the economy and the impact of the pandemic to the sector and GDP will be significant. In this era, it is evident the importance of the Sustainable Development Goals and effective Destination Management that will take into consideration all aspects of the local communities. Customer satisfaction is crucial to improving strategies that destinations must follow to service quality and satisfaction management strategies. Recent consumer and technological trends make customer satisfaction more important than ever. This paper aims to investigate the characteristics, preferences, images, satisfaction levels, and the overall experience gained by the tourists visiting Lesvos island in the North Aegean Region Greece. Primary research was conducted and the airport of the island during departure in 2019. The useful gathered questionnaires (201) provided helpful information to the island's DMO related to the visitors' demographic characteristics, destination perception, awareness and competitiveness, satisfaction and overall experience. The basic research findings were the strong impression of the visitors about the authenticity of the destination. They also believe that prices are excellent and the rate of value for money is high. At the same time, visitors think that the island is not promoted very good and the image/brand of the island is not very clear and well defined. It is the first research conducted to visitors departing from Lesvos island to the authors' best knowledge. The results and discussion of this study will be useful to the islands' DMO and the island's tourism authorities and the North Aegean Region and other similar island destinations, which wish to maximize the benefits of tourism development.

Spyros Langkos

DOI: 10.13140/2.1.3231.1683 INDEPENDENT STUDΥ - THESIS " Athens as an international tourism destination: An empirical investigation to the city’s imagery and the role of local DMO’s.” The aim of this project was to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens. The result of this study indicated that DMOs are playing a crucial role for the promotion of Athens as a tourist destination. DMOs key responsibilities include: development of sophisticated online marketing strategies, creation of high quality published material, participation in international tourism fairs for developing relationships with key stakeholders and development of network synergies with airline companies, and international tourism organizations. Athens is a destination with great potential for future growth and for that reason DMOs have designed certain plans for the next three years in order to exploit the opportunities which are presented. The future plans of the DMOs give particular emphasis in the opening in new tourist markets and more particularly in the markets of Russia, Turkey China, and USA. Besides, DMOs will focus in five forms of tourism which can be developed successfully in Athens, namely: 1) cultural tourism, 2) health tourism, 3) luxury tourism, 4) city break tourism, and 5) convention tourism On the other hand, the executives of the DMOs underlined several problems which prevent the tourism development of Athens. The majority of these problems are related with the business environment in Greece which has become less competitive due to the crisis. Besides, the city as a destination faces the problems of seasonality as well as missing infrastructures. Finally, the research showed that DMOs have established strong and long term relationships with DMOs in foreign countries. These partnerships allow the Greek DMOs to be updated concerning the trends of the global tourism market as well as enhance the movement of tourists between cooperating countries. Nevertheless, the promotion of Athens as a tourism destination requires a more concerted effort between the public and the private stakeholders which are involved in the tourism industry. The benefits will be multiplied for businesses, the state and the society in general. Keywords & terms: Destination Marketing Organizations, DMO’s, tourism destination, tourist product, popularity & imagery, interviews, online marketing strategies, Athens, Greece, international tourism fairs, stakeholder relationships, network synergies, airline companies, future growth, tourist markets, cultural tourism, health tourism, luxury tourism, city break tourism, convention tourism, tourism development of Athens, business environment in Greece, seasonality, infrastructures


Aan Jaelani (SCOPUS ID: 57195963463)

Dear Participant, I am Spyros Langkos and I am collecting data from you which will be used in my dissertation for: Athens as an international tourism destination. An empirical investigation to the city’s imagery and the role of local DMO’s, as part of my MSc in Marketing Management at the University of Derby. The objective of the dissertation research, will be to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing and the information you will be asked to provide will be used to help to provide insights to achieve this objective. The data you provide will only be used for the dissertation, and will not be disclosed to any third party, except as part of the dissertation findings, or as part of the supervisory or assessment processes of the University of Derby. The data you provide will be kept until the 31st of December 2014, so that it is available for scrutiny by the University of Derby as part of the assessment process. If you feel uncomfortable with any of the questions being asked, you may decline to answer specific questions. You may also withdraw from the study completely, and your answers will not be used. And, if you later decide that you wish to withdraw from the study, please write to me at Spyros Langkos, email: [email protected] no later than the 30th of March 2014 and I will be able to remove your response from my analysis and findings, and destroy your response. The Researcher Spyros Langkos

