how to write engineering methodology

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Methodologies

The Methodology is one of the most important and neglected sections in engineering writing. In some documents, such as an undergraduate lab report, the methodology section can be as short as a one-sentence reference to relevant section of the lab manual. But in more advanced labs, the methodology can be a very significant part of the report. In fact, the methodology is often the product of engineering related research: researchers are often looking for appropriate ways of testing or evaluating products, forces, etc., or new methods for accomplishing a required task. In a proposal, the methodology can even be the most important part of the document – the proposal argues that its method for achieving a certain task is the best.

The methodology section of report should accomplish two tasks:

• Should allow readers to, if necessary, reproduce your experiment, design, or method for achieving a task
• Should help readers to anticipate your results

Writing a methodology that does both requires attention to detail and precision. In the following example from a lab report, key elements of the method are missing:

We poured out some distilled water into the container. We then added some of mixture A. We shook the mixture and observed what happened, taking some measurements.

This statement of method is missing some essential elements:

• How much distilled water did you pour?
• How much of the mixture did you add?
• How did you shake it (length, technique)?
• What did you observe, measure?

It is also missing some key details that may or may not be relevant to the experiment:

• What was the container made of?
• How big was it?
• Did you let it settle?

The composition of the container ma be significant because the mixture may react with certain materials; its size is significant, because it may tell us how accurate your measurements were (for example, measuring 5ml in a 1000 ml container would probably result in less accurate measurements than measuring 5ml in a 100ml container). Whether or not the mixture was allowed to settle, and how much time was required, may also determine the results of the reaction.

In revising this statement of method, we want to ensure that we include all of these details to help the reader reproduce the experiment and to anticipate a set of results:

We poured 250ml of distilled water into the 1000ml glass beaker. We then added 50mg of Mixture A. We shook the mixture by gently twirling the beaker around for two minutes. We observed and recorded the changes in mixture color and transparency during our mixing process. Immediately after stopping the mixing process, we recorded the color, translucency, and temperature of the new solution; we repeated these measurements after letting the solution settle for five minutes.

After reading this method, readers should already have expectations for the results: specifically, readers should see three key readings, color, transparency, and temperature taken at three different times, during, immediately after shaking, and after settling (but no temp reading for during stage).

Passive versus Active Voice: The methods section of your report should not be written in an imperative mode – that is, you are not giving people instructions or commands, but describing what was done. But the choice between active and passive voice in your methods is a contentious one. Some readers will prefer the active voice, while others prefer the passive. Both are acceptable; deciding on what voice to use will require some audience analysis (i.e. ask your professor or supervisor). The above passage can easily and unobtrusively be converted to passive:

250ml of distilled water was poured into a 1000ml glass beaker. 50mg of Mixture A was then added to the water. The mixture was gently shaken for two minutes. Changes in mixture color and transparency during our mixing process were observed and recorded. The color, translucency, and temperature of the new solution were recorded immediately after shaking, and after five minutes of settling.

Writing Methods for Other Types of Reports: The above example was taken from a student lab report: you should apply the same attention to detail in writing methods sections for proposals and other types of reports.

The key difference between the methodology in lab report and other types of reports is that in the lab report, the method is often given in the procedure from the manual. In research reports and proposals, the method is something you devise on your own. This adds two tasks to writing the methods: organization and justification.

1. Organization: Organization of the methodology section seems simple enough: the most obvious structure is chronological. However, while organization by chronology is usually the dominant mode of organization, you may not want to describe everything in the order that you did them. For example, you might start a different stage of the methods while waiting for the previous one to finish. Trying to adhere to a strict chronological mode of organization here would not be a good idea. Organizing a methodology section well involves:

Dividing and subdividing the steps into the appropriate key stages/sub-stages Choosing headings / key words that reflect the nature of the stages (i.e. Sample Preparation) Providing an overview of the entire methodology at the beginning of the section

2. Justification: If your method is of your own making, you may also need to justify your choices. Explain clearly why you chose the method that you did – for accuracy, simplicity, etc. – and also identify the implications of using your methods. For example, there may be some limitations that you were forced to accept because of time, cost, or other constraints. Identify these, state why they are acceptable or necessary, and explain the effect they may have on your results (take these into account in your Discussion as well).

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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 .

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?

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.

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.

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.

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!

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

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.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• 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, frequently asked questions about methodology.

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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 ?

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 generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/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 4–8 July 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.

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 analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’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.

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 here.

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Next, you should indicate how you processed and analysed 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 analysing 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 : Categorising 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 behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised 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 generalisable.

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.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

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 test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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

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

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Journal Article: Methods

Criteria for success.

A successful Methods section contains the following elements:

• The rationale for selecting your methodology and constructing your apparatus. What are the advantages and limitations? What are the constraints? How does your methodology help you determine what you want to know?
• Sufficient information about the setup to enable a reader to replicate your findings. What materials or techniques did you use? What did you build versus what was purchased off-the-shelf? Characterize the relevant performance specifications of your components.

A Methods section describes how you will approach the questions and knowledge gap posed in the Introduction. Not all readers will be interested in this information. For those who are, the Methods section has two purposes:

• Allow readers to judge whether the results and conclusions of the study are valid.

The interpretation of your results depends on the methods you used to obtain them. A reader who is skeptical of your results will read your Methods section to see if they can be trusted. They’ll want to know that you chose the most appropriate apparatus, that your assumptions were reasonable, and that you performed the necessary controls. Without this content, skeptical readers may think your data, and therefore any conclusions drawn from it, are unreliable.

• Allow readers to repeat the study.

For readers interested in replicating your study, the Methods section should provide enough information for them to obtain the same or similar results. This applies equally for the skeptical reader as for one who needs similar or extended data.

Analyze your audience

Typically, only readers in your field will want to replicate your study or have the knowledge to assess your methodology. More general audiences will read the Introduction and then proceed straight to the Results. You can therefore typically assume that people reading your Methods understand methodologies that are frequently used in your field. To gauge the level of detail necessary for a given method, you can look at articles previously published in your target journal.

If your paper is designed to appeal to experts in more than one field, you still need to write your Methods for a targeted set of experts. For example, say you implemented a novel numerical technique to study the behavior of bouncing fluid droplets. Is your goal to show fluid dynamicists additional insights from the numerical techniques, or engage applied mathematicians to improve the numerical model’s performance? In the former case, assume less computational expertise. In the latter, assume less fluids experience, i.e., explain what assumptions the current model makes.

