qualitative research in engineering

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• > Cambridge Handbook of Engineering Education Research
• > Framing Qualitative Methods in Engineering Education Research

Book contents

• Frontmatter
• Contributors
• Acknowledgments

Introduction

• Chapter 1 Chronological and Ontological Development of Engineering Education as a Field of Scientific Inquiry
• Part 1 Engineering Thinking and Knowing
• Part 2 Engineering Learning Mechanisms and Approaches
• Part 3 Pathways into Diversity and Inclusiveness
• Part 4 Engineering Education and Institutional Practices
• Part 5 Research Methods and Assessment
• Chapter 24 Studying Teaching and Learning in Undergraduate Engineering Programs
• Chapter 25 Design-Based Research in Engineering Education
• Chapter 26 Quantitative and Mixed Methods Research
• Chapter 27 Framing Qualitative Methods in Engineering Education Research
• Chapter 28 Conducting Interpretive Research in Engineering Education Using Qualitative and Ethnographic Methods
• Chapter 29 The Science and Design of Assessment in Engineering Education
• Part 6 Cross-Cutting Issues and Perspectives

Chapter 27 - Framing Qualitative Methods in Engineering Education Research

Established and Emerging Methodologies

Published online by Cambridge University Press:  05 February 2015

In science and engineering research, meth-odologies based on quantitative methods of data collection are prominent, based on their power for building predictive models of the natural world. Research in the social world, of which engineering education is a subset, is only partially described by quantitative models. Much of the subtlety of human interaction rests in complex models of causality that require the use of qualitative data for building explanatory theory.

This chapter provides an introduction to the use of qualitative methods for engineering education researchers. A more substantial consideration than that of methods, however, is the way in which an argument is developed for the validity of the knowledge generated from the analysis of qualitative data. These arguments are encapsulated in a discussion of methodology, which can be defined as referring to a theoretical justification for the methods used in a study (Burton, 2002; Clough & Nutbrown, 2002). This chapter focuses on methodology as a crucial area with which researchers need to grapple in order for the quality and scope of research to continue to develop. It is argued that to be able to answer the research questions at hand, methodological decisions need to be more explicitly represented in reports of research; and researchers need to consider a broad range of methodological options, in particular those methodologies that could be considered to be “emerging” in engineering education research.

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• Framing Qualitative Methods in Engineering Education Research
• By Jennifer M. Case , University of Cape Town, Gregory Light , Northwestern University
• Edited by Aditya Johri , Virginia Polytechnic Institute and State University , Barbara M. Olds
• Book: Cambridge Handbook of Engineering Education Research
• Online publication: 05 February 2015
• Chapter DOI: https://doi.org/10.1017/CBO9781139013451.034

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• USU Library

EED 7040 Qualitative Methods in Engineering Education: Qualitative Research Examples

Qualitative research examples.

• Writing & Citing (APA)
• Guides & Additional Resources
• Plagiarism & Copyright

Ethnography:

Baba, M. L., & Pawlowski, D. (2001, August). Creating culture change: An ethnographic approach to the transformation of engineering education. In  International Conference on Engineering Education. Retrieved January  (Vol. 15, p. 2009). https://www.researchgate.net/publication/239538831_Creating_culture_change_An_ethnograethn_approach_to_the_transformation_of_engineering_education

Crede, E., & Borrego, M. (2013). From ethnography to items: A mixed methods approach to developing a survey to examine graduate engineering student retention.  Journal of Mixed Methods Research ,  7 (1), 62-80. https://journals-sagepub-com.dist.lib.usu.edu/doi/full/10.1177/1558689812451792

Lucena, J., Downey, G., Jesiek, B., & Elber, S. (2008). Competencies beyond countries: The re-organization of engineering education in the United States, Europe, and Latin America. Journal Of Engineering Education , 97 (4), 433-447. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=35725497&site=ehost-live

Stevens, R., O'Connor, K., Garrison, L., Jocuns, A., & Amos, D. M. (2008). Becoming an engineer: Toward a three dimensional view of engineering learning. Journal Of Engineering Education , 97 (3), 355-368. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=34435804&site=ehost-live

Crede, E., & Borrego, M. (2013). From ethnography to items: A mixed methods approach to developing a survey to examine graduate engineering student retention. Journal Of Mixed Methods Research , 7 (1), 62-80. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ989388&site=ehost-live

Carroll, M. P. (2014). Shoot for the moon! the mentors and the middle schoolers explore the intersection of design thinking and STEM. Journal Of Pre-College Engineering Education Research , 4 (1), 14-30. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ1060017&site=ehost-live

Case Study:

Matusovich, H. M., Streveler, R. A., & Miller, R. L. (2010). Why do students choose engineering? A qualitative, longitudinal investigation of students' motivational values.  Journal of Engineering Education ,  99 (4), 289-303. https://onlinelibrary-wiley-com.dist.lib.usu.edu/doi/10.1002/j.2168-9830.2010.tb01064.x

Runeson, P. & Höst, M. (2009.) Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering , 14 , 131-164. http://link.springer.com/article/10.1007%2Fs10664-008-9102-8

Magin, D. J., & Churches, A. E. (1995). Peer tutoring in engineering design: A case study. Studies In Higher Education , 20 (1), 73-85. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ503298&site=ehost-live

Action Research:

Gelles, L. A. (2019). Career prospects and resources of domestic engineering doctoral students. https://digitalcommons.usu.edu/etd/7650/

Jorgensen, F., & Kofoed, L. B. (2007). Integrating the development of continuous improvement and innovation capabilities into engineering education. European Journal Of Engineering Education , 32 (2), 181-191. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ828267&site=ehost-live