Turismo y Sociedad

Andres Camacho-Murillo

One of the most important tools for conducting research on tourism topics is the utilised research method. Leguizamón’s book shows the adequacy of the scientific method to examine research problems in tourism issues. The systematic process described in the book includes the research problem delimitation, research hypothesis formulation, data collection, hypothesis testing and analysis and interpretation of results. The scientific method has been applied in experimental studies (whether causal or quasi-experiments as noted by the author) on diverse tourism issues, including the demand for international (schiff & Becken, 2011) and domestic (Alegre, Mateo, & pou, 2013) tourism, on competition in the package tour industry (Davies & Downward, 1998), on the impact of tourism on economic growth (Ivanov & Webster, 2007) and poverty alleviation (Croes & Vanegas, 2008), among other issues. The volume provides other research designs besides experimental research that can be applied to tourism related issues, including exploratory, descriptive and evaluative designs.

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This abstract book includes all the summaries of the papers presented at the 9th Annual International Conference on Tourism 10-13 June 2013, organized by the Sciences and Engineering Research Division of the Athens Institute for Education and Research. In total there were 34 papers and 45 presenters, coming from 19 different countries (Australia, Canada, China, Cyprus, Egypt, Hong Kong, Hungary, Ireland, Israel, Lithuania, Poland, Portugal, South Africa Spain, Taiwan, Turkey, UAE, UK, USA). The conference was organized into IX sessions that included areas of Tourism Marketing Issues, Tourism Destination and Development, Special Tourism Themes Entrepreneurship, Economics and Business in the Tourism Industry and other related fields. As it is the publication policy of the Institute, the papers presented in this conference will be considered for publication in one of the books of ATINER.

The tourism industry in Greece is one of the most important sectors of the country’s economy it terms of value (Hellenic Statistical Authority, 2014). There are several public and private organizations which are involved in the tourism industry in Greece for promoting destinations such as the Destination Management Organizations (DMOs). In this context, the aim of this project is to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing. More specifically, the project has the following objectives:  To identify the activities which are performed by DMOs for promoting Athens and to evaluate the strategic role of DMO’s.  To identify the importance of destination marketing through its application in the Greek Tourism Industry and the particular case of Athens.  To portrait the opinions and activity planning of Greek DMO’s Executives, who are considered to be experts in the tourism field.  To provide insights and new trends of high informational value about the Tourism Industry in Athens.  To highlight the latest incentives and programming concerning the city’s future developments.  To identify the key problems that Athens faces as a tourist destination and to recommend points for improvement from the DMOs perspective.

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parts of chapter 3 research methodology

Microsoft’s AI Access Principles: Our commitments to promote innovation and competition in the new AI economy

Feb 26, 2024 | Brad Smith - Vice Chair & President

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parts of chapter 3 research methodology

As we enter a new era based on artificial intelligence, we believe this is the best time to articulate principles that will govern how we will operate our AI datacenter infrastructure and other important AI assets around the world. We are announcing and publishing these principles – our “AI Access Principles” – today at the Mobile World Congress in Barcelona in part to address Microsoft’s growing role and responsibility as an AI innovator and a market leader.

Like other general-purpose technologies in the past, AI is creating a new sector of the economy. This new AI economy is creating not just new opportunities for existing enterprises, but new companies and entirely new business categories. The principles we’re announcing today commit Microsoft to bigger investments, more business partnerships, and broader programs to promote innovation and competition than any prior initiative in the company’s 49-year history. By publishing these principles, we are committing ourselves to providing the broad technology access needed to empower organizations and individuals around the world to develop and use AI in ways that will serve the public good.

These new principles help put in context the new investments and programs we’ve announced and launched across Europe over the past two weeks, including $5.6 billion in new AI datacenter investments and new AI skilling programs that will reach more than a million people. We’ve also launched new public-private partnerships to advance responsible AI adoption and protect cybersecurity, new AI technology services to support network operators, and a new partnership with France’s leading AI company, Mistral AI. As much as anything, these investments and programs make clear how we will put these principles into practice, not just in Europe, but in the United States and around the world.