State the reasons for choosing your methodology

A reader looking to assess your methodology will read your Methods section to judge your experimental design. When describing your approach, place more emphasis on how and why you applied a method rather than on how you performed the method. For example, you don’t need to explain how to build an instrumentation amplifier, but you might want to describe why a linear Lorentz-force actuator is more appropriate than a solenoid or ball screw linear actuator for your apparatus (and, potentially, why you didn’t use another method).

Use subheadings to organize content

As recommended for your Results section, use subheadings within your Methods to group related experiments and establish a logical flow. Write your Results section first, and then follow the order of Results subheadings when writing your Methods. The parallel structure will make it easy for readers to locate corresponding information in the two sections.

Subheadings for Methods and Results may not exactly correspond. Sometimes you may need multiple Methods subheadings to explain one Results subheading. Other times one Method subheading is enough to explain multiple Result subheadings .

Note: For some journals, especially for letter-style submissions, subheadings are not allowed or recommended by the editors. Check the journal style guidelines before committing to subheadings.

Provide minimal essential detail

For readers to replicate your study, you must provide enough detail to allow them to reach the same conclusions as you do in your paper. Include only those details—anything more is extraneous. Specify any factor that might change the conclusions in your paper. State the accuracy limits of the instrumentation you used and any uncertainties in material properties.

You can cite papers for standard methods, but any modifications or alterations should be clearly stated. When citing methods, cite the original paper in which a method was described instead of a paper that used the method. This helps avoid chains of citations that your reader must follow to find information about the method.

Depending on the journal, field, and novelty of the techniques, it is sometimes most appropriate to describe minimal detail in the article body, and then to include a more detailed appendix or supplemental section that describes the methodology in enough detail to be reproduced exactly.

Resources and Annotated Examples

Annotated example 1.

This is the methods section of a fluid mechanics paper published in Nature Physics. 417 KB

Annotated Example 2

This is the methods section of a fluid mechanics paper published in the Journal of Fluid Mechanics. 88 KB

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Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
• How to Write a Great Title
• How to Write an Abstract
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

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Writing the parts of scientific reports

18 Writing the methodology chapter

The title of this chapter can vary, such as Procedure(s) or Experiments or Materials and Methods , depending on the discipline, the project or subject of the study. The location of this chapter within a paper can vary. In a simple thesis it typically precedes the Results chapter. In a project report there might not be a separate chapter with the title ‘Methodology”, but this might be part of the description of the project tasks or research design.

Purpose of the methodology section

Overall, the purpose of this section is to provide the reader with information on the methods used to answer the research question (s), to achieve the objectives of the projects. Another important aspect is the justification for each method, which means why they were used, and also how. This involves a restatement of the research aim/ objectives and explains to the reader how the chosen research method (s) help answer the research questions. At this stage ethical issues or limitations of the research can also be stated.

In  other studies, the primary goal of this section is to convey to the reader the validity of the research which has been undertaken. The reader must be able to replicate the experiment and obtain essentially the same result.

Writing up the methods

In social science readers are often not only interested in the findings but in the methods you used to obtain them such as how you chose your sample, how representative it is, the questions posed in the survey or asked in the interview. The method section then becomes a detailed account of the steps undertaken in your research. Methods sections of projects using a non-experimental approach most likely have the three components of description , explanation and justification of data and method.

Therefore, the methods section fulfills three purposes (Lea, 2014):

• Describe the data and method(s) used
• Explain how the data were collected and how the method (s) were employed in the research
• Justify why the data were collected and why particular methods were chosen.

Overall structure

The overall structure follows the general-to-specific pattern, and also the logical organization of your project.

Experimental method

If experiments are used as a method, procedures followed and how results were calculated have to be presented. A typical structure:

• Apparatus: briefly describe the equipment, hardware used. Be as precise as possible: full details, including photographs, drawings or sketches should be placed in the appendix.
• Materials: list the materials used- be specific.
• Procedure: present a chronological account of how the experiment was conducted.

Use present tense to restate the aim/ purpose of your paper: this paper investigates the effects….

Most parts of this section use past tense + passive:

How it was done (passive voice + by ….. ing): the test was carried out by using a saturated solution of …

Why it was done (passive voice + to + verb): the …. was used to measure …

Use of sub-headings

This section often has subheadings which should, whenever possible, match those to be used in the results section.

Describing sequence

Sequence, or order, is important in describing processes. The table below shows some common expressions.

You may also want to explain:

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

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

Structure of Research Methodology

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

I. Introduction

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

II. Research Design

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

III. Data Collection Methods

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

IV. Data Analysis Methods

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

V. Ethical Considerations

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

VI. Limitations

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

VII. Conclusion

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

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

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

Qualitative Research Methodology

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

Mixed-Methods Research Methodology

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

Case Study Research Methodology

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

Action Research Methodology

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

Experimental Research Methodology

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

Survey Research Methodology

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

Grounded Theory Research Methodology

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

Research Methodology Example

An Example of Research Methodology could be the following:

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

Introduction:

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

Research Design:

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

Participants:

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

Intervention :

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

Data Collection:

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

Data Analysis:

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

Ethical Considerations:

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

Data Management:

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

Limitations:

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

Conclusion:

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

How to Write Research Methodology

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

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

When to Write Research Methodology

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

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

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

Applications of Research Methodology

Here are some of the applications of research methodology:

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

Purpose of Research Methodology

Research methodology serves several important purposes, including:

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

Advantages of Research Methodology

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

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

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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4 Writing the Materials and Methods (Methodology) Section

The Materials and Methods section briefly describes how you did your research. In other words, what did you do to answer your research question? If there were materials used for the research or materials experimented on you list them in this section. You also describe how you did the research or experiment. The key to a methodology is that another person must be able to replicate your research—follow the steps you take. For example if you used the internet to do a search it is not enough to say you “searched the internet.” A reader would need to know which search engine and what key words you used.

Open this section by describing the overall approach you took or the materials used. Then describe to the readers step-by-step the methods you used including any data analysis performed. See Fig. 2.5 below for an example of materials and methods section.

Writing tips:

• Explain procedures, materials, and equipment used
• Example: “We used an x-ray fluorescence spectrometer to analyze major and trace elements in the mystery mineral samples.”
• Order events chronologically, perhaps with subheadings (Field work, Lab Analysis, Statistical Models)
• Use past tense (you did X, Y, Z)
• Quantify measurements
• Include results in the methods! It’s easy to make this mistake!
• Example: “W e turned on the machine and loaded in our samples, then calibrated the instrument and pushed the start button and waited one hour. . . .”

