engineering dissertation methodology

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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Home » Dissertation Methodology – Structure, Example and Writing Guide

Dissertation Methodology – Structure, Example and Writing Guide

• Table of Contents

Dissertation Methodology

In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

Here are the basic elements that are typically included in a dissertation methodology:

• Introduction : This section should explain the importance and goals of your research .
• Research Design : Outline your research approach and why it’s appropriate for your study. You might be conducting an experimental research, a qualitative research, a quantitative research, or a mixed-methods research.
• Data Collection : This section should detail the methods you used to collect your data. Did you use surveys, interviews, observations, etc.? Why did you choose these methods? You should also include who your participants were, how you recruited them, and any ethical considerations.
• Data Analysis : Explain how you intend to analyze the data you collected. This could include statistical analysis, thematic analysis, content analysis, etc., depending on the nature of your study.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your study. For instance, you could discuss measures taken to reduce bias, how you ensured that your measures accurately capture what they were intended to, or how you will handle any limitations in your study.
• Ethical Considerations : This is where you state how you have considered ethical issues related to your research, how you have protected the participants’ rights, and how you have complied with the relevant ethical guidelines.
• Limitations : Acknowledge any limitations of your methodology, including any biases and constraints that might have affected your study.
• Summary : Recap the key points of your methodology chapter, highlighting the overall approach and rationalization of your research.

Types of Dissertation Methodology

The type of methodology you choose for your dissertation will depend on the nature of your research question and the field you’re working in. Here are some of the most common types of methodologies used in dissertations:

Experimental Research

This involves creating an experiment that will test your hypothesis. You’ll need to design an experiment, manipulate variables, collect data, and analyze that data to draw conclusions. This is commonly used in fields like psychology, biology, and physics.

Survey Research

This type of research involves gathering data from a large number of participants using tools like questionnaires or surveys. It can be used to collect a large amount of data and is often used in fields like sociology, marketing, and public health.

Qualitative Research

This type of research is used to explore complex phenomena that can’t be easily quantified. Methods include interviews, focus groups, and observations. This methodology is common in fields like anthropology, sociology, and education.

Quantitative Research

Quantitative research uses numerical data to answer research questions. This can include statistical, mathematical, or computational techniques. It’s common in fields like economics, psychology, and health sciences.

Case Study Research

This type of research involves in-depth investigation of a particular case, such as an individual, group, or event. This methodology is often used in psychology, social sciences, and business.

Mixed Methods Research

This combines qualitative and quantitative research methods in a single study. It’s used to answer more complex research questions and is becoming more popular in fields like social sciences, health sciences, and education.

Action Research

This type of research involves taking action and then reflecting upon the results. This cycle of action-reflection-action continues throughout the study. It’s often used in fields like education and organizational development.

Longitudinal Research

This type of research involves studying the same group of individuals over an extended period of time. This could involve surveys, observations, or experiments. It’s common in fields like psychology, sociology, and medicine.

Ethnographic Research

This type of research involves the in-depth study of people and cultures. Researchers immerse themselves in the culture they’re studying to collect data. This is often used in fields like anthropology and social sciences.

Structure of Dissertation Methodology

The structure of a dissertation methodology can vary depending on your field of study, the nature of your research, and the guidelines of your institution. However, a standard structure typically includes the following elements:

• Introduction : Briefly introduce your overall approach to the research. Explain what you plan to explore and why it’s important.
• Research Design/Approach : Describe your overall research design. This can be qualitative, quantitative, or mixed methods. Explain the rationale behind your chosen design and why it is suitable for your research questions or hypotheses.
• Data Collection Methods : Detail the methods you used to collect your data. You should include what type of data you collected, how you collected it, and why you chose this method. If relevant, you can also include information about your sample population, such as how many people participated, how they were chosen, and any relevant demographic information.
• Data Analysis Methods : Explain how you plan to analyze your collected data. This will depend on the nature of your data. For example, if you collected quantitative data, you might discuss statistical analysis techniques. If you collected qualitative data, you might discuss coding strategies, thematic analysis, or narrative analysis.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your research. This might include steps you took to reduce bias or increase the accuracy of your measurements.
• Ethical Considerations : If relevant, discuss any ethical issues associated with your research. This might include how you obtained informed consent from participants, how you ensured participants’ privacy and confidentiality, or any potential conflicts of interest.
• Limitations : Acknowledge any limitations in your research methodology. This could include potential sources of bias, difficulties with data collection, or limitations in your analysis methods.
• Summary/Conclusion : Briefly summarize the key points of your methodology, emphasizing how it helps answer your research questions or hypotheses.

How to Write Dissertation Methodology

Writing a dissertation methodology requires you to be clear and precise about the way you’ve carried out your research. It’s an opportunity to convince your readers of the appropriateness and reliability of your approach to your research question. Here is a basic guideline on how to write your methodology section:

1. Introduction

Start your methodology section by restating your research question(s) or objective(s). This ensures your methodology directly ties into the aim of your research.

2. Approach

Identify your overall approach: qualitative, quantitative, or mixed methods. Explain why you have chosen this approach.

• Qualitative methods are typically used for exploratory research and involve collecting non-numerical data. This might involve interviews, observations, or analysis of texts.
• Quantitative methods are used for research that relies on numerical data. This might involve surveys, experiments, or statistical analysis.
• Mixed methods use a combination of both qualitative and quantitative research methods.

3. Research Design

Describe the overall design of your research. This could involve explaining the type of study (e.g., case study, ethnography, experimental research, etc.), how you’ve defined and measured your variables, and any control measures you’ve implemented.

4. Data Collection

Explain in detail how you collected your data.

• If you’ve used qualitative methods, you might detail how you selected participants for interviews or focus groups, how you conducted observations, or how you analyzed existing texts.
• If you’ve used quantitative methods, you might detail how you designed your survey or experiment, how you collected responses, and how you ensured your data is reliable and valid.

5. Data Analysis

Describe how you analyzed your data.

• If you’re doing qualitative research, this might involve thematic analysis, discourse analysis, or grounded theory.
• If you’re doing quantitative research, you might be conducting statistical tests, regression analysis, or factor analysis.

Discuss any ethical issues related to your research. This might involve explaining how you obtained informed consent, how you’re protecting participants’ privacy, or how you’re managing any potential harms to participants.

7. Reliability and Validity

Discuss the steps you’ve taken to ensure the reliability and validity of your data.

• Reliability refers to the consistency of your measurements, and you might discuss how you’ve piloted your instruments or used standardized measures.
• Validity refers to the accuracy of your measurements, and you might discuss how you’ve ensured your measures reflect the concepts they’re supposed to measure.

8. Limitations

Every study has its limitations. Discuss the potential weaknesses of your chosen methods and explain any obstacles you faced in your research.

9. Conclusion

Summarize the key points of your methodology, emphasizing how it helps to address your research question or objective.

Example of Dissertation Methodology

An Example of Dissertation Methodology is as follows:

Chapter 3: Methodology

• Introduction

This chapter details the methodology adopted in this research. The study aimed to explore the relationship between stress and productivity in the workplace. A mixed-methods research design was used to collect and analyze data.

Research Design

This study adopted a mixed-methods approach, combining quantitative surveys with qualitative interviews to provide a comprehensive understanding of the research problem. The rationale for this approach is that while quantitative data can provide a broad overview of the relationships between variables, qualitative data can provide deeper insights into the nuances of these relationships.

Data Collection Methods

Quantitative Data Collection : An online self-report questionnaire was used to collect data from participants. The questionnaire consisted of two standardized scales: the Perceived Stress Scale (PSS) to measure stress levels and the Individual Work Productivity Questionnaire (IWPQ) to measure productivity. The sample consisted of 200 office workers randomly selected from various companies in the city.

Qualitative Data Collection : Semi-structured interviews were conducted with 20 participants chosen from the initial sample. The interview guide included questions about participants’ experiences with stress and how they perceived its impact on their productivity.

Data Analysis Methods

Quantitative Data Analysis : Descriptive and inferential statistics were used to analyze the survey data. Pearson’s correlation was used to examine the relationship between stress and productivity.

Qualitative Data Analysis : Interviews were transcribed and subjected to thematic analysis using NVivo software. This process allowed for identifying and analyzing patterns and themes regarding the impact of stress on productivity.

Reliability and Validity

To ensure reliability and validity, standardized measures with good psychometric properties were used. In qualitative data analysis, triangulation was employed by having two researchers independently analyze the data and then compare findings.

Ethical Considerations

All participants provided informed consent prior to their involvement in the study. They were informed about the purpose of the study, their rights as participants, and the confidentiality of their responses.

Limitations

The main limitation of this study is its reliance on self-report measures, which can be subject to biases such as social desirability bias. Moreover, the sample was drawn from a single city, which may limit the generalizability of the findings.

Where to Write Dissertation Methodology

In a dissertation or thesis, the Methodology section usually follows the Literature Review. This placement allows the Methodology to build upon the theoretical framework and existing research outlined in the Literature Review, and precedes the Results or Findings section. Here’s a basic outline of how most dissertations are structured:

• Acknowledgements
• Literature Review (or it may be interspersed throughout the dissertation)
• Methodology
• Results/Findings
• References/Bibliography

In the Methodology chapter, you will discuss the research design, data collection methods, data analysis methods, and any ethical considerations pertaining to your study. This allows your readers to understand how your research was conducted and how you arrived at your results.

Advantages of Dissertation Methodology

The dissertation methodology section plays an important role in a dissertation for several reasons. Here are some of the advantages of having a well-crafted methodology section in your dissertation:

• Clarifies Your Research Approach : The methodology section explains how you plan to tackle your research question, providing a clear plan for data collection and analysis.
• Enables Replication : A detailed methodology allows other researchers to replicate your study. Replication is an important aspect of scientific research because it provides validation of the study’s results.
• Demonstrates Rigor : A well-written methodology shows that you’ve thought critically about your research methods and have chosen the most appropriate ones for your research question. This adds credibility to your study.
• Enhances Transparency : Detailing your methods allows readers to understand the steps you took in your research. This increases the transparency of your study and allows readers to evaluate potential biases or limitations.
• Helps in Addressing Research Limitations : In your methodology section, you can acknowledge and explain the limitations of your research. This is important as it shows you understand that no research method is perfect and there are always potential weaknesses.
• Facilitates Peer Review : A detailed methodology helps peer reviewers assess the soundness of your research design. This is an important part of the publication process if you aim to publish your dissertation in a peer-reviewed journal.
• Establishes the Validity and Reliability : Your methodology section should also include a discussion of the steps you took to ensure the validity and reliability of your measurements, which is crucial for establishing the overall quality of your research.

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• Dissertation
• 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.

Prevent plagiarism, run a free check.

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.

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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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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Writing the Dissertation - Guides for Success: The Methodology

• Writing the Dissertation Homepage
• Overview and Planning
• The Literature Review
• The Methodology
• The Results and Discussion
• The Conclusion
• The Abstract
• Getting Started
• What to Avoid

Overview of writing the methodology

The methodology chapter precisely outlines the research method(s) employed in your dissertation and considers any relevant decisions you made, and challenges faced, when conducting your research. Getting this right is crucial because it lays the foundation for what’s to come: your results and discussion.

Disciplinary differences

Please note: this guide is not specific to any one discipline. The methodology can vary depending on the nature of the research and the expectations of the school or department. Please adapt the following advice to meet the demands of your dissertation and the expectations of your school or department. Consult your supervisor for further guidance; you can also check out our  Writing Across Subjects guide .

