does quantitative research use inductive reasoning

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1-2 The Nature of Evidence: Inductive and Deductive Reasoning

Renate Kahlke; Jonathan Sherbino; and Sandra Monteiro

Different philosophies of science (Chapter 1-1) inform different approaches to research, characterized by different goals, assumptions, and types of data. In this chapter, we discuss how post-positivist, interpretivist, constructivist, and critical philosophies form a foundation for two broad approaches to generating knowledge: quantitative and qualitative research. There are several often-discussed distinctions between these two approaches, stemming from their philosophical roots, specifically, a commitment to objectivity versus subjective interpretation, numbers versus words, generalizability versus transferability, and deductive versus inductive reasoning. While these distinctions often distinguish qualitative and quantitative research, there are always exceptions. These exceptions demonstrate that quantitative and qualitative research approaches have more in common than what superficial descriptions imply.

Key Points of the Chapter

By the end of this chapter the learner should be able to:

Describe quantitative research methodologies
Describe qualitative research methodologies
Compare and contrast these two approaches to interpreting data

Rayna (they/their) stared at the blinking cursor on her screen. They had been recently invited to revise and resubmit their first qualitative research manuscript. Most of the editor and reviewer comments had been relatively easy to handle, but when Rayna reached Reviewer 2’s comments, they were caught off guard. The reviewer acknowledged that they came from a quantitative background, but then went on to write: “I am worried about the generalizability of this study, with a sample of only 25 residents. Wouldn’t it be better to do a survey to get more perspectives?”

Rayna hadn’t seen any other qualitative studies talk about generalizability, so they weren’t entirely sure how to address this comment. Maybe the study won’t be valuable if the results aren’t generalizable! Reviewer 2 might be right that the sample size is too small! They immediately panic-email one of their co-authors:

Hi Rayna, We get these kinds of reviews all the time. Don’t worry, it’s not a flaw in your study. That said, I think we can build our field’s knowledge about qualitative research approaches by explaining some of these concepts. Start with the one by Monteiro et al. and then read the one by Wright and colleagues. These are both good primers and will give you some language to help respond to this reviewer.

With a sigh of relief, Rayna reads through the attached papers (1-8) and continues her mission to craft a response.

Deeper Dive on this Concept

Qualitative and quantitative research are often talked about as two different ways of thinking and generating knowledge. We contrast a reliance on words with a reliance on numbers, a focus on subjectivity with a focus on objectivity. And to some extent these approaches are different, in precisely the ways described. However, many experienced researchers on both sides of the line will tell you that these differences only go so far.

Drawing on Chapter 1-1, there are different philosophies of science that inform researchers and their research products. Generally, quantitative research is associated with post-positivism; researchers seek to be objective and reduce their bias in order to ensure that their results are as close as possible to the truth. Post-positivists, unlike positivists, acknowledge that a singular truth is impossible to define. However, truth can be defined within a spectrum of probability. Quantitative researchers generate and test hypotheses to make conclusions about a theory they have developed. They conduct experiments that generate numerical data that define the extent to which an observation is “true.” The strength of the numerical data suggests whether the observation can be generalized beyond the study population. This process is called deductive reasoning because it starts with a general theory that narrows to a hypothesis that is tested against observations that support (or refute) the theory.

Qualitative researchers, on the other hand, tend to be guided by interpretivism, constructivism, critical theory or other perspectives that value subjectivity. These analytic approaches do not address bias because bias assumes a misrepresentation of “truth” during collection or analysis of data. Subjectivity emphasizes the position and perspective assumed during analysis, articulating that there is no external objective truth, uninformed by context. ( See Chapter 1-1 .) Qualitative methods seek to deeply understand a phenomenon, using the rich meaning provided by words, symbols, traditions, structures, identity constructs and power dynamics (as opposed to simply numbers). Rather than testing a hypothesis, they generate knowledge by inductively generating new insights or theory (i.e. observations are collected and analyzed to build a theory). These insights are contextual, not universal. Qualitative researchers translate their results within a rich description of context so that readers can assess the similarities and differences between contexts and determine the extent to which the study results are transferable to their setting.

While these distinctions can be helpful in distinguishing quantitative and qualitative research broadly, they also create false divisions. The relationships between these two approaches are more complex, and nuances are important to bear in mind, even for novices, lest we exacerbate hierarchies and divisions between different types of knowledge and evidence.

As an example, the interpretation of quantitative results is not always clear and obvious – findings do not always support or refute a hypothesis. Thus, both qualitative and quantitative researchers need to be attentive to their data. While quantitative research is generally thought to be deductive, quantitative researchers often do a bit of inductive reasoning to find meaning in data that hold surprises. Conversely, qualitative data are stereotypically analyzed inductively, making meaning from the data rather than proving a hypothesis. However, many qualitative researchers apply existing theories or theoretical frameworks, testing the relevance of existing theory in a new context or seeking to explain their data using an existing framework. These approaches are often characterized as deductive qualitative work.

As another example, quantitative researchers use numbers, but these numbers aren’t always meaningful without words. In surveys, interpretation of numerical responses may not be possible without analyzing them alongside free-text responses. And while qualitative researchers rarely use numbers, they do need to think through the frequency with which certain themes appear in their dataset. An idea that only appears once in a qualitative study may have great value to the analysis, but it is also important that researchers acknowledge the views that are most prevalent in a given qualitative dataset.

Key Takeaways

Quantitative research is generally associated with post-positivism. Since researchers seek to get as close to ‘the truth’, as they can, they value objectivity and seek generalizable results. They generate hypotheses and use deductive reasoning and numerical data numbers to prove or disprove their hypotheses. Replicating patterns of data to validate theories and interpretations is one way to evaluate ‘the truth’.
Qualitative research is generally associated with worldviews that value subjectivity. Since qualitative researchers seek to understand the interaction between person, place, history, power, gender and other elements of context, they value subjectivity (e.g. interpretivism, constructivism, critical theory). Qualitative research does not seek generalizability of findings (i.e. a universal, decontextualized result), rather it produces results that are inextricably linked to the context of the data and analysis. Data tend to take the form of words, rather than numbers, and are analyzed inductively.
Qualitative and quantitative research are not opposites. Qualitative and quantitative research are often marked by a set of apparently clear distinctions, but there are always nuances and exceptions. Thus, these approaches should be understood as complementary, rather than diametrically opposed.

Vignette Conclusion

Rayna smiled and read over their paper once more. They had included an explanation of qualitative research, and how it’s about depth of information and nuance of experience. The depth of data generated per participant is significant, therefore fewer people are typically recruited in qualitative studies. Rayna also articulated that while the interpretivist approach she used for analysis isn’t focused on generalizing the results beyond a specific context, they were able to make an argument for how the results can transfer to other, similar contexts. Cal provided a bunch of margin comments in her responses to the reviews, highlighting how savvy Rayna had been in addressing the concerns of Reviewer 2 without betraying their epistemic roots. The editor certainly was right – adding more justification and explanation had made the paper stronger, and would likely help others grow to better understand qualitative work. Now the only challenge left was figuring out how to upload the revised documents to the journal submission portal…

Wright, S., O’Brien, B. C., Nimmon, L., Law, M., & Mylopoulos, M. (2016). Research Design Considerations. Journal of Graduate Medical Education, 8(1), 97–98. doi: 10.4300/JGME-D-15-00566.1
Monteiro S, Sullivan GM, Chan TM. Generalizability theory made simple (r): an introductory primer to G-studies. Journal of graduate medical education. 2019 Aug;11(4):365-70. doi: 10.4300/JGME-D-19-00464.1
Goldenberg MJ. On evidence and evidence-based medicine: lessons from the philosophy of science. Social science & medicine. 2006 Jun 1;62(11):2621-32. doi: 10.1016/j.socscimed.2005.11.031
Varpio L, MacLeod A. Philosophy of science series: Harnessing the multidisciplinary edge effect by exploring paradigms, ontologies, epistemologies, axiologies, and methodologies. Academic Medicine. 2020 May 1;95(5):686-9. doi: 10.1097/ACM.0000000000003142
Morse JM, Mitcham C. Exploring qualitatively-derived concepts: Inductive—deductive pitfalls. International journal of qualitative methods. 2002 Dec;1(4):28-35. https://doi.org/10.1177/160940690200100404
Armat MR, Assarroudi A, Rad M, Sharifi H, Heydari A. Inductive and deductive: Ambiguous labels in qualitative content analysis. The Qualitative Report. 2018;23(1):219-21. https://www.proquest.com/docview/2122314268
Tavakol M, Sandars J. Quantitative and qualitative methods in medical education research: AMEE Guide No 90: Part I. Medical Teacher. 2014 Sep 1;36(9):746-56. doi: 10.3109/0142159X.2014.915298
Tavakol M, Sandars J. Quantitative and qualitative methods in medical education research: AMEE Guide No 90: Part II. Medical teacher. 2014 Oct 1;36(10):838-48. doi: 10.3109/0142159X.2014.915297

About the authors

institution: McMaster University

Renate Kahlke is an Assistant Professor within the Division of Education & Innovation, Department of Medicine. She is a scientist within the McMaster Education Research, Innovation and Theory (MERIT) Program.

Sandra Monteiro is an Associate Professor within the Department of Medicine, Division of Education and Innovation, Faculty of Health Sciences, McMaster University. She holds a joint appointment within the Department of Health Research Methods, Evidence, and Impact , Faculty of Health Sciences, McMaster University.

1-2 The Nature of Evidence: Inductive and Deductive Reasoning Copyright © 2022 by Renate Kahlke; Jonathan Sherbino; and Sandra Monteiro is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Inductive Approach (Inductive Reasoning)

Inductive approach, also known in inductive reasoning, starts with the observations and theories are proposed towards the end of the research process as a result of observations [1] . Inductive research “involves the search for pattern from observation and the development of explanations – theories – for those patterns through series of hypotheses” [2] . No theories or hypotheses would apply in inductive studies at the beginning of the research and the researcher is free in terms of altering the direction for the study after the research process had commenced.

It is important to stress that inductive approach does not imply disregarding theories when formulating research questions and objectives. This approach aims to generate meanings from the data set collected in order to identify patterns and relationships to build a theory; however, inductive approach does not prevent the researcher from using existing theory to formulate the research question to be explored. [3] Inductive reasoning is based on learning from experience. Patterns, resemblances and regularities in experience (premises) are observed in order to reach conclusions (or to generate theory).

Application of Inductive Approach (Inductive Reasoning) in Business Research

Inductive reasoning begins with detailed observations of the world, which moves towards more abstract generalisations and ideas [4] . When following an inductive approach, beginning with a topic, a researcher tends to develop empirical generalisations and identify preliminary relationships as he progresses through his research. No hypotheses can be found at the initial stages of the research and the researcher is not sure about the type and nature of the research findings until the study is completed.

As it is illustrated in figure below, “inductive reasoning is often referred to as a “bottom-up” approach to knowing, in which the researcher uses observations to build an abstraction or to describe a picture of the phenomenon that is being studied” [5]

Inductive approach (inductive reasoning)

Here is an example:

My nephew borrowed $100 last June but he did not pay back until September as he had promised (PREMISE). Then he assured me that he will pay back until Christmas but he didn’t (PREMISE). He also failed in to keep his promise to pay back in March (PREMISE). I reckon I have to face the facts. My nephew is never going to pay me back (CONCLUSION).

Generally, the application of inductive approach is associated with qualitative methods of data collection and data analysis, whereas deductive approach is perceived to be related to quantitative methods . The following table illustrates such a classification from a broad perspective:

However, the statement above is not absolute, and in some instances inductive approach can be adopted to conduct a quantitative research as well. The following table illustrates patterns of data analysis according to type of research and research approach .

When writing a dissertation in business studies it is compulsory to specify the approach of are adopting. It is good to include a table comparing inductive and deductive approaches similar to one below [6] and discuss the impacts of your choice of inductive approach on selection of primary data collection methods and research process.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research approaches. The e-book also explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy , research design , methods of data collection , data analysis and sampling are explained in this e-book in simple words.

John Dudovskiy

[1] Goddard, W. & Melville, S. (2004) “Research Methodology: An Introduction” 2nd edition, Blackwell Publishing

[2] Bernard, H.R. (2011) “Research Methods in Anthropology” 5 th edition, AltaMira Press, p.7

[3] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th edition, Pearson Education Limited

[4] Neuman, W.L. (2003) “Social Research Methods: Qualitative and Quantitative Approaches” Allyn and Bacon

[5] Lodico, M.G., Spaulding, D.T &Voegtle, K.H. (2010) “Methods in Educational Research: From Theory to Practice” John Wiley & Sons, p.10

[6] Source: Alexandiris, K.T. (2006) “Exploring Complex Dynamics in Multi Agent-Based Intelligent Systems” Pro Quest

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4.1 Inductive and deductive reasoning

Learning objectives.

Learners will be able to…

Describe inductive and deductive reasoning and provide examples of each
Identify how inductive and deductive reasoning are complementary

Pre-awareness check (Emotion)

When you gain more knowledge, it is normal to experience discomfort as you question what you previously believed to be true. Have you experienced any internal struggles as you have been reading this text? Have any of your preconceived notions regarding your research topic been altered? Do you have any remaining challenges, biases, or blind spots that need to be addressed as you start this chapter?

[NEED AN INTRO TO THIS CHAPTER SECTION]

Inductive and deductive approaches to research are quite different, they can also be complementary. Let’s start by looking at each one and how they differ from one another. Then we’ll move on to thinking about how they complement one another.

Inductive reasoning

A researcher using inductive reasoning begins by collecting a substantial amount of data that is relevant to their topic of interest. The researcher will then step back from data collection to get a bird’s eye view of their data and look for patterns in the data, working to develop a theory that could explain those patterns. Thus, when researchers take an inductive approach, they start with a particular set of observations and move to a more general set of propositions about those experiences. In other words, they move from data to theory, or from the specific to the general. Figure 4.1 outlines the steps involved with an inductive approach to research.

A researcher moving from a more particular focus on data to a more general focus on theory by looking for patterns

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating study in which the researchers took an inductive approach is Katherine Allen, Christine Kaestle, and Abbie Goldberg’s (2011) [1] study of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young cisgender men in which the men described how they learned about menstruation, what they thought of it when they first learned about it, and what they think of it now. By looking for patterns across all 23 cisgender men’s narratives, the researchers were able to develop a general theory of how boys and young men learn about this aspect of girls’ and women’s biology. They conclude that sisters play an important role in boys’ early understanding of menstruation. Menstruation tends to make boys feel separated from girls, but as they gradually develop into adulthood and form romantic relationships, they simultaneously develop more mature attitudes about menstruation. Note how this study began with the data—men’s narratives of learning about menstruation—and worked to develop a theory.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011) [2] analyzed empirical data to better understand how to meet the needs of young people who are experiencing homelessness. The authors analyzed focus group data from 20 youth at a shelter. From the data they developed a set of recommendations for those interested in applied interventions that serve youth experiencing homelessness. The researchers also developed hypotheses for others who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test their hypotheses, their study ends where most deductive investigations begin: with a theory and a hypothesis derived from that theory. Section 4.4 discusses the use of mixed methods research as a way for researchers to test hypotheses created in a previous component of the same research project.

As in these examples, inductive reasoning is most commonly found in studies using qualitative methods, such as focus groups and interviews. Because inductive reasoning involves the creation of a new theory, researchers need very nuanced data on how the key concepts in their working research question operate in the real world. Qualitative data are often drawn from lengthy interactions and observations with the individuals and phenomena under examination. For this reason, inductive reasoning is most often associated with qualitative methods, though it is used in both quantitative and qualitative research.

Deductive reasoning

If inductive reasoning is about creating theories from raw data, deductive reasoning is about testing theories using data. Researchers using deductive reasoning take the steps described earlier for inductive research and reverse their order. They start with a compelling social theory, create a hypothesis about how the world should work, collect raw data, and analyze whether their hypothesis was confirmed or not. That is, deductive approaches move from a more general level (theory) to a more specific (data); whereas inductive approaches move from the specific (data) to general (theory).

Deductive approach to research are what many people think of when they think of scientific investigation. Students in English-dominant countries that may be confused by inductive vs. deductive research can rest part of the blame on Sir Arthur Conan Doyle, creator of the Sherlock Holmes character. As Craig Vasey points out in his breezy introduction to logic book chapter , Sherlock Holmes more often used inductive rather than deductive reasoning (despite claiming to use the powers of deduction to solve crimes). By noticing subtle details in how people act, behave, and dress, Holmes finds patterns that others miss. Using those patterns, he creates a theory of how the crime occurred, dramatically revealed to the authorities just in time to arrest the suspect. Indeed, it is these flashes of insight into the patterns of data that make Holmes such a keen inductive reasoner. In social work practice, rather than detective work, inductive reasoning is supported by the intuitions and practice wisdom of social workers, just as Holmes’ reasoning is sharpened by his experience as a detective.

So, if deductive reasoning isn’t Sherlock Holmes’ observation and pattern-finding, how does it work? Figure 4.2 outlines the steps involved with a deductive approach to research.