Technical Writing @ SLCC Copyright © 2020 by Department of English, Linguistics, and Writing Studies at SLCC is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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• EDITOR'S PICK 🎯

Glass sensors 1,000x smaller than sand grain, 3D-printed on optical fiber

The method surpasses challenges in shaping optical fiber tips with silica glass, eliminating the need for high-temperature treatments..

Jijo Malayil

The setup for printing silica glass microstructures on an optical fiber.

David Callahan

Marking a significant advancement in communications, Swedish researchers 3D-printed silica glass micro-optics on the tips of optic fibers, which have surfaces as small as the cross-section of a human hair.

More sensitive remote sensors for the environment and healthcare are among the innovations that can be made possible by integrating silica glass optical devices with optical fibers .

According to the team at KTH Royal Institute of Technology in Stockholm, the approach combines the superior material properties of glass with the plug-and-play nature of optical fibers. It enables promising applications in fiber sensing, optical microelectromechanical systems (MEMS), and quantum photonics.

“These structures are so small you could fit 1,000 of them on the surface of a grain of sand, which is about the size of sensors being used today,” said Po-Han Huang, the study’s co-author, in a statement.

Next-gen optical fiber tips

In recent decades, integrating functional materials and structures on optical fiber tips has opened up numerous applications in sensing, imaging, and optical trapping.

The light-coupled platform of optical fiber tips enables interaction between the guided light and the device on the tip, offering a small footprint, low insertion loss, and compatibility with standard optoelectronic components.

However, researchers highlight that fiber tips’ small, delicate nature poses challenges for standard microfabrication processes designed for planar substrates.

Researchers claim that their approach also solves long-standing issues with the silica glass structure of optical fiber tips. These tips frequently call for high-temperature treatments that jeopardize the integrity of temperature-sensitive fiber coatings.

Unlike other approaches, the process starts with a non-carbon-containing basic material. This implies that the glass structure can be made transparent without requiring high temperatures to remove carbon.

Glass printing enhances photonics

The team’s 3D printing of inorganic glass structures on optical fiber tips involves four steps. First, a single-mode optical fiber is cut to the desired length and cleaved at both ends. The fiber is then threaded through a customized aluminum holder and fixed to a motorized stage.

In the second step, a 40 percent hydrogen silsesquioxane (HSQ) solution in toluene is drop-casted onto the fiber tip, forming a dome-shaped layer about 100 μm thick. The HSQ solution is dried, leaving a hard layer on the fiber tip.

In the third step, 650 nm laser light is injected to illuminate the fiber core, aiding alignment. Finally, in the fourth step, a femtosecond laser with a 1040 nm wavelength and less than 400 fs pulse width is used for direct laser writing (DLW).

The laser selectively cures the HSQ, removing the uncured HSQ and leaving a 3D-printed silica glass structure on the fiber tip.

Results show that the work solves the problem of high-temperature requirements in 3D direct laser writing glass methods, allowing the creation of glass structures on optical fiber tips without damaging temperature-sensitive coatings.

“We demonstrated a glass refractive index sensor integrated onto the fiber tip that allowed us to measure the concentration of organic solvents. This measurement is challenging for polymer-based sensors due to the corrosiveness of the solvents,” said Lee-Lun Lai, the study ‘s lead author.

Additionally, the refractive indices of acetone and methanol mixtures at near-infrared wavelengths were measured for the first time. A fiber-tip polarization beam splitter (PBS) demonstrated that light polarization and beam steering can be manipulated, which is useful for fiber-to-chip coupling and integrated quantum photonic circuits.

Researchers claim that photonics can reach new heights with the capacity to 3D print any kind of glass structure directly on the fiber tip.

“By bridging the gap between 3D printing and photonics, the implications of this research are far-reaching, with potential applications in microfluidic devices, MEMS accelerometers, and fiber-integrated quantum emitters,” said Po-Han Huang, the study’s co-author.

The details of the team’s research were published in the journal ACS Nano .

Integration of functional materials and structures on the tips of optical fibers has enabled various applications in micro-optics, such as sensing, imaging, and optical trapping. Direct laser writing is a 3D printing technology that holds promise for fabricating advanced micro-optical structures on fiber tips. To date, material selection has been limited to organic polymer-based photoresists because existing methods for 3D direct laser writing of inorganic materials involve high-temperature processing that is not compatible with optical fibers. However, organic polymers do not feature stability and transparency comparable to those of inorganic glasses. Herein, we demonstrate 3D direct laser writing of inorganic glass with a subwavelength resolution on optical fiber tips. We show two distinct printing modes that enable the printing of solid silica glass structures (“Uniform Mode”) and self-organized subwavelength gratings (“Nanograting Mode”), respectively. We illustrate the utility of our approach by printing two functional devices: (1) a refractive index sensor that can measure the indices of binary mixtures of acetone and methanol at near-infrared wavelengths and (2) a compact polarization beam splitter for polarization control and beam steering in an all-in-fiber system. By combining the superior material properties of glass with the plug-and-play nature of optical fibers, this approach enables promising applications in fields such as fiber sensing, optical microelectromechanical systems (MEMS), and quantum photonics.

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Facility for Rare Isotope Beams

At michigan state university, international research team uses wavefunction matching to solve quantum many-body problems, new approach makes calculations with realistic interactions possible.

FRIB researchers are part of an international research team solving challenging computational problems in quantum physics using a new method called wavefunction matching. The new approach has applications to fields such as nuclear physics, where it is enabling theoretical calculations of atomic nuclei that were previously not possible. The details are published in Nature (“Wavefunction matching for solving quantum many-body problems”) .

Ab initio methods and their computational challenges

An ab initio method describes a complex system by starting from a description of its elementary components and their interactions. For the case of nuclear physics, the elementary components are protons and neutrons. Some key questions that ab initio calculations can help address are the binding energies and properties of atomic nuclei not yet observed and linking nuclear structure to the underlying interactions among protons and neutrons.

Yet, some ab initio methods struggle to produce reliable calculations for systems with complex interactions. One such method is quantum Monte Carlo simulations. In quantum Monte Carlo simulations, quantities are computed using random or stochastic processes. While quantum Monte Carlo simulations can be efficient and powerful, they have a significant weakness: the sign problem. The sign problem develops when positive and negative weight contributions cancel each other out. This cancellation results in inaccurate final predictions. It is often the case that quantum Monte Carlo simulations can be performed for an approximate or simplified interaction, but the corresponding simulations for realistic interactions produce severe sign problems and are therefore not possible.