Guide contents

As part of the Writing the Dissertation series, this guide covers the most common conventions found in a methodology chapter, giving you the necessary knowledge, tips and guidance needed to impress your markers!  The sections are organised as follows:

• Getting Started  - Defines the methodology and its core characteristics.
• Structure  - Provides a detailed walk-through of common subsections or components of the methodology.
• What to Avoid  - Covers a few frequent mistakes you'll want to...avoid!
• FAQs  - Guidance on first- vs. third-person, secondary literature and more.
• Checklist  - Includes a summary of key points and a self-evaluation checklist.

Training and tools

• The Academic Skills team has recorded a Writing the Dissertation workshop series to help you with each section of a standard dissertation, including a video on writing the method/methodology .
• For more on methods and methodologies, you can check out USC's methodology research guide  and Huddersfield's guide to writing the methodology of an undergraduate dissertation .
• The dissertation planner tool can help you think through the timeline for planning, research, drafting and editing.
• iSolutions offers training and a Word template to help you digitally format and structure your dissertation.

What is the methodology?

The methodology of a dissertation is like constructing a house of cards. Having strong and stable foundations for your research relies on your ability to make informed and rational choices about the design of your study. Everything from this point on – your results and discussion –  rests on these decisions, like the bottom layer of a house of cards.

The methodology is where you explicitly state, in relevant detail, how you conduced your study in direct response to your research question(s) and/or hypotheses. You should work through the linear process of devising your study to implementing it, covering the important choices you made and any potential obstacles you faced along the way.

Methods or methodology?

Some disciplines refer to this chapter as the research methods , whilst others call it the methodology . The two are often used interchangeably, but they are slightly different. The methods chapter outlines the techniques used to conduct the research and the specific steps taken throughout the research process. The methodology also outlines how the research was conducted, but is particularly interested in the philosophical underpinning that shapes the research process. As indicated by the suffix, -ology , meaning the study of something, the methodology is like the study of research, as opposed to simply stating how the research was conducted.

This guide focuses on the methodology, as opposed to the methods, although the content and guidance can be tailored to a methods chapter. Every dissertation is different and every methodology has its own nuances, so ensure you adapt the content here to your research and always consult your supervisor for more detailed guidance.

What are my markers looking for?

Your markers are looking   for your understanding of the complex process behind original (see definition) research. They are assessing your ability to...

• Demonstrate   an understanding of the impact that methodological choices can have on the reliability and validity of your findings, meaning you should engage with ‘why’ you did that, as opposed to simply ‘what’ you did.
• Make   informed methodological choices that clearly relate to your research question(s).

But what does it mean to engage in 'original' research? Originality doesn’t strictly mean you should be inventing something entirely new. Originality comes in many forms, from updating the application of a theory, to adapting a previous experiment for new purposes – it’s about making a worthwhile contribution.

Structuring your methodology

The methodology chapter should outline the research process undertaken, from selecting the method to articulating the tool or approach adopted to analyse your results. Because you are outlining this process, it's important that you structure your methodology in a linear way, showing how certain decisions have impacted on subsequent choices.

Scroll to continue reading, or click a link below to jump immediately to that section:

The 'research onion'

To ensure you write your methodology in a linear way, it can be useful to think of the methodology in terms of layers, as shown in the figure below.

Figure: 'Research onion' from Saunders et al. (2007).

You don't need to precisely follow these exact layers as some won't be relevant to your research. However, the layered 'out to in' structure developed by Saunders et al. (2007) is appropriate for any methodology chapter because it guides your reader through the process in a linear fashion, demonstrating how certain decisions impacted on others. For example, you need to state whether your research is qualitative, quantitative or mixed before articulating your precise research method. Likewise, you need to explain how you collected your data before you inform the reader of how you subsequently analysed that data.

Using this linear approach from 'outer' layer to 'inner' layer, the next sections will take you through the most common layers used to structure a methodology chapter.

Introduction and research outline

Like any chapter, you should open your methodology with an introduction. It's good to start by briefly restating the research problem, or gap, that you're addressing, along with your research question(s) and/or hypotheses. Following this, it's common to provide a very condensed statement that outlines the most important elements of your research design. Here's a short example:

This study adopted qualitative research through a series of semi-structured interviews with seven experienced industry professionals.

Like any other introduction, you can then provide a brief statement outlining what the chapter is about and how it's structured (e.g., an essay map ).

Restating the research problem (or gap) and your research question(s) and/or hypotheses creates a natural transition from your previous review of the literature - which helped you to identify the gap or problem - to how you are now going to address such a problem. Your markers are also going to assess the relevance and suitability of your method and methodological choices against your research question(s), so it's good to 'frame' the entire chapter around the research question(s) by bringing them to the fore.

Research philosophy

A research philosophy is an underlying belief that shapes the way research is conducted. For this reason, as featured in the 'research onion' above, the philosophy should be the outermost layer - the first methodological issue you deal with following the introduction and research outline - because every subsequent choice, from the method employed to the way you analyse data, is directly influenced by your philosophical stance.

You can say something about other philosophies, but it's best to directly relate this to your research and the philosophy you have selected - why the other philosophy isn't appropriate for you to adopt, for instance. Otherwise, explain to your reader the philosophy you have selected (using secondary literature), its underlying principles, and why this philosophy, therefore, is particularly relevant to your research.

The research philosophy is sometimes featured in a methodology chapter, but not always. It depends on the conventions within your school or discipline , so only include this if it's expected.

The reason for outlining the research philosophy is to show your understanding of the role that your chosen philosophy plays in shaping the design and approach of your research study. The philosophy you adopt also indicates your worldview (in the context of this research), which is an important way of highlighting the role you, the researcher, play in shaping new knowledge.

Research method

This is where you state whether you're doing qualitative, quantitative or mixed-methods research before outlining the exact instrument or strategy (see definition) adopted for research (interviews, case study, etc.). It's also important that you explain why you have chosen that particular method and strategy. You can also explain why you're not adopting an alternate form of research, or why you haven't used a particular instrument, but keep this brief and use it to reinforce why you have chosen your method and strategy.

Your research method, more than anything else, is going to directly influence how effectively you answer your research question(s). For that reason, it's crucial that you emphasise the suitability of your chosen method and instrument for the purposes of your research.                       

Data collection

The data collection part of your methodology explain the process of how you accessed and collected your data. Using an interview as a qualitative example, this might include the criteria for selecting participants, how you recruited the participants and how and where you conducted the interviews. There is often some overlap with data collection and research method, so don't worry about this. Just make sure you get the essential information across to your reader.

The details of how you accessed and collected your data are important for replicability purposes - the ability for someone to adopt the same approach and repeat the study. It's also important to include this information for reliability and consistency purposes (see  validity and reliability  on the next tab of this guide for more).

Data analysis

After describing how you collected the data, you need to identify your chosen method of data analysis. Inevitably, this will vary depending on whether your research is qualitative or quantitative (see note below).

Qualitative research tends to be narrative-based where forms of ‘coding’ are employed to categorise and group the data into meaningful themes and patterns (Bui, 2014). Quantitative deals with numerical data meaning some form of statistical approach is taken to measure the results against the research question(s).

Tell your reader which data analysis software (such as SPSS or Atlast.ti) or method you’ve used and why, using relevant literature. Again, you can mention other data analysis tools that you haven’t used, but keep this brief and relate it to your discussion of your chosen approach. This isn’t to be confused with the results and discussion chapters where you actually state and then analyse your results. This is simply a discussion of the approach taken, how you applied this approach to your data and why you opted for this method of data analysis.

Detail of how you analysed your data helps to contextualise your results and discussion chapters. This is also a validity issue (see next tab of guide), as you need to ensure that your chosen method for data analysis helps you to answer your research question(s) and/or respond to your hypotheses. To use an example from Bui (2014: 155), 'if one of the research questions asks whether the participants changed their behaviour before and after the study, then one of the procedures for data analysis needs to be a comparison of the pre- and postdata'.

Validity and reliability

Validity simply refers to whether the research method(s) and instrument(s) applied are directly suited to meet the purposes of your research – whether they help you to answer your research question(s), or allow you to formulate a response to your hypotheses.

Validity can be separated into two forms: internal and external. The difference between the two is defined by what exists inside the study (internal) and what exists outside the study (external).

• Internal validity is the extent to which ‘the results obtained can be attributed to the manipulation of the independent variable' (Salkind, 2011: 147).
• External validity refers to the application of your study’s findings outside the setting of your study. This is known as generalisability , meaning to what extent are the results applicable to a wider context or population.

Reliability

Reliability refers to the consistency with which you designed and implemented your research instrument(s). The idea behind this is to ensure that someone else could replicate your study and, by applying the instrument in the exact same way, would achieve the same results. This is crucial to quantitative and scientific based research, but isn’t strictly the case with qualitative research given the subjective nature of the data.

With qualitative data, it’s important to emphasise that data was collected in a consistent way to avoid any distortions. For example, let’s say you’ve circulated a questionnaire to participants. You would want to ensure that every participant receives the exact same questionnaire with precisely the same questions and wording, unless different questionnaires are required for different members of the sample for the purposes of the research.

Ethical considerations

Any research involving human participants needs to consider ethical factors. In response, you need to show your markers that you have implemented the necessary measures to cover the relevant ethical issues. These are some of the factors that are typically included:

• How did you gain the consent of participants, and how did you formally record this consent?
• What measures did you take to ensure participants had enough understanding of their role to make an informed decision, including the right to withdraw at any stage?
• What measures did you take to maintain the confidentiality of participants during the research and, potentially, for the write-up?
• What measures did you take to store the raw data and protect it from external access and use prior to the write-up?

These are only a few examples of the ethical factors you need to write about in your methodology. Depending on the nature of your research, ethical considerations might form a significant part of your methodology chapter, or may only constitute a few sentences. Either way, it’s imperative that you show your markers that you’ve considered the relevant ethical implications of your research.

Limitations

Don’t make the mistake of ignoring the limitations of your study (see the next tab, 'What to Avoid', for more on this) – it’s a common part of research and should be confronted. Limitations of research can be diverse, but tend to be logistical issues relating to time, scope and access . Whilst accepting that your study has certain limitations, the key is to put a positive spin on it, like the example below:

Despite having a limited sample size compared to other similar studies, the number of participants is enough to provide sufficient data, whilst the in-depth nature of the interviews facilitates detailed responses from participants.

• Bui, Y. N. (2014) How to Write a Master’s Thesis. 2dn Edtn. Thousand Oaks, CA: Sage.
• Guba, E. G. and Lincoln, Y. S. (1994) ‘Competing paradigms in qualitative research’, in Denzin, N. K. and Lincoln, N. S. (eds.) Handbook of Qualitative Research. Thousand Oaks, CA: Sage, pp. 105-117.
• Salkind, N. J. (2011) ‘Internal and external validity’, in Moutinho, L. and Hutchenson, G. D. (eds.) The SAGE Dictionary of Quantitative Management Research . Thousand Oaks, CA: Sage, pp. 147-149.
• Saunders, M., Lewis, P. and Thornhill, A. (2007) Research Methods for Business Students . 4th Edtn. Harlow: Pearson.

What to avoid

This portion of the guide will cover some common missteps you should try to avoid in writing your methodology.

Ignoring limitations

It might seem instinctive to hide any flaws or limitations with your research to protect yourself from criticism. However, you need to highlight any problems you encountered during the research phase, or any limitations with your approach. Your markers are expecting you to engage with these limitations and highlight the kind of impact they may have had on your research.

Just be careful that you don’t overstress these limitations. Doing so could undermine the reliability and validity of your results, and your credibility as a researcher.

Literature review of methods

Don’t mistake your methodology chapter as a detailed review of methods employed in other studies. This level of detail should, where relevant, be incorporated in the literature review chapter, instead (see our Writing the Literature Review guide ). Any reference to methodological choices made by other researchers should come into your methodology chapter, but only in support of the decisions you made.