Moving from general to specific using deductive reasoning

While not all researchers follow a deductive approach, many do. We’ll now take a look at a couple examples of deductive research.

In a study of U.S. law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009) [3] hypothesized that law enforcement’s response would be less vigorous in areas of the country that had a stronger history of racial violence. The authors developed their hypothesis from prior research and theories on the topic. They tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis and illustrated an important application of critical race theory.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011) [4] studied the effects of different classroom environments on first graders’ mental health. Based on prior research and theory, Milkie and Warner hypothesized that negative classroom features, such as a lack of basic supplies and heat, would be associated with emotional and behavioral problems in children. One might associate this research with Maslow’s hierarchy of needs or systems theory. The researchers found support for their hypothesis, demonstrating that policymakers should be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011). [5]

Complementary approaches

While inductive and deductive approaches to research seem quite different, they can actually be rather complementary. In some cases, researchers will plan for their study to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to conduct either inductive or deductive research, but then discovers along the way that the other approach is needed to help illuminate findings. Here is an example of each such case.

Dr. Amy Blackstone (2012), author of Principles of sociological inquiry: Qualitative and quantitative methods , relates a story about her mixed methods research on sexual harassment.

We began the study knowing that we would like to take both a deductive and an inductive approach in our work. We therefore administered a quantitative survey, the responses to which we could analyze in order to test hypotheses, and also conducted qualitative interviews with a number of the survey participants. The survey data were well suited to a deductive approach; we could analyze those data to test hypotheses that were generated based on theories of harassment. The interview data were well suited to an inductive approach; we looked for patterns across the interviews and then tried to make sense of those patterns by theorizing about them. For one paper (Uggen & Blackstone, 2004) [6] , we began with a prominent feminist theory of the sexual harassment of adult women and developed a set of hypotheses outlining how we expected the theory to apply in the case of younger women’s and men’s harassment experiences. We then tested our hypotheses by analyzing the survey data. In general, we found support for the theory that posited that the current gender system, in which heteronormative men wield the most power in the workplace, explained workplace sexual harassment—not just of adult women but of younger women and men as well. In a more recent paper (Blackstone, Houle, & Uggen, 2006), [7] we did not hypothesize about what we might find but instead inductively analyzed interview data, looking for patterns that might tell us something about how or whether workers’ perceptions of harassment change as they age and gain workplace experience. From this analysis, we determined that workers’ perceptions of harassment did indeed shift as they gained experience and that their later definitions of harassment were more stringent than those they held during adolescence. Overall, our desire to understand young workers’ harassment experiences fully—in terms of their objective workplace experiences, their perceptions of those experiences, and their stories of their experiences—led us to adopt both deductive and inductive approaches in the work. (Blackstone, n.d., p. 21) [8]

Researchers may not always set out to employ both approaches in their work but sometimes find that their use of one approach leads them to the other. One such example is described eloquently in Russell Schutt’s Investigating the Social World (2006). [9] As Schutt describes, researchers Sherman and Berk (1984) [10] conducted an experiment to test two competing theories of the effects of punishment on deterring deviance (in this case, domestic violence). Specifically, Sherman and Berk hypothesized that deterrence theory (see Williams, 2005 [11] for more information on that theory) would provide a better explanation of the effects of arresting accused perpetrators than labeling theory . Deterrence theory predicts that arresting an accused perpetrators of domestic violence will reduce future incidents of violence. Conversely, labeling theory predicts that arresting alleged perpetrators will increase future incidents (see Policastro & Payne, 2013 [12] for more information on that theory). Figure 4.3 summarizes the two competing theories and the hypotheses Sherman and Berk set out to test.

A table showing deterrence theory predicts arrests lead to lower violence while labeling theory predicts higher violence

Research from these follow-up studies were mixed. In some cases, arrest deterred future incidents of violence. In other cases, it did not. This left the researchers with new data that they needed to explain. The researchers therefore took an inductive approach in an effort to make sense of their latest empirical observations. The new studies revealed that arrest seemed to have a deterrent effect for those who were married and employed, but that it led to increased offenses for those who were unmarried and unemployed. Researchers thus turned to control theory, which posits that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation (see Davis et al., 2000 [14] for more information on this theory).

What the original Sherman and Berk study, along with the follow-up studies, show us is that we might start with a deductive approach to research, but then, if confronted by new data we must make sense of, we may move to an inductive approach. We will expand on these possibilities in section 4.5 when we discuss mixed methods research.

Predictions of control theory on incidents of domestic violence

Ethical and critical considerations

Inductive and deductive reasoning, just like other components of the research process comes with ethical and cultural considerations for researchers. Specifically, deductive research is limited by existing theory. Because scientific inquiry has been shaped by oppressive forces such as sexism, racism, and colonialism, what is considered theory is largely based in Western, white-male-dominant culture. Thus, researchers doing deductive research may artificially limit themselves to ideas that were derived from this context. Non-Western researchers, international social workers, and practitioners working with non-dominant groups may find deductive reasoning of limited help if theories do not adequately describe other cultures.

While these flaws in deductive research may make inductive reasoning seem more appealing, on closer inspection you’ll find similar issues apply. A researcher using inductive reasoning applies their intuition and lived experience when analyzing participant data. They will take note of particular themes, conceptualize their definition, and frame the project using their unique psychology. Since everyone’s internal world is shaped by their cultural and environmental context, inductive reasoning conducted by Western researchers may unintentionally reinforcing lines of inquiry that derive from cultural oppression.

Inductive reasoning is also shaped by those invited to provide the data to be analyzed. For example, I recently worked with a student who wanted to understand the impact of child welfare supervision on children born dependent on opiates and methamphetamine. Due to the potential harm that could come from interviewing families and children who are in foster care or under child welfare supervision, the researcher decided to use inductive reasoning and to only interview child welfare workers.

Talking to practitioners is a good idea for feasibility, as they are less vulnerable than clients. However, any theory that emerges out of these observations will be substantially limited, as it would be devoid of the perspectives of parents, children, and other community members who could provide a more comprehensive picture of the impact of child welfare involvement on children. Notice that each of these groups has less power than child welfare workers in the service relationship. Attending to which groups were used to inform the creation of a theory and the power of those groups is an important critical consideration for social work researchers.

As you can see, when researchers apply theory to research they must wrestle with the history and hierarchy around knowledge creation in that area. In deductive studies, the researcher is positioned as the expert, similar to the positivist paradigm (which will be presented in Chapter 5 ). We’ve discussed a few of the limitations on the knowledge of researchers in this subsection, but the position of the “researcher as expert” is inherently problematic. However, it should also not be taken to an extreme. A researcher who approaches inductive inquiry as a naïve learner is also inherently problematic. Just as competence in social work practice requires a baseline of knowledge prior to entering practice, so does competence in social work research. Because a truly naïve intellectual position is impossible—we all have preexisting ways we view the world and are not fully aware of how they may impact our thoughts—researchers should be well-read in the topic area of their research study but humble enough to know that there is always much more to learn.

Key Takeaways

Inductive reasoning begins with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
Deductive reasoning begins with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test the truth of those hypotheses.
Inductive and deductive reasoning can be employed together for a more complete understanding of the research topic.
Though researchers don’t always set out to use both inductive and deductive reasoning in their work, they sometimes find that new questions arise in the course of an investigation that can best be answered by employing both approaches.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Identify one theory and how it helps you understand your topic and working research question. Try to find a specific theory from this topic area, rather than relying only on the broad theoretical perspectives like systems theory or the strengths perspective. Those broad theoretical perspectives are okay, but searching for theories about your topic will help you conceptualize and design your research project.
Using the theory you identified, describe what you expect the answer to be to your working research question.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

You are interested in researching teen dating violence and teenagers’ levels of depressive symptoms and self-esteem.

Develop a working research question for this topic.
Identify one theory and describe how it helps you understand your topic and working research question. Try to find a specific theory from this topic area, rather than relying only on the broad theoretical perspectives like systems theory or the strengths perspective. Those broad theoretical perspectives are okay, but searching for theories about your topic will help you conceptualize and design your research project.
Allen, K. R., Kaestle, C. E., & Goldberg, A. E. (2011). More than just a punctuation mark: How boys and young men learn about menstruation. Journal of Family Issues, 32 , 129–156. ↵
Ferguson, K. M., Kim, M. A., & McCoy, S. (2011). Enhancing empowerment and leadership among homeless youth in agency and community settings: A grounded theory approach. Child and Adolescent Social Work Journal, 28 , 1–22 ↵
King, R. D., Messner, S. F., & Baller, R. D. (2009). Contemporary hate crimes, law enforcement, and the legacy of racial violence. American Sociological Review, 74 , 291–315. ↵
Milkie, M. A., & Warner, C. H. (2011). Classroom learning environments and the mental health of first grade children. Journal of Health and Social Behavior, 52 , 4–22. ↵
The American Sociological Association wrote a press release on Milkie and Warner’s findings: American Sociological Association. (2011). Study: Negative classroom environment adversely affects children’s mental health. Retrieved from: https://www.sciencedaily.com/releases/2011/03/110309073717.htm ↵
Uggen, C., & Blackstone, A. (2004). Sexual harassment as a gendered expression of power. American Sociological Review, 69 , 64–92. ↵
Blackstone, A., Houle, J., & Uggen, C. “At the time I thought it was great”: Age, experience, and workers’ perceptions of sexual harassment. Presented at the 2006 meetings of the American Sociological Association. ↵
Blackstone, A. (2012). Inductive or deductive? Two different approaches. Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Foundation. ↵
Schutt, R. K. (2006). Investigating the social world: The process and practice of research . Thousand Oaks, CA: Pine Forge Press. ↵
Sherman, L. W., & Berk, R. A. (1984). The specific deterrent effects of arrest for domestic assault. American Sociological Review, 49 , 261–272. ↵
Williams, K. R. (2005). Arrest and intimate partner violence: Toward a more complete application of deterrence theory. Aggression and Violent Behavior , 10 (6), 660-679. ↵
Policastro, C., & Payne, B. K. (2013). The blameworthy victim: Domestic violence myths and the criminalization of victimhood. Journal of Aggression, Maltreatment & Trauma , 22 (4), 329-347. ↵
Berk, R., Campbell, A., Klap, R., & Western, B. (1992). The deterrent effect of arrest in incidents of domestic violence: A Bayesian analysis of four field experiments. American Sociological Review, 57, 698–708; Pate, A., & Hamilton, E. (1992). Formal and informal deterrents to domestic violence: The Dade county spouse assault experiment. American Sociological Review, 57, 691–697; Sherman, L., & Smith, D. (1992). Crime, punishment, and stake in conformity: Legal and informal control of domestic violence. American Sociological Review, 57, 680–690. ↵
Taylor, B. G., Davis, R. C., & Maxwell, C. D. (2001). The effects of a group batterer treatment program: A randomized experiment in Brooklyn. Justice Quarterly, 18(1), 171-201. ↵

when a researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences

starts by reading existing theories, then testing hypotheses and revising or confirming the theory

a statement describing a researcher’s expectation regarding what they anticipate finding

when researchers use both quantitative and qualitative methods in a project

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8.1: Inductive and deductive reasoning

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Matthew DeCarlo, Cory Cummings, & Kate Agnelli

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Open Social Work Education

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Learning Objectives

Learners will be able to…

Describe inductive and deductive reasoning and provide examples of each
Identify how inductive and deductive reasoning are complementary

Congratulations! You survived the chapter on theories and paradigms. My experience has been that many students have a difficult time thinking about theories and paradigms because they perceive them as “intangible” and thereby hard to connect to social work research. I even had one student who said she got frustrated just reading the word “philosophy.”

Rest assured, you do not need to become a theorist or philosopher to be an effective social worker or researcher. However, you should have a good sense of what theory or theories will be relevant to your project, as well as how this theory, along with your working question, fit within the three broad research paradigms we reviewed. If you don’t have a good idea about those at this point, it may be a good opportunity to pause and read more about the theories related to your topic area.

Theories structure and inform social work research. The converse is also true: research can structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students when they consider the relationships between theory and research in inductive and deductive approaches to research. In both cases, theory is crucial. But the relationship between theory and research differs for each approach.

While inductive and deductive approaches to research are quite different, they can also be complementary. Let’s start by looking at each one and how they differ from one another. Then we’ll move on to thinking about how they complement one another.

Inductive reasoning

A researcher using inductive reasoning begins by collecting data that is relevant to their topic of interest. Once a substantial amount of data have been collected, the researcher will then step back from data collection to get a bird’s eye view of their data. At this stage, the researcher looks for patterns in the data, working to develop a theory that could explain those patterns. Thus, when researchers take an inductive approach, they start with a particular set of observations and move to a more general set of propositions about those experiences. In other words, they move from data to theory, or from the specific to the general. Figure 8.1 outlines the steps involved with an inductive approach to research.

Figure 8.1 Inductive reasoning

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating study in which the researchers took an inductive approach is Katherine Allen, Christine Kaestle, and Abbie Goldberg’s (2011)$^1$ study of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young cisgender men in which the men described how they learned about menstruation, what they thought of it when they first learned about it, and what they think of it now. By looking for patterns across all 23 cisgender men’s narratives, the researchers were able to develop a general theory of how boys and young men learn about this aspect of girls’ and women’s biology. They conclude that sisters play an important role in boys’ early understanding of menstruation, that menstruation makes boys feel somewhat separated from girls, and that as they enter young adulthood and form romantic relationships, young men develop more mature attitudes about menstruation. Note how this study began with the data—men’s narratives of learning about menstruation—and worked to develop a theory.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011)$^2$ analyzed empirical data to better understand how to meet the needs of young people who are homeless. The authors analyzed focus group data from 20 youth at a homeless shelter. From these data they developed a set of recommendations for those interested in applied interventions that serve homeless youth. The researchers also developed hypotheses for others who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test their hypotheses, their study ends where most deductive investigations begin: with a theory and a hypothesis derived from that theory. Section 8.4 discusses the use of mixed methods research as a way for researchers to test hypotheses created in a previous component of the same research project.

You will notice from both of these examples that inductive reasoning is most commonly found in studies using qualitative methods, such as focus groups and interviews. Because inductive reasoning involves the creation of a new theory, researchers need very nuanced data on how the key concepts in their working question operate in the real world. Qualitative data is often drawn from lengthy interactions and observations with the individuals and phenomena under examination. For this reason, inductive reasoning is most often associated with qualitative methods, though it is used in both quantitative and qualitative research.

Deductive reasoning

A deductive approach to research is the one that people typically associate with scientific investigation. Students in English-dominant countries that may be confused by inductive vs. deductive research can rest part of the blame on Sir Arthur Conan Doyle, creator of the Sherlock Holmes character. As Craig Vasey points out in his breezy introduction to logic book chapter , Sherlock Holmes more often used inductive rather than deductive reasoning (despite claiming to use the powers of deduction to solve crimes). By noticing subtle details in how people act, behave, and dress, Holmes finds patterns that others miss. Using those patterns, he creates a theory of how the crime occurred, dramatically revealed to the authorities just in time to arrest the suspect. Indeed, it is these flashes of insight into the patterns of data that make Holmes such a keen inductive reasoner. In social work practice, rather than detective work, inductive reasoning is supported by the intuitions and practice wisdom of social workers, just as Holmes’ reasoning is sharpened by his experience as a detective.

So, if deductive reasoning isn’t Sherlock Holmes’ observation and pattern-finding, how does it work? It starts with what you have already done in Chapters 3 and 4, reading and evaluating what others have done to study your topic. It continued with Chapter 5, discovering what theories already try to explain how the concepts in your working question operate in the real world. Tapping into this foundation of knowledge on their topic, the researcher studies what others have done, reads existing theories of whatever phenomenon they are studying, and then tests hypotheses that emerge from those theories. Figure 8.2 outlines the steps involved with a deductive approach to research.

Figure 8.2 Deductive reasoning

While not all researchers follow a deductive approach, many do. We’ll now take a look at a couple excellentrecent examples of deductive research.

In a study of US law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009)$^3$ hypothesized that law enforcement’s response would be less vigorous in areas of the country that had a stronger history of racial violence. The authors developed their hypothesis from prior research and theories on the topic. They tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis and illustrated an important application of critical race theory.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011)$^4$ studied the effects of different classroom environments on first graders’ mental health. Based on prior research and theory, Milkie and Warner hypothesized that negative classroom features, such as a lack of basic supplies and heat, would be associated with emotional and behavioral problems in children. One might associate this research with Maslow’s hierarchy of needs or systems theory. The researchers found support for their hypothesis, demonstrating that policymakers should be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011).$^5$

Complementary approaches

Dr. Amy Blackstone (n.d.), author of Principles of sociological inquiry: Qualitative and quantitative methods , relates a story about her mixed methods research on sexual harassment.

We began the study knowing that we would like to take both a deductive and an inductive approach in our work. We therefore administered a quantitative survey, the responses to which we could analyze in order to test hypotheses, and also conducted qualitative interviews with a number of the survey participants. The survey data were well suited to a deductive approach; we could analyze those data to test hypotheses that were generated based on theories of harassment. The interview data were well suited to an inductive approach; we looked for patterns across the interviews and then tried to make sense of those patterns by theorizing about them.