Euro announcements over past 12 days

These principles also reflect the responsible and important role we must play as a company. They build in part on the lessons we have learned from our experiences with previous technology developments. In 2006, after more than 15 years of controversies and litigation relating to Microsoft Windows and the company’s market position in the PC operating system market, we published a set of “Windows Principles.” Their purpose was to govern the company’s practices in a manner that would both promote continued software innovation and foster free and open competition.

I’ll never forget the reaction of an FTC Commissioner who came up to me after I concluded the speech I gave in Washington, D.C. to launch these principles. He said, “If you had done this 10 years ago, I think you all probably would have avoided a lot of problems.”

Close to two decades have gone by since that moment, and both the world of technology and the AI era we are entering are radically different. Then, Windows was the computing platform of the moment. Today, mobile platforms are the most popular gateway to consumers, and exponential advances in generative AI are driving a tectonic shift in digital markets and beyond. But there is wisdom in that FTC Commissioner’s reaction that has stood the test of time: As a leading IT company, we do our best work when we govern our business in a principled manner that provides broad opportunities for others.

The new AI era requires enormous computational power to train, build, and deploy the most advanced AI models. Historically, such power could only be found in a handful of government-funded national laboratories and research institutions, and it was available only to a select few. But the advent of the public cloud has changed that. Much like steel did for skyscrapers, the public cloud enables generative AI.

Today, datacenters around the world house millions of servers and make vast computing power broadly available to organizations large and small and even to individuals as well. Already, many thousands of AI developers – in startups, enterprises, government agencies, research labs, and non-profit organizations around the world – are using the technology in these datacenters to create new AI foundation models and applications.

These datacenters are owned and operated by cloud providers, which include larger established firms such as Microsoft, Amazon, Google, Oracle, and IBM, as well as large firms from China like Alibaba, Huawei, Tencent, and Baidu. There are also smaller specialized entrants such as Coreweave, OVH, Aruba, and Denvr Dataworks Corporation, just to mention a few. And government-funded computing centers clearly will play a role as well, including with support for academic research. But building and operating those datacenters is expensive. And the semiconductors – or graphical processing units (GPUs) – that are essential to power the servers for AI workloads remain costly and in short supply. Although governments and companies are working hard to fill the gap, doing so will take some time.

With this reality in mind, regulators around the world are asking important questions about who can compete in the AI era. Will it create new opportunities and lead to the emergence of new companies? Or will it simply reinforce existing positions and leaders in digital markets?

I am optimistic that the changes driven by the new AI era will extend into the technology industry itself. After all, how many readers of this paragraph had, two years ago, even heard of OpenAI and many other new AI entrants like Anthropic, Cohere, Aleph Alpha, and Mistral AI? In addition, Microsoft, along with other large technology firms are dynamically pivoting to meet the AI era. The competitive pressure is fierce, and the pace of innovation is dizzying. As a leading cloud provider and an innovator in AI models ourselves and through our partnership with OpenAI, we are mindful of our role and responsibilities in the evolution of this AI era.

Throughout the past decade, we’ve typically found it helpful to define the tenets – in effect, the goals that guide our thinking and drive our actions as we navigate a complex topic. We then apply these tenets by articulating the principles we will apply as we make the decisions needed to govern the development and use of technology. I share below the new tenets on which we are basing our thinking on this topic, followed by our 11 AI Access Principles.

Our AI Access Tenets

Fundamentally, there are five tenets that define Microsoft’s goals as we focus on AI access, including our role as an infrastructure and platforms provider.

First, we have a responsibility to enable innovation and foster competition. We believe that AI is a foundational technology with a transformative capability to help solve societal problems, improve human productivity, and make companies and countries more competitive. As with prior general-purpose technologies, from the printing press to electricity, railroads, and the internet itself, the AI era is not based on a single technology component or advance. We have a responsibility to help spur innovation and competition across the new AI economy that is rapidly emerging.

AI is a dynamic field, with many active participants based on a technology stack that starts with electricity and connectivity and the world’s most advanced semiconductor chips at the base. It then runs up through the compute power of the public cloud, public and proprietary data for training foundation models, the foundation models themselves, tooling to manage and orchestrate the models, and AI-powered software applications. In short, the success of an AI-based economy requires the success of many different participants across numerous interconnected markets.