Unnecessary detail

It’s important to be thorough in a methodology chapter. However, don’t include unnecessary levels of detail. You should provide enough detail that allows other researchers to replicate or adapt your study, but don’t bore your reader with obvious or extraneous detail.

Any materials or content that you think is worth including, but not essential in the chapter, could be included in an appendix (see definition). These don’t count towards your word count (unless otherwise stated), and they can provide further detail and context for your reader. For instance, it’s quite common to include a copy of a questionnaire in an appendix, or a list of interview questions.

Q: Should the methodology be in the past or present tense?

A: The past tense. The study has already been conducted and the methodological decisions have been implemented, meaning the chapter should be written in the past tense. For example...

Data was collected over the course of four weeks.

I informed participants of their right to withdraw at any time.

The surveys included ten questions about job satisfaction and ten questions about familial life (see Appendix).

Q: Should the methodology include secondary literature?

A: Yes, where relevant. Unlike the literature review, the methodology is driven by what you did rather than what other people have done. However, you should still draw on secondary sources, when necessary, to support your methodological decisions.

Q: Do you still need to write a methodology for secondary research?

A: Yes, although it might not form a chapter, as such. Including some detail on how you approached the research phase is always a crucial part of a dissertation, whether primary or secondary. However, depending on the nature of your research, you may not have to provide the same level of detail as you would with a primary-based study.

For example, if you’re analysing two particular pieces of literature, then you probably need to clarify how you approached the analysis process, how you use the texts (whether you focus on particular passages, for example) and perhaps why these texts are scrutinised, as opposed to others from the relevant literary canon.

In such cases, the methodology may not be a chapter, but might constitute a small part of the introduction. Consult your supervisor for further guidance.

Q: Should the methodology be in the first-person or third?

A: It’s important to be consistent , so you should use whatever you’ve been using throughout your dissertation. Third-person is more commonly accepted, but certain disciplines are happy with the use of first-person. Just remember that the first-person pronoun can be a distracting, but powerful device, so use it sparingly. Consult your supervisor for further guidance.

It’s important to remember that all research is different and, as such, the methodology chapter is likely to be very different from dissertation to dissertation. Whilst this guide has covered the most common and essential layers featured in a methodology, your methodology might be very different in terms of what you focus on, the depth of focus and the wording used.

What’s important to remember, however, is that every methodology chapter needs to be structured in a linear, layered way that guides the reader through the methodological process in sequential order. Through this, your marker can see how certain decisions have impacted on others, showing your understanding of the research process.

Here’s a final checklist for writing your methodology. Remember that not all of these points will be relevant for your methodology, so make sure you cover whatever’s appropriate for your dissertation. The asterisk (*) indicates any content that might not be relevant for your dissertation. You can download a copy of the checklist to save and edit via the Word document, below.

• Methodology self-evaluation checklist

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Dissertations - systems engineering department,                                  systems engineering student dissertations.

All Dissertations can be found at the Dudley Knox Library Website on the Calhoun webpage . 

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USEFUL MEASURES OF COMPLEXITY: A MODEL OF ASSESSING DEGREE OF COMPLEXITY IN ENGINEERED SYSTEMS AND COMPLEX PROJECTS

Abstract:   Many modern systems are very complex, a reality which can affect their safety and reliability of operations. Systems engineers need new ways to measure problem complexity. This research lays the groundwork for measuring the complexity of systems engineering (SE) projects. This research proposes a project complexity measurement model (PCMM) and associated methods to measure complexity. To develop the PCMM, we analyze four major types of complexity (structural complexity, temporal complexity, organizational complexity, and technological complexity) and define a set of complexity metrics. Through a survey of engineering projects, we also develop project profiles for three types of software projects typically used in the U.S. Navy to provide empirical evidence for the PCMM. The results of our work on these projects show that as a project increases in complexity, the more difficult and expensive it is for a project to meet all requirements and schedules because of changing interactions and dynamics among the project participants and stakeholders. The three projects reveal reduction of project complexity by setting a priority and a baseline in requirements and project scope, concentrating on the expected deliverable, strengthening familiarity of the systems engineering process, eliminating redundant processes, and clarifying organizational roles and decision-making processes to best serve the project teams while also streamlining on business processes and information systems.

Read the full dissertation at https://calhoun.nps.edu/handle/10945/68753 

Cuong Ton (Graduated March 27, 2021)

Advisors/Committee

Wei Kang (Member), Robert Harney (Supervisor), Clifford Whitcomb (Member), Ronald Giachetti (Supervisor - CHAIR), Douglas Van Bossuyt (Member), Robert Semmens (Member)

RESILIENCE ASSESSMENT OF ISLANDED RENEWABLE ENERGY MICROGRIDS

Abstract:   The military’s installations on remote islands have the highest power costs and demand resilient and reliable power for mission assurance. These installations have no electrical connection to an external utility provider and encounter numerous challenges in incorporating renewable energy, and there is a pronounced gap in both defining resilience and measuring it for off-grid islanded microgrids at islanded naval installations (INIs). This work’s research objective was to develop a methodology to choose renewable energy microgrid designs that maximize resilience and minimize costs on remote islands with applications for INIs. The deliverable is a tool that incorporates the methodology to identify the cost of resilience using a measure that captures the area under the resilience curve. The tool uses the models developed in this research to create the resilience and cost trade-off curves for different microgrid design and maintenance options to enable decision makers to choose an optimal microgrid design primarily for remote islanded military installations like San Nicolas Island. The research concluded that resilience can be improved by using optimal power capacity ratios for a renewable energy microgrids, that redundancy improves resilience for less costs, and that more maintenance only improves resilience when the generation capacity is closer to the demand and for microgrids with less redundancy.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/66574

William W. Anderson (Graduated December 18, 2020)

Giovanna Oriti (Member), Fotis Papoulias (Member), Ronald Giachetti (Supervisor - CHAIR), Douglas Van Bossuyt (Member), Richard Carlin (Non-NPS Member)

IDENTIFICATION OF BEHAVIOR PATTERNS IN SYSTEMS-OF-SYSTEMS ARCHITECTURES

Abstract:   This dissertation presents a methodology to derive inherent behavior patterns in system-of-systems architectures. When considering the possibility of not only the intended behaviors of a system but also the alternative, unintended behaviors of the system, the developer needs to evaluate hundreds of possible outcomes, even for a simple model. This set of outcomes grows to tens of thousands for a reasonably complex model, driving the need for some means of systematic analysis that ensures the evaluation of all possibilities. This dissertation outlines a methodology that enables the developer to describe the behaviors of a particular system; define the interaction of the system with other systems, users, and the environment; add constraints to the model; derive patterns from the set of possible outcomes; analyze and interpret the results; and re-use the model for other problems of interest. The methodology employs the Monterey Phoenix behavior modeling concepts and tools as a light-weight, formal method. The dissertation details a cross-domain example that demonstrates the methodology and outlines multiple applications, serving as verification of the approach.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/64902

John J. Quartuccio (Graduated March 27, 2020)

Mikhail Auguston (Member), Clifford Whitcomb (Member), Kristin Giammarco (Supervisor - CHAIR), Raymond Madachy (Member), Ronald Giachetti (Member)

A FRAMEWORK FOR ENGINEERED COMPLEX ADAPTIVE SYSTEMS OF SYSTEMS

Abstract:   This dissertation presents a theory for complex adaptive systems of systems (CASoS) as a new class of systems that can be engineered as solutions to highly complex problems. The exponential growth in technology, demands from a warfighting community to rapidly address operational challenges, and dynamic, highly complex environments overwhelm traditional engineering approaches. This study followed a grounded theory methodology. Thorough examination of systems and complexity theory knowledge domains and engineering disciplines resulted in a conceptual CASoS theory. The theory establishes the definition, characteristics, and principles of this new class of systems. Implications for this new class of systems identify unique capability requirements that are the bases for developing an engineering solution: 1) CASoS adjust to their environment through complex interactions among their self-organizing constituent systems, giving rise to purposeful emergent multi-level and multi-minded behavior, and 2) CASoS require an adaptive architecture that enables intelligent constituent systems with the ability to discover knowledge and predict the outcomes and effects of their actions. The CASoS systems engineering approach is a top-down and adaptive process that relies on continuous and ongoing design and development in parallel with operations. In defining a new systems domain, this research offers a framework to develop an engineered CASoS solution to highly complex problems.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/63463

Bonnie W. Johnson (Graduated September 27, 2019)

Wayne Porter (Member), Alejandro Hernandez (Supervisor - CHAIR), Clifford Whitcomb (Member), Anthony Pollman (Member), Karen Holness (Member)

A SET-BASED APPROACH TO SYSTEMS DESIGN

Abstract:   A set-based design (SBD) approach is proposed as an alternative to traditional point-based design (PBD) methodologies. SBD is compared to other common engineering, decision-making, and optimization methods to illustrate how conventional methods do not ordinarily embrace set-based thinking (SBT) or SBD methodologies. The predominant features of Toyota's approach are summarized, leading to seven characteristics and two principles required to identify a design approach as set-based. Several Latin hypercube (LHC) sampling strategies and the distinguishing characteristics of each are described for use in creating and refining sets. Methods of set reduction and elimination are introduced, and topics related to engineering reasoning in set reduction, expectations, SBT, when to use SBD, benefits, challenges, and metrics are discussed. Improved SBD process steps are proposed and demonstrated in an unmanned air system (UAS) example. A specific type of LHC is chosen to generate points in the design space, which are then used as inputs into a simulation tool. Approaching the UAS example problem in a set-based way results in more viable options with higher system-level performance for comparable cost than if a PBD approach were used.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/62256

Jamie M. Gumina (Graduated March 29, 2019)

Rudolph Darken (Member), Alejandro Hernandez (Member), Eugene Paulo (Member), Clifford Whitcomb (Supervisor), Paul Beery (Member)

OPERATIONAL MISSION ARCHITECTURE FRAMEWORK: A BLENDED ARCHITECTURE METHODOLOGY FOR ENABLING OPERATIONAL CAPABILITY

Abstract:   When called upon, the Department of Defense (DoD) typically organizes and integrates mission capabilities from across the enterprise, operates as a joint force, disassembles when the mission is complete, and prepares for the next potential mission. This dissertation presents an organizing construct and associative mapping tool that enables the systems engineering of this episodic joint operational mission capability. The Operational Mission Architecture Framework (OMAF) organizes the key elements of joint operational capability into an intuitive framework, orienting systems engineers to this critical perspective. With operational mission capability now in architecture form, enterprise architecture methodologies can then be applied directly to operational missions. The Operational Blended Architecture Map (OBAM) serves as the integrating mechanism. This blended approach allows the operational community to communicate in its own terminology with systems engineers, who, in turn, can execute enterprise-architecting activities in their own terminology, facilitated by this associative mapping matrix. OMAF/OBAM enables the desired top-down systems engineering effort for joint operational capability and Systems of Systems development. The cumulative effect of OMAF/OBAM provides the integrating function for a DoD capability development enterprise architecture. Without an enterprise approach, the DoD will continue to be challenged to deliver 21st century joint warfighting capability.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/60413

Thomas Irwin (Graduated September 21, 2018)

Wayne Porter (Member), Eugene Paulo (Supervisor - CHAIR), Paul Beery (Member), Anthony Pollman (Member), Steve Gillespie (Non-NPS Member)