Using ‘plastic surgery’ to make calculations possible

The new wavefunction-matching approach is designed to solve such computational problems. The research team—from Gaziantep Islam Science and Technology University in Turkey; University of Bonn, Ruhr University Bochum, and Forschungszentrum Jülich in Germany; Institute for Basic Science in South Korea; South China Normal University, Sun Yat-Sen University, and Graduate School of China Academy of Engineering Physics in China; Tbilisi State University in Georgia; CEA Paris-Saclay and Université Paris-Saclay in France; and Mississippi State University and the Facility for Rare Isotope Beams (FRIB) at Michigan State University (MSU)—includes  Dean Lee , professor of physics at FRIB and in MSU’s Department of Physics and Astronomy and head of the Theoretical Nuclear Science department at FRIB, and  Yuan-Zhuo Ma , postdoctoral research associate at FRIB.

“We are often faced with the situation that we can perform calculations using a simple approximate interaction, but realistic high-fidelity interactions cause severe computational problems,” said Lee. “Wavefunction matching solves this problem by doing plastic surgery. It removes the short-distance part of the high-fidelity interaction, and replaces it with the short-distance part of an easily computable interaction.”

This transformation is done in a way that preserves all of the important properties of the original realistic interaction. Since the new wavefunctions look similar to that of the easily computable interaction, researchers can now perform calculations using the easily computable interaction and apply a standard procedure for handling small corrections called perturbation theory.  A team effort

The research team applied this new method to lattice quantum Monte Carlo simulations for light nuclei, medium-mass nuclei, neutron matter, and nuclear matter. Using precise ab initio calculations, the results closely matched real-world data on nuclear properties such as size, structure, and binding energies. Calculations that were once impossible due to the sign problem can now be performed using wavefunction matching.

“It is a fantastic project and an excellent opportunity to work with the brightest nuclear scientist s in FRIB and around the globe,” said Ma. “As a theorist , I'm also very excited about programming and conducting research on the world's most powerful exascale supercomputers, such as Frontier , which allows us to implement wavefunction matching to explore the mysteries of nuclear physics.”

While the research team focused solely on quantum Monte Carlo simulations, wavefunction matching should be useful for many different ab initio approaches, including both classical and  quantum computing calculations. The researchers at FRIB worked with collaborators at institutions in China, France, Germany, South Korea, Turkey, and United States.

“The work is the culmination of effort over many years to handle the computational problems associated with realistic high-fidelity nuclear interactions,” said Lee. “It is very satisfying to see that the computational problems are cleanly resolved with this new approach. We are grateful to all of the collaboration members who contributed to this project, in particular, the lead author, Serdar Elhatisari.”

This material is based upon work supported by the U.S. Department of Energy, the U.S. National Science Foundation, the German Research Foundation, the National Natural Science Foundation of China, the Chinese Academy of Sciences President’s International Fellowship Initiative, Volkswagen Stiftung, the European Research Council, the Scientific and Technological Research Council of Turkey, the National Natural Science Foundation of China, the National Security Academic Fund, the Rare Isotope Science Project of the Institute for Basic Science, the National Research Foundation of Korea, the Institute for Basic Science, and the Espace de Structure et de réactions Nucléaires Théorique.

Michigan State University operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.

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Professor Emeritus Jerome Connor, pioneer in structural mechanics, dies at 91

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Jerome J. Connor ’53, SM ’54, ScD ’59, professor emeritus in the Department of Civil and Environmental Engineering and a member of the MIT faculty since 1959, died on March 31. He was 91 years old.

Over a remarkable career spanning nearly six decades at the Institute, Connor was a prolific scholar and highly respected mentor to several generations of students, many of whom now hold notable positions in academia and industry around the world. His earliest research contributed to the pioneering numerical methods widely used today in structural engineering, such as the finite element method, and was also an early pioneer of the boundary element method. In addition, Connor was the lead proponent of the technical discipline referred to as motion-based design, which is based on limiting displacements against earthquake effects by means of structural control. His leadership role in the application of numerical methods to structural engineering led to significant advances in the numerical simulation of structural and material behavior.

“He was well-known for his intellectual leadership, exceptional dedication to the department, and extraordinary mentoring of students, faculty, and staff,” says Oral Buyukozturk, the George Macomber Professor in Construction Management, who first met Connor when he was an adjunct associate professor at Brown University and was invited to lecture at MIT.

Connor led the department in new teaching and research directions, advocating the importance of materials research and of design education in the civil engineering curriculum. For over 20 years, Connor led the high-performance structures track in the Master of Engineering (MEng) program as faculty advisor. In addition to classroom teaching, he helped MEng students think outside of the box in their design of skyscrapers and bridges. He often accompanied students on weeklong national and international visits to prominent construction sites during MIT’s Independent Activities Period. With his wife Barbara and their family, he regularly entertained students at their summer home on Cape Cod. His dedication and development of the program contributed to its success and recognition at peer institutions as one of the best professional MEng programs in the nation — eagerly sought out by students in structural engineering.

“Connor was truly devoted to our students and he was passionate about the field of structural design. He introduced a number of pedagogical innovations that we still use today, such as semester-long design projects as well as on-site visits to innovative, signature projects together with their design engineers,” says John Ochsendorf, professor of architecture and civil and environmental engineering, who taught with Connor for 10 years and currently leads the structural mechanics and design track of the MEng program.

Adoring mentor and visionary

Connor was a beloved mentor, and from 2007 to 2014 organized and managed MIT undergraduates’ participation in the National Steel Bridge competition. Buyukozturk recalls how “he was always coming up with new and innovative concepts for the competition; several times his team was selected as top in the nation and year after year his students were placed in the top three.”

MIT professor emeritus of civil and environmental engineering Eduardo Kausel, who was a graduate student of Connor’s and then later a colleague, remembers him fondly as an incredible teacher and colleague.

"Jerry was an excellent teacher and I enjoyed taking his advanced computational mechanics class. He was brilliant in computational mechanics and excelled in everything he did,” says Kausel. “As a colleague, he was always kind and had a gentle demeanor; I never saw him getting angry or voicing harsh words. He also had this fantastic ability to mentor students who would go on not only to become very successful as outstanding professionals, but also very wealthy,” Kausel says.

Kausel also remembers Connor’s uncanny ability to look into the future and know where the next big trend occurred in the field. Connor was one of the first researchers to work on the boundary element method in structural engineering. The method is effective in understanding how fluid interacts with structures to ensure its stability, safety, and efficiency. Connor also experimented with artificial intelligence well before it became popular and played a significant role in leading a team of MIT researchers in the development of the STRUDL computer code, which became a highly influential software package for structural analysis and design.

In addition to structural mechanics, he pursued computational fluid mechanics, helping develop early finite element analysis in both the time and frequency domains. His models had applications to offshore engineering, including tidal circulation, and the behavior and design of marine structures for resiliency in withstanding extreme events, including those related to climate change.