The Tech Stack for AI

You can see here the technology stack that defines the new AI era. While one company currently produces and supplies most of the GPUs being used for AI today, as one moves incrementally up the stack, the number of participants expands. And each layer enables and facilitates innovation and competition in the layers above. In multiple ways, to succeed, participants at every layer of the technology stack need to move forward together. This means, for Microsoft, that we need to stay focused not just on our own success, but on enabling the success of others.

Second, our responsibilities begin by meeting our obligations under the law. While the principles we are launching today represent a self-regulatory initiative, they in no way are meant to suggest a lack of respect for the rule of law or the role of regulators. We fully appreciate that legislators, competition authorities, regulators, enforcers, and judges will continue to evolve the competition rules and other laws and regulations relevant to AI. That’s the way it should be.

Technology laws and rules are changing rapidly. The European Union is implementing its Digital Markets Act and completing its AI Act, while the United States is moving quickly with a new AI Executive Order. Similar laws and initiatives are moving forward in the United Kingdom, Canada, Japan, India, and many other countries. We recognize that we, like all participants in this new AI market, have a responsibility to live up to our obligations under the law, to engage constructively with regulators when obligations are not yet clear, and to contribute to the public dialogue around policy. We take these obligations seriously.

Third, we need to advance a broad array of AI partnerships . Today, only one company is vertically integrated in a manner that includes every AI layer from chips to a thriving mobile app store. As noted at a recent meeting of tech leaders and government officials, “The rest of us, Microsoft included, live in the land of partnerships.”

People today are benefiting from the AI advances that the partnership between OpenAI and Microsoft has created. Since 2019, Microsoft has collaborated with OpenAI on the research and development of OpenAI’s generative AI models, developing the unique supercomputers needed to train those models. The ground-breaking technology ushered in by our partnership has unleashed a groundswell of innovation across the industry. And over the past five years, OpenAI has become a significant new competitor in the technology industry. It has expanded its focus, commercializing its technologies with the launch of ChatGPT and the GPT Store and providing its models for commercial use by third-party developers.

Innovation and competition will require an extensive array of similar support for proprietary and open-source AI models, large and small, including the type of partnership we are announcing today with Mistral AI, the leading open-source AI developer based in France. We have also invested in a broad range of other diverse generative AI startups. In some instances, those investments have provided seed funding to finance day-to-day operations. In other instances, those investments have been more focused on paying the expenses for the use of the computational infrastructure needed to train and deploy generative AI models and applications. We are committed to partnering well with market participants around the world and in ways that will accelerate local AI innovations.

Goals that Guide US

Fourth, our commitment to partnership extends to customers, communities, and countries. More than for prior generations of digital technology, our investments in AI and datacenters must sustain the competitive strengths of customers and national economies and address broad societal needs. This has been at the core of the multi-billion-dollar investments we recently have announced in Australia, the United Kingdom, Germany, and Spain. We need constantly to be mindful of the community needs AI advances must support, and we must pursue a spirit of partnership not only with others in our industry, but with customers, governments, and civil society. We are building the infrastructure that will support the AI economy, and we need the opportunities provided by that infrastructure to be widely available.

Fifth, we need to be proactive and constructive , as a matter of process, in working with governments and the IT industry in the design and release of new versions of AI infrastructure and platforms. We believe it is critical for companies and regulators to engage in open dialogue, with a goal of resolving issues as quickly as possible – ideally, while a new product is still under development. For our part, we understand that Microsoft must respond fully and cooperatively to regulatory inquiries so that we can have an informed discussion with regulators about the virtues of various approaches. We need to be good listeners and constructive problem solvers in sorting through issues of concern and identifying practical steps and solutions before a new product is completed and launched.