AGENT AND COMPONENT OBJECT FRAMEWORK FOR CONCEPT DESIGN MODELING OF MOBILE CYBER-PHYSICAL SYSTEMS

Abstract:   Military intelligent ground vehicle systems (MIGVS) have a wide variation in computationally controlled behavior logic that involves the interaction of both cyber and physical components as well as more typical systems engineering modeling needs and constraints. Current system concept design methods do not sufficiently address either the variation in cyber behavior linked to mission effectiveness or the integrated dependencies and interaction between the cyber and physical components. In this work, model-based concepts are developed to capture the required behavior logic as solution and assembly independent state-based agents and objects. These logical objects can be realized by alternative implementations and assembly aggregations, to include “human assemblies.” The approach contributes a more thorough and robust model of the subject problem domain. These concepts include an agent and component object system data metamodel, supporting structural system classes, and state-based behavior concepts. The concepts are applied to a case study project to produce a solution-independent system concept design.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/58286

Curtis Adams (Graduated March 30, 2018)

Marcus Stefanou (Member), Raymond Madachy (Member), Ronald Giachetti (Supervisor - CHAIR), Bryan O'Halloran (Member), John Reed (Non-NPS Member)

MISSION-BASED ARCHITECTURE FOR SWARM COMPOSABILITY (MASC)

Abstract:   This research introduces the Mission-based Architecture for Swarm Composability (MASC) and methodology. This dissertation applies a mission engineering approach with model based systems engineering foundations to formalize a swarm architecture, which is an example of a complex adaptive system. This architectural framework and methodology extend current swarm system design methods, which are primarily bottom-up approaches focused on the behavior of individual agents. MASC introduces a top-down, hierarchical approach with an overarching mission decomposed into phases, tactics, plays, and algorithms. MASC is applied to three unmanned aerial vehicle swarm case studies and assessed for incorporating mission doctrine, enhancing architecture reusability, and improving user accessibility. The assessment of these three factors indicates that MASC improves the state-of-the art methods in complex adaptive system architecture design.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/58300

CDR Kathleen Giles (Graduated March 30, 2018)

Eugene Paulo (Member), Kristin Giammarco (Supervisor - CHAIR), Timothy Chung (Member), Ronald Giachetti (Member), Raymond Buettner (Member)

METHODOLOGY FOR THE SYSTEMS INTEGRATION OF ADAPTIVE RESILIENCE IN ARMOR 

Abstract:   This dissertation introduces a novel augmentation to system-engineering methodology based on the integration of adaptive capacity, which produces enhanced resilience in technological systems that operate in complex operating environments. The implementation of this methodology enhances system resistance to top-level function failure or accelerates the system's functional recovery in the event of a top-level function failure due to functional requirement shift, evolutions or perturbations. Specifically, the dissertation defines and proposes a methodology to integrate adaptive resilience and demonstrates its implementation in a relevant armor system case study. The conceptual validity of the methodology is proven through a physical comparative test and evaluation of the system described in the case study. The research and resulting methodology supplements and enhances traditional system-engineering processes by offering systems designers the opportunity to integrate adaptive capacity into systems, enhancing their resilient resistance or recovery to top-level function failure in complex operating environments. The research expands traditional and contemporary systems engineering, design, and integration methodologies, which currently do not explicitly address system adaptation and resilience. The methodology accomplishes this objective by defining adaptive design considerations, identifying controllable adaptive performance factors, characterizing adaptive performance factors and configurations, mapping and integrating adaptive components, and verifying and validating the adaptive components and configurations that achieve system requirements and adaptive design considerations. The utility of this research and methodology is demonstrated through development of an adaptive resilient armor system called the mechanically adaptive armor linkage (MAAL), which was designed, developed, and validated using the methodology for the system integration of adaptive resilience (MSIAR).

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/50515

LTC Joseph Cannon (Graduated September 23, 2016)

Claudia Luhrs (Member), Robert Harney (Member), Douglas Nelson (Member), Eugene Paulo (Supervisor - CHAIR), John Reed (Non-NPS Member)

A MODEL-BASED SYSTEMS ENGINEERING METHODOLOGY FOR EMPLOYING ARCHITECTURE IN SYSTEMS ANALYSIS:  DEVELOPING SIMULATION MODELS USING SYSTEMS MODELING LANGUAGE PRODUCTS TO LINK ARCHITECTURE AND ANALYSIS

Abstract:  This dissertation contributes to model-based systems engineering (MBSE) by formally defining an MBSE methodology for employing architecture in system analysis (MEASA) that presents a comprehensive framework detailing the relationship between system architecture products and external models and simulations used to analyze system performance and feasibility. Specifically, the research combines the use of Systems Modeling Language (SysML) products and operational simulation models to support assessment of system requirements for systems engineering. The MBSE MEASA transforms operational needs into preferred system configurations through the analysis of detailed simulation models. The research does this by using designed experiments to generate architecture tradespace visualizations that highlight the impact that system design parameters, system-environment interactions, system operational implementation, and system component interactions have on system performance. The research demonstrates a procedure for iterations of the methodology when analysis suggests potentially impactful design, operational, or environmental variables (as well as potential interactions between those variables). The research develops and analyzes notional architecture products and simulation models of United States Navy mine warfare systems to demonstrate an application of the MBSE MEASA.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/49363

Paul Beery (Graduated June 17, 2016)

Rudolph Darken (Member), Matthew Boensel (Member), Douglas Nelson (Member), Eugene Paulo (Supervisor - CHAIR), Kristin Giammarco (Member)

THE SYSTEM OF SYSTEMS ARCHITECTURE FEASIBILITY ASSESSMENT MODEL

Abstract:  This research presents the system of systems (SoS) tradespace definition methodology (SoS-TDM) and SoS architecture feasibility assessment model (SoS-AFAM). Together, these extend current model-based systems engineering (MBSE) and SoS engineering (SoSE) methodologies. In particular, they extend the methods of tradespace exploration to considerations of multiple perspectives of an SoS—the physical, process, and organization. In considering multiple perspectives of an SoS, one better defines the SoS and is more likely to correctly represent its performance in an analysis model. The SoS-TDM defines an SoS tradespace by progressively winnowing the design space with increasingly strict definitions of feasibility and then exhaustively analyzing the remaining points. The SoS-AFAM defines and assesses SoS architecture feasibility through a variety of tests that consider the aforementioned aspects of an SoS. Together, these methods may be integrated with existing MBSE and SoSE methodologies and extend their utility.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/49467

CPT Stephen Gillespie (Graduated June 17, 2016)

Rudolph Darken (Member), Alejandro Hernandez (Member), Eugene Paulo (Supervisor - CHAIR), Paul Beery (Member), Ronald Giachetti (Member)

UN-BUILDING BLOCKS:  DEVELOPING A MODEL OF REVERSE ENGINEERING AND SEARCHING FOR APPLICABLE HEURISTICS

Abstract:  Reverse engineering is the problem-solving activity that ensues when one takes a human-made system, whole or in part, and attempts—through systematic analysis of its physical characteristics and other available evidence—to answer one or more of the following questions: What is this for? What does it do? How does it do it? What is inside it? How was it made? A model developed from a synthesis of the technical literature is used to infer modes of failure in the process of reverse engineering and identify and catalog applicable experience-based techniques known as heuristics. The model is then cast in an executable formal language in order to further test its assumptions, and explore its implications. Hands-on, historic, and virtual case studies are used to validate and refine the model. The modes of failure, heuristics, and the model itself in its original and formal language expressions, introduce a new descriptive terminology of reverse engineering and provide a new framework to interpret real world reverse engineering activity.

Read the full dissertation at  https://calhoun.nps.edu/handle/10945/47948

CDR Jorge Garcia (Graduated December 18, 2015)

Robert Harney (Supervisor - CHAIR), Douglas Nelson (Member), Ravi Vaidyanathan (Member), Kristin Giammarco (Member), Donald Brutzman (Member)

Research methods in engineering design: a synthesis of recent studies using a systematic literature review

• Original Paper
• Open access
• Published: 16 January 2023
• Volume 34 , pages 221–256, ( 2023 )

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• David Escudero-Mancebo   ORCID: orcid.org/0000-0003-0849-8803 1 , 2 ,
• Nieves Fernández-Villalobos   ORCID: orcid.org/0000-0003-2559-0652 1 , 3 ,
• Óscar Martín-Llorente   ORCID: orcid.org/0000-0001-9200-5797 1 &
• Alejandra Martínez-Monés   ORCID: orcid.org/0000-0003-3201-0345 2  

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The relation between scientific research and engineering design is fraught with controversy. While the number of academic PhD programs on design grows, because the discipline is in its infancy, there is no consolidated method for systematically approaching the generation of knowledge in this domain. This paper reviews recently published papers from four top-ranked journals in engineering design to analyse the research methods that are frequently used. The research questions consider the aim and contributions of the papers, as well as which experimental design and which sources of data are being used. Frequency tables show the high variety of approaches and aims of the papers, combining both qualitative and quantitative empirical approaches and analytical methods. Most of the papers focus on methodological concerns or on delving into a particular aspect of the design process. Data collection methods are also diverse without a clear relation between the type of method and the objective or strategy of the research. This paper aims to act as a valuable resource for academics, providing definitions related to research methods and referencing examples, and for researchers, shedding light on some of the trends and challenges for current research in the domain of engineering design.

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1 Introduction

Doctoral studies have a long tradition in higher education systems (Bogle 2018 ). Doctoral studies are highly relevant because they are considered as a key for technical development and industrial excellence in developed countries. Normally, a PhD diploma is compulsory for pursuing and it is highly valued for getting involved in research projects in companies. The goal of doctoral programs is to provide postgraduates with competences for the generation of knowledge in a given domain. The means to generate knowledge depends on the area, being research methods and techniques potentially different, and evolving in parallel with the development of the domain. In young domains such as Engineering Design, the discussion about which research procedures and paradigms should be employed is still open.

Simon ( 1996 ), in his book The Science of Design ,  defined design as a search for an optimum in a space of alternatives that take into account the specifications and restrictions of a given problem. Hatchuel ( 2001 ) highlighted limitations of Simon’s position discussing that designing cannot be reduced to taking decisions among a bounded set because the number of concepts related to the problem and the possible number of decisions to be taken could be expandable and uncountable, not only due to human creativity but also to social interaction. (Subrahmanian et al. 2020 ) place Simon and Hatchuel’s approaches in a historical timeline that describes different models of how designing is understood, evidencing the challenges for research design as a discipline that defines a common language that includes the impact of context and users in designing, in addition to the problems.. Probably due to the youth of design as a research discipline, or due to its socio-technical nature, it does not yet have a consolidated research methods and techniques. Blessing and Chakrabarti ( 2009 ) proposed the DRM (Design Research Methodology) motivated by “ frustration about the lack of a common terminology, benchmarked research method and a common research methodology in design”. Through the analysis of recent research papers, this work has the aim to confirm how these visions about research in engineering design are projected in current state-of-the-art publications.

Since the work of Blessing and Chakrabarti, there have been some relevant proposals that have shed light on different aspects of the global design research landscape. Koskinen et al. ( 2011 ) proposed the term ‘constructive design research’ and presented alternatives to integrate research within the practice of design. Joost et al. ( 2016 ) used the term ‘design as research’ in a volume that compiled discourses of experts about questions on design research and its relationship with other disciplines. Vaughan ( 2017 ) presented a survey that collected different points of view related to doctoral education in the opinions of design graduates about practice-based research design. Redström ( 2017 ) presented an essay about how to develop theory -knowledge- by practice, experimentation and making -design. These works are a multi-faceted compendium of practical experiences and visions of experts on how to perform activities related to research in the domain of design. Although many examples and discussions presented in the cited books focus on the topic of research through/by design, rather than on research in engineering design, all of them agree on the relevance of research into the design due to the increasing number of PhD programs that could benefit from background knowledge about this topic. In this paper, we present an alternative approach to shed light on the relations between research and design: instead of collecting the personal visions of experts, we summarise and classify research papers on research in Engineering Design in terms of aims and contributions, methods and approaches, data collection techniques, and research instruments used for the collection of data. To this end, we have carried out a systematic review of the literature on research in engineering design. The overarching research question (RQ) that drives the review is: What is the current landscape of research methods in engineering design?