Buyukozturk credits the way the department has evolved into what it is today because of Connor’s direction and vision. “Priorities for research change over time, but Jerry set forth a basic roadmap for prioritizing research in computational mechanics, engineering design, and the development of sustainable materials that cut across the entire department in a wider scope,” he says. 

Influential wide-ranging career

Born in Dorchester, Massachusetts, on May 19, 1932, Connor attended Boston College High School and received his bachelor’s, master’s, and PhD degrees in civil engineering from MIT. Before he returned to MIT to become a faculty member, he went to work at the Army Materials Lab in Watertown, designing missile systems during the Vietnam War. While on sabbatical in 1983, he served as the dean of the Department of Engineering at Northeastern University and the director of the MIT Sea Grant Program.

Over the span of his career, Connor’s research in structural mechanics attracted the interest of the international community. He spoke at conferences around the world and consulted on many engineering projects, including the Hancock Tower glass crisis, the Twin Towers in New York, and the Parthenon in Greece, among many others. His papers were cited and published among the top engineering journals, and he was honored with numerous awards, including an honorary doctorate from the University of Thessaloniki in Greece. He authored many books on structural engineering, the boundary element method, motion-based design, and computational fluid mechanics. His books have been used in doctoral programs at universities around the world.  

Connor led a rich and adventurous life outside of his academic one. Known as “Jerry” to his friends and colleagues, Connor traveled to more than 25 different countries around the world with his wife, Barbara, but was especially fond of the Provence in southern France. Some of his memorable adventures included taking the family by Volkswagen bus throughout Europe during the holiday periods and, during a sabbatical from MIT in 1970, sailing to England on the Queen Elizabeth 2 with his then-young children.

Connor is survived by his wife Barbara, and by his six children: Patricia and her husband Richard, Stephen and his wife Madeline, Brian and his wife Michele, Michael and his wife Christine, Mark and his wife Kathy, Tracy and her husband Maurice, and 14 grandchildren. Gifts in Connor’s memory can be made to Boston College High School .

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AI Tool Instantly Assesses Self-Harm Risk

Behavioral economics principles allow researchers to predict suicidal thoughts and behaviors, the problem.

Suicidality hit a new record high in the US in 2022.

A new assessment tool able to predict whether participants exhibited suicidal thoughts and behaviors using a quick and simple combination of variables.

Why it Matters

Determining who is most at risk for self-harm is a crucial but difficult task that must be done quickly.

Professor Aggelos Katsaggelos, PhD student Shamal Shashi Lalvani

Trigger Warning: Sensitive Content

A new assessment tool that leverages powerful artificial intelligence was able to predict whether participants exhibited suicidal thoughts and behaviors using a quick and simple combination of variables.

Developed by researchers at Northwestern University, the University of Cincinnati (UC), Aristotle University of Thessaloniki, and Massachusetts General Hospital/Harvard School of Medicine, the system focuses on a simple picture-ranking task along with a small set of contextual/demographic variables rather than extensive psychological data. 

The tool was on average 92 percent effective at predicting four variables related to suicidal thoughts and behaviors.

“A system that quantifies the judgment of reward and aversion provides a lens through which we may understand preference behavior,” said first author Shamal Shashi Lalvani, a PhD student in electrical engineering at Northwestern Engineering. “By using interpretable variables describing human behavior to predict suicidality, we open an avenue toward a more quantitative understanding of mental health and make connections to other disciplines such as behavioral economics.”

The study,  published in the journal Nature Mental Health , concludes that a small set of behavioral and social measures play a key role in predicting suicidal thoughts and behaviors. The current work details the components of a tool that could be an app for medical professionals, hospitals, or the military to provide assessment of who is most at risk of self-harm.

“It’s reported we have about 20 suicides daily among veterans in the US, and a salient number of students. We all can cite statistics to how the American medical system is at a breaking point. I wish we’d had this technology sooner. The data strongly argues it would change outcomes,” said Hans Breiter, contact PI for the study, and a professor in computer science and biomedical engineering at UC. 

“People have developed good techniques with big data,” Breiter said, “but we have problems interpreting the meaning of many predictions based on big data. Having a small number of variables grounded in mathematical psychology appears to get around this issue and is needed if current machine learning is ever going to approach the issue of artificial general intelligence.”

Data was collected from surveys completed in 2021 by 4,019 participants ages 18 to 70 across the United States. Identities of participants were protected and not shared with researchers and participants gave informed consent.

Participants were asked to rank a random sequence of 48 pictures on a seven-point like-to-dislike scale of 3 to -3 in six categories: sports, disasters, cute animals, aggressive animals, nature, and adults in bathing suits. Researchers also collected a limited set of demographics about age, sex assigned at birth, race or ethnicity, highest education level achieved, and handedness. 

“The usage of a picture-rating task may seem simple but understanding individual preferences and how one evaluates reward and aversion plays a large role in shaping personality and behavior,” said co-PI for the study and co-senior author Aggelos Katsaggelos , the Joseph Cummings Professor of Electrical and Computer Engineering at the McCormick School of Engineering and director of the AI in Multimedia-Image and Video Processing Lab at Northwestern.

“We find that our results in predicting suicidality exceed typical methods of measurement without using extensive electronic health records or other forms of big data,” Katsaggelos said. 

Along with the picture ratings, participants completed a limited set of mental health questions and were asked to rank perceived loneliness on a five-point scale. 

When the data was plugged into an artificial intelligence system developed by Northwestern and the University of Cincinnati, the software was able to predict four measures of suicidal thoughts and behaviors: passive suicidal ideation (desire without a plan); active ideation (current and specific thoughts); planning for suicide; and planning coping strategies to prevent self-harm.

Researchers noted that respondents in other countries could have unique cultural influences that might affect prediction success, although race and gender effect were the least predictive of any measures used. Another potential limitation, the researchers said is the surveys were self-reported rather than through clinical assessments, adding that it’s difficult to see how a prospective study of suicide might be performed. Lastly, the cohort was sampled during the COVID-19 pandemic at a time that has seen higher-than-normal rates of loneliness and self-harm.

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Using AI to improve building energy use and comfort

University of Waterloo researchers have developed a new method that can lead to significant energy savings in buildings. The team identified 28 major heat loss regions in a multi-unit residential building with the most severe ones being at wall intersections and around windows. A potential energy savings of 25 per cent is expected if 70 per cent of the discovered regions are fixed.   