For one paper (Uggen & Blackstone, 2004)$^6$, we began with a prominent feminist theory of the sexual harassment of adult women and developed a set of hypotheses outlining how we expected the theory to apply in the case of younger women’s and men’s harassment experiences. We then tested our hypotheses by analyzing the survey data. In general, we found support for the theory that posited that the current gender system, in which heteronormative men wield the most power in the workplace, explained workplace sexual harassment—not just of adult women but of younger women and men as well. In a more recent paper (Blackstone, Houle, & Uggen, 2006),$^7$ we did not hypothesize about what we might find but instead inductively analyzed interview data, looking for patterns that might tell us something about how or whether workers’ perceptions of harassment change as they age and gain workplace experience. From this analysis, we determined that workers’ perceptions of harassment did indeed shift as they gained experience and that their later definitions of harassment were more stringent than those they held during adolescence. Overall, our desire to understand young workers’ harassment experiences fully—in terms of their objective workplace experiences, their perceptions of those experiences, and their stories of their experiences—led us to adopt both deductive and inductive approaches in the work. (Blackstone, n.d., p. 21)$^8$

Researchers may not always set out to employ both approaches in their work but sometimes find that their use of one approach leads them to the other. One such example is described eloquently in Russell Schutt’s Investigating the Social World (2006).$^9$ As Schutt describes, researchers Sherman and Berk (1984)$^{10}$ conducted an experiment to test two competing theories of the effects of punishment on deterring deviance (in this case, domestic violence).Specifically, Sherman and Berk hypothesized that deterrence theory (see Williams, 2005 $^{11}$ for more information on that theory) would provide a better explanation of the effects of arresting accused batterers than labeling theory . Deterrence theory predicts that arresting an accused spouse batterer will reduce future incidents of violence. Conversely, labeling theory predicts that arresting accused spouse batterers will increase future incidents (see Policastro & Payne, 2013$^{12}$ for more information on that theory). Figure 8.3 summarizes the two competing theories and the hypotheses Sherman and Berk set out to test.

Deterrence theory predicts arrests lead to lower violence while labeling theory predicts higher violence

Figure 8.3 Predicting the effects of arrest on future spouse battery: Two different theories

Sherman and Berk found, after conducting an experiment with the help of local police in one city, that arrest did in fact deter future incidents of violence, thus supporting their hypothesis that deterrence theory would better predict the effect of arrest. After conducting this research, they and other researchers went on to conduct similar experiments in six additional cities (Berk, Campbell, Klap, & Western, 1992; Pate & Hamilton, 1992; Sherman & Smith, 1992).$^{13}$hat the original Sherman and Berk study, along with the follow-up studies, show us is that we might start with a deductive approach to research, but then, if confronted by new data we must make sense of, we may move to an inductive approach. We will expand on these possibilities in section 8.4 when we discuss mixed methods research.

Figure 8.4 Predicting the effects of arrest on future spouse battery: A third theory

Ethical and critical considerations

Deductive and inductive reasoning, just like other components of the research process comes with ethical and cultural considerations for researchers. Specifically, deductive research is limited by existing theory. Because scientific inquiry has been shaped by oppressive forces such as sexism, racism, and colonialism, what is considered theory is largely based in Western, white-male-dominant culture. Thus, researchers doing deductive research may artificially limit themselves to ideas that were derived from this context. Non-Western researchers, international social workers, and practitioners working with non-dominant groups may find deductive reasoning of limited help if theories do not adequately describe other cultures.

As you can see, when researchers apply theory to research they must wrestle with the history and hierarchy around knowledge creation in that area. In deductive studies, the researcher is positioned as the expert, similar to the positivist paradigm presented in Chapter 5. We’ve discussed a few of the limitations on the knowledge of researchers in this subsection, but the position of the “researcher as expert” is inherently problematic. However, it should also not be taken to an extreme. A researcher who approaches inductive inquiry as a naïve learner is also inherently problematic. Just as competence in social work practice requires a baseline of knowledge prior to entering practice, so does competence in social work research. Because a truly naïve intellectual position is impossible—we all have preexisting ways we view the world and are not fully aware of how they may impact our thoughts—researchers should be well-read in the topic area of their research study but humble enough to know that there is always much more to learn.

Key Takeaways

Inductive reasoning begins with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
Deductive reasoning begins with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test the truth of those hypotheses.
Inductive and deductive reasoning can be employed together for a more complete understanding of the research topic.
Though researchers don’t always set out to use both inductive and deductive reasoning in their work, they sometimes find that new questions arise in the course of an investigation that can best be answered by employing both approaches.
Identify one theory and how it helps you understand your topic and working question.

I encourage you to find a specific theory from your topic area, rather than relying only on the broad theoretical perspectives like systems theory or the strengths perspective. Those broad theoretical perspectives are okay…but I promise that searching for theories about your topic will help you conceptualize and design your research project.

Using the theory you identified, describe what you expect the answer to be to your working question.

Inductive and Deductive Reasoning — Strategic approach for conducting research

Karl questioned his research approach before finalizing the hypothesis of his research study. He laid a plan and a procedure that consists of steps of broad assumptions to detailed methods of data collection, analysis, and interpretation, wondering how to reason with your findings!

His supervisor provided him the insights about understanding his role in driving the research question by developing a research approach. The supervisor quoted, “In all the disciplines, research plays an essential role in allowing researchers to expand their theoretical knowledge in the field of their study to verify and justify the existing theories. A well-planned research approach will assist one in understanding and building a relationship between theory and objective of the research study.”

Obediently, Karl jotted down the keywords to research on the internet, understand the reasoning, and define the research approach for his research study.

Table of Contents

What Is a Research Approach?

A research approach is a procedure selected by a researcher to collect, analyze, and interpret data. Based on the methods of data collection and data analysis, research approach methods are of three types: quantitative, qualitative, and mixed methods. However, considering the general plan and procedure for conducting a study, the research approach is divided into three categories:

1. Inductive Approach

The inductive approach begins with a researcher collecting data that is relevant to the research study. Post-data collection, a researcher will analyze this data broadly, looking for patterns in the data to develop a theory that could explain the patterns. Therefore, an inductive approach starts with a set of observations and then moves toward developing a theory.

2. Deductive Approach

The deductive approach is the reverse of the inductive approach. It always starts with a theory, such as one or more general statements or premises, and reaches a logical conclusion. Scientists use this type of reasoning approach to prove their research hypothesis .

3. Abductive Approach

This type of reasoning approach is set to answer the weakness associated with deductive and inductive approaches. While following the abductive reasoning approach, researchers start the process with surprising facts or puzzles while studying some empirical phenomena which cannot be explained with the existing theories. Abductive reasoning will assist researchers in explaining the facts or puzzles. Despite its popularity, the abductive approach is challenging to implement and researchers are advised to use traditional deductive or inductive approaches.

Inductive Vs Deductive Reasoning

What Is Inductive Reasoning?

Inductive reasoning moves away from more specific observations to broader generalizations and theories. Usually, this is against the scientific method, an empirical method of acquiring results based on experimental findings. Inductive reasoning makes generalizations by observing patterns and drawing inferences.

Inductive reasoning is based on strong and weak arguments. When the premise is true then the conclusion of the argument is likely to be true. Such an argument is termed a strong or cogent argument. Meanwhile, weak arguments may be false even if the premises they are based upon are true. An argument is a cogent argument if it is weak or the premise is false.

Types of Inductive Reasoning

1. inductive generalization.

Inductive generalization uses observations about a sample to conclude the population from which the sample was chosen. In simple terms, you use statistical results from samples to make statements about populations. One can evaluate large samples or random sampling using inductive generalizations.

2. Statistical Generalization

Statistical generalization uses specific numbers to create statements about populations. This generalization is a subtype of inductive generalization, and it is also termed statistical syllogism.

3. Causal Reasoning

Causal reasoning links cause and effect between different aspects of the research study. A casual reasoning statement starts with a premise about two events that occur simultaneously, followed by choosing a specific direction of causality or refuting any other direction, and concluding a causal statement about the relationship between two things.

4. Sign Reasoning

Sign reasoning makes correlational connections between different things. Inductive reasoning works on a correlational relationship where nothing causes the other thing to occur. However, sign reasoning proposes that one event may be a ‘sign’ to impact another event’s occurrence.

5. Analogical Reasoning

Analogical reasoning concludes something based on its similarities to another thing. It links two things together and then concludes based on the attributes of one thing which holds for another thing. Analogical reasoning could be literal or figurative. However, literal comparison usually uses a much stronger case while reasoning.

Stages of Inductive Research Approach

Begin with an observation
Seek patterns in the observation
Develop a theory or preliminary conclusion based on the patterns observed

Limitations of an Inductive Approach

A conclusion drawn based on inductive reasoning cannot be proven completely, but it can be invalidated.

What Is Deductive Reasoning?

Deductive reasoning starts with one or more general statements to derive a logical conclusion. Moreover, while conducting deductive research, a researcher starts with a theory. This theory could be derived from inductive reasoning. The approach of deductive reasoning is used to test the stated theory. If the general statement or theory is true, the conclusion derived is valid and vice-versa.

Deductive reasoning produces arguments that may be valid or invalid. If the logic is correct, conclusions flow from the general statement or theory and the arguments are valid. Researchers use deductive reasoning to prove their hypotheses. However, if there is no theory yet, then one cannot conduct deductive research.

Types of Deductive Reasoning

There are three common types of deductive reasoning:

1. Syllogism

Syllogism takes two conditional statements and forms a conclusion by combining the hypothesis of one statement with the conclusion of another. For example —

If brakes fail, the car does not stop.

If the car does not stop, it will cause an accident., therefore, brake failure causes the accident., 2. modus ponens.

Modus ponens is another type of deductive reasoning that follows a pattern that affirms the condition of the reasoning. For example —

If a person is born after 1997, then they are a Gen Z

Ryan was born in 1998., therefore, ryan belongs to gen z..

This type of reasoning affirms the previous statement. Meanwhile, the first premise sets the conditional statement to be affirmed.

3. Modus Tollens

Modus tollens is yet another type of deductive reasoning known as ‘the law of contrapositive’. It is the opposite of modus ponens because it negates the condition of the reasoning. For example —

Bruce is not a Gen Z.

Therefore, bruce was not born after 1997., stages of deductive research approach.

Begin with an existing theory and create a problem statement
Formulate a hypothesis based on the existing theory
Collect and analyze data to test the hypothesis
Decide if you could reject or accept the hypothesis

Limitations of the Deductive Approach

The conclusions drawn from deductive reasoning can only be true if the theory set in the inductive study is true and the terms are clear.

Combination of Inductive and Deductive Reasoning in Research

When researchers conduct a large research project, they begin with an inductive study. This inductive reasoning assists them in constructing an efficient working theory. Therefore, post inductive reasoning, a deductive reasoning could confirm and conclude the working theory. This helps researchers formulate a structured project and mitigates the risk of research bias in the research study.

After doing thorough research in understanding inductive and deductive reasoning, Karl concluded that:

Inductive reasoning is known for constructing hypotheses based on existing knowledge and predictions.
Deductive reasoning could be used to test an inductive research approach.
People tend to rely on information that is easily accessible and available in the world. While theorizing a research hypothesis , this tendency could introduce biases in the study.
Inductive reasoning could cause biases which can distort the proper application of inductive argument.
A good scientific research study must be highly focused and requires both inductive and deductive research approaches.

After a few hours of focused research, Karl understood his supervisor’s approach to creating a well-planned research hypothesis for his research study. Karl dived deeper and understood that he had only touched the tip of an iceberg, and there is much more to induce and deduce before he holds his doctorate!

Have you ever encountered a situation like Karl’s? Trying to understand which research approach to use? Did you find this blog informative? Do write to us or comment below and tell us what you feel!

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The potential of working hypotheses for deductive exploratory research

Open access
Published: 08 December 2020
Volume 55 , pages 1703–1725, ( 2021 )

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Mattia Casula ORCID: orcid.org/0000-0002-7081-8153 1 ,
Nandhini Rangarajan 2 &
Patricia Shields ORCID: orcid.org/0000-0002-0960-4869 2

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While hypotheses frame explanatory studies and provide guidance for measurement and statistical tests, deductive, exploratory research does not have a framing device like the hypothesis. To this purpose, this article examines the landscape of deductive, exploratory research and offers the working hypothesis as a flexible, useful framework that can guide and bring coherence across the steps in the research process. The working hypothesis conceptual framework is introduced, placed in a philosophical context, defined, and applied to public administration and comparative public policy. Doing so, this article explains: the philosophical underpinning of exploratory, deductive research; how the working hypothesis informs the methodologies and evidence collection of deductive, explorative research; the nature of micro-conceptual frameworks for deductive exploratory research; and, how the working hypothesis informs data analysis when exploratory research is deductive.

Reflections on Methodological Issues

Research: Meaning and Purpose

Research Design and Methodology

Avoid common mistakes on your manuscript.

1 Introduction

Exploratory research is generally considered to be inductive and qualitative (Stebbins 2001 ). Exploratory qualitative studies adopting an inductive approach do not lend themselves to a priori theorizing and building upon prior bodies of knowledge (Reiter 2013 ; Bryman 2004 as cited in Pearse 2019 ). Juxtaposed against quantitative studies that employ deductive confirmatory approaches, exploratory qualitative research is often criticized for lack of methodological rigor and tentativeness in results (Thomas and Magilvy 2011 ). This paper focuses on the neglected topic of deductive, exploratory research and proposes working hypotheses as a useful framework for these studies.

To emphasize that certain types of applied research lend themselves more easily to deductive approaches, to address the downsides of exploratory qualitative research, and to ensure qualitative rigor in exploratory research, a significant body of work on deductive qualitative approaches has emerged (see for example, Gilgun 2005 , 2015 ; Hyde 2000 ; Pearse 2019 ). According to Gilgun ( 2015 , p. 3) the use of conceptual frameworks derived from comprehensive reviews of literature and a priori theorizing were common practices in qualitative research prior to the publication of Glaser and Strauss’s ( 1967 ) The Discovery of Grounded Theory . Gilgun ( 2015 ) coined the terms Deductive Qualitative Analysis (DQA) to arrive at some sort of “middle-ground” such that the benefits of a priori theorizing (structure) and allowing room for new theory to emerge (flexibility) are reaped simultaneously. According to Gilgun ( 2015 , p. 14) “in DQA, the initial conceptual framework and hypotheses are preliminary. The purpose of DQA is to come up with a better theory than researchers had constructed at the outset (Gilgun 2005 , 2009 ). Indeed, the production of new, more useful hypotheses is the goal of DQA”.

DQA provides greater level of structure for both the experienced and novice qualitative researcher (see for example Pearse 2019 ; Gilgun 2005 ). According to Gilgun ( 2015 , p. 4) “conceptual frameworks are the sources of hypotheses and sensitizing concepts”. Sensitizing concepts frame the exploratory research process and guide the researcher’s data collection and reporting efforts. Pearse ( 2019 ) discusses the usefulness for deductive thematic analysis and pattern matching to help guide DQA in business research. Gilgun ( 2005 ) discusses the usefulness of DQA for family research.

Given these rationales for DQA in exploratory research, the overarching purpose of this paper is to contribute to that growing corpus of work on deductive qualitative research. This paper is specifically aimed at guiding novice researchers and student scholars to the working hypothesis as a useful a priori framing tool. The applicability of the working hypothesis as a tool that provides more structure during the design and implementation phases of exploratory research is discussed in detail. Examples of research projects in public administration that use the working hypothesis as a framing tool for deductive exploratory research are provided.

In the next section, we introduce the three types of research purposes. Second, we examine the nature of the exploratory research purpose. Third, we provide a definition of working hypothesis. Fourth, we explore the philosophical roots of methodology to see where exploratory research fits. Fifth, we connect the discussion to the dominant research approaches (quantitative, qualitative and mixed methods) to see where deductive exploratory research fits. Sixth, we examine the nature of theory and the role of the hypothesis in theory. We contrast formal hypotheses and working hypotheses. Seven, we provide examples of student and scholarly work that illustrates how working hypotheses are developed and operationalized. Lastly, this paper synthesizes previous discussion with concluding remarks.

2 Three types of research purposes

The literature identifies three basic types of research purposes—explanation, description and exploration (Babbie 2007 ; Adler and Clark 2008 ; Strydom 2013 ; Shields and Whetsell 2017 ). Research purposes are similar to research questions; however, they focus on project goals or aims instead of questions.

Explanatory research answers the “why” question (Babbie 2007 , pp. 89–90), by explaining “why things are the way they are”, and by looking “for causes and reasons” (Adler and Clark 2008 , p. 14). Explanatory research is closely tied to hypothesis testing. Theory is tested using deductive reasoning, which goes from the general to the specific (Hyde 2000 , p. 83). Hypotheses provide a frame for explanatory research connecting the research purpose to other parts of the research process (variable construction, choice of data, statistical tests). They help provide alignment or coherence across stages in the research process and provide ways to critique the strengths and weakness of the study. For example, were the hypotheses grounded in the appropriate arguments and evidence in the literature? Are the concepts imbedded in the hypotheses appropriately measured? Was the best statistical test used? When the analysis is complete (hypothesis is tested), the results generally answer the research question (the evidence supported or failed to support the hypothesis) (Shields and Rangarajan 2013 ).