Access to doctoral studies normally requires candidates to have a Master’s degree in which they have taken courses about research methodologies. Doctoral studies normally culminate with the defense of a PhD thesis in which postgraduates have to show their capabilities to generate knowledge in a specific field. Submitting a PhD thesis that includes activities previously reviewed in scientific journals is generally considered as a quality warranty of the research performed by the student. Although publishing journal papers is not the only way to assess the excellence of the research work performed in a PhD thesis, the quasi-exponential increase of scientific publications we are witnessing (Tenopir and King 2014 ) indicates that it is probably becoming a universal standard for rating the quality of research. Therefore, being aware of the kind of works published in scientific journals related to engineering design could be of outstanding importance for scholars who have to configure the contents of the courses related to research methodologies in this field, as well as for PhD supervisors and students to focalize efforts for being more productive in terms of publications. The analysis of scientific papers about research in engineering design performed presented in this paper aims to contribute to this aim.

There are many possible ways to analyse, categorise or classify research works because there are many dimensions of analysis. Creswell ( 2009 ) presents a classical distinction between (1) quantitative, (2) qualitative and (3) mixed-methods (combining qualitative and quantitative research methods). For quantitative methods experimental designs, non-experimental design are distinguished. For qualitative, narrative research, ethnographic research, phenomenological research, grounded theory and case study research are distinguished. For mixed-methods, sequential, concurrent and transformative methods are distinguished. Blessing and Chakrabarti ( 2009 ) identified the following ones: (1) paradigm, that includes empiricism (Randolph 2003 ; Solomon 2007 ) and ethno-methodology (Atkinson 1988 ), methodologies, theories, views and assumptions (Kothari 2004 ); (2) aim, research questions and hypotheses; (3) nature of the study, including observational vs interventional (Thiese 2014 ), comparative vs non-comparative; (4) units of analysis; (5) data collection methods including recordings, interview, questionnaires (De Leeuw 2008 ); (6) role of the researcher (Fink 2000 ); (7) time constraints, duration and continuation of the research process; (8) observed processes including layout drawing, prototype or product; (9) setting referring to laboratory or field research (Paluck and Cialdini 2014 ); (10) tasks including type and complexity and nature; (11) number of cases, case size and participants (Diggle et al. 2011 ); (12) object of analysis distinguishing objects, companies, projects, documents… (13) coding and analysis, analysis and (14) verification methods (Brewer and Crano 2014 ); or (15) findings, that is, statement models or conclusions resulting from the study. Reich and Subrahmanian ( 2021 ) use the PSI framework (Problem, Social and Institutional space) to analyse and categorise research design works focussing on dimensions related to the problem being addressed concerning (1) disciplinary, (2) structural complexity and (3) knowledge availability; dimensions related with who is included in designing concerning (4) the perspective required to formulate the problem, (5) the inclusion of participants in the design process and the (6) capabilities of the design team; and finally dimensions related with how designing is executed taking into account (7) the ties or connections between actors, (8) the accessibility to knowledge and (9) the institutional complexity (Reich and Subrahmanian 2020 ). The dimensions presented by Blessing and Chakrabari have the ambition to classify different aspects to be taken into account when research in engineering design works are tackled. The dimensions proposed by Reich and Subrahmian are complementary and arise when they analyse the factors influencing success in engineering design projects. When analysing papers, some of the details related to some of the listed dimensions could be missing in the descriptions (timing, success validation etc.) so that we had to devise alternative proposals.

Our analysis pivots around the division between empirical qualitative, quantitative research and mixed-methods proposed by (Creswell 2009 ). This classification was complemented with analytical research methods, as specified by (Adrion 1993 ), cited by (Glass 1995 ) (defined in Sect. 2.2). From this germinal division, data-collection methods, strategies, and contributions of the studies are reported in cross-analysis tables. We aim to identify the main goals and results pursued or obtained by researchers (dimensions 2 and 15 of Blessing and Chakrabarti 2009 ), the strategies of enquiry and methodologies they follow (dimensions 1, 3, 9, 10 of Blessing and Chakrabarti 2009 ), and which data sources and instruments are most (and least) commonly used (rest of dimensions of Blessing and Chakrabarti 2009 ) in the domain of engineering design.

The structure of the document is the following: First, we present the review method and the categories used to classify the papers. We then present the quantitative results of the number of papers in each of the categories and the cross relations of the different classes, shedding light on the relative weight of each of the qualitative and quantitative approaches and the most frequent data-collection methods used. Next, we discuss the usefulness of the obtained results for academics and professionals interested in research design and the paper ends with the conclusions. Complementary material is provided with a brief description of each of the analysed papers.

We follow Kitchenham et al. ( 2009 ) as a guideline for performing the systematic review. The nature of the research question did not suit a usual search in the databases, as we were interested in analysing the approaches to research published in the field of engineering design. For this reason, we focused on identifying papers published in relevant journals in the field. The data sources are journal papers in the field of engineering design.

A simple search in the Journal of Citation Reports using the term “Design” as a key search title criterion, generates a list of 99 journals indexed in different categories. Only 80 are indexed in 2020, the rest of them in previous years. As we aimed to high-impact journals reporting research in engineering design, we focused on the journals indexed in SCIE (Science Citation Index Expanded) related to Science and Technology, discarding the 22 journals indexed in ESCI (Emerging Sources Citation Index), the 10 indexed in AHCI (Arts and Humanities Citation Index) and the 5 indexed in SSCI (Social Sciences Citation Index). Among the 43 remaining journals indexed in SCIE, 13 of them correspond to categories related to Chemistry and Biology (for example Anti-Cancer Drug Design or Molecular System Design & Engineering ) 11 of them to Computer Science or Electrics (for example Design Codes and Cryptography or Computer Aid Design ); 3 with Mathematics (for example Journal of Combinatorial Design ) and 2 with Building ( Architectural Engineering and Design Management or Structural Design of Tall and Special Building ). Closer to engineering design are the 14 remaining journals: 4 indexed in Mechanics Journal of Mechanical Design, Mechanics Based Design of Structures and Machines, Journal of Advanced Mechanical Design Systems for Manufacturing and Journal of Strain Analysis for Engineering Design ), 4 related to Materials ( Materials & Design, Proceedings of the Institution of Mechanical Engineers, International Journal of Mechanics and Materials in Design and Road Materials and Pavement Design ); and 2 related with vehicle design ( Journal of Ship Production and Design , and International Journal of Vehicle Design ). In spite of being closer to the topic of research in engineering design, we discarded these journals for being too specific. The remaining 4 journals were: (i) Design Studies (DS), (ii) the International Journal of Design (IJD), (iii) the Journal of Engineering Design (JED) and (iv) Research on Engineering Design (RED). Table 1 shows that these journals share the category denominated “Engineering Multidisciplinary”. In this category, there are 6 journals that have the term “Design” in the title, the four selected plus International Journal of Technology and Design Education (also indexed in SSCI), Artificial Intelligence for Engineering Design Analysis and Manufacturing (also indexed in Computer Science) that were discarded for being specialized in education and in artificial intelligence with applications in engineering design, respectively, and therefore, out of the focus of our research.

Each of the selected journals declare in their presentation their aims and audience: RED focuses on design theory and methodology, DS focuses on design processes, JED focuses on different aspects of the design of engineered products and systems, and IJD publishes research papers in all fields of design. The audience of DS, JEC and IJD is broader than the one of RED, which focuses on mechanical, civil, architectural, and manufacturing engineering. Overall, the four journals constitute a rich and representative sample that includes works of diverse nature, applying a variety of research methods and approaches to different problems in the context of research in engineering design.

Sample selection in systematic literature reviews must be structured, comprehensive, and transparent (Hiebl 2021 ). To comply with these three requirements, we established a recent and limited temporal window and applied random selection to select the sample. We collected 17 papers from each journal, as 17 is the number of papers available in one of the journals under analysis (IJD) and we chose to use the same number of papers per journal to avoid bias (i.e., giving more importance to one journal than another) in the study. For the journals with more than 17 papers in the period of analysis, random selection was applied. We focused on papers published between November 2018 and November 2019, which was the most recent available time window when this work was started.

This methodology led to a final total of 68 papers. We followed a collaborative team-coding approach (Saldaña 2021 ). Papers were selected and assigned randomly to a pair of reviewers. Each reviewer coded two papers every two weeks. Disagreements and new code proposals were resolved in periodic meetings involving the four researchers/authors. The first author of this paper played the role of “codebook editor” (MacQueen and Guest 2008 ), updating the code list after the meetings and he used the data from the analysis to build the final tables and present the resulting themes derived from the study.

With the aim of answering the general question of this review, RQ:, “What is the current landscape of research methods in engineering design?”, we focused on the following more specific sub-questions:

What are the research goals pursued by the analysed works?

What are the main experimental approaches found in the reviewed papers?

What data collection methods are employed in the reviewed works?

Which instruments are normally used to collect these data?

To answer these questions, we followed an anticipated data condensation approach (Miles et al. 2020 ). We defined four overarching topics corresponding to the research sub-questions: aims and contributions of the research; research approach; data collection techniques; and instruments for the collection of data. For each topic, we defined a set of categories, based on our revision of engineering design methods (see Sect. 2). During the iterative coding work, emerging categories were included when required. The new categories were used to re-codify all the works. This combination of deductive and inductive coding enabled us to derive new meanings from the data.

In the rest of this section, we present the categories that were identified in the analysis under each topic. Appendix shows complementary information with representative examples of the categories.

2.1 Aims and contributions

Concerning the aims/contributions of the research (RQ1), we started from an empty list of research targets which was enriched as the number of reviewed papers increased. Finally, the following research goals were identified through the coding process:

To study or propose a methodology, that focuses on papers whose main objective is to study an existing design methodology by analysing its validity in works that propose a new design methodology or that develop a part of it more deeply.

To delve into a given aspect of design, which includes papers that focus on exploring an aspect of a design (team communication, sketching, generation of ideas, materials...) or that explore one area of design that is recognised as challenging (social design, inclusive design, ecological design...).

To design, develop, or test a specific product , which includes those papers that set out the process of creation or development of a specific product or a group of them. Some of these works describe the overall process of creating a product, and others focus on a specific phase of its development (research, ideation, testing, and validation).

To make recommendations or propose guidelines, which include articles whose main aim is to systematize the results of their research to provide advice, either at a methodological level or in the design of new products.

Proposing a theory includes those articles that use logical reasoning or mental operations, such as imagination, intuition, abstraction, and deduction, with the aim of enunciating concepts or creating models, explanations, or theories about the phenomena under study.

Proposing a framework of analysis or a taxonomy that enables concepts or objects to be classified into categories.

More than one code could be assigned to each of the papers. This could be the case of a paper that aims to develop a specific product and ends by proposing guidelines.

2.2 Research approach

Concerning experimental approaches found in the reviewed papers (RQ2), as explained in the introduction, we propose the use of the distinction between quantitative, qualitative, mixed, and analytical research methods, defined as:

Quantitative empirical studies are those that aim at testing theories by examining relationships between variables, based on the collection of numerical data which is analysed using statistical procedures.

Qualitative empirical studies are those that aim at exploring and understanding in depth the meaning that individuals or groups give to a problem. They usually involve the collection of non-numerical data obtained in the participants’ settings and follow inductive analysis approaches in which the researchers interpret the meanings of the collected data.