Building enclosures rely on heat and moisture control to avoid significant energy loss due to airflow leakage, which makes buildings less comfortable and more costly to maintain. This problem will likely be compounded by climate change due to volatile temperature fluctuations. Since manual inspection is time-consuming and infrequently done due to a lack of trained personnel, energy inefficiency becomes a widespread problem for buildings.  

Researchers at Waterloo, which is a leader in sustainability research and education and a catalyst for environmental innovation, solutions and talent, created an autonomous, real-time platform to make buildings more energy efficient. The platform combines artificial intelligence, infrared technology, and a mathematical model that quantifies heat flow to better identify areas of heat loss in buildings.

Using the new method, the researchers conducted an advanced study on a multi-unit residential building in the extreme climate of Canadian prairies, where elderly residents reported discomfort and higher electricity bills due to increased demand for heating in their units. Using AI tools, the team trained the program to examine thermal images in real time, achieving 81 percent accuracy in detecting regions of heat loss in the building envelope.  

"The almost 10 per cent increase in accuracy with this AI-based model is impactful, as it enhances occupants' comfort as well as reduces energy bills," said Dr. Mohamad Araji, director of Waterloo's Architectural Engineering Program and head of the Symbiosis Lab, an interdisciplinary group at the university that specializes in developing innovative building systems and building more environmentally friendly buildings.  

The new AI tools helped to remove the element of human error in examining the results and increased the speed of getting the data analyzed by a factor of 12 compared to traditional building inspection methods. 

Future expansions to this work will include utilizing drones equipped with cameras to inspect high-rise buildings. 

"The hope is that our methodology can be used to analyze buildings and lead to millions in energy savings in a much faster way than previously possible," Araji said.  

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Materials provided by University of Waterloo . Note: Content may be edited for style and length.

Journal Reference :

• Ali Waqas, Mohamad T. Araji. Machine learning-aided thermography for autonomous heat loss detection in buildings . Energy Conversion and Management , 2024; 304: 118243 DOI: 10.1016/j.enconman.2024.118243

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How to use ChatGPT to write code: What it can and can't do for you

One of the more intriguing discoveries about ChatGPT is that it can write pretty good code. I first tested this out last year when I asked it to write a WordPress plugin my wife could use on her website. ChatGPT did a fine job, but it was a very simple project. 

How to use ChatGPT to write: Resumes  | Excel formulas | Essays | Cover letters  

So, how can you use ChatGPT to write code as part of your daily coding practice? Here's a quick summary:

• ChatGPT can produce both useful and unusable code. For best results, provide clear and detailed prompts.
• ChatGPT excels in assisting with specific coding tasks or routines, rather than building complete applications from scratch.
• Use ChatGPT to find and choose the right coding libraries for specific purposes, and engage in an interactive discussion to narrow down options.
• Be cautious about the ownership of AI-generated code and always verify the code's reliability. Don't blindly trust the generated output.
• Treat interactions with ChatGPT as a conversation. Refine your questions based on the AI's responses to get closer to the desired output.

Now, let's explore ChatGPT in considerably more depth.

What types of coding can ChatGPT do well?

There are two important facts about ChatGPT and coding. The first is that the AI can, in fact, write useful code. 

The second is that it can get completely lost, fall down a rabbit hole, chase its own tail, and produce unusable garbage.

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I found this out the hard way. After I finished the WordPress plugin, I decided to see how far ChatGPT could go. 

I wrote out a very careful prompt for a Mac application, including detailed descriptions of user interface elements, interactions, what would be provided in settings, how they would work, and so on. Then, I fed the prompt to ChatGPT.

ChatGPT responded with a flood of text and code. Then, it stopped mid-code. When I asked it to continue, it vomited out even more code and text. I requested continue after continue, and it dumped out more and more code. But... none of it was usable . It didn't identify where the code should go, how to construct the project, and -- when I looked carefully at the code produced -- it left out major operations I requested, leaving in simple text descriptions stating "program logic goes here".

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After a bunch of repeated tests, it became clear to me that if you ask ChatGPT to deliver a complete application, it will fail. A corollary to this observation is that if you know nothing about coding and want ChatGPT to build you something, it will fail.

Where ChatGPT succeeds -- and does so very well -- is in helping someone who already knows how to code to build specific routines and get specific tasks done. Don't ask for an app that runs on the menu bar. But if you ask ChatGPT for a routine to put a menu on the menu bar, and then paste that into your project, the tool will do quite well.

Also, keep in mind that while ChatGPT appears  to have a tremendous amount of domain-specific knowledge (and it often does), it lacks wisdom . As such, the tool may be able to write code, but it won't be able to write code containing the nuances for very specific or complex problems that require deep experience to understand.

Also:  How to use ChatGPT to create an app

Use ChatGPT to demo techniques, write small algorithms, and produce subroutines. You can even get ChatGPT to help you break down a bigger project into chunks, and then you can ask it to help you code those chunks.

So, with that in mind, let's look at some specific steps for how ChatGPT can help you write code.

How to use ChatGPT to write code

1. narrow down and sharpen up your request.

This first step is to decide what you are going to ask of ChatGPT -- but not yet ask it anything. Decide what you want your function or routine to do, or what you want to learn about to incorporate into your code. Decide on the parameters you're going to pass into your code and what you want to get out. And then look at how you're going to describe it.

Also: How to write better ChatGPT prompts

Imagine you're paying a human programmer to do this task. Are you giving that person enough information to be able to work on your assignment? Or are you too vague and the person you're paying is more likely to either ask questions or turn in something entirely unrelated to what you want?

Here's an example. Let's say I want to be able to summarize any web page. I want to feed it something like this article and get back a short summary that's well-considered and appropriate. As my input, I'll specify a web page URL. As my output, it's a block of text with a summary.

2. Use ChatGPT to explore libraries and resources

Continuing with the example above, a very old school way of extracting web page data was to find the text between HTML paragraph tags.

But with the rise of AI tools , it makes more sense to use an AI library to do an intelligent extract and summary. One of the places ChatGPT excels (and it's also an area you can easily verify to avoid its authoritative-but-wrong behavior pattern) is finding libraries and resources. 

Also: How to make ChatGPT provide sources and citations

OpenAI (the maker of ChatGPT) sells API access to the GPT-3 and GPT-4 engines that will do exactly what we want. But in the case of this example, let's assume we don't want to pay transaction fees.

So let's look at interacting with ChatGPT to figure out how to use such a tool, for free, with a project that runs in PHP.

I started with a prompt that was designed to elicit information about what libraries would provide the functionality I wanted. A library (for those of you reading along who aren't programmers) is a body of code a programmer can access that does a lot of the heavy lifting for a specific purpose. A big part of modern programming is finding and choosing the right libraries, so this is a good starting point.