Descriptive research addresses the “What” question and is not primarily concerned with causes (Strydom 2013 ; Shields and Tajalli 2006 ). It lies at the “midpoint of the knowledge continuum” (Grinnell 2001 , p. 248) between exploration and explanation. Descriptive research is used in both quantitative and qualitative research. A field researcher might want to “have a more highly developed idea of social phenomena” (Strydom 2013 , p. 154) and develop thick descriptions using inductive logic. In science, categorization and classification systems such as the periodic table of chemistry or the taxonomies of biology inform descriptive research. These baseline classification systems are a type of theorizing and allow researchers to answer questions like “what kind” of plants and animals inhabit a forest. The answer to this question would usually be displayed in graphs and frequency distributions. This is also the data presentation system used in the social sciences (Ritchie and Lewis 2003 ; Strydom 2013 ). For example, if a scholar asked, what are the needs of homeless people? A quantitative approach would include a survey that incorporated a “needs” classification system (preferably based on a literature review). The data would be displayed as frequency distributions or as charts. Description can also be guided by inductive reasoning, which draws “inferences from specific observable phenomena to general rules or knowledge expansion” (Worster 2013 , p. 448). Theory and hypotheses are generated using inductive reasoning, which begins with data and the intention of making sense of it by theorizing. Inductive descriptive approaches would use a qualitative, naturalistic design (open ended interview questions with the homeless population). The data could provide a thick description of the homeless context. For deductive descriptive research, categories, serve a purpose similar to hypotheses for explanatory research. If developed with thought and a connection to the literature, categories can serve as a framework that inform measurement, link to data collection mechanisms and to data analysis. Like hypotheses they can provide horizontal coherence across the steps in the research process.

Table 1 demonstrated these connections for deductive, descriptive and explanatory research. The arrow at the top emphasizes the horizontal or across the research process view we emphasize. This article makes the case that the working hypothesis can serve the same purpose as the hypothesis for deductive, explanatory research and categories for deductive descriptive research. The cells for exploratory research are filled in with question marks.

The remainder of this paper focuses on exploratory research and the answers to questions found in the table:

What is the philosophical underpinning of exploratory, deductive research?

What is the Micro-conceptual framework for deductive exploratory research? [ As is clear from the article title we introduce the working hypothesis as the answer .]

How does the working hypothesis inform the methodologies and evidence collection of deductive exploratory research?

How does the working hypothesis inform data analysis of deductive exploratory research?

3 The nature of exploratory research purpose

Explorers enter the unknown to discover something new. The process can be fraught with struggle and surprises. Effective explorers creatively resolve unexpected problems. While we typically think of explorers as pioneers or mountain climbers, exploration is very much linked to the experience and intention of the explorer. Babies explore as they take their first steps. The exploratory purpose resonates with these insights. Exploratory research, like reconnaissance, is a type of inquiry that is in the preliminary or early stages (Babbie 2007 ). It is associated with discovery, creativity and serendipity (Stebbins 2001 ). But the person doing the discovery, also defines the activity or claims the act of exploration. It “typically occurs when a researcher examines a new interest or when the subject of study itself is relatively new” (Babbie 2007 , p. 88). Hence, exploration has an open character that emphasizes “flexibility, pragmatism, and the particular, biographically specific interests of an investigator” (Maanen et al. 2001 , p. v). These three purposes form a type of hierarchy. An area of inquiry is initially explored . This early work lays the ground for, description which in turn becomes the basis for explanation . Quantitative, explanatory studies dominate contemporary high impact journals (Twining et al. 2017 ).

Stebbins ( 2001 ) makes the point that exploration is often seen as something like a poor stepsister to confirmatory or hypothesis testing research. He has a problem with this because we live in a changing world and what is settled today will very likely be unsettled in the near future and in need of exploration. Further, exploratory research “generates initial insights into the nature of an issue and develops questions to be investigated by more extensive studies” (Marlow 2005 , p. 334). Exploration is widely applicable because all research topics were once “new.” Further, all research topics have the possibility of “innovation” or ongoing “newness”. Exploratory research may be appropriate to establish whether a phenomenon exists (Strydom 2013 ). The point here, of course, is that the exploratory purpose is far from trivial.

Stebbins’ Exploratory Research in the Social Sciences ( 2001 ), is the only book devoted to the nature of exploratory research as a form of social science inquiry. He views it as a “broad-ranging, purposive, systematic prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life” (p. 3). It is science conducted in a way distinct from confirmation. According to Stebbins ( 2001 , p. 6) the goal is discovery of potential generalizations, which can become future hypotheses and eventually theories that emerge from the data. He focuses on inductive logic (which stimulates creativity) and qualitative methods. He does not want exploratory research limited to the restrictive formulas and models he finds in confirmatory research. He links exploratory research to Glaser and Strauss’s ( 1967 ) flexible, immersive, Grounded Theory. Strydom’s ( 2013 ) analysis of contemporary social work research methods books echoes Stebbins’ ( 2001 ) position. Stebbins’s book is an important contribution, but it limits the potential scope of this flexible and versatile research purpose. If we accepted his conclusion, we would delete the “Exploratory” row from Table 1 .

Note that explanatory research can yield new questions, which lead to exploration. Inquiry is a process where inductive and deductive activities can occur simultaneously or in a back and forth manner, particularly as the literature is reviewed and the research design emerges. Footnote 1 Strict typologies such as explanation, description and exploration or inductive/deductive can obscures these larger connections and processes. We draw insight from Dewey’s ( 1896 ) vision of inquiry as depicted in his seminal “Reflex Arc” article. He notes that “stimulus” and “response” like other dualities (inductive/deductive) exist within a larger unifying system. Yet the terms have value. “We need not abandon terms like stimulus and response, so long as we remember that they are attached to events based upon their function in a wider dynamic context, one that includes interests and aims” (Hildebrand 2008 , p. 16). So too, in methodology typologies such as deductive/inductive capture useful distinctions with practical value and are widely used in the methodology literature.

We argue that there is a role for exploratory, deductive, and confirmatory research. We maintain all types of research logics and methods should be in the toolbox of exploratory research. First, as stated above, it makes no sense on its face to identify an extremely flexible purpose that is idiosyncratic to the researcher and then basically restrict its use to qualitative, inductive, non-confirmatory methods. Second, Stebbins’s ( 2001 ) work focused on social science ignoring the policy sciences. Exploratory research can be ideal for immediate practical problems faced by policy makers, who could find a framework of some kind useful. Third, deductive, exploratory research is more intentionally connected to previous research. Some kind of initial framing device is located or designed using the literature. This may be very important for new scholars who are developing research skills and exploring their field and profession. Stebbins’s insights are most pertinent for experienced scholars. Fourth, frameworks and deductive logic are useful for comparative work because some degree of consistency across cases is built into the design.

As we have seen, the hypotheses of explanatory and categories of descriptive research are the dominate frames of social science and policy science. We certainly concur that neither of these frames makes a lot of sense for exploratory research. They would tend to tie it down. We see the problem as a missing framework or missing way to frame deductive, exploratory research in the methodology literature. Inductive exploratory research would not work for many case studies that are trying to use evidence to make an argument. What exploratory deductive case studies need is a framework that incorporates flexibility. This is even more true for comparative case studies. A framework of this sort could be usefully applied to policy research (Casula 2020a ), particularly evaluative policy research, and applied research generally. We propose the Working Hypothesis as a flexible conceptual framework and as a useful tool for doing exploratory studies. It can be used as an evaluative criterion particularly for process evaluation and is useful for student research because students can develop theorizing skills using the literature.

Table 1 included a column specifying the philosophical basis for each research purpose. Shifting gears to the philosophical underpinning of methodology provides useful additional context for examination of deductive, exploratory research.

4 What is a working hypothesis

The working hypothesis is first and foremost a hypothesis or a statement of expectation that is tested in action. The term “working” suggest that these hypotheses are subject to change, are provisional and the possibility of finding contradictory evidence is real. In addition, a “working” hypothesis is active, it is a tool in an ongoing process of inquiry. If one begins with a research question, the working hypothesis could be viewed as a statement or group of statements that answer the question. It “works” to move purposeful inquiry forward. “Working” also implies some sort of community, mostly we work together in relationship to achieve some goal.

Working Hypothesis is a term found in earlier literature. Indeed, both pioneering pragmatists, John Dewey and George Herbert Mead use the term working hypothesis in important nineteenth century works. For both Dewey and Mead, the notion of a working hypothesis has a self-evident quality and it is applied in a big picture context. Footnote 2

Most notably, Dewey ( 1896 ), in one of his most pivotal early works (“Reflex Arc”), used “working hypothesis” to describe a key concept in psychology. “The idea of the reflex arc has upon the whole come nearer to meeting this demand for a general working hypothesis than any other single concept (Italics added)” (p. 357). The notion of a working hypothesis was developed more fully 42 years later, in Logic the Theory of Inquiry , where Dewey developed the notion of a working hypothesis that operated on a smaller scale. He defines working hypotheses as a “provisional, working means of advancing investigation” (Dewey 1938 , pp. 142). Dewey’s definition suggests that working hypotheses would be useful toward the beginning of a research project (e.g., exploratory research).

Mead ( 1899 ) used working hypothesis in a title of an American Journal of Sociology article “The Working Hypothesis and Social Reform” (italics added). He notes that a scientist’s foresight goes beyond testing a hypothesis.

Given its success, he may restate his world from this standpoint and get the basis for further investigation that again always takes the form of a problem. The solution of this problem is found over again in the possibility of fitting his hypothetical proposition into the whole within which it arises. And he must recognize that this statement is only a working hypothesis at the best, i.e., he knows that further investigation will show that the former statement of his world is only provisionally true, and must be false from the standpoint of a larger knowledge, as every partial truth is necessarily false over against the fuller knowledge which he will gain later (Mead 1899 , p. 370).

Cronbach ( 1975 ) developed a notion of working hypothesis consistent with inductive reasoning, but for him, the working hypothesis is a product or result of naturalistic inquiry. He makes the case that naturalistic inquiry is highly context dependent and therefore results or seeming generalizations that may come from a study and should be viewed as “working hypotheses”, which “are tentative both for the situation in which they first uncovered and for other situations” (as cited in Gobo 2008 , p. 196).

A quick Google scholar search using the term “working hypothesis” show that it is widely used in twentieth and twenty-first century science, particularly in titles. In these articles, the working hypothesis is treated as a conceptual tool that furthers investigation in its early or transitioning phases. We could find no explicit links to exploratory research. The exploratory nature of the problem is expressed implicitly. Terms such as “speculative” (Habib 2000 , p. 2391) or “rapidly evolving field” (Prater et al. 2007 , p. 1141) capture the exploratory nature of the study. The authors might describe how a topic is “new” or reference “change”. “As a working hypothesis, the picture is only new, however, in its interpretation” (Milnes 1974 , p. 1731). In a study of soil genesis, Arnold ( 1965 , p. 718) notes “Sequential models, formulated as working hypotheses, are subject to further investigation and change”. Any 2020 article dealing with COVID-19 and respiratory distress would be preliminary almost by definition (Ciceri et al. 2020 ).

5 Philosophical roots of methodology

According to Kaplan ( 1964 , p. 23) “the aim of methodology is to help us understand, in the broadest sense not the products of scientific inquiry but the process itself”. Methods contain philosophical principles that distinguish them from other “human enterprises and interests” (Kaplan 1964 , p. 23). Contemporary research methodology is generally classified as quantitative, qualitative and mixed methods. Leading scholars of methodology have associated each with a philosophical underpinning—positivism (or post-positivism), interpretivism or constructivist and pragmatism, respectively (Guba 1987 ; Guba and Lincoln 1981 ; Schrag 1992 ; Stebbins 2001 ; Mackenzi and Knipe 2006 ; Atieno 2009 ; Levers 2013 ; Morgan 2007 ; O’Connor et al. 2008 ; Johnson and Onwuegbuzie 2004 ; Twining et al. 2017 ). This section summarizes how the literature often describes these philosophies and informs contemporary methodology and its literature.

Positivism and its more contemporary version, post-positivism, maintains an objectivist ontology or assumes an objective reality, which can be uncovered (Levers 2013 ; Twining et al. 2017 ). Footnote 3 Time and context free generalizations are possible and “real causes of social scientific outcomes can be determined reliably and validly (Johnson and Onwuegbunzie 2004 , p. 14). Further, “explanation of the social world is possible through a logical reduction of social phenomena to physical terms”. It uses an empiricist epistemology which “implies testability against observation, experimentation, or comparison” (Whetsell and Shields 2015 , pp. 420–421). Correspondence theory, a tenet of positivism, asserts that “to each concept there corresponds a set of operations involved in its scientific use” (Kaplan 1964 , p. 40).

The interpretivist, constructivists or post-modernist approach is a reaction to positivism. It uses a relativist ontology and a subjectivist epistemology (Levers 2013 ). In this world of multiple realities, context free generalities are impossible as is the separation of facts and values. Causality, explanation, prediction, experimentation depend on assumptions about the correspondence between concepts and reality, which in the absence of an objective reality is impossible. Empirical research can yield “contextualized emergent understanding rather than the creation of testable theoretical structures” (O’Connor et al. 2008 , p. 30). The distinctively different world views of positivist/post positivist and interpretivist philosophy is at the core of many controversies in methodology, social and policy science literature (Casula 2020b ).

With its focus on dissolving dualisms, pragmatism steps outside the objective/subjective debate. Instead, it asks, “what difference would it make to us if the statement were true” (Kaplan 1964 , p. 42). Its epistemology is connected to purposeful inquiry. Pragmatism has a “transformative, experimental notion of inquiry” anchored in pluralism and a focus on constructing conceptual and practical tools to resolve “problematic situations” (Shields 1998 ; Shields and Rangarajan 2013 ). Exploration and working hypotheses are most comfortably situated within the pragmatic philosophical perspective.

6 Research approaches

Empirical investigation relies on three types of methodology—quantitative, qualitative and mixed methods.

6.1 Quantitative methods

Quantitative methods uses deductive logic and formal hypotheses or models to explain, predict, and eventually establish causation (Hyde 2000 ; Kaplan 1964 ; Johnson and Onwuegbunzie 2004 ; Morgan 2007 ). Footnote 4 The correspondence between the conceptual and empirical world make measures possible. Measurement assigns numbers to objects, events or situations and allows for standardization and subtle discrimination. It also allows researchers to draw on the power of mathematics and statistics (Kaplan 1964 , pp. 172–174). Using the power of inferential statistics, quantitative research employs research designs, which eliminate competing hypotheses. It is high in external validity or the ability to generalize to the whole. The research results are relatively independent of the researcher (Johnson & Onwuegbunzie 2004 ).

Quantitative methods depend on the quality of measurement and a priori conceptualization, and adherence to the underlying assumptions of inferential statistics. Critics charge that hypotheses and frameworks needlessly constrain inquiry (Johnson and Onwuegbunzie 2004 , p. 19). Hypothesis testing quantitative methods support the explanatory purpose.

6.2 Qualitative methods

Qualitative researchers who embrace the post-modern, interpretivist view, Footnote 5 question everything about the nature of quantitative methods (Willis et al. 2007 ). Rejecting the possibility of objectivity, correspondence between ideas and measures, and the constraints of a priori theorizing they focus on “unique impressions and understandings of events rather than to generalize the findings” (Kolb 2012 , p. 85). Characteristics of traditional qualitative research include “induction, discovery, exploration, theory/hypothesis generation and the researcher as the primary ‘instrument’ of data collection” (Johnson and Onwuegbunzie 2004 , p. 18). It also concerns itself with forming “unique impressions and understandings of events rather than to generalize findings” (Kolb 2012 , p. 85). The data of qualitative methods are generated via interviews, direct observation, focus groups and analysis of written records or artifacts.

Qualitative methods provide for understanding and “description of people’s personal experiences of phenomena”. They enable descriptions of detailed “phenomena as they are situated and embedded in local contexts.” Researchers use naturalistic settings to “study dynamic processes” and explore how participants interpret experiences. Qualitative methods have an inherent flexibility, allowing researchers to respond to changes in the research setting. They are particularly good at narrowing to the particular and on the flipside have limited external validity (Johnson and Onwuegbunzie 2004 , p. 20). Instead of specifying a suitable sample size to draw conclusions, qualitative research uses the notion of saturation (Morse 1995 ).

Saturation is used in grounded theory—a widely used and respected form of qualitative research, and a well-known interpretivist qualitative research method. Introduced by Glaser and Strauss ( 1967 ), this “grounded on observation” (Patten and Newhart 2000 , p. 27) methodology, focuses on “the creation of emergent understanding” (O’Connor et al. 2008 , p. 30). It uses the Constant Comparative method, whereby researchers develop theory from data as they code and analyze at the same time. Data collection, coding and analysis along with theoretical sampling are systematically combined to generate theory (Kolb 2012 , p. 83). The qualitative methods discussed here support exploratory research.