Microsoft AI access principles

Our AI Access Principles

  • The aforementioned tenets come together to shape the new principles we are announcing below. It’s important to note that, given the safety, security, privacy, and other issues relating to responsible AI, we need to apply all these principles subject to objective and effective standards to comply with our legal obligations and protect the public. These are discussed further below. Subject to these requirements, we are committed to the following 11 principles:

Provide access and support for AI developers who create models and applications

We are committed to enabling AI innovation and fostering competition by making our cloud computing and AI infrastructure, platforms, tools, and services broadly available and accessible to software developers around the world. We want Microsoft Azure to be the best place for developers to train, build, and deploy AI models and to use those models safely and securely in applications and solutions. This means:

  • As we grow chip capacity, we are expanding Microsoft’s cloud computing AI infrastructure to enable the training and deployment of more foundation models, both proprietary and open source, and large and small

Today, our partnership with OpenAI is supporting the training of the next generation of OpenAI models and increasingly enabling customers to access and use these models and Microsoft’s CoPilot applications in local datacenters. At the same time, we are committed to supporting other developers, training, and deploying proprietary and open-source AI models, both large and small.

Today’s important announcement with Mistral AI launches a new generation of Microsoft’s support for technology development in Europe. It enables Mistral AI to accelerate the development and deployment of its next generation Large Language Models (LLMs) with access to Azure’s cutting-edge AI infrastructure. It also makes the deployment of Mistral AI’s premium models available to customers through our Models-as-a-Service (MaaS) offering on Microsoft Azure, which model developers can use to publish and monetize their AI models. By providing a unified platform for AI model management, we aim to lower the barriers and costs of AI model development around the world for both open source and proprietary development. In addition to Mistral AI, this service is already hosting more than 1,600 open source and proprietary models from companies and organizations such as Meta, Nvidia, Deci, and Hugging Face, with more models coming soon from Cohere and G42 .

We are committed to expanding this type of support for additional models in the months and years ahead.

  • We are making AI models and development tools broadly available to software applications developers around the world, so every nation can build its own AI economy

As reflected in Microsoft’s Copilots and OpenAI’s ChatGPT itself, the world is rapidly benefiting from the use of a new generation of software applications that access and use the power of AI models. But our applications will represent just a small percentage of the AI-powered applications the world will need and create. For this reason, we’re committed to ongoing and innovative steps to make the AI models we host and the development tools we create broadly available to AI software applications developers around the world in ways that are consistent with responsible AI principles.

This includes the Azure OpenAI service, which enables software developers who work at start-ups, established IT companies, and in-house IT departments to build software applications that call on and make use of OpenAI’s most powerful models. It extends through Models as a Service to the use of other open source and proprietary AI models from other companies, including Mistral AI, Meta, and others.

We are also committed to empowering developers to build customized AI solutions by enabling them to fine-tune existing models based on their own unique data sets and for their specific needs and scenarios. With Azure Machine Learning, developers can easily access state-of-the-art pre-trained models and customize them with their own data and parameters, using a simple drag-and-drop interface or code-based notebooks. This helps companies, governments, and non-profits create AI applications that help advance their goals and solve their challenges, such as improving customer service, enhancing public safety, or promoting social good. This is rapidly democratizing AI and fostering a culture of even broader innovation and collaboration among developers.

We are also providing developers with tools and repositories on GitHub that enable them to create, share, and learn from AI solutions. GitHub is the world’s largest and most trusted platform for software development, hosting over 420 million repositories and supporting more than 100 million developers, including 90% of the Fortune 100. We are committed to supporting the AI developer community by making our AI tools and resources available on GitHub, giving developers access to the latest innovations and best practices in AI development, as well as the opportunity to collaborate with other developers and contribute to the open source community. As one example, just last week we made available an open automation framework to help red team generative AI systems.

Ensure choice and fairness across the AI economy

We understand that AI innovation and competition require choice and fair dealing. We are committed to providing organizations, AI developers, and data scientists with the flexibility to choose which AI models to use wherever they are building solutions. For developers who choose to use Microsoft Azure, we want to make sure they are confident we will not tilt the playing field to our advantage. This means:

  • We are making available public APIs to enable developers to access and use AI models we host on Microsoft Azure

The AI models that we host on Azure, including the Microsoft Azure OpenAI API service, are all accessible via public APIs. Microsoft publishes documentation on its website explaining how developers can call these APIs and use the underlying models. This enables any application, whether it is built and deployed on Azure or other private and public clouds, to call these APIs and access the underlying models.