Mixed-methods studies are those that combine both quantitative and qualitative approaches at diverse levels (data sources, analytical methods, etc.), so that the overall study is stronger than using each of the two approaches (i.e., quantitative, or qualitative) separately.

Analytical studies are those that focus on the formalization of a model and its demonstration. They start out by proposing a formal model with a mathematical formulation, derive results using deductive approaches, and, if possible, compare these results with empirical observations.

With respect to quantitative empirical studies, we subcategorize them into experiments, quasi-experiments and non-experiments, depending on the way the subjects of interest are assigned to an experimental group or to a control group:

Experiments: the assignment of subjects to the experimental or to the control group is random.

Quasi-experiments: there is not a random assignment of a subject to the groups.

Non-experiments: there is not control on the grouping of subjects.

When a known qualitative strategy of inquiry is used, it is also tagged. According to the definition proposed by Creswell ( 2009 ), strategies of inquiry are types of methods, designs or models that provide specific direction for procedures in a research design.

Ethnographic research documents the beliefs and practices of a particular cultural group or phenomenon in its natural environment from the perspective of insiders (Lapan et al. 2012 ). The researcher stays on site for a considerable amount of time to analyse practices and behaviours of groups, by observing, interviewing and (sometimes) participating in the process under analysis. Very popular in social sciences, it is also used in architecture (Cranz 2016 ).

In phenomenological research , the researcher identifies the essence of human experiences about a phenomenon as described by participants, while the researcher sets aside his or her own perspective (Wilson 2015 ).

Grounded theory is a strategy of inquiry in which the researcher derives a general theory grounded in the views of participants, involving the use of multiple stages of data collection (Jørgensen 2001 ).

Hermeneutics inquiry focuses on disclosing how participants’ interpretations of a phenomenon determine the way they live in the world (Stigliano 1989 ). This technique is popular in architecture (Pérez-Gómez 1999 ) .

Case study research is an empirical strategy of inquiry that investigates a contemporary phenomenon within its real-life context (Yin 2009 ). It uses descriptions of programs, events, or other phenomena to construct a complete portrayal of a case for interpretation and possible action (Lapan et al. 2012 ).

Eikeland ( 2006 ) describes different approaches to action research that involve applied research, moving experimentation from laboratories to field, inviting the subjects of research to join the community of researchers and involving practitioners in research with the insistence of thinking through personal practices. Action research is a very popular approach in social sciences (Stringer 2008 ; Clark et al. 2020 ) and it is also proposed for architecture (Herr 2015 ) and for the practice of product design (Swann 2002 ). This method is related to the terms research-through-design, practice-based-design research or research-by-design (Redström 2017 ; Vaughan 2017 ), that has been discussed to be a kind of action research in works like (Kennedy-Clark 2013 ; Motta-Filho 2021 ).

Case study is generally used for exploratory research or for pre-testing some research hypotheses (Blessing and Chakrabarti 2009 ). Action research requires a high degree of flexibility and is usually qualitative, data-driven, participatory, and makes use of multiple data sources. Case study and action research also appear in the following criteria of classification, following the proposal of Blessing and Chakrabarti ( 2009 ) referring to data-collection techniques.

2.3 Data-collection techniques

In this subsection, we present the list of data-collection techniques we have tagged, to analyse what is proposed in RQ3. Following the list of data-collection methods presented in section A.4 of Blessing and Chakrabarti ( 2009 ), excluding experiments, case studies and action research we prefer to include in the list of inquiry research strategies presented in the previous subsection.

Observation is a technique in which the researcher records, in real time, what is happening, either by hand, recording it or using measuring equipment. As Blessing and Chakrabarti ( 2009 ) explain: ‘The quality of observational data is highly dependent on the skill, training and competency of the observer’ (Blessing and Chakrabarti 2009 ). Observations are the main source of data in ethnographic studies (see Sect. 2.1), but this strategy is also commonly used in social sciences (Creswell 2009 ) and in visual design (Goodwin 2000 ), architecture (Cuff 1992 ) and product design practice (Wasson 2000 ).

Simultaneous verbalization refers to the situation in which the participants speak aloud while using a system, with the aim of providing information about the cognitive behaviour of the participants, which may not be obtained through normal observation (Ohnemus and Biers 1993 ). Often used to analyse problem-solving behaviour, its most important feature is the real-time aspect. Simultaneous verbalization sessions usually last a few hours and never more than a day, due to the effort required by both the participants and the researchers in their corresponding analysis. Although audio recordings are sometimes used to record simultaneous verbalization, they are understood as inappropriate for a process such as design, which usually involves drawings and gestures, so video recordings are considered more appropriate.

Collecting technical documents consists of obtaining technical documents related to a particular project, topic or product, from various sources (Rapley and Rees 2018 ). Analysis of these documents is often used early in a research project to understand the organisation, the background of the project and the experience of the designers. It is commonly employed in most observational studies. However, if it is used as a single source of information, it can result in such limitations as the usual lack of data on the context in which the documents were created and the reason for their content. It is, therefore, convenient to complement them with other methods such as interviews.

Collecting physical objects involves mock-ups, prototypes and other physical models that may be relevant for developing a product or testing it. The model or prototype could refer to a part of the product or the whole product. For traditional engineering research, which focuses, for example, on the analysis of product behaviour, the products are the main source of data (Blessing and Chakrabarti 2009 ). In our review, we consider those works that start collecting different objects to carry out a study on their usefulness, or on the behaviour of users, for example. The object is a general term that can refer both to drawings and physical objects. Among the former, we find all those sketches, drawings and diagrams that have emerged throughout the conception of a product or its development, or throughout a research process, which could yield important information to organise ideas and draw conclusions.

Questionnaires are used to collect people´s thoughts or opinions about a certain product, process or method (Radhakrishna 2007 ). A priori, they seem easier to use than real-time methods, such as observation or simultaneous verbalization, and they are useful to obtain data from a greater number of cases. However, some of its disadvantages, such as the time required by the participants and the potential bias of the results, must also be taken into account.

Interviews have the same purpose as the questionnaires but are carried out face-to-face (King et al. 2019 ). Sometimes they are not carried out individually but using a group dynamic known as focus group: a group interview that mixes aspects of interviews and observations, as it provides information from the study of the interactions between participants. Focus groups can provide richer information than interviews, but they can have a negative effect on the contribution of specific participants.

2.4 Instruments for the collection of data

Data collection methods are supported by instrumentation. This section describes the categories we found to respond to RQ4, exposing the instruments that are normally used to collect these data. Independently of the strategy of inquiry applied, there are several instruments that are used to keep records of the observations. These recordings are important to keep evidence and to enable the reproducibility of the analysis. We tagged the papers depending on the use of classical audio, video and image recordings and the more recent technique of eye tracking (Bergstrom and Schall 2014 ).

In experiments and case studies, we are also interested in physical measurements that are used to objectify observations.

When questionnaires and/or interviews are the data-collection techniques, we tagged who is the attendee, distinguishing between stakeholders , users of products or participants (observed people) in the research and experts or designers. We also found it relevant to tag when the study uses workshops as a means to obtain information.

The last topic of interest that has been tagged is the fact that the research work uses simulation algorithms or tools as a source of information. We use this tag when the simulation tools are a fundamental part of the research, as it provides the information analysed in the paper (Behera et al. 2019 ), or because the tool or the algorithm itself is the main contribution (Mathias et al. 2019 ).

3.1 Aims and contributions of the reviewed papers

Table 2 shows the codes assigned to each of the papers analysed. This section summarises the results related to RQ1 (research goals). As shown in Table 2 , most of the works focus on methodologies or on the analysis of a specific aspect of the design processes. The presentation of a product and the building up of knowledge with taxonomies, guidelines, theories, or reviews, are exceptions.

Five papers propose a theory: (Comi et al. 2019 ) present the concept of shared professional vision; (Benavides and Lara-Rapp 2019 ) present the principle of weaker dependencies in axiomatic design; (Martinec et al. 2019 ) introduce the state-transition model (synthesis, analysis, evaluation) in conceptual design and Lloyd ( 2019 ) defends the theory of the social turn in design, Aktas and Mäkelä ( 2019 ) focus on the relation between craft, materials, makers.

Six works focus on the evaluation of a specific product: a software product in Takahashi et al. ( 2018 ) and Belkadi et al. ( 2019 ); or physical objects in the case of Roesler et al. ( 2019 ), Hyysalo et al. ( 2019b ) and McKinnon and Sade ( 2019 ).

Concerning the works related to methodologies, we find papers that propose a method based on analytical methods or algorithmic solutions such as those related to axiomatic design (Chen et al. 2019a ) and those related to such methods as research-through-design, where the importance of the method followed is prominent in the study (Tsai and Van Den Hoven 2018 ; Hyysalo et al. 2019b ; McKinnon and Sade 2019 ; Hanrahan et al. 2019 ); or methodologies for product development such as Daalhuizen et al. ( 2019 ), with emphasis on different aspects such as work in groups (Gyory et al. 2019 ), sustainability (Santolaya et al. 2019 ) or democratised design (Hyysalo et al. 2019a ).

A good number of papers present frameworks of analysis or classifications with different purposes. Bresciani ( 2019 ) for classifying visualization dimensions, McDonald and Michela ( 2019 ) to classify moral goods, Roy and Warren ( 2019 ) for card sets, Park-Lee and Person ( 2018 ) identify three practices on briefing, Vegt et al. ( 2019 ) deduce 3 types of invasiveness evoked by the rules in gamified brainstorming, Valverde et al. ( 2019 ) classify the type of feedback in automotive push buttons, Cooper ( 2019 ) presents the five waves in design research, Luck ( 2019 ) describes the framework to distinguish between design, design research, architectural design research and practice, Hobye and Ranten ( 2019 ) present five behavioural strategies for interactive products and Van Kuijk et al. ( 2019 ) presents a framework to analyse usability concepts of electronic products and Petreca et al. ( 2019 ) for analysing the relation between sensors and textile. We also include in this category the papers related to ontologies, that are used to represent knowledge.

Proposing recommendations is a common result in the analysed research papers, including a variety of themes such as recommendations on the use of guidelines by new designers (Reimlinger et al. 2019 ); the use of specific materials (Genç et al. 2018 ; Pedgley et al. 2018 ; Aktas and Mäkelä 2019 ; Petreca et al. 2019 ); how to orient future studies on the use of mobile technology by elderly people (Li and Luximon 2018 ), or about design and poverty (Jagtap 2019 ) or ethnographic studies in developing countries (Wood and Mattson 2019 ); appliance design (Selvefors et al. 2018 ); use of games in brainstorming (Vegt et al. 2019 ); or specifying requirements (Morkos et al. 2019 ). Cooper ( 2019 ) proposes interprets the history of design research through five waves.

The most frequent type of works delve into a particular aspect of product design such as sketching (Sung et al. 2019 ; Self 2019 ), prototyping (Menold et al. 2019 ; Mathias et al. 2019 ), material (Pedgley et al. 2018 ; Aktas and Mäkelä 2019 ; Barati et al. 2019 ; Petreca et al. 2019 ), interaction (Hobye and Ranten 2019 ; Valverde et al. 2019 ), briefing (Park-Lee and Person 2018 ), working in groups (Graeff et al. 2019 ), iterations and testing (Tahera et al. 2019 ; Piccolo et al. 2019 ); behavioural complexity (Hobye and Ranten 2019 ), manufacturing (Yang et al. 2019 ), or usability (Van Kuijk et al. 2019 ).