In this case, I'm looking at blocks of code written by other people that will summarize text. Here's my first prompt:

Describe ten different open source AI libraries (and the languages they work with) that I can use to generate a summary of the main core contents of any web page, ignoring any ads or embedded materials.

This prompt gave me exactly what I wanted, including a mention of OpenAI's offerings. I think OpenAI would do great here, but for this hypothetical project, I don't want to budget for API fees. So. I'll narrow down the question:

Are any of these free?

ChatGPT hedged its bets with its answer. Here's what it said: "Yes, all ten of these AI libraries are open source and free to use. However, some of them may have usage limits or require payment for access to additional features or resources." So, based on that, I clarified my query:

Which of these libraries have no usage limits and don't require any additional payment or licensing?

Notice how this is very much a conversation. I don't have to re-ask the originating question. I'm just drilling down in the same way I might if I had an expert at hand and was seeking clarification. In this case, ChatGPT gave me eight library choices, but none of them mentioned the PHP language that I was planning to code in. So, here's the next prompt:

Of those 8 libraries, can I use any with PHP?

It returned three libraries, but I wasn't sure about what each did. So, another question:

What's the difference between Sumy, Gensim, and NLTK?

I still wasn't sure, so I clarified my use plan and then asked:

If I want to create summaries of web page news articles, which library would work better?

The answer I got was clear and promising: "Sumy is specifically designed for text summarization, which is the task of creating a summary that captures the most important information from a piece of text." So, now it was time to see what was involved in using Sumy with PHP. I asked my last question for this part of the project:

Can you explain how to use Sumy from PHP?

Feel free to play along on your computer and paste these prompts into your instance of ChatGPT. Notice that, in step one, I decided what program module I was going to get help on. Then, in this step, I had a conversation with ChatGPT to decide what library to use and how to integrate it into my project.

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That may not seem like programming, but I assure you it is. Programming isn't just blasting lines of code onto a page. Programming is figuring out how to integrate all the various resources and systems together, and how to talk to all the various components of your solution. Here, ChatGPT helped me do that integration analysis.

By the way, I was curious whether Google's Gemini AI (formerly Bard) could help in the same way. Gemini can't actually write code, but it did give some extra insights into the planning aspect of programming over ChatGPT's responses. So, don't hesitate to use multiple tools to triangulate on answers you want. Here's that story: Gemini vs. ChatGPT: Can Gemini help you code?  Since I wrote that article, Google added some coding capabilities to Gemini, but they're not all that great. You can read about it here: I tested Google Gemini's new coding skills. It didn't go well . And even more recently, I dug into Gemini Advanced . It's still not passing many tests.

Also: How I test an AI chatbot's coding ability - and you can too

Coding is next. 

3. Ask ChatGPT to write example code

OK, let's pause here. This article is entitled "How to use ChatGPT to write code." And it will. But what we're really doing is asking ChatGPT to write example code.

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Let's be clear: Unless you're writing a very small function (like the line sorter/randomizer ChatGPT wrote for my wife), ChatGPT isn't going to be able to write your final code. First, you're going to have to maintain it. ChatGPT is terrible at modifying already-written code. Terrible, as in, it doesn't do it. So, to get new code, you have to ask ChatGPT to generate something new. As I found previously, even if your prompt is virtually identical, ChatGPT may change what it gives you in very unexpected ways.

So, bottom line: ChatGPT can't maintain your code, or even tweak it.

That limitation means you have to do it yourself. As we know, the first draft of a piece of code is rarely the final code. So, even if you were to expect ChatGPT to generate final code, it would really be a starting point, one where you need to take it to completion, integrate it into your bigger project, test it, refine it, debug it, and so on.

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But that doesn't mean the example code is worthless -- far from it. Let's take a look at a prompt I wrote based on the project I described earlier. Here's the first part:

Wite a PHP function called summarize_article. As input, summarize_article will be passed a URL to an article on a news-related site like ZDNET.com or Reuters.com.

I'm telling ChatGPT the programming language it should use. I'm also telling it the input but, while doing so, providing two sites as samples to help ChatGPT understand the style of article. Honestly, I'm not sure ChatGPT didn't ignore that bit of guidance. Next, I'll tell it how to do the bulk of the work:

Inside summarize_article, retrieve the contents of the web page at the URL provided. Using the library Sumy from within PHP and any other libraries necessary, extract the main body of the article, ignoring any ads or embedded materials, and summarize it to approximately 50 words. Make sure the summary consists of complete sentences. You can go above the 50 words to finish the last sentence, if necessary.

This is very similar to how I'd instruct an employee. I'd want that person to know that they weren't only restricted to Sumy. If they needed another tool, I wanted them to use it. 

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I also specified an approximate number of words to create bounds for what I wanted as a summary. A later version of the routine might take that number as a parameter. I then ended by saying what I wanted as a result:

Once processing is complete, code summarize_article so it returns the summary in plain text.

The resulting code is pretty simple. ChatGPT did call on another library (Goose) to retrieve the article contents. It then passed that summary to Sumy with a 50-word limit and then returned the result. But once the basics are written, it's a mere matter of programming to go back in and add tweaks, customize what's passed to the two libraries, and delivering the results.

One interesting point of note. When I originally tried this test in early 2023, ChatGPT created a sample call to the routine it wrote, using a URL from after 2021. At that time, in March 2023, ChatGPT's dataset only went to 2021. Now, the ChatGPT knowledge base extends to the end of December 2023. But my point is that ChatGPT made up a sample link that it couldn't possibly know about:

https://www.reuters.com/business/retail-consumer/teslas-musk-says-fremont-california-factory-may-be-sold-chip-shortage-bites-2022-03-18/

I checked that URL against both Reuters' site and the Wayback Machine, and it doesn't exist. Never assume ChatGPT is accurate. Always double-check everything it gives you.

Does ChatGPT replace programmers? 

Not now -- or, at least -- not yet. ChatGPT programs at the level of a talented first-year programming student, but it's lazy (like that first-year student). The tool might reduce the need for entry-level programmers, but at its current level, I think it will just make life easier for entry-level programmers (and even programmers with more experience) to write code and look up information. It's definitely a time-saver, but there are few programming projects it can do on its own -- at least now. In 2030? Who knows.

How do I get coding answers in ChatGPT?

Just ask it. You saw above how I used an interactive discussion dialog to narrow down the answers I wanted. When you're working with ChatGPT, don't expect one question to magically do all your work for you. But use ChatGPT as a helper and resource, and it will give you a lot of very helpful information. Of course, test that information -- because, as John Schulman, a co-founder of OpenAI, says , "Our biggest concern was around factuality, because the model likes to fabricate things."