  • We are supporting a common public API to enable network operators to support software developers

Network operators are playing a vital role in accelerating the AI transformation of customers around the world, including for many national and regional governments. This is one reason we are supporting a common public API through the Open Gateway initiative driven by the GSM Association, which advances innovation in the mobile ecosystem. The initiative is aligning all operators with a common API for exposing advanced capabilities provided by their networks, including authentication, location, and quality of service. It’s an indispensable step forward in enabling network operators to offer their advanced capabilities to a new generation of AI-enabled software developers. We have believed in the potential of this initiative since its inception at GSMA, and we have partnered with operators around the world to help bring it to life.

Today at Mobile World Congress, we are launching the Public Preview of Azure Programmable Connectivity (APC). This is a first-class service in Azure, completely integrated with the rest of our services, that seamlessly provides access to Open Gateway for developers. It means software developers can use the capabilities provided by the operator network directly from Azure, like any other service, without requiring specific work for each operator.

  • Developers may choose how to distribute and sell their AI models, tools and applications for deployment and use on Microsoft Azure, whether via the Azure Marketplace or directly to customers

We are committed to maintaining Microsoft Azure as an open cloud platform, much as Windows has been for decades and continues to be. That means in part ensuring that developers can choose how they want to distribute and sell their AI software to customers for deployment and use on Microsoft Azure. We provide a marketplace on Azure through which developers can list and sell their AI software to Azure customers under a variety of supported business models. Developers who choose to use the Azure Marketplace are also free to decide whether to use the transaction capabilities offered by the marketplace (at a modest fee ) or whether to sell licenses to customers outside of the marketplace (at no fee). And, of course, developers remain free to sell and distribute AI software to Azure customers however they choose, and those customers can then upload, deploy, and use that software on Azure.

  • We respect the needs of developers by ensuring we do not use any non-public information or data from the training, building, deployment, or use of developers’ AI models in Microsoft Azure to compete with those models

We believe that trust is central to the success of Microsoft Azure. We build this trust by serving the interests of AI developers and customers who choose Microsoft Azure to train, build, and deploy foundation models. In practice, this also means that we avoid using any non-public information or data from the training, building, deployment, or use of developers’ AI models to compete against them.

  • We enable customers using Microsoft Azure to switch to another cloud provider by enabling them to easily export and transfer their data

We know that customers can and do use multiple cloud providers to meet their AI and other computing needs. And we understand that the data our customers store on Microsoft Azure is their data. So, we are committed to enabling customers to easily export and transfer their data if they choose to switch to another cloud provider. We recognize that different countries are considering or have enacted laws limiting the extent to which we can pass along the costs of such export and transfer. We will comply with those laws.

Meet our societal responsibilities

We recognize that new AI technologies raise an extraordinary array of critical questions. These involve important societal issues such as privacy, safety, security, the protection of children, and the safeguarding of elections from deepfake manipulation, to name just a few. These and other issues require that tech companies create guardrails for their AI services, adapt to new legal and regulatory requirements, and work proactively in multistakeholder efforts to meet broad societal needs. We’re committed to fulfilling these responsibilities, including through the following priorities:

  • We are supporting the physical and cybersecurity needs of all the AI models and applications that run in our AI datacenters

We are committed to safeguarding the physical security of our AI datacenters, as they host the infrastructure and data that power AI solutions. We follow strict security protocols and standards to ensure that our datacenters are protected from unauthorized access, theft, vandalism, fire, or natural disasters. We monitor and audit our datacenters to detect and prevent any potential threats or breaches. Our datacenter staff are trained and certified in security best practices and are required to adhere to a code of conduct that respects the privacy and confidentiality of our customers’ data.

We are also committed to safeguarding the cybersecurity of our AI models and applications, as they process and generate sensitive information for our customers and society. We use state-of-the-art encryption, authentication, and authorization mechanisms to protect data in transit and at rest, as well as the integrity and confidentiality of AI models and applications. We also use AI to enhance our cybersecurity capabilities, such as detecting and mitigating cyberattacks, identifying and resolving vulnerabilities, and improving our security posture and resilience.

We’re building on these efforts with our new Secure Future Initiative (SFI) . This brings together every part of Microsoft and has three pillars. It focuses on AI-based cyber defenses, advances in fundamental software engineering, and advocacy for stronger application of international norms to protect civilians from cyber threats.