A close look at the two philosophies and assumptions of quantitative and qualitative research suggests two contradictory world views. The literature has labeled these contradictory views the Incompatibility Theory, which sets up a quantitative versus qualitative tension similar to the seeming separation of art and science or fact and values (Smith 1983a , b ; Guba 1987 ; Smith and Heshusius 1986 ; Howe 1988 ). The incompatibility theory does not make sense in practice. Yin ( 1981 , 1992 , 2011 , 2017 ), a prominent case study scholar, showcases a deductive research methodology that crosses boundaries using both quantaitive and qualitative evidence when appropriate.

6.3 Mixed methods

Turning the “Incompatibility Theory” on its head, Mixed Methods research “combines elements of qualitative and quantitative research approaches … for the broad purposes of breadth and depth of understanding and corroboration” (Johnson et al. 2007 , p. 123). It does this by partnering with philosophical pragmatism. Footnote 6 Pragmatism is productive because “it offers an immediate and useful middle position philosophically and methodologically; it offers a practical and outcome-oriented method of inquiry that is based on action and leads, iteratively, to further action and the elimination of doubt; it offers a method for selecting methodological mixes that can help researchers better answer many of their research questions” (Johnson and Onwuegbunzie 2004 , p. 17). What is theory for the pragmatist “any theoretical model is for the pragmatist, nothing more than a framework through which problems are perceived and subsequently organized ” (Hothersall 2019 , p. 5).

Brendel ( 2009 ) constructed a simple framework to capture the core elements of pragmatism. Brendel’s four “p”’s—practical, pluralism, participatory and provisional help to show the relevance of pragmatism to mixed methods. Pragmatism is purposeful and concerned with the practical consequences. The pluralism of pragmatism overcomes quantitative/qualitative dualism. Instead, it allows for multiple perspectives (including positivism and interpretivism) and, thus, gets around the incompatibility problem. Inquiry should be participatory or inclusive of the many views of participants, hence, it is consistent with multiple realities and is also tied to the common concern of a problematic situation. Finally, all inquiry is provisional . This is compatible with experimental methods, hypothesis testing and consistent with the back and forth of inductive and deductive reasoning. Mixed methods support exploratory research.

Advocates of mixed methods research note that it overcomes the weaknesses and employs the strengths of quantitative and qualitative methods. Quantitative methods provide precision. The pictures and narrative of qualitative techniques add meaning to the numbers. Quantitative analysis can provide a big picture, establish relationships and its results have great generalizability. On the other hand, the “why” behind the explanation is often missing and can be filled in through in-depth interviews. A deeper and more satisfying explanation is possible. Mixed-methods brings the benefits of triangulation or multiple sources of evidence that converge to support a conclusion. It can entertain a “broader and more complete range of research questions” (Johnson and Onwuegbunzie 2004 , p. 21) and can move between inductive and deductive methods. Case studies use multiple forms of evidence and are a natural context for mixed methods.

One thing that seems to be missing from mixed method literature and explicit design is a place for conceptual frameworks. For example, Heyvaert et al. ( 2013 ) examined nine mixed methods studies and found an explicit framework in only two studies (transformative and pragmatic) (p. 663).

7 Theory and hypotheses: where is and what is theory?

Theory is key to deductive research. In essence, empirical deductive methods test theory. Hence, we shift our attention to theory and the role and functions of the hypotheses in theory. Oppenheim and Putnam ( 1958 ) note that “by a ‘theory’ (in the widest sense) we mean any hypothesis, generalization or law (whether deterministic or statistical) or any conjunction of these” (p. 25). Van Evera ( 1997 ) uses a similar and more complex definition “theories are general statements that describe and explain the causes of effects of classes of phenomena. They are composed of causal laws or hypotheses, explanations, and antecedent conditions” (p. 8). Sutton and Staw ( 1995 , p. 376) in a highly cited article “What Theory is Not” assert the that hypotheses should contain logical arguments for “why” the hypothesis is expected. Hypotheses need an underlying causal argument before they can be considered theory. The point of this discussion is not to define theory but to establish the importance of hypotheses in theory.

Explanatory research is implicitly relational (A explains B). The hypotheses of explanatory research lay bare these relationships. Popular definitions of hypotheses capture this relational component. For example, the Cambridge Dictionary defines a hypothesis a “an idea or explanation for something that is based on known facts but has not yet been proven”. Vocabulary.Com’s definition emphasizes explanation, a hypothesis is “an idea or explanation that you then test through study and experimentation”. According to Wikipedia a hypothesis is “a proposed explanation for a phenomenon”. Other definitions remove the relational or explanatory reference. The Oxford English Dictionary defines a hypothesis as a “supposition or conjecture put forth to account for known facts.” Science Buddies defines a hypothesis as a “tentative, testable answer to a scientific question”. According to the Longman Dictionary the hypothesis is “an idea that can be tested to see if it is true or not”. The Urban Dictionary states a hypothesis is “a prediction or educated-guess based on current evidence that is yet be tested”. We argue that the hypotheses of exploratory research— working hypothesis — are not bound by relational expectations. It is this flexibility that distinguishes the working hypothesis.

Sutton and Staw (1995) maintain that hypotheses “serve as crucial bridges between theory and data, making explicit how the variables and relationships that follow from a logical argument will be operationalized” (p. 376, italics added). The highly rated journal, Computers and Education , Twining et al. ( 2017 ) created guidelines for qualitative research as a way to improve soundness and rigor. They identified the lack of alignment between theoretical stance and methodology as a common problem in qualitative research. In addition, they identified a lack of alignment between methodology, design, instruments of data collection and analysis. The authors created a guidance summary, which emphasized the need to enhance coherence throughout elements of research design (Twining et al. 2017 p. 12). Perhaps the bridging function of the hypothesis mentioned by Sutton and Staw (1995) is obscured and often missing in qualitative methods. Working hypotheses can be a tool to overcome this problem.

For reasons, similar to those used by mixed methods scholars, we look to classical pragmatism and the ideas of John Dewey to inform our discussion of theory and working hypotheses. Dewey ( 1938 ) treats theory as a tool of empirical inquiry and uses a map metaphor (p. 136). Theory is like a map that helps a traveler navigate the terrain—and should be judged by its usefulness. “There is no expectation that a map is a true representation of reality. Rather, it is a representation that allows a traveler to reach a destination (achieve a purpose). Hence, theories should be judged by how well they help resolve the problem or achieve a purpose ” (Shields and Rangarajan 2013 , p. 23). Note that we explicitly link theory to the research purpose. Theory is never treated as an unimpeachable Truth, rather it is a helpful tool that organizes inquiry connecting data and problem. Dewey’s approach also expands the definition of theory to include abstractions (categories) outside of causation and explanation. The micro-conceptual frameworks Footnote 7 introduced in Table 1 are a type of theory. We define conceptual frameworks as the “way the ideas are organized to achieve the project’s purpose” (Shields and Rangarajan 2013 p. 24). Micro-conceptual frameworks do this at the very close to the data level of analysis. Micro-conceptual frameworks can direct operationalization and ways to assess measurement or evidence at the individual research study level. Again, the research purpose plays a pivotal role in the functioning of theory (Shields and Tajalli 2006 ).

8 Working hypothesis: methods and data analysis

We move on to answer the remaining questions in the Table 1 . We have established that exploratory research is extremely flexible and idiosyncratic. Given this, we will proceed with a few examples and draw out lessons for developing an exploratory purpose, building a framework and from there identifying data collection techniques and the logics of hypotheses testing and analysis. Early on we noted the value of the Working Hypothesis framework for student empirical research and applied research. The next section uses a masters level student’s work to illustrate the usefulness of working hypotheses as a way to incorporate the literature and structure inquiry. This graduate student was also a mature professional with a research question that emerged from his job and is thus an example of applied research.

Master of Public Administration student, Swift ( 2010 ) worked for a public agency and was responsible for that agency’s sexual harassment training. The agency needed to evaluate its training but had never done so before. He also had never attempted a significant empirical research project. Both of these conditions suggest exploration as a possible approach. He was interested in evaluating the training program and hence the project had a normative sense. Given his job, he already knew a lot about the problem of sexual harassment and sexual harassment training. What he did not know much about was doing empirical research, reviewing the literature or building a framework to evaluate the training (working hypotheses). He wanted a framework that was flexible and comprehensive. In his research, he discovered Lundvall’s ( 2006 ) knowledge taxonomy summarized with four simple ways of knowing ( Know - what, Know - how, Know - why, Know - who ). He asked whether his agency’s training provided the participants with these kinds of knowledge? Lundvall’s categories of knowing became the basis of his working hypotheses. Lundvall’s knowledge taxonomy is well suited for working hypotheses because it is so simple and is easy to understand intuitively. It can also be tailored to the unique problematic situation of the researcher. Swift ( 2010 , pp. 38–39) developed four basic working hypotheses:

WH1: Capital Metro provides adequate know - what knowledge in its sexual harassment training

WH2: Capital Metro provides adequate know - how knowledge in its sexual harassment training

WH3: Capital Metro provides adequate know - why knowledge in its sexual harassment training

WH4: Capital Metro provides adequate know - who knowledge in its sexual harassment training

From here he needed to determine what would determine the different kinds of knowledge. For example, what constitutes “know what” knowledge for sexual harassment training. This is where his knowledge and experience working in the field as well as the literature come into play. According to Lundvall et al. ( 1988 , p. 12) “know what” knowledge is about facts and raw information. Swift ( 2010 ) learned through the literature that laws and rules were the basis for the mandated sexual harassment training. He read about specific anti-discrimination laws and the subsequent rules and regulations derived from the laws. These laws and rules used specific definitions and were enacted within a historical context. Laws, rules, definitions and history became the “facts” of Know-What knowledge for his working hypothesis. To make this clear, he created sub-hypotheses that explicitly took these into account. See how Swift ( 2010 , p. 38) constructed the sub-hypotheses below. Each sub-hypothesis was defended using material from the literature (Swift 2010 , pp. 22–26). The sub-hypotheses can also be easily tied to evidence. For example, he could document that the training covered anti-discrimination laws.

WH1: Capital Metro provides adequate know - what knowledge in its sexual Harassment training

WH1a: The sexual harassment training includes information on anti-discrimination laws (Title VII).

WH1b: The sexual harassment training includes information on key definitions.

WH1c: The sexual harassment training includes information on Capital Metro’s Equal Employment Opportunity and Harassment policy.

WH1d: Capital Metro provides training on sexual harassment history.

Know-How knowledge refers to the ability to do something and involves skills (Lundvall and Johnson 1994 , p. 12). It is a kind of expertise in action. The literature and his experience allowed James Smith to identify skills such as how to file a claim or how to document incidents of sexual harassment as important “know-how” knowledge that should be included in sexual harassment training. Again, these were depicted as sub-hypotheses.

WH2: Capital Metro provides adequate know - how knowledge in its sexual Harassment training

WH2a: Training is provided on how to file and report a claim of harassment

WH2b: Training is provided on how to document sexual harassment situations.

WH2c: Training is provided on how to investigate sexual harassment complaints.

WH2d: Training is provided on how to follow additional harassment policy procedures protocol

Note that the working hypotheses do not specify a relationship but rather are simple declarative sentences. If “know-how” knowledge was found in the sexual harassment training, he would be able to find evidence that participants learned about how to file a claim (WH2a). The working hypothesis provides the bridge between theory and data that Sutton and Staw (1995) found missing in exploratory work. The sub-hypotheses are designed to be refined enough that the researchers would know what to look for and tailor their hunt for evidence. Figure 1 captures the generic sub-hypothesis design.

A Common structure used in the development of working hypotheses

When expected evidence is linked to the sub-hypotheses, data, framework and research purpose are aligned. This can be laid out in a planning document that operationalizes the data collection in something akin to an architect’s blueprint. This is where the scholar explicitly develops the alignment between purpose, framework and method (Shields and Rangarajan 2013 ; Shields et al. 2019b ).

Table 2 operationalizes Swift’s working hypotheses (and sub-hypotheses). The table provide clues as to what kind of evidence is needed to determine whether the hypotheses are supported. In this case, Smith used interviews with participants and trainers as well as a review of program documents. Column one repeats the sub-hypothesis, column two specifies the data collection method (here interviews with participants/managers and review of program documents) and column three specifies the unique questions that focus the investigation. For example, the interview questions are provided. In the less precise world of qualitative data, evidence supporting a hypothesis could have varying degrees of strength. This too can be specified.

For Swift’s example, neither the statistics of explanatory research nor the open-ended questions of interpretivist, inductive exploratory research is used. The deductive logic of inquiry here is somewhat intuitive and similar to a detective (Ulriksen and Dadalauri 2016 ). It is also a logic used in international law (Worster 2013 ). It should be noted that the working hypothesis and the corresponding data collection protocol does not stop inquiry and fieldwork outside the framework. The interviews could reveal an unexpected problem with Smith’s training program. The framework provides a very loose and perhaps useful ways to identify and make sense of the data that does not fit the expectations. Researchers using working hypotheses should be sensitive to interesting findings that fall outside their framework. These could be used in future studies, to refine theory or even in this case provide suggestions to improve sexual harassment training. The sensitizing concepts mentioned by Gilgun ( 2015 ) are free to emerge and should be encouraged.

Something akin to working hypotheses are hidden in plain sight in the professional literature. Take for example Kerry Crawford’s ( 2017 ) book Wartime Sexual Violence. Here she explores how basic changes in the way “advocates and decision makers think about and discuss conflict-related sexual violence” (p. 2). She focused on a subsequent shift from silence to action. The shift occurred as wartime sexual violence was reframed as a “weapon of war”. The new frame captured the attention of powerful members of the security community who demanded, initiated, and paid for institutional and policy change. Crawford ( 2017 ) examines the legacy of this key reframing. She develops a six-stage model of potential international responses to incidents of wartime violence. This model is fairly easily converted to working hypotheses and sub-hypotheses. Table 3 shows her model as a set of (non-relational) working hypotheses. She applied this model as a way to gather evidence among cases (e.g., the US response to sexual violence in the Democratic Republic of the Congo) to show the official level of response to sexual violence. Each case study chapter examined evidence to establish whether the case fit the pattern formalized in the working hypotheses. The framework was very useful in her comparative context. The framework allowed for consistent comparative analysis across cases. Her analysis of the three cases went well beyond the material covered in the framework. She freely incorporated useful inductively informed data in her analysis and discussion. The framework, however, allowed for alignment within and across cases.

9 Conclusion

In this article we argued that the exploratory research is also well suited for deductive approaches. By examining the landscape of deductive, exploratory research, we proposed the working hypothesis as a flexible conceptual framework and a useful tool for doing exploratory studies. It has the potential to guide and bring coherence across the steps in the research process. After presenting the nature of exploratory research purpose and how it differs from two types of research purposes identified in the literature—explanation, and description. We focused on answering four different questions in order to show the link between micro-conceptual frameworks and research purposes in a deductive setting. The answers to the four questions are summarized in Table 4 .

Firstly, we argued that working hypothesis and exploration are situated within the pragmatic philosophical perspective. Pragmatism allows for pluralism in theory and data collection techniques, which is compatible with the flexible exploratory purpose. Secondly, after introducing and discussing the four core elements of pragmatism (practical, pluralism, participatory, and provisional), we explained how the working hypothesis informs the methodologies and evidence collection of deductive exploratory research through a presentation of the benefits of triangulation provided by mixed methods research. Thirdly, as is clear from the article title, we introduced the working hypothesis as the micro-conceptual framework for deductive explorative research. We argued that the hypotheses of explorative research, which we call working hypotheses are distinguished from those of the explanatory research, since they do not require a relational component and are not bound by relational expectations. A working hypothesis is extremely flexible and idiosyncratic, and it could be viewed as a statement or group of statements of expectations tested in action depending on the research question. Using examples, we concluded by explaining how working hypotheses inform data collection and analysis for deductive exploratory research.

Crawford’s ( 2017 ) example showed how the structure of working hypotheses provide a framework for comparative case studies. Her criteria for analysis were specified ahead of time and used to frame each case. Thus, her comparisons were systemized across cases. Further, the framework ensured a connection between the data analysis and the literature review. Yet the flexible, working nature of the hypotheses allowed for unexpected findings to be discovered.

The evidence required to test working hypotheses is directed by the research purpose and potentially includes both quantitative and qualitative sources. Thus, all types of evidence, including quantitative methods should be part of the toolbox of deductive, explorative research. We show how the working hypotheses, as a flexible exploratory framework, resolves many seeming dualisms pervasive in the research methods literature.

To conclude, this article has provided an in-depth examination of working hypotheses taking into account philosophical questions and the larger formal research methods literature. By discussing working hypotheses as applied, theoretical tools, we demonstrated that working hypotheses fill a unique niche in the methods literature, since they provide a way to enhance alignment in deductive, explorative studies.

In practice, quantitative scholars often run multivariate analysis on data bases to find out if there are correlations. Hypotheses are tested because the statistical software does the math, not because the scholar has an a priori, relational expectation (hypothesis) well-grounded in the literature and supported by cogent arguments. Hunches are just fine. This is clearly an inductive approach to research and part of the large process of inquiry.