3.2 Strategies of inquiry and methodologies

This section summarises the results related to RQ2 (main experimental approaches founded): qualitative approaches are a majority, but the number of quantitative or mixed-methods studies is also relevant. Other approaches, such as the use of analytical methods, are less frequent. Table 3 shows that, when the goal of the paper is related to proposing or studying a methodology (first column in Table 3 ), the percentage of pure quantitative papers is lower than in the rest of the cases. Regarding whether there is a tendency towards any methodology depending on the journal; Table 2 shows that the Journal on Engineering Design seems to focus more than the other journals on non-qualitative strategies of inquiry.

When quantitative methods are used, experiments are more frequent than quasi-experiments and non-experiments (14 out of the 17 quantitative studies present an experiment). We found 26 experimental studies, with 5 quasi-experiments (Saliminamin et al. 2019 ; Vegt et al. 2019 ; Sung et al. 2019 ; Self 2019 ; Santolaya et al. 2019 ) and 4 non-experiments (Selvefors et al. 2018 ; Morkos et al. 2019 ; Roesler et al. 2019 ; Piccolo et al. 2019 ).

The use of case studies is pervasive in qualitative research (more than half the studies that classified as qualitative base the research on a case study). Furthermore, many quantitative studies support results from case studies; for example, some analytical studies in which case studies are used as proof of concept of the proposed models (Chen et al. 2019b ; Zhang and Thomson 2019 ; Li et al. 2019a ).

Nevertheless, other qualitative methods, such as ethnography, hermeneutics, action research and phenomenological studies, are also used. The use of specific methods related to design is scarce (the discussion about this concern is dealt with in detail below). Ethnography is used in three cases (Roesler et al. 2019 ; Van der Linden et al. 2019a ; Comi et al. 2019 )—also the annotation as observation in the tables—and one more paper uses ethnography as the study focus (Wood and Mattson 2019 ). Hermeneutics is used by (McDonald and Michela 2019 ; Cooper 2019 ; Lloyd 2019 ; Luck 2019 ).

Action research is used by Pakkanen et al. ( 2019 ) to investigate, in combination with case studies, modular systems in industrial environments. The work of Bresciani ( 2019 ) could be considered an action research study with the goal of building a grounded theory evaluation technique for visual thinking. McKinnon and Sade ( 2019 ) align their work in the field of research through design using a set of gadgets to obtain information about environmental home good practices. Research through design is also used by Genç et al. ( 2018 ) to explore new materials and Tsai and Van Den Hoven ( 2018 ) to explore user experience. Hyysalo et al. ( 2019b ) and present the evaluation of a panel following the principles of research through design. Close to this method is that presented by Barati et al.( 2019 ), who complement their study with workshops where a group of students explores their proposals.

3.3 Data-collection methods

Results regarding RQ3 (data collection methods) are summarised in this section. Table 3 shows which main methods and techniques for collecting data are used in the different studies. The analysis of the sources of information is completed with a revision of the instruments used to collect data and with a discussion about the role of human input presented in the following sections. None of the data-collection methods identified seem to be dominant in the papers studied.

Technical documents of diverse nature are the main source of information used (Table 2 reports 23 out of the 68 papers analysed using technical documents). Interviewing is also frequent (22 times reported in Table 2 ). Expert and user opinions are both used as sources of information, but neither is a majority (22 and 20 papers, respectively, reported in Table 2 . Observation is mostly used in qualitative studies, where almost half use this technique. Concerning quantitative studies, apart from measurements, expert opinions appear as a frequent resource. This is because it is common to collect the opinions of experts in questionnaires or in evaluation templates that convert opinions into numeric values.

Verbalization is used in Martinec et al. ( 2019 ) and Gyory et al. ( 2019 ) for team work analysis and in (Khalaj and Pedgley 2019 ), where designers and users had to verbalize impressions.

Objects are collected as a data source in a relevant number of studies. Some are the results of students’ work as in Gralla et al. ( 2019 ); brainstorming outputs (Vegt et al. 2019 ); prototypes (Feijs and Toeters 2018 ; Barati et al. 2019 ), or commercial products (Roy and Warren 2019 ). Sketches are the type of object analysed in (Genç et al. 2018 ; Martinec et al. 2019 ; Gyory et al. 2019 ; Goucher-Lambert and Cagan 2019 ; Comi et al. 2019 ); while for (Li and Luximon 2018 ; Sung et al. 2019 ) sketches are the main concern of the research.

Questionnaires are less frequently used, and when this happens, they are designed ad-hoc for each study. Given the wide variety of topics and aims of the reviewed works, no standardised questionnaires have been found. Questionnaires, therefore, take different formats: Amazon Mechanical Turk is used once (Goucher-Lambert and Cagan 2019 ); a Likert scale tool evaluation (Graeff et al. 2019 ); binary and open questions (Pakkanen et al. 2019 ); ranking of preferences (Franceschini and Maisano 2019 ); or ad-hoc software tools (Li et al. 2019a ).

Interviews are frequently used as a source of information in qualitative and mixed strategies of inquiry. Interviews are associated with phenomenological studies (Li and Luximon 2018 ; Park-Lee and Person 2018 ; Selvefors et al. 2018 ) and also in ethnographic studies (Roesler et al. 2019 ; Van der Linden et al. 2019a ; Wood and Mattson 2019 ; Comi et al. 2019 ). The interviewed population can be a group of users of a given technology (Li and Luximon 2018 ) or a group of experts (Bresciani 2019 ).

Concerning the sample size used in the 24 papers whose research method has been classified as experimental, and taking into account that the sample may refer to studied objects or to participants/users, which, in turn, may be individuals or teams, the number of participants/users varies between 4, in Martinec et al. ( 2019 ), and 169, in Ozer and Cebeci ( 2019 ). The number of studied objects also varies from 6, in Mathias et al. ( 2019 ) to 256, in Li et al. ( 2019b ). In Santolaya et al. ( 2019 ) a methodology is experimentally tested in 2 case studies.

3.4 Instruments

Results regarding RQ4 (instruments used to collect data) are summarised in this section. Measurements refer both to metrics obtained with a physical device and to qualitative ratings obtained from human-based scores. In the first group, we can mention the metrics of energetic consumption (Selvefors et al. 2018 ; Santolaya et al. 2019 ), mass material (Santolaya et al. 2019 ), volumes of objects (Mathias et al. 2019 ), displacement of buttons (Valverde et al. 2019 ), online shopping user interaction data (Ozer and Cebeci 2019 ), or the timing of tasks in (Mathias et al. 2019 ). In the second group, we can cite (Saliminamin et al. 2019 ; Gyory et al. 2019 ), which score the quality of design proposals, and (Franceschini and Maisano 2019 ), who use design preferences as the input for an analytical model.

Simulations and/or software developments of algorithms take on an important role in several papers. Belkadi et al. ( 2019 ) present a software tool; Chen et al. ( 2019a ), Feijs and Toeters ( 2018 ), Mathias et al. ( 2019 ) and Takahashi et al. ( 2018 ) present or test software tools for different goals, such as analysing Lego buildings, and generating fashion patterns for projecting requirements into design parameters. Li et al. ( 2019a ) focus on modelling knowledge; Piccolo et al. ( 2019 ) use analysis and visualization tools to present results; while Ozer and Cebeci ( 2019 ) and Saravanan and Jerald ( 2019 ) use machine learning techniques such as neural networks and clustering. De Lessio et al. ( 2019 ) present a software tool to support planning and Yang et al. ( 2019 ) to support manufacturing. Boussuge et al. ( 2019 ) propose using ontologies to capture high-level modelling and idealisation decisions, characterising the simulations of CAE models from CAD assemblies. Other papers related to ontologies use software to model them (Cheong and Butscher 2019 ; Hagedorn et al. 2019 ; Wang et al. 2019 ).

Workshops are frequently used for evaluating results and sharing experiences by a group of experts with discussions (Van der Linden et al. 2019a , b ; McKinnon and Sade 2019 ; Self 2019 ; Wlazlak et al. 2019 ). In (Genç et al. 2018 ; Martinec et al. 2019 ), the workshops become designing activities in the research-through-design methodology. In Takahashi et al. ( 2018 ), workshops are used to observe users while they interact with a system and, in Pakkanen et al. ( 2019 ), to collect information from experts. In Garcia et al. ( 2019 ), workshops are meetings with stakeholders.

The opinions of stakeholders can be the core of the research study (Self 2019 ) or they can be used as part of usability tests (Takahashi et al. 2018 ). Most often, questionnaires and interviews are performed with users of a product (Selvefors et al. 2018 ; Roesler et al. 2019 ; Hanrahan et al. 2019 ; Ozer and Cebeci 2019 ); by active participants of the process under analysis, such as professionals in companies (Reimlinger et al. 2019 ; Wlazlak et al. 2019 ); or by students that are required to do a project (Vegt et al. 2019 ; Li et al. 2019a ; Abi Akle et al. 2019 ; Graeff et al. 2019 ). The experts that participate in questionnaires or interviews are designers, architects, engineers (Li and Luximon 2018 ; Park-Lee and Person 2018 ; Pakkanen et al. 2019 ), or academic staff evaluating results (Morkos et al. 2019 ; Sung et al. 2019 ; McKinnon and Sade 2019 ). In interviews occurring in ethnographic studies, the subjects providing information could be considered the topic of analysis (Wood and Mattson 2019 ), but at the same time, they could be experts (Comi et al. 2019 ).

4 Discussion

4.1 variety of aims and approaches.

The principal finding of our research is that there is a very high diversity in the works we have analysed in the journals related to engineering design. This variety affects the aims and scopes of the research works, the methods, and the data sources. Table 4 shows that variety affects the papers in the four journals analysed with only minor differences among them. Thus, DS (Design Studies) and RED (Research in Engineering Design) seem to focus more on methodological aspects, while IJD (International Journal of Design) and JED (Journal of Engineering Design) focus more on delving into particular aspects of the design process or on products, but at most 7 papers out of the 17 falls into one of the categories. According to the results, DS and IJD journals attract more papers with a qualitative approach (only 2 papers in each journal are purely quantitative), while most of the papers from JED and RED follow a quantitative or analytical approach (only 3 and 7 papers, respectively, are purely qualitative). However, we have found papers with both approaches in all the journals. RED uses less self-reported data (interviews, questionnaires or workshops), while DS uses this source of data the most, but in both journals there are exceptions, such as the works of Mathias et al. ( 2019 ) in DS or Garcia et al. ( 2019 ) in RED.

Despite this broad spectrum of papers, we found a clear interest in methodologies and the in-depth analysis of a given aspect of the whole process of designing generally applied to a particular case study. The interest in both topics is justified by the nature of the design and the youth of the discipline. As a process of searching for optimum solutions, design is clearly related to methodological concerns. As a young discipline, the space for contributing to the different tasks of the whole design process is huge. The analysis of the process of engineering design has evolved from being considered from a purely technical perspective to being studied as a socio-technical process. From a technical point of view, (Beitz et al. 1996 ) distinguished between conceptual design and embodied design for identifying a list of tasks that contribute to facing problems of engineering design in an effective and systematic way. From a socio-technical perspective, different authors have pointed out that the design process is influenced by aspects related to teamwork capabilities (Dorst 2004 ), the inclusion of participants (Van der Bijl-Brouwer and Dorst 2017 ) or by the institutional complexity (Reich and Subrahmanian 2020 ). Our study shows that there is space for research works that focus on both perspectives of analysis, being found works that are closely related to tasks that affect conceptual design (Martinec et al. 2019 ; Benavides and Lara-Rapp 2019 ; Self 2019 ), embodied design (Petreca et al. 2019 ) and also to social aspects of the design process (Piccolo et al. 2019 ).