Grounded Theory:

Kinnunen, P., & Simon, B. (2012). Phenomenography and grounded theory as research methods in computing education research field.  Computer Science Education ,  22 (2), 199-218. http://dist.lib.usu.edu/login?url=https://doi.org/10.1080/08993408.2012.692928

Jonassen, D., Strobel, J., & Lee, C. B. (2006). Everyday problem solving in engineering: Lessons for engineering educators. Journal of Engineering Education , 95 (2), 139–151. http://onlinelibrary.wiley.com/doi/10.1002/j.2168-9830.2006.tb00885.x/abstract

Khiat, H. (2010). A grounded theory approach: Conceptions of understanding in engineering mathematics learning. Qualitative Report , 15 (6), 1459-1488. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=EJ914018&site=ehost-live

Phenomenology:

Seeman, K. (2003.) Basic principles in holistic technology education. Journal of Technology Education , 14 (2). http://scholar.lib.vt.edu/ejournals/JTE/v14n2/seemann.html

Arnold, M. (2003). On the phenomenology of technology: the “Janus-faces” of mobile phones. Information & Organization , 13 (4), 231. doi:10.1016/S1471-7727(03)00013-7 http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=10117392&site=eds-live

Chari, D., Irving, P., Howard, R., & Bowe, B. (2012). Identifying knowledge, skill and competence for nanoscience and nanotechnology research: A study of postgraduate researchers' experiences. International Journal Of Engineering Education , 28 (5), 1046-1055. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=83519746&site=ehost-live

Lee, C. S., McNeill, N. J., Douglas, E. P., Koro-Ljungberg, M. E., & Therriault, D. J. (2013). Indispensable resource? A phenomenological study of textbook use in engineering problem solving. Journal Of Engineering Education , 102 (2), 269-288. doi:10.1002/jee.20011 http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=89219021&site=ehost-live

Narrative Inquiry:

Villanueva, I., & Di Stefano, M. (2017). Narrative inquiry on the teaching of STEM to blind high school students. Education Sciences ,  7 (4), 89. https://www.mdpi.com/2227-7102/7/4/89

Lahenius, K., & Martinsuo, M. (2011). Different types of doctoral study processes. Scandinavian Journal of Educational Research , 55 (6), 609-623. doi:10.1080/00313831.2011.555924 http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=66788237&site=ehost-live

Marshall, D., & Case, J. (2010). Rethinking 'disadvantage' in higher education: a paradigmatic case study using narrative analysis. Studies in Higher Education , 35 (5), 491-504. doi:10.1080/03075070903518386 http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=52497739&site=ehost-live

Epistemology:

Downey, G.L., & Lucena, J.C. (2004.) Knowledge and professional identity in engineering. History and Technology 20 (4): 393-420. http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=15496342&site=ehost-live

Qualitative Research Considerations and Confusions In Engineering Education:

Baillie, C., & Douglas, E. P. (2014). Confusions and conventions: Qualitative research in engineering education. Journal of Engineering Education ,  103 (1), 1.                                                                                               

http://dist.lib.usu.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=asn&AN=94447542&site=ehost-live 

Beddoes, K., Schimpf, M. C. M., & Pawley, A. L. (2014). New metaphors for new understandings: Ontological questions about developing grounded theories in engineering education.  ASEE Paper ID 9010. https://peer.asee.org/new-metaphors-for-new-understandings-ontological-questions-about-developing-grounded-theories-in-engineering-education

Borrego, M., Douglas, E. P., & Amelink, C. T. (2009). Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education , 98 (1), 53-66. https://onlinelibrary-wiley-com.dist.lib.usu.edu/doi/10.1002/j.2168-9830.2009.tb01005.x

Case, J. M., & Light, G. (2011). Emerging research methodologies in engineering education research. Journal of Engineering Education, 100 (1), 186-210. https://onlinelibrary-wiley-com.dist.lib.usu.edu/doi/10.1002/j.2168-9830.2011.tb00008.x

Walther, J., Sochacka, N. W., Benson, L. C., Bumbaco, A. E., Kellam, N., Pawley, A. L., & Phillips, C. M. (2017). Qualitative research quality: A collaborative inquiry across multiple methodological perspectives. Journal of Engineering Education, 106 (3), 398-430. https://onlinelibrary-wiley-com.dist.lib.usu.edu/doi/10.1002/jee.20170

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on September 5, 2024.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Socio-Technical Grounded Theory: An Overview

• First Online: 10 June 2024

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• Rashina Hoda   ORCID: orcid.org/0000-0001-5147-8096 2  

This chapter provides an overview of socio-technical grounded theory (STGT) for conducting qualitative research within qualitative and mixed-methods research studies. First, we will learn about the underlying socio-technical research framework which acts as the sweet spot for applying STGT and more generally for conducting qualitative empirical research in the modern digital world. Next, we will be introduced to STGT including an overview of its philosophical foundations, methodological steps and procedures for qualitative research, and published examples. We will learn about the possible applications of the full STGT method or in a limited capacity as STGT for data analysis . We will also learn about an application selection guide to help us decide how best to apply STGT and when possibly not to. Finally, we will be introduced to the evaluation guidelines. This chapter serves to provide a birds-eye view of STGT, encouraging further exploration in the remaining chapters.

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Hoda, R. (2024). Socio-Technical Grounded Theory: An Overview. In: Qualitative Research with Socio-Technical Grounded Theory. Springer, Cham. https://doi.org/10.1007/978-3-031-60533-8_3

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