In 1958 , Philosophers of Science, Oppenheim and Putnam use the notion of Working Hypothesis in their title “Unity of Science as Working Hypothesis.” They too, use it as a big picture concept, “unity of science in this sense, can be fully realized constitutes an over-arching meta-scientific hypothesis, which enables one to see a unity in scientific activities that might otherwise appear disconnected or unrelated” (p. 4).

It should be noted that the positivism described in the research methods literature does not resemble philosophical positivism as developed by philosophers like Comte (Whetsell and Shields 2015 ). In the research methods literature “positivism means different things to different people….The term has long been emptied of any precise denotation …and is sometimes affixed to positions actually opposed to those espoused by the philosophers from whom the name derives” (Schrag 1992 , p. 5). For purposes of this paper, we are capturing a few essential ways positivism is presented in the research methods literature. This helps us to position the “working hypothesis” and “exploratory” research within the larger context in contemporary research methods. We are not arguing that the positivism presented here is anything more. The incompatibility theory discussed later, is an outgrowth of this research methods literature…

It should be noted that quantitative researchers often use inductive reasoning. They do this with existing data sets when they run correlations or regression analysis as a way to find relationships. They ask, what does the data tell us?

Qualitative researchers are also associated with phenomenology, hermeneutics, naturalistic inquiry and constructivism.

See Feilzer ( 2010 ), Howe ( 1988 ), Johnson and Onwuegbunzie ( 2004 ), Morgan ( 2007 ), Onwuegbuzie and Leech ( 2005 ), Biddle and Schafft ( 2015 ).

The term conceptual framework is applicable in a broad context (see Ravitch and Riggan 2012 ). The micro-conceptual framework narrows to the specific study and informs data collection (Shields and Rangarajan 2013 ; Shields et al. 2019a ) .

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Casula, M., Rangarajan, N. & Shields, P. The potential of working hypotheses for deductive exploratory research. Qual Quant 55 , 1703–1725 (2021). https://doi.org/10.1007/s11135-020-01072-9

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Dr Deborah Gabriel

Inductive and deductive approaches to research

The main difference between inductive and deductive approaches to research is that whilst a deductive approach is aimed and testing theory, an inductive approach is concerned with the generation of new theory emerging from the data.

A deductive approach usually begins with a hypothesis, whilst an inductive approach will usually use research questions to narrow the scope of the study.

For deductive approaches the emphasis is generally on causality, whilst for inductive approaches the aim is usually focused on exploring new phenomena or looking at previously researched phenomena from a different perspective.

Inductive approaches are generally associated with qualitative research, whilst deductive approaches are more commonly associated with quantitative research. However, there are no set rules and some qualitative studies may have a deductive orientation.

One specific inductive approach that is frequently referred to in research literature is grounded theory, pioneered by Glaser and Strauss.

This approach necessitates the researcher beginning with a completely open mind without any preconceived ideas of what will be found. The aim is to generate a new theory based on the data.

Once the data analysis has been completed the researcher must examine existing theories in order to position their new theory within the discipline.

Grounded theory is not an approach to be used lightly. It requires extensive and repeated sifting through the data and analysing and re-analysing multiple times in order to identify new theory. It is an approach best suited to research projects where there the phenomena to be investigated has not been previously explored.

The most important point to bear in mind when considering whether to use an inductive or deductive approach is firstly the purpose of your research; and secondly the methods that are best suited to either test a hypothesis, explore a new or emerging area within the discipline, or to answer specific research questions.

Citing This Article

Gabriel, D. (2013). Inductive and deductive approaches to research. Accessed on ‘date’ from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Gabriel, D., 2013. Inductive and deductive approaches to research. Accessed on ‘date’ from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

How to write a literature review

Whether you are writing a 2000-word literature review for an undergraduate dissertation or something closer to 10,000-words for a PhD thesis, the process is the same.

Using Conceptual Frameworks in Qualitative Research

I recently delivered this presentation to final year undergraduate students about to embark on their dissertation projects.

Qualitative data analysis

The most important task that you will undertake in your PhD research project is the analysis of data. The integrity and rigour of your research is utterly dependent on getting the analysis right and it will be intensely scrutinised by the external examiners.

200 thoughts on “ Inductive and deductive approaches to research ”

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Hi, yes the explanation was helpful because it was simple to read and pretty much, straight to the point. It has given me a brief understanding for what I needs. Thanks — Chantal

It was very supportive for me!! thank you!!

thank you so much for the information. it was simple to read and also brief concise and straight to the point. thank you.

Deborah, thanks for this elaboration. but I am asking is it possible to conduct a deductive inclined research and also generate a theory, or add to the theory. I have been asked by my supervisor whether I am just testing hypothesi or my aim is to contribute/generate a theory. my studies is more of a quantitative nature. Thanks

Deductive research is more aimed towards testing a hypothesis and therefore is an approach more suited to working with quantitative data. The process normally involves reproducing a previous study and seeing if the same results are produced. This does not lend itself to generating new theories since that is not the object of the research. Between inductive and deductive approaches there is also a third approach which I will write a post on shortly – abdductive.

Dear Deborah, it has been very long time since you posted this article. However, I can testify that it is very helpful for a novis reseracher like me.

Ms. Deborah, thank you for given info, but i was in confusion reg, differences between deductive, inductive, abductive and (new one) Hypothetico-deductive approaches. Can it be possible to email the differences, its applications, tools used and scientific nature, to build a theory using quantitative survey method. My email Id is: [email protected] Iam writing my PhD thesis based on this . it is part of my 3 chapter. Thanking you awaiting for your mail soon.

Hi Madhu. As a PhD student you need to take the time to read the appropriate literature on research approaches and attend workshops/conferences to better understand methodology. You cannot take short cuts by by asking someone (me) to simply provide you with ready answers to your queries – especially when I do not have the time to do so!

Great saying…every student at every level, should be critical analysts and critical thinkers.

Thank u.. but there is a little mistake which is deductive approach deals with qualitative and inductive appoech deals with quantitative

No – you have it the wrong way round – I suggest you read the article again and also engage in wider reading on research methods to gain a deeper understanding.

A clear cut concept with example.

Deborah I appreciate so much in this article…I got what I am looking for… thanks for your contribution.

Thanks so much

It is simple, easy to differentiate and understandable.

Thank u for the information, it really helps me.

Exactly, your work is simple and clear, that there are two research approaches, Inductive and deductive.Qualitative and Quantative approaches You gave clear differences in a balance, simple to understand, I suppose you are a teacher by profession. This is how we share knowledge,and you become more knowledgable

Thank you Lambawi, I am glad that these posts are proving useful. I will endeavour to add some more in the coming weeks!

This has been helpful. thanks for posting

Thank you very much for sharing knowledge with me. It really much helpful while preparing my college exam. Thank –:)

Thank you ever so much for making it simple and easily understandable. Would love to see more posts.

Best wishes

The explanation is simple and easy to understand it has helped to a lot thank you

very helpful and explained simply. thanks

Explanation is simple…. it was a great help for my exam preparation.

Excellent presentation please!

Very helpful information and a clear, simple explanation. Thank you

Thanks; this has been helpful in preparation for my forthcoming exams

This is fantastic, I have greatly beneffitted from this straight forward illustration

Thanks…i will benifited to read this

Thanks for your help. Keep it like that so that will be our guide towards our destinations.

Thank-you for your academic insights.

Thank you for your clarification. Well understood.

Hi, I had a question would you call process tracing technique an inductive or deductive approach? or maybe both? Hope you can help me with the same.

Hi Achin. Process tracing is a qualitative analytical tool and therefore inductive rather than deductive, since its purpose is to identify new phenomena. You might find this journal article useful:

http://www.ukcds.org.uk/sites/default/files/uploads/Understanding-Process-Tracing.pdf

Preparing for my Research Methods exams and I'm grateful for your explanations. This is a full lecture made simple. Thank you very much.

I am very thankful for this information, madam you are just good. If you are believer, allow me to say, May God bless you with more knowledge and good health.

Hi Rasol, glad that the post was useful and – yes – I am a believer so thank you for the blessing!

Dear Deborah

I am currently doing Btech in forensic with Unisa would you be so kind and help with this question below and may I use your services while Iam doing this degree Please

Hi I have question that goes like these "If the reseacher wanted to conduct reseach in a specific context to see whether it supports an establisblished theory" the reseacher would be conducting

1. case study

2. deductive research

3. exploratory resarch

4. inductive methods

please help me to choose the right one

Yours Faithfully

Baba Temba

Deductive, which is not exploratory but designed to test a hypothesis. So this is unlike to be case study research but a quantitative study.

Hi Deborah, I have been struggling with my research methods proposal, in finding the right methodology for my study. This is the only explanation out of all the books that I have read which really enables me to truly understand the meaning of Grounded Theory for which you describe as an inductive. I just would like to say thank you for your explanation as this has helped me in a way, which I thought I would never get. Thank you Destini

You are most welcome!

Very useful piece of information. Thank you!

Very impressing work, may god bless you with more mighty knowledge.

In fact this has been very usefull information for me in my research,. It's very clear and easy to understand looking at the choice of language ,etc God bless you!!

Hi Ms Deborah Gabriel, I am from the backward area of Pakistan, which is known as “North Waziristan”. Unfortunately famous for terrorism, as from that background, you can understand the weakness of my educational background. I am struggling for MBA degree, and I was searching for Deductive and Inductive approaches, and then I found your best explained article here, already praised by many people. I will just add this “Thank you, May GOD Bless You” I will be highly honoured if you would like to contact me on my email. My email is [email protected] Thanking you for your time and efforts.

Is it possible to use deductive approach in research concerning what has happened in an industry?

If you are seeking to test a hypothesis then yes.

Thank you very much this information has been extremely helpful. I can now progress with my dissertation.

Thanks for that good work Deborah. It has taken me quite a short time to read and understand. Kindly please help me understand what am required to write in this case where my teacher gave me this question: "Explain the process of deduction and induction research approaches".

Please refer to the recommended reading: https://deborahgabriel.com/recommended-reading/

Many thanks to you, I really appreciate u on ur information provided basically on theories and approaches to understanding research.

Thank you very much.

Good work Deborah.

Thank you so much!! The distinction between the two approaches is clear and concise. Most other websites tend to go into long discussions without really getting to the point. This was very helpful.

Thank you , useful explanation

It is a very fruitful post. I would like to ask if the objective of my research is to develop an extended process from the existing processes. And I am going to use qualitative and quantitative research methods, because my research phenomenon requires to study the individual meanings and perceptions and then uses the findings from the qualitative study and also the theoretical study as inputs for the quantitative study. Finally, I will use the findings of the theoretical study and the quantitative study in developing the extended process. So, which approach to follow in this case?

Dear Tamer, Your question is too hypothetical for me to offer a response. But in any event, you are the only one who can decide whether an inductive or deductive approach is appropriate for your research project. This is where methodology comes in – which is about determining what research methods will be most effective in answering your research questions and which are in sync with your approach (e.g. critical, feminist etc).

my dissertation is in the same situation. and I also feel struggle to choose my research approach. I guess its a combination of inductive and deductive. using the deductive approach to test what was found in the literature, and use an inductive approach to examine the themes that emerged from qualitative data.

Thanks much!

What do you think about the approach with quantitative analyses that start with data to generate theories? Typically data mining techniques fit into my example.

This is a question of methodology – research methods must be selected based on the discipline, research questions and approach to the study. For example, If you are seeking to ascertain how many people read the news on their smartphones then a quantitiative method is most appropriate. On the other hand. if you are seeking to delve into why some people read the news on their smartphones, then clearly a qualitative method is required.

Awesome response, I was looking at the same thing in my postgraduate class.

What if I’m using secondary sources? Which would be more appropriate qualitative or quantitative?

The question of inductive or deductive approaches arise only in relation to ‘primary’ research – that is when you are undertaking your own study. In your own study, secondary sources would appear under a Literature Review. However, if you are doing a dissertation, say for an undergraduate degree where you are not undertaking primary research then inductive or deductive approaches are not applicable. I hope this clarifies.

Your comments are really good and easy to understand. Hope to contact you for my project. keep up the good work. thank you

The last paragraph stated ‘The most important point to bear in mind when considering whether to use an inductive or deductive approach is firstly the purpose of your research; and secondly the methods that are best suited to either test a hypothesis, explore a new or emerging area within the discipline, or to answer specific research questions. However my question is if my research is about answering specific research questions in a qualitative research. Am I to use the inductive or the deductive or the mixture of the two?

Hi Ola, if your research questions are qualitatively focused – that is seeking to find out the whys and the hows as opposed to ‘what’ and ‘how many’ then certainly an inductive approach is most appropriate. This is because inductive aims to find new theories emerging from the data whereas deductive is centred on testing a hypothesis rather than exploring research questions.

Thank u so much.it was difficult for me to understand but with ur help the job is complete

Points of distinction top notch. Absolutely fantastic. Straight to the point. Was really helpful. Keep up the good work.

Thanks for the inforation Deborah. It was useful

Thank you so much, this was something I was never able to grasp so well! I found this site while searching the difference between the two on Google. I am a PHD Scholar, now it seems I will be visiting this site frequently and seeking your help 🙂

Hi Deborah. Thank you for the input.It clearly exemplifies the difference. In your response to one of the questions, you have highlighted a lot of 'what' will qualify the research as quantitative. I have developed 4 research questions, 3 are on 'what's and 1 'why'. The what is because my sample of analysis is multimodal text. Will my study still fall under qualitative? Thank you in advance, Deborah. I appreciate it very much.

Hi Zilla, It is hard to provide a definitive answer without knowing what your research questions are (although time does not permit me to provide individual responses). So I will reiterate that the question of whether to adopt an inductive or deductive approach to a research project is relevant for ‘primary’ research – that is, research that you undertake yourself. Factors that influence your decision should rest on whether you are seeking to explore the ‘whys’ and ‘hows’ of human experience, generating new levels or understanding or simply wish to test a hypothesis or use a large sample in order to generalise results to the wider population. You say that your sample is multimodal text – that is simply text plus media such as videos, pictures etc. My question to you is whether this multimodal text has been generated from primary research – i.e interviews you conducted, photos you took and/or videos that you filmed of research subjects? If that is the case then I would presume that this would be a qualitative research project that would lend itself to an inductive approach,since I cannot imagine that you would be able to work with a very large sample of multimodal text. If the multimodal text is not generated from your own primary resesarch then this is secondary research that might be included in your literature review but would fall outside the scope of your analysis.

Dear Deborah I just want to ask you to help me with generation of theory. Steps that need to be followed

Mongwai Michael

Thanks a lot for showing me the best way to understand the basic difference between two approaches of research.

Dear Aliyu, time does not permit me to provide responses on your individual projects. Therefore, my aim is to equip you with the understanding of different approaches so that you have both the confidence and competence to make appropriate decisions on the most suitable methodological approaches to your research.

Beautiful stuff you are giving us Deborah.

Deborah, thank you so much for your explanation, I'm clear now.

I am gathering quantitative data to develop a model to represent the behavior of a material using an existing model. I subsequently used this model to simulate the material behavior with a computer program. this is a reversal approach to previour reaeasch in these area. usually the computer simulation is used to obtain quantitative data without experiment. Could you please kindly let me know what is my reasearch method Thanks

Please see my response to Aliyu on 8 November.

Dr, your explanation about inductive research and deductive, is meaningful to postgraduate students. What is your suggestion on my research topic: use of handset by primary six pupils for games, rather for home works and readings, what is the research approach that will be suitable?

Very brief and well explained. Thank you Deborah.

Thank you Dr. Gabriel, good informationl; will come back.

It has actually helped – a similar question was asked last year in my schools,that prompted me to search for it while preparing for my exam. Today the same question appeared and I used your explanation as my response to the question. Thanks.

Hi Deborah, your explanations are comprehensible. I understand this topic thanks to you. May I ask you question? What are the similarities between inductive and deductive reasoning?

That’s like asking what the similarities are between quantitative and qualitative research! Focus on what your research objectives are and then choose the approach that will be most efefctive in meeting these objectives.

Thanks so much Have got what I really want here

Enlightening facts. Thank you.

Thanks Deborah for the explanation but, i want to ask if descriptive is inductive or deductive approach? God bless you

it is really good explanation

Can I ask one question? I am going to research how technology is changing the hotel industry particularly at the hotel front desk so is that inductive or deductive approach? I believe deductive approach because the aim of my research is to investigate current used technology at hotel front desk. So what do you think please let me know Thank you very much indeed.

Please refer to my post on conceptual frameworks to take you through the key steps in developing a research project – you will find your answer there: https://deborahgabriel.com/2015/02/14/using-conceptual-frameworks-in-qualitative-research/

The information provide is quite helpful, thanks after all….

I was confused about these approaches but your information has helped me a lot – reasonable and authentic.

I've got the answers,thx.

It is very clear and concise.

Thank you, it was right on point.

Thank you, I used this solution for my assignment.

Thank you so much Deborah. I am currently doing my dissertation and most of my lecturers have recommeded us to use Research Methods for Business Students by Mark Saunders, Philip Lewis and Adrian Thornhill. I have found the book very hard to understand especially when I'm wrtiting up the methodology section as I have to talk about deductive and inductive approaches. You have simplified it and explained it well. Also you have made it so so easy to understand. Everyone should be reading this. Thank you so so, so much.