  • We are applying a strong Responsible AI Standard to keep people at the center of AI design decisions and respect enduring values, including fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability

As AI becomes more pervasive and impactful, we recognize the need to ensure that our technology is developed and deployed in a way that is ethical, trustworthy, and aligned with human values. That is why we have created the Microsoft Responsible AI Standard , a comprehensive framework that guides our teams on how to build and use AI responsibly.

The standard covers six key dimensions of responsible AI: fairness; reliability and safety; privacy and security; inclusiveness; transparency; and accountability. For each dimension, we define what these values mean and how to achieve our goals in practice. We also provide tools, processes, and best practices to help our teams implement the standard throughout the AI lifecycle, from design and development to deployment and monitoring. The approach that the standard establishes is not static, but instead evolves and improves based on the latest research, feedback, and learnings.

  • We are investing in initiatives to spread AI skilling broadly around the world

We recognize that countries need more than advanced AI chips and datacenters to sustain their competitive edge and unlock economic growth. AI is changing jobs and the way people work, requiring that people master new skills to advance their careers. That’s why we’re committed to marrying AI infrastructure capacity with AI skilling capability, combining the two to advance innovation.

In just the past few months, we’ve combined billions of dollars of infrastructure investments with new programs to bring AI skills to millions of people in countries like Australia , the United Kingdom , Germany , and Spain . We’re launching training programs focused on building AI fluency, developing AI technical skills, supporting AI business transformation, and promoting safe and responsible AI development. Our work includes the first Professional Certificate on Generative AI.

Typically, our skilling programs involve a professional network of Microsoft certified training services partners and multiple industry partners, universities, and nonprofit organizations. Increasingly, we find that major employers want to launch new AI skilling programs for their employees, and we are working with them actively to provide curricular materials and support these efforts.

One of our most recent and important partnerships is with the AFL-CIO, the largest federation of labor unions in the United States. It’s the first of its kind between a labor organization and a technology company to focus on AI and will deliver on three goals: (1) sharing in-depth information with labor leaders and workers on AI technology trends; (2) incorporating worker perspectives and expertise in the development of AI technology; and (3) helping shape public policy that supports the technology skills and needs of frontline workers.

We’ve learned that government institutions and associations can typically bring AI skilling programs to scale. At the national and regional levels, government employment and educational agencies have the personnel, programs, and expertise to reach hundreds of thousands or even millions of people. We’re committed to working with and supporting these efforts.

Through these and other initiatives, we aim to democratize access to AI education and enable everyone to harness the potential of AI for their own lives and careers.

  • We are managing our AI datacenters in an environmentally sensitive manner and using AI to advance environmental sustainability needs

In 2020, Microsoft set ambitious goals to be carbon negative, water positive and zero waste by 2030 . We recognize that our datacenters play a key part in achieving these goals. Being responsible and sustainable by design also has led us to take a first-mover approach, making long-term investments to bring as much or more carbon-free electricity than we will consume onto the grids where we build datacenters and operate.

We also apply a holistic approach to the Scope 3 emissions relating to our investments in AI infrastructure , from the construction of our datacenters to engaging our supply chain. This includes supporting innovation to reduce the embodied carbon in our supply chain and advancing our water positive and zero waste goals throughout our operations.

At the same time, we recognize that AI can be a vital tool to help accelerate the deployment of sustainability solutions from the discovery of new materials to better predicting and responding to extreme weather events. This is why we continue to partner with others to use AI to help advance breakthroughs that previously would have taken decades, underscoring the important role AI technology can play in addressing some of our most critical challenges to realizing a more sustainable future.

*** We know that the principles governing our approach are only a first step. We expect that we will need to evolve these principles and our approach as AI technology and industry moves forward and the applicable law and regulations change. We look forward to continuing dialogue with the many stakeholders that are now playing critical roles in building the new AI economy. If experience teaches us anything, it’s that we’ll all need to succeed together.

Update 2/27/2024: This blog was updated to reflect the latest stats from GitHub.

Tags: ChatGPT , datacenters , generative ai , Github , Mobile World Congress , open ai , Responsible AI

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