Is the code generated by ChatGPT guaranteed to be error-free?

Hell, no! But you also can't trust the code human programmers write. I certainly don't trust any code I write. Code comes out of the code-making process incredibly flawed. There are always bugs. Before you ship, you need to test, test, and test again. Then, alpha test with a few chosen victims. Then beta test with your wider user community. Even after all that, there will be bugs. Just because an AI is playing at this coding thing doesn't mean it can do bug-free code. Do not trust. Always verify. And you still won't have it fully bug-free. Such is the nature of the universe.

How detailed should my description of a programming issue be when asking ChatGPT?

Detailed. Look at it this way: the more you leave open for interpretation, the more the AI will go its own way. When I give prompts to ChatGPT to help me while programming, I imagine I'm assigning a programming task to one of my students or someone who works for me. Did I give that person enough details to go off and create a first draft or will that person have to ask me a ton of additional questions? Worse, will that person have so little guidance that they'll go off in entirely the wrong direction? Don't be lazy here. ChatGPT can save you hours or even days programming (it has for me), but only if you give it useful instructions to begin with.

If I use ChatGPT to write my code, who owns it?

As it turns out, there's not a lot of case law yet to definitively answer this question. The US, Canada, and the UK require something that's copyrighted to have been created by human hands, so code generated by an AI tool may not be copyrightable. There are also issues of liability based on where the training code came from and how the resulting code is used. ZDNET did a deep dive on this topic, spoke to legal experts, and produced the following three articles. If you're concerned about this issue (and if you're using AI to help with code, you should be), I recommend you give them a read.

• Who owns the code? If ChatGPT's AI helps write your app, does it still belong to you?
• If you use AI-generated code, what's your liability exposure?
• A thorny question: Who owns code, images, and narratives generated by AI?

What programming languages does ChatGPT know?

Most of them.  I tested common modern languages , like PHP, Python, Java, Kotlin, Swift, C#, and more. But then I had the tool  write code in obscure dark-age languages like COBOL, Fortran, Forth, LISP, ALGOL, RPG (the report program generator, not the role-playing game), and even IBM/360 assembly language. 

As the icing on the cake, I gave it this prompt:

Write a sequence that displays 'Hello, world' in ascii blinking lights on the front panel of a PDP 8/e

The PDP 8/e was my very first computer , and ChatGPT actually gave me instructions for toggling in a program using front-panel switches. I was impressed, gleeful, and ever so slightly afraid.

Can ChatGPT help me with data analysis and visualization tasks?

Yes, and a lot of it can be done without code. Check out my entire article on this topic:  The moment I realized ChatGPT Plus was a game-changer for my business .

I also did a piece on generated charts and tables:  How to use ChatGPT to make charts and tables

But here's where it gets fun. In the article above, I asked ChatGPT Plus "Make a bar chart of the top five cities in the world by population," and it did. But do you want code? Try asking:

Make a bar chart of the top five cities in the world by population in Swift. Pull the population data from online. Be sure to include any necessary libraries.

By adding "in Swift," you're specifying the programming language. By specifying where the data comes from and forcing ChatGPT Plus to include libraries, it knows to bring in the other resources the program needs. That's why, fundamentally, programming with an AI's help requires you to know things about programming. But if you do, it's cool. Because three sentences can get you a nice chunk of annotated code. Cool, huh?  

How does ChatGPT handle the differences between dialects and implementations of a given programming language?

We don't have exact details on this issue from OpenAI, but our understanding of how ChatGPT is trained can shed some light on this question. Keep in mind that dialects and implementations of programming languages (and their little quirks) change much more rapidly than the full language itself. This reality makes it harder for ChatGPT (and many programming professionals) to keep up.

Also:  How I used ChatGPT to write a custom JavaScript bookmarklet

As such, I'd work off these two assumptions:

• The more recent the dialectic change, the less likely ChatGPT knows about it, and
• The more popular a language overall, the more training data it likely has learned from, and therefore the more accurate it will be.

What's the bottom line? ChatGPT can be a very helpful tool. Just don't ascribe superpowers to it. Yet.

You can follow my day-to-day project updates on social media. Be sure to follow me on Twitter at @DavidGewirtz , on Facebook at Facebook.com/DavidGewirtz , on Instagram at Instagram.com/DavidGewirtz , and on YouTube at YouTube.com/DavidGewirtzTV .

How to use ChatGPT (and what you can use it for)

Code faster with generative ai, but beware the risks when you do, how i test an ai chatbot's coding ability - and you can too.

Researchers’ innovative technology to improve computational fluid dynamics models to be presented at ACM PASC24 Conference

Lucas Johnson

May 15, 2024, 2:29 PM

Vanderbilt researchers’ novel technology that integrates machine learning with traditional fluid simulation methods to enhance the accuracy and efficiency of computational fluid dynamics models will be discussed at the prestigious ACM PASC24 Conference in Zurich, Switzerland, June 3-5.

David Hyde , assistant professor of computer science, will be presenting the paper: “Toward Improving Boussinesq Flow Simulations by Learning with Compressible Flow.” Nurshat Mangnike , a second-year Ph.D. student majoring in computer science, is the paper’s first author.

Researchers say the main problem the paper tries to solve is that simulating complex fluid flows can require solving the compressible Euler equations, which are computationally expensive to solve. To mitigate this, they correct the results of more efficient equations – the Boussinesq approximation – using a neural network they trained to learn the difference between Boussinesq flow simulations and compressible flow simulations.

“It turns out that the inference time for this neural network is negligible,” says Hyde. “So, we get the best of both worlds: a computationally efficient fluid simulation algorithm that is almost as accurate as a traditional compressible flow solver.  We only trained our network and tested our method on a small class of problems, but we believe that this methodology could apply to various kinds of fluid simulation challenges.”

Mangnike says he considers it an honor to have the paper presented at the conference.

“The opportunity to have our work featured in such a respected forum validates our innovative approach,” he says. “This accolade is not only a motivator for us to continue our efforts but also an opportunity to share our insights with the global scientific community, fostering further research and collaboration.”

The PASC Conference series is an international and interdisciplinary platform for the exchange of knowledge in scientific computing and computational science with a strong focus on methods, tools, algorithms, workflows, application challenges, and novel techniques in the context of scientific usage of high-performance computing.

Hyde, who will be making the presentation, received a competitive travel grant award sponsored by ACM SIGHPC .

Contact: Lucas Johnson,  [email protected]

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