You’re very welcome. Good luck with your dissertation!

Thank you D now I am aware of these two!

I found it so easy to understand the difference between deductive theory and inductive- it's so helpful. Many Thanks.

Deborah, your work is precise,well organized and relevant.Thank you very much.

Thanks for the explanation, it has cleared my doubts.

Thanks Dr. Deb, I am satisfied – it was really useful.

Hi Doc, thank you for making things simpler for me. I will always be incontact with your website. Stay forever blessed.

Thank you for the information. It really helps me.

Hi Deborah, i just went through the abductive approach which is combination of inductive and deductive Approach. I found it a little confusing when I tried to know by my own from e sources. But after going through the conversation in this page helped me a lot. Thank u very much. If u can share your email I can share my report made for my pre PhD comprehensive viva. My profession is teaching and my area of research is International HRM. Title is Knowledge and Learning Model among effective repatriation . If anybody is doing reserach in the same area, plse feel free to reach me at [email protected]. Thank u all again

Thx for the information.

Hi Deborah Thank you very much for the article. it is informative. My question is what approach am i supposed to take if i am doing a research that is both qualitative and quantitative. I am doing research on the feasibility of establishing renewable energy systems in a developing country. I am using a simulation software to generate a model to analyse the technical and economic data (Quantitative) but i have to use interviews to capture social and polical views from industry experts (Qualitative). So which approach is best in such a scenario? Thank you

In a mixed methods study, the quanitiative dimension of the study usually functions to capture preliminary data, with the qualitative dimension being the primary method that answers the research questions. In any case, in a mixed methods study you must peform both quantitative and qualitative data analysis – separately. In reference to your specifc study you need to refer back to your reearch questions and the aims and objectives of your study. Is your primary objective to develop a model for a renewable energy system or is it to determine whether industry experts see the viability of the model? If it is the latter then the approach should be inductive. I would advise you to consult your supervisor or someone in your discipline, as I am not an engineer.

Very informative.

Your explaination of inductive & deductive approaches to research is clear to understand.

Your explanation of concepts is succint and easily conceivable. Helpful.

Thank you so much Deborah. I am currently writing my research on resource curse theory and I will like to have your private mail for private discussions. Thank you

No problem – you can use the contact form and your message will go directly to my email address.

Thanks for differentiating the two in easy and pragmetic manner.

Thank you Deborah, that was a simple, clear explanation helpful for sure.

I like the way you simplified everything,was really helpful for my assignment. Please how do I reference this work? Thank you

Reference it as an online source:

Gabriel,D.(2013). Inductive and deductive approaches to research. Avaolable from: https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Thank you Deborah, this is very helpful to me and others.

Great insight, simple and clear; I now get the difference thanks for sharing.

Thanks for the very good explanation and comparison.

A very simple and straightforward guidance to students.

Hi Debrah , It is really interesting to get valuable points from your statments about deductive reasoning. However it seems short . It will be helpful for us if you write more. Thanks

Dear Almaz, thank you for your feedback. The post was only intended to provide a brief overview of the subject – to better understand inductive and deductive approaches to research I strongly recommend immersing yourself in the available literature.

Perhaps, you can suggest 1 or 2 widely cited scholars (to read) that argue that deductive approaches can also be used in a qualitative methodology which is interpretive/subjective in nature.

Thank you once again!

You are definitely on point.

Thank you so much.

Thank you so much Deborah.

Such inspiring work.

Your literature is helping us a lot here at the Ivory Tower Makerere University Kampala Uganda.

I am glad to hear that. What are you studying?

Hi Deborah. I am Iftikhar from United Arab Emirates (UAE). I am conducting a research on learning preferences of Generation Z youth, and one of my research questions is "What are the learning preferences of UAE Gen Z youth and how matching of L&D program delivery with these learning preferences affect Gen Z interest in organizational L&D programs?" Now as for existing literature, a lot has already been written on this but in the West; there is practically no formal research literature available on this topic in UAE. Therefore, I am taking the Western literature outcomes and applying these in UAE context to see the results. My questions are: a. Will this research be treated as "Deductive' or "Inductive"? b. Should I choose 'Quantitative" or "Qualitative' approach? Wishing you all the best.

Thanks Deborah. but I'm confused. You said deductive approach is used in quantitative research and it test a theory and inductive approach is used in qualitative research to generate a theory. So what is grounded theory?

Thanks a lot for such a good explanation, Deborah!

Thank you very much. It was simple to understand.

Lovely….this was very helpful…simple and straightforward.

Thanks. It is so useful. Best Regards.

This has been troubling me for a while. It is often said that the interpretive paradigm typically goes with inductive approaches and methods involving observation, interviews and research into archives. But then if concepts are to emerge from the data without theoretical preconceptions, how come it is often said that the research design, choice of case studies, and initial coding in thematic analysis can be theory driven? Wouldn’t that make the approach deductive (i.e. about testing theory). Or, how does theory coming before the research design fit with an inductive approach? In my experience so far authors seem to evade this point.

HALLO D! I REALLY LIKE YOUR WORK IT REALLY HELPED ME IN MY RESEARCH.

Thank You so very much Deborah. I really got to uncover what puzzled me on deductive versus inductive approaches.

Thanks Dr.Gabriel. It was very simple and useful. Now I understood the differecne b/w deductive and inductive method.

Thanx for sharing with us. It is very useful for my dissertation. Your topic clarifies the difference between inductive and deductive research.

Hi Dr.Gabriel, I am doing a research to apply a theory into service industry which is more commonly practiced in manufacturing industry ( known as Lean approach), my aim is to apply this approach into banking operation, the objective is to find the elements/processes in the bank operation that actually increase the cost or decrease the service quality. If I want to conduct a research to find those elements in a bank operation. should I use Inductive approach? what is your advise? Thanks

Hi Deborah, Thank you for a great article! It made it very clear the differenece between deductive and inductive. I'd like to ask you the following: – Is possible to have inductive study with hypotheses and use semi-structured interviews to answer these hypotheses and research questions? thank you very much for your reply! Alina

Hi Alina, I’m glad my post has been useful for you. In answer to your question, I think maybe you are confusing research questions with hypotheses. Research questions guide the overall study and ensure that when designing interview questions – they are structured in the most effective way to elicit responses that address the research questions. Hypotheses are linked to deductive studies where researchers aim to test presumptions/predictions about phenomenon – this is not the same as research questions.

Hi Deborah Thanks for an intersting piece of work presented. Am kindly inquiring how i can get along with literature review and conceptual framemework on the topic 'IDPs and Solid Waste Management' and objectives; exploring everyday practice around solid waste management; finding out how social networks move and merge into new spaces for waste management and establish connections between waste management and social lfestyle. Thanks Hakimu

Hi Deborah thank you for a great article . I have a question for you I am doing my research work and I have some issue about theory construction. Basically I am a beginner in social research – I have no idea about constructing new theory. Please let me know about theory construction and what is a procedure – how can I construct theory and also about steps of this method? I hope you will understand my words. Thank you.

Dear Amna, Welcome to the world of research – we all have to start somewhere! If you're new to social research I would recommend you join the Social Research Association (SRA) who provide training and a wealth of resources for researchers. With regards to theory – unless you are researching new phenomena that has never been researched before or are developing a completely new approach (unlikely) you will not be creating 'new' theory with your research project. You will be using existing theory in your approach and embed theoretical perspectives into your methodology. You will also likely use relevant theories when analysing your data. However, before you think about theory you need to develop your methodology – see my other post: methods and methodology .

Thanks so much. This post was very helpful and easy to understand!

Hi Deborah, Thank you for the precise and helpful information .. I need your help as I feel a little bit confused. i am doing a case study of airline corporate image. it is the newest crisis scenario in my country related to our regional carrier. I think, i among the pioneers doing the case study research for this airline company. I used the conceptual framework from other previous conducted study. It was conducted in quantitative manner. If i used the conceptual framework as my guidance for my literature review and interview question construction, is that okay if i do not use inductive for the case study because i do not build a new theory. If i just compare and argue with the previous finding and the model used, is it consider as deductive approach in case study? Based on my reading, i found some researchers used deductive approach in their case study. they tested the hypotheses..but i just compare my finding with the model used from the previous research. For your information, i did documentation, direct observation and interview (trigulation) with ex-passengers and aviation expert. What do you think? .Please help me..i am stuck. Thank you

Thank you Doctor, it’s straightforward valuable piece of knowledge. It may require a little bit of referencing. Furthermore, adding citation line below will be useful for academic use.

Thank you very much!

Thank you for a crisp and nice post. It helped me.

I am very beginner in research, and its really very helpful.

Thanks writer,

Thank you very much…

wow wow wow great work Deborah. I now have a clear insight of the differences,,,, kudos!

Thank you …it’s helpful for me.

Good job.It helped me to find the question’s answer in my mid-term. Thenk you.

My study is ethnographic research specifically it studies about culture, tradition and lifestyle of an ethnic groups. I think my research is inductive, is it right?

Thank you, I feel same as most the above commentators. Very well written – written in a way that I (who for the first time heard of these two types of researcher methods,) felt like I got a gist of what they are and how they are different.

Thanks Deborah G! Your articles have helped me a lot.

your article is simple to understand and please keep it up thanks.

Hi, It is really helpful me to get sorted these concepts in research field in simple manner.Thanks for that and really appreciate it.

Very clear explanation about inductive and deductive appraoches in research. I like it.

I appreciate your clear and precise explanations, thank you.

Thanks for this, it’s clear, concise and easy to understand – very helpful.

That was just perfect. I have been reading about these methods from my course book offered by university for 3 days but I couldn’t understand their differences. Now it is completely clear. Thanks a lot.

Is that possible to have both in our research? I mean, what if we choose an inductive approach and then when we go forward make some assumptions to answer research questions?

If you are undertaking inductive research then you don’t make any assumptions as you are looking for micro theories to emerge from your analysis of the data. You cannot start with inductive and then switch to deductive – it must be one or the other. You don’t make assumptions to answer research questions – you analyse the findings to do that.

It is precise and clear. Thanks

Great work and explanation and also the researcher herself is very energetic and motivated to help others… world is because of people like you. thanks

Dear madam, I’m a undergraduate studying engineering and I want to know famous researches done solely based on inductive and deductive separately….if you can give me some examples it will be very helpful to me….

You can look this up yourself, through your library and learning resources at your institution…

Dear Deborah, Thank you for the precise explanation of inductive and deductive approaches.

When analysing data in a qualitative study, could you use both inductive and deductive methods as a triangulation technique?

Hi Irene, a mixed methods study might involve both a quantitative method – e.g. survey and qualitative – e.g. interviews. But the overall approach would still be inductive as the quantitative element normally shapes the qualitative and the overall aim would still be to gain in-depth understandings rather than generalise findings. Mixed methods does as you say, create academic rigour through triangulation.

Dear Deborah Thank you in advance for using your precious time to reply to my question. God bless you. l am doing my master degree dissertation on Green Supply Chain Management practices in the United Kingdom automotive industry. The research philosophy that I adopt is this: interpretivist epistemological and constructivism ontological. The methodology is as follows: Interpretivism – inductive – mono-method qualitative -survey. My question is this: Can online survey questionnaire be used with the inductive approach? Most books that l am reading are linking online survey with quantitative data.

Quite educative. Thank you Dr Deborah, it’s so useful to my study.

Thank you Dr Gabriel. It was really useful abstract. Kindly help me to enlighten with more details on grounded theory, dependent and independent variables.

Thank you Dr Gabriel for sharing your knowledge with all of us. Highly appreciated. I have just started my Ph.D. program, and I’m still struggled to deciding which paradigm I have to use! I have one question: My research idea talks about the readiness of an organization toward IoT (factors that affect the organization readiness towards IoT). note*: The IoT technology is still not implemented in the organization context that I want to study that is why I’m going to study the readiness. so what is your suggestion for me regarding which paradigm I have to use?

Hi Mutasem, Congratulations on embarking on your PhD program! Your research paradigm should reflect your positionality, your values and in essence, how you view the world. You need to think critically and reflectively about this. For example, you say you plan to study the readiness of an organization to implement IT. One approach to this study could be examining what factors might shape that readiness – i.e power structures that confer equality/inequality, and there is also the question of how the adoption of IT could help to create more inclusivity and diversity (which contribute to greater productivity & profitability). This of course is a different proposition from merely focusing on technical issues as opposed to social/political ones that also shape technology use.

THANK YOU SO MUCH FOR YOUR KIND RESPONSE

Dear Dr Gabriel

I am currently busy with my Masters in Interior design. My research aim is to determine (possibly explore as Its not currently making sense) the discrepancy that exists between the designer and a specific user group of a Healthcare environment. I have used provisional coding as a first cycle method (which identified a set of themes by which to analyze a Healthcare environment). These themes (conceptual framework) informed my interviews etc. From there the findings were analysed to see to what extent the designer aims correspond with the way in which the user group experiences the space (through the various themes). I initially thought that it was a deductive study but as it is a qualitative study I was abit worried. From what I understood from previous comments, the inductive/deductive is only applicable in the primary research, would that then mean that my study takes the form of a inductive approach? (although my questions are ‘what’ and ‘how’).

Kind regards Anienke

Hi Deborah, your posts are quite simple and useful. Great! Thanks for posts!

Thanks for posting this, I would say that this article is one of the most useful explanation I have found so far. But I also have a question, hopefully you will be able to help me out. If my research question is about understanding “How important are loyalty programmes for customers in welness field. I use quantitative method (Online survey) to collect data from respondents, what is my approach for research inductive and deductive?

Dr Deborah Gabriel, Honestly, I have gone through your explanation on inductive and deductive approaches to research work and I’m very pleased with the write up. I want to sincerely thank you for your contribution to the existing body of knowledge. Regards, Clemduze.

Thank you Dr Gabriel for explaining the differences between inductive and deductive approaches in research. Your explanation helped me understand these two concepts as I am working on the early portions of my dissertation in General Psychology.

Thank you so much. This has been very useful. I now know I can use both Inductive and deductive if i am carrying out a mixed method research. Both can be used depending on my research questions.

It was helpful.

How can I be at this time replying to this worthy and simple explanation? It’s well placed and and explained. Thank you D.

Thanks for your article. How do I reference you in my work?

APA Gabriel, D. (2013). Inductive and deductive approaches to research. Accessed on ‘date’ from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Harvard Gabriel, D., 2013. Inductive and deductive approaches to research. Accessed on ‘date’ from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Can social research be purely deductive?

‘Social’ research – i.e research within the social sciences, can be qualitative or quantitative, and therefore can be inductive or deductive. It depends on what the research objectives are as to which approach is taken – and as my article states, these questions are explored through research methodology.

Very important points are discussed in your article in simplified terms.

Thanks very much!

Appreciated the discussion – it is well simplified and easy to understand.

Comments are closed.

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Korean J Med Educ
v.31(4); 2019 Dec

Reasoning processes in clinical reasoning: from the perspective of cognitive psychology

Hyoung seok shin.

Department of Medical Education, Korea University College of Medicine, Seoul, Korea

Clinical reasoning is considered a crucial concept in reaching medical decisions. This paper reviews the reasoning processes involved in clinical reasoning from the perspective of cognitive psychology. To properly use clinical reasoning, one requires not only domain knowledge but also structural knowledge, such as critical thinking skills. In this paper, two types of reasoning process required for critical thinking are discussed: inductive and deductive. Inductive and deductive reasoning processes have different features and are generally appropriate for different types of tasks. Numerous studies have suggested that experts tend to use inductive reasoning while novices tend to use deductive reasoning. However, even experts sometimes use deductive reasoning when facing challenging and unfamiliar problems. In clinical reasoning, expert physicians generally use inductive reasoning with a holistic viewpoint based on a full understanding of content knowledge in most cases. Such a problem-solving process appears as a type of recognition-primed decision making only in experienced physicians’ clinical reasoning. However, they also use deductive reasoning when distinct patterns of illness are not recognized. Therefore, medical schools should pursue problem-based learning by providing students with various opportunities to develop the critical thinking skills required for problem solving in a holistic manner.

Introduction

It is hard to describe clinical reasoning in a sentence, because it has been studied by a number of researchers from various perspectives, such as medical education, cognitive psychology, clinical psychology, and so forth, and they have failed to reach an agreement on its basic characteristics [ 1 ]. Accordingly, clinical reasoning has been defined in various ways. Some researchers defined clinical reasoning as a crucial skill or ability that all physicians should have for their clinical decision making, regardless of their area of expertise [ 2 , 3 ]. Others focused more on the processes of clinical reasoning; thus, they defined it as a complex process of identifying the clinical issues to propose a treatment plan [ 4 - 6 ]. However, these definitions are not so different. Taking this into account, it can be concluded that clinical reasoning is used to analyze patients’ status and arrive at a medical decision so that doctors can provide the proper medical treatment.