It has been observed that there are a relatively low number of papers proposing recommendations, guidelines, frameworks, and taxonomies. We understand how difficult it is generalizing and classifying a discipline with multiple tasks, agents, approaches and sub-domains. Nevertheless, generating these types of representations of knowledge could be a substrate for the growth of the discipline. Design is a context-specific endeavour, but trying to generalize results so that other authors could reuse the generated knowledge in other domains would be positive for the growth of the discipline. The selected papers include product development and engineering design, which are two different areas, albeit overlapping. Recommendations and guidelines are always useful for the practice of engineering design, but more importantly, classifying concepts and types of activities with frameworks and taxonomies is an essential process in the building of knowledge in any research area. The variety of aims and approaches is probably the reason for this deficit, but research in engineering design would benefit from works analysing the many methodologies proposed from a meta level that permits obtaining general concepts that are domain-independent and universally applicable.

Results presented in Table 2 and summarised in Table 3 could be used to derive patterns or preferred styles in research design. Papers using analytical approaches mainly use case studies to validate the proposed models and they use simulations to compare results with expectations. Here, the case studies are used as proof of concept of the proposed models. They do not consider human input as a main feature of analysis. The ones related to methodological concerns are the papers focusing on axiomatic design and the ones relating to specific aspects or to frameworks are the ones related to ontologies. Most papers with quantitative approaches use experimental setups in which they compare different configurations of a given problem. The means to collect numerical data highly depend on the type of work, with no outstanding method or instrument. This approach is mainly used when the goal is to study a given aspect of design, which is coherent with the fact that experiments are meant to measure variables that can be isolated, and therefore these studies need to focus on specific features of the design process. Like analytical papers, qualitative approaches are mainly based on case studies. The main difference is related to the nature of these case studies. In qualitative approaches, the case studies aim at gaining insight into the complexity of the studied design processes from the point of view of the participants. In consequence, the preferred data collection methods are observations and interviews and/or workshops, to collect data from users and experts. They use rich data sources (audio, photography, video or software tools) to make observations rigorously. Qualitative approaches are the most used methods, independently of the aim of the paper, but they are dominant for proposing frameworks of analysis or deriving guidelines and recommendations, probably because the active interpretation of experts is a must for these concerns. Papers using mixed methods triangulate the information obtained in quantitative experiments with information obtained with qualitative methods. Therefore, their pattern is closer to one of the papers using quantitative methods than to the ones using qualitative methods.

The application of one approach or another should respond to what Subrahmanian et al. ( 2020 ) call the different models of designing. When the artifact or the process is clear, analytical, and quantitative methods, closer to approaches followed in natural science can be applied. When people, culture, society, and politics must be taken into consideration, the use of analytical and quantitative methods is not appropriate. When individual designers play a role, and, especially, when social aspects and context must be taken into consideration, design processes become more complex and dynamic, involving aspects that are better studied by qualitative approaches that are able to capture the complexity of the object of study and the participants' perspectives.

4.2 Implications for the research in the engineering design community

As mentioned in the introduction, one of the objectives of this paper was to provide suggestions about the course contents that doctoral studies in the domain of engineering design must carry out. The first implication of our analysis relates to the type of research methodologies that students must be introduced to. According to the analysed papers, it seems essential that future researchers receive training in both qualitative and quantitative methods. The analysis shows that qualitative research is very common and that rich sources of data, such as observations or users and experts opinions collected through interviews are frequent. Furthermore, pure qualitative research approaches, like ethnography and phenomenology are commonly found. Nevertheless, experimental approaches should also have a relevant role in the student curricula because it is frequently used as well. We understand that this qualitative-quantitative duality responds to the nature of engineering design, a complex field that requires both technical background and the consideration of behavioural and social aspects related to design.

A second implication has to do with the instruments and data collection methods that researchers on engineering design must get familiar with. Research studies in this domain could require accessing real design scenarios that are authentic field studies rather than controlled lab studies. This is a relevant divergence with respect to other research domains that permit isolating variables and participants. There are implications for the instruments used for collecting data, with the need of considering techniques that permit collecting information in real settings and during longer periods of time. but also, that human fact is a relevant variable that affects both design teams managements, communication with users and social aspects. This fact justifies the use of technical reports, questionnaires, and observation as the main sources of information in these studies.

It must be noted that publishing in a journal should not be an end in itself, and the real value of a paper does not rely on the journal in which it is published but on its contribution to the growth of the discipline (Bladek 2014 ). However, there is a universal tendency to identify research quality and impact with these publications, and students that pursue a research career usually need to accomplish certain goals related to publishing. For this reason, we think that doctoral students in engineering design can find this work useful, as it provides an overview and pointers to different types of research work published in four top-quality journals in the field, and this may give them tips on the kind of knowledge they need to acquire to have their work published in these journals or similar ones.

4.3 Relation to other surveys

Probably due to the youth of engineering design as a research discipline, the number of papers devoted to literature reviews in these fields is still sparse. From the few reviews found, most refer to particular aspects of engineering design: such as inspiration and fixation (Crilly 2019 ); sustainability (Coskun et al. 2015 ); user value (Boztepe 2007 ); Alzheimer and play experience (Anderiesen et al. 2015 ); performance in industrial design(Candi and Gemser 2010 ); relation between creativity, functionality, and aesthetics (Han et al. 2021 ); fuzzy front-ends for product development (Park et al. 2021 ); surrogate models and computational complexity (Alizadeh et al. 2020 ); smart design (Pessôa and Becker 2020 ); design and poverty (Jagtap 2019 ); mass customization (Ferguson et al. 2014 ); product stigma (Schröppel et al. 2021 ); uncertainty (Han et al. 2020 ); decision-making methods (Renzi et al. 2017 ); modular product design (Bonvoisin et al. 2016 ); or product-service systems (Vasantha et al. 2012 ).

More interesting, for their similarity with respect to the present study, are the works presented by Tempczyk ( 1986 ) and Cantamessa ( 2003 ), both presenting reviews or surveys about research and studies on engineering design. These two works and the one presented in this paper differ in their sources of information. Tempczyk ( 1986 ) made a survey by sending questionnaires to academic staff concerning research subjects and methods; Cantamessa ( 2003 ) made a review of the proceedings of two editions of the conference on engineering design. There is a temporal distance of 17 years between the work of Tempczyk ( 1986 ) and the one of Cantamessa ( 2003 ) and 18 years between the work of Cantamessa ( 2003 ) and the present study, but we must highlight the fact that the three studies report methodologies as one of the main topics of research. Computer-aided products are reported by Tempczyk ( 1986 ) as a relevant topic, and Cantamessa ( 2003 ) also refers to software tools as a recurrent topic, while we also identified a category named simulation which included software tools and algorithms. The three works also report a high variety of approaches and themes. The main difference between these studies and the present one is that Tempczyk ( 1986 ) reports on training as an important concern for researchers and Cantamessa ( 2003 ) observes different streams of research, loosely coupled with an excess of referencing to previous works. As regards references to training concerns, we did not find any paper related to training, probably because, nowadays, there are journals specifically devoted to learning in the domain of engineering and design. As regards the criticism of Cantamessa ( 2003 ) concerning the notable amount of self-references in the analysed papers, we did not observe such a circumstance in the journal papers we have reviewed. On the contrary, our review has found that the papers reviewed contain complete state-of-the-art sections in which other research groups are referenced and other studies are discussed. This finding partially contradicts what Cantamessa ( 2003 ) found in his review. We think that the nature of the sources of data in his review, based on proceedings which are shorter could have influenced these divergent results. Our study may point to a more mature stage of research that builds on the knowledge already offered in the community. This finding may be based on the fact we are working on journal papers that offer more mature results.

4.4 Limitations

The systematic literature review presented in this paper covers a recent period of time spanning one year of publications. The sample is representative of recent research in engineering design, but it does not provide information about tendencies in the field. For example, we have observed a relevant number of quantitative studies in comparison to qualitative ones, but we cannot say if this is a tendency. Future work would be required to compare our results with those of a longitudinal study covering a larger period of years. We expect that our work can be considered as the first step in this longer-term study that could provide useful information about the evolution of research into the young discipline of engineering design.

By selecting Blessing and Chakrabarti ( 2009 ) as a framework to categorize research papers, we did not pay attention to the important concern of the success of the research which could be a critical point for connecting the study aim, with the approach, research method, etc. Reich and Subrahmanian ( 2021 ) show that it is possible to use the PSI framework (Problem, Social and Institutional space) to describe what researchers and designers did in case studies to analyse the matching of methods, aims and approaches with the success of the projects. In spite of our work being merely descriptive of the aims, methods and techniques used by authors, we offer a corpus of categorised research papers for analysing in future works on whether the research design is appropriate for its goals.

The analysis of the sample of journal papers selected has permitted us to build a consistent set of categories for classifying research works in engineering design. We consider this sample comprehensive, based on a saturation analysis carried out on the sample, that showed that all the categories used in the analysis could be identified with 69% of the papers that were actually used in the analysis. Nevertheless, while selecting 68 papers from only four journals, we could have discarded other works that could include other alternative approaches also valid for research in engineering design. Moreover, the choice of a single year-window is another limitation of this study, as it does not enable us to provide a full vision of the field and its evolution. Nevertheless, we think that the classification presented in this paper could be the basis for subsequent studies, which should consider a broader timeframe, and therefore, a larger selection of papers across several years. Other approaches for selecting the analysed papers like sampling at the same rate in all the journals could also have led to representative results.

5 Conclusions

In this paper, we have presented a systematic review of recent literature on research methods and instruments used in a one-year period of research papers in the field of engineering design. By taking this approach, we offer a "fixed image" of recent research in the area and point to some gaps and challenges in the field.

The review shows that there is no single methodological approach accepted as the standard in the field; and that there is a large variety of goals, approaches, data collection methods and instruments to collect them. In spite of this variety, we have observed a certain preference towards qualitative methods, which can be justified by the increasing consideration of engineering design as a complex process affecting humans and their contexts.

We think that this paper contributes to research in engineering design by providing initial evidence for researchers about the kind of work that are expected by high-impact scientific journals in this domain. Additionally, academics can find in this paper a list of topics (methodologies, data-collection procedures, instruments, etc.…) that must be part of the programme of courses on research in engineering design.

6 Appendix: Coding scheme: categories and examples

The tables included in this Appendix have aim to present the knowledge generated in this paper in the form of a coding scheme, that can be used as an instrument to describe the taxonomy of research aims (Table 5 ), approaches (Table 6 ), data collection techniques (Table 7 ), and instruments (Table 8 ) in engineering design.

Data Availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors acknowledge financial support from Universidad de Valladolid (PID 2020 21 038), Agencia Estatal de Investigación (PID2020-118216RB-I00) and Agencia Estatal de Investigación (PID2020-112584RB-C32).

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Escuela de Ingenierías Industriales, Universidad de Valladolid, Paseo del Cauce Paseo del Cauce 59, 47011, Valladolid, Spain

David Escudero-Mancebo, Nieves Fernández-Villalobos & Óscar Martín-Llorente

Escuela de Ingeniería Informática, Universidad de Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain

David Escudero-Mancebo & Alejandra Martínez-Monés

Escuela Técnica Superior de Arquitectura, Universidad de Valladolid, Avenida Salamanca 18, 47014, Valladolid, Spain

Nieves Fernández-Villalobos

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Escudero-Mancebo, D., Fernández-Villalobos, N., Martín-Llorente, Ó. et al. Research methods in engineering design: a synthesis of recent studies using a systematic literature review. Res Eng Design 34 , 221–256 (2023). https://doi.org/10.1007/s00163-022-00406-y

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Received : 08 November 2021

Revised : 26 October 2022

Accepted : 23 December 2022

Published : 16 January 2023

Issue Date : April 2023

DOI : https://doi.org/10.1007/s00163-022-00406-y

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