In reality, properly working clinical reasoning requires three domains of knowledge: diagnostic knowledge, etiological knowledge, and treatment knowledge [ 6 ]. From the perspective of cognitive psychology, structural knowledge is needed to integrate domain knowledge and find solutions based on the learner’s prior knowledge and experience [ 7 ], and structural knowledge can be constructed as a form of mental model by understanding the relations between the interconnected factors involved in clinical issues [ 8 , 9 ]. In this cognitive process, critical thinking skills such as causal reasoning and systems thinking can play a pivotal role in developing deeper understanding of given problem situations. Causal reasoning is the ability to identify causal relationships between sets of causes and effects [ 10 ]. Causality often involves a series or chain of events that can be used to infer or predict the effects and consequences of a particular cause [ 10 - 13 ]. Systems thinking is a thinking paradigm or conceptual framework where understanding is defined in terms of how well one is able to break a complex system down into its component parts [ 14 , 15 ]. It is based on the premise that a system involves causality between factors that are parts of the system as a whole [ 14 ]. Systems thinking is a process for achieving a deeper understanding of complex phenomena that are composed of components that are causally interrelated [ 14 - 16 ]. As a result, causal reasoning and systems thinking are skills that can help people to better understand complex phenomena in order to arrive at effective and targeted solutions that address the root causes of complex problems [ 10 , 12 , 15 ].

If cognitive skills work properly, one can make correct decisions all of the time. However, human reasoning is not always logical, and people often make mistakes in their reasoning. The more difficult the problems with which they are presented, the more likely they are to choose wrong answers that are produced by errors or flaws in the reasoning process [ 17 , 18 ]. Individual differences in reasoning skills—such as systems thinking, causal reasoning, and thinking processes—may influence and explain observed differences in their understanding. Therefore, to better assist learners in solving problems, instructors should focus more on facilitating the reasoning skills required to solve given problems successfully.

In this review paper, the author focuses on the reasoning processes involved in clinical reasoning, given that clinical reasoning is considered as a sort of problem-solving process. Therefore, this paper introduces concepts related to the reasoning processes involved in clinical reasoning and their influences on novices and experts in the field of medical education from the perspective of cognitive psychology. Then, based on the contents discussed, the author will be able to propose specific instructional strategies associated with reasoning processes to improve medical students’ reasoning skills to enhance their clinical reasoning.

Concepts and nature of reasoning processes

Generally, reasoning processes can be categorized into two types: inductive/forward and deductive/backward [ 19 ]. In an inductive reasoning process, one observes several individual facts first, then makes a conclusion about a premise or principle based on these facts. Yet there may be the possibility that a conclusion is not true even though a premise or principle in support of that conclusion is true, because the conclusion is generalized from the facts observed by the learner, but the learner does not observe all relevant examples [ 20 ].

In general, in a deductive reasoning process, according to Johnson-Laird [ 20 ], one establishes a mental model or a set of models to solve given problems considering general knowledge and principles based on a solid foundation. Then, one makes a conclusion or finds a solution based on the mental model or set of models. To verify a mental model, one needs to check the validity of the conclusions or solutions by searching for counterexamples. If one cannot find any counterexamples, the conclusions can be accepted as true and the solutions as valid. Consequently, the initial mental model or set of models can be used for deductive reasoning.

Anderson [ 17 ] proposed three different ways of solving complex problems: means-ends analysis, working backward, and planning by simplification. A means-ends analysis is a process that gets rid of differences between the current state and the ideal state in order to determine sub-goals in solving problems, and the process can be repeated until the major goal is achieved [ 21 - 23 ]. It can be considered an inductive reasoning process, because the distinct feature of means-ends analysis where it achieves sub-goals in consecutive order is similar to inductive reasoning. Working backward is addressed as an opposite concept to means-ends analysis [ 17 ], because it needs to set up a desired result to find causes by measuring the gap between the current state and the ideal state; then, this process is repeated until the root causes of a problem are identified. According to Anderson [ 17 ], means-ends analysis (inductive reasoning) is more useful in finding a solution quickly when a limited number of options are given or many sub-goals should be achieved for the major goal; whereas working backward (deductive reasoning) spends more time removing wrong answers or inferences to find the root causes of a problem. In conclusion, inductive and deductive reasoning processes have different features and can play different roles in solving complex problems.

The use of reasoning processes

A number of researchers across different fields have used inductive and deductive approaches as reasoning processes to solve complex problems or complete tasks. For example, Scavarda et al. [ 24 ] used both approaches in their study to collect qualitative data through interviews with experts, and they found that experts with a deductive approach used a top-down approach and those with an inductive approach used a bottom-up approach to solve a given problem. In a study of Overmars et al. [ 25 ], the results showed that a deductive approach explicitly illustrated causal relations and processes in 39 geographic contexts and it was appropriate for evaluating various possible scenarios; whereas an inductive approach presented associations that did not guarantee causality and was more useful for identifying relatively detailed changes.

Sharma et al. [ 26 ] found that inductive or deductive approaches can both be useful depending on the characteristics of the tasks and resources available to solve problems. An inductive approach is considered a data-driven approach, which is a way to find possible outcomes based on rules detected from undoubted facts [ 26 ]. Therefore, if there is a lot of available data and an output hypothesis, then it is effective to use an inductive approach to discover solutions or unexpected and interesting findings [ 26 , 27 ]. An inductive approach makes it possible to directly reach conclusions via thorough reasoning that involves the following procedures: (1) recognize, (2) select, and (3) act [ 28 ]. These procedures are recurrent, but one cannot know how long they should be continued to complete a task, because a goal is not specified [ 26 ]. Consequently, an inductive approach is useful when analyzing an unstructured data set or system [ 29 ].

On the other hand, a deductive approach sets up a desired goal first, then finds a supporting basis—such as information and rules—for the goals [ 26 ]. For this, a backward approach, which is considered deductive reasoning, gradually gets rid of things proved unnecessary for achieving the goal while reasoning; therefore, it is regarded as a goal-driven approach [ 28 ]. If the output hypothesis is limited and it is necessary to find supporting facts from data, then a deductive approach would be effective [ 26 , 28 ]. This implies that a deductive approach is more appropriate when a system or phenomenon is well-structured and relationships between the components are clearly present [ 29 ]. Table 1 shows a summary of the features and differences of the inductive and deductive reasoning processes.

Features of Inductive and Deductive Reasoning Processes

The classification according to the reasoning processes in the table is dichotomous, but they do not always follow this classification absolutely. This means that each reasoning process shows such tendencies.

Considering the attributes of the two reasoning processes, an inductive approach is effective for exploratory tasks that do not have distinct goals—for example, planning, design, process monitoring, and so on, while a deductive approach is more useful for diagnostic and classification tasks [ 26 ]. In addition, an inductive approach is more useful for discovering solutions from an unstructured system. On the other hand, a deductive approach can be better used to identify root causes in a well-structured context. While both reasoning approaches are useful in particular contexts, it can be suggested that inductive reasoning is more appropriate than deductive reasoning in clinical situations, which focus on diagnosis and treatment of diseases rather than on finding their causes.

Reasoning processes by novices and experts

As mentioned above, which reasoning process is more effective for reaching conclusions can be generally determined depending on the context and purpose of the problem solving. In reality, however, learners’ choices are not always consistent with this suggestion, because they are affected not only by the problem itself, but also by the learner. Assuming that learners or individuals can be categorized into two types, novices and experts, based on their level of prior knowledge and structural knowledge, much research has shown that novices and experts use a different reasoning process for problem solving. For example, in a study of Eseryel et al. [ 30 ], novice instructional designers who possessed theoretical knowledge but little experience showed different patterns of ill-structured problem solving compared to experts with real-life experience. Given that each learner has a different level of prior knowledge relating to particular topics and critical thinking skills, selecting the proper reasoning process for each problem is quite complex. This section focuses on which reasoning process an individual uses depending on their content and structural knowledge.

Numerous studies have examined which reasoning processes are used by experts, who have sufficient content and structural knowledge, and novices, who have little content and structural knowledge, for problem solving. The result of a study of Hong et al. [ 31 ] showed that children generally performed better when using cause-effect inferences (inductive approach) than effect-cause inferences (considered a deductive approach). According to Anderson [ 17 ], people are faced with some difficulties when they solve problems using induction. The first difficulty is in formulating proper hypotheses and the second is that people do not know how to interpret negative evidence when it is given and reach a conclusion based on that evidence [ 17 ]. Nevertheless, most students use a type of inductive reasoning to solve problems that they have not previously faced [ 32 ]. Taken together, the studies suggest that novices generally prefer an inductive approach to a deductive approach for solving problems because they may feel comfortable and natural using an inductive approach but tend to experience difficulties during problem-solving processes. From these findings, it can be concluded that novices are more likely to use inductive reasoning, but it is not always productive.

Nevertheless, there is still a controversy about which reasoning processes are used by experts or novices [ 33 ]. For example, experts in specific domains use an inductive approach to solving problems, but novices, who have a lower level of prior knowledge in specific domains, tend to use a deductive approach [ 23 ]. In contrast, according to Smith [ 34 ], studies in which more familiar problems were used concluded that experts preferred an inductive approach, whereas in studies that employed relatively unfamiliar problems that required more time and effort to solve, experts tended to prefer a deductive approach. In line with this finding, in solving physics problems, experts mostly used inductive reasoning that was faster and had fewer errors for problem solving only when they encountered easy or familiar problems where they could gain a full understanding of the situation quickly, but novices took more time to deductively reason by planning and solving each step in the process of problem solving [ 35 ].

Assuming that an individual’s prior knowledge consists of content knowledge such as knowledge of specific domains as well as structural knowledge such as the critical thinking skills required for problem solving in the relevant field, it seems experts use an inductive approach when faced with relatively easy or familiar problems; while a deductive approach is used for relatively challenging, unfamiliar, or complex problems. In the case of novices, it may be better to use deductive reasoning for problem solving considering that they have a lower level of prior knowledge and that even experts use deductive reasoning to solve complex problems.

Inductive and deductive reasoning in clinical reasoning

In medicine, concepts of inductive and deductive reasoning apply to gathering appropriate information and making a clinical diagnosis considering that the medical treatment process is a form of problem solving. Inductive reasoning is used to make a diagnosis by starting with an analysis of observed clinical data [ 36 , 37 ]. Inductive reasoning is considered as scheme-inductive problem solving in medicine [ 36 ], because in inductive reasoning, one first constructs his/her scheme (also considered a mental model) based on one’s experiences and knowledge. It is generally used for a clinical presentation-based model, which has been most recently applied to medical education [ 38 ].

In contrast, deductive reasoning entails making a clinical diagnosis by testing hypotheses based on systematically collected data [ 39 ]. Deductive reasoning is considered an information-gathering method, because one constructs a hypothesis first then finds supporting or refuting facts from data [ 36 , 40 ]. It has been mostly used for discipline-based, system-based, and case-based models in medical education [ 38 ].

Inductive and deductive reasoning by novice and expert physicians

A growing body of research explores which reasoning processes are mainly used by novices and experts in clinical reasoning. Novice physicians generally use deductive reasoning, because limited knowledge restricts them from using deductive reasoning [ 1 , 38 ]. Also, it is hard to consider deductive reasoning as an approach generally used by experts, since they do not repeatedly test a hypothesis based on limited knowledge in order to move on to the next stage in the process of problem solving [ 38 ]. Therefore, it seems that deductive reasoning is generally used by novices, while inductive reasoning is used by expert physicians in general. However, this may be too conclusive and needs to be further examined in the context of clinical reasoning.

In clinical reasoning, inductive reasoning is more intuitive and requires a holistic view based on a full understanding of content knowledge, including declarative and procedural knowledge, but also structural knowledge; thus, it occurs only when physicians’ knowledge structures of given problems are highly organized [ 38 ]. Expert physicians recognize particular patterns of symptoms through repeated application of deductive reasoning, and the pattern recognition process makes it possible for them to apply inductive reasoning when diagnosing patients [ 10 ]. As experts automate a number of cognitive sequences required for problem solving in their own fields [ 35 ], expert physicians automatically make appropriate diagnoses following a process of clinical reasoning when they encounter patients who have familiar or typical diseases. Such a process of problem solving is called recognition-primed decision making (RPDM) [ 41 , 42 ]. It is a process of finding appropriate solutions to ill-structured problems in a limited timeframe [ 10 ]. In RPDM, expert physicians are aware of what actions should be taken when faced with particular situations based on hundreds of prior experiences [ 10 ]. These prior experiences are called illness scripts in diagnostic medicine [ 10 ], and this is a concept similar to a mental model or schema in problem solving.

However, expert physicians do not always use inductive reasoning in their clinical reasoning. Jonassen [ 10 ] categorized RPDM into three forms of variations in problem solving by experts, and the first form of variation is the simplest and easiest one based on inductive reasoning, as mentioned above. The second type of variation occurs when an encountered problem is somewhat atypical [ 10 ]. Even expert physicians are not always faced with familiar or typical diseases when treating patients. Expert physicians’ RPDM does not work automatically when faced with atypical symptoms, because they do not have sufficient experiences relevant to the atypical symptoms. In this case, it can be said that they have weak illness scripts or mental models of the given symptoms. In the second variation, experts need more information and will attempt to connect it to their prior knowledge and experiences [ 10 ]. Deductive reasoning is involved in this process so that problem solvers can test their hypotheses in order to find new patterns and construct new mental models based on the newly collected data and previous experiences. The third variation of RPDM is when expert physicians have no previous experience or prior knowledge of given problem situations; in other words, no illness script or mental model [ 10 ]. Jonassen [ 10 ] argued that a mental simulation is conducted to predict the consequences of various actions by experts in the third variation. This process inevitably involves repetitive deductive reasoning to test a larger number of hypotheses when making a diagnosis.

Similarly, from the perspective of dual process theory as a decision-making process, decision making is classified into two approaches based on the reasoning style: type 1 and type 2 (or system 1 and system 2) [ 43 , 44 ]. According to Croskerry [ 44 ], the type 1 decision-making process is intuitive and based on experiential-inductive reasoning, while type 2 is an analytical and hypothetico-deductive decision-making process [ 44 , 45 ]. A feature that distinguishes the two processes is whether a physician who encounters a patient’s symptoms succeeds in pattern recognition. If a physician recognizes prominent features of the visual presentation of illness, type 1 processes (or system 1) are operated automatically, whereas type 2 (or system 2) processes work if any distinct feature of illness presentation is not recognized [ 44 ].

Only experienced expert physicians can use RPDM [ 10 , 46 ] or type 1 and 2 processes [ 43 ], because it can occur solely based on various experiences and a wide range of prior knowledge that can be gained as a result of a huge amount of deductive reasoning since they were novices. Consequently, it can be concluded that expert physicians generally use more inductive reasoning when they automatically recognize key patterns of given problems or symptoms, while sometimes they also use deductive reasoning when they additionally need processes of hypothesis testing to recognize new patterns of symptoms.

From the perspective of cognitive processes, clinical reasoning is considered as one of the decision-making processes that finds the best solutions to patients’ illnesses. As a form of decision making for problem solving, two reasoning processes have been considered: inductive and deductive reasoning. Deductive reasoning can be used to make a diagnosis if physicians have insufficient knowledge, sufficient time, and the ability to analyze the current status of their patients. However, in reality, it is inefficient to conduct thorough deductive reasoning at each stage of clinical reasoning because only a limited amount of time is allowed for both physicians and patients to reach a conclusion in most cases. A few researchers have suggested that using deductive reasoning is more likely to result in diagnostic errors than inductive reasoning, because evidence-based research, such as deductive reasoning, focuses mainly on available and observable evidence and rules out the possibility of any other possible factors influencing the patient’s symptoms [ 37 , 38 ]. However, when a physician encounters unfamiliar symptom and the degree of uncertainty is high, deductive reasoning is required to reach the correct diagnosis through analytical and slow diagnostic processes by collecting data from resources [ 44 ]. Taken together, in order to make the most of a limited timeframe and reduce diagnostic errors, physicians should be encouraged to use inductive reasoning in their clinical reasoning as far as possible given that patterns of illness presentation are recognized.

Unfortunately, it is not always easy for novice physicians to apply inductive or deductive reasoning in all cases. Expert physicians have sufficient capabilities to use both inductive and deductive reasoning and can also automate their clinical reasoning based on inductive reasoning, because they have already gathered the wide range of experiences and knowledge required to diagnose various symptoms. Novice physicians should make a greater effort to use inductive reasoning when making diagnoses; however, it takes experiencing countless deductive reasoning processes to structure various illness scripts or strong mental models until they reach a professional level. As a result, teaching not only clinical reasoning as a whole process but also the critical thinking skills required for clinical reasoning is important in medical schools [ 47 ]. For this, medical schools should pursue problem-based learning by providing students with various opportunities to gain content knowledge as well as develop the critical thinking skills —such as data analysis skills, metacognitive skills, causal reasoning, systems thinking, and so forth—required for problem solving in a holistic manner so that they can improve their reasoning skills and freely use both inductive and deductive approaches in any context. Further studies will be reviewed to provide detailed guidelines or teaching tips on how to develop medical students’ critical thinking skills.

Acknowledgments

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Author contributions

All work was done by HS.

COMMENTS

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