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  • Inductive vs. Deductive Research Approach | Steps & Examples

Inductive vs. Deductive Research Approach | Steps & Examples

Published on April 18, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory .

In other words, inductive reasoning moves from specific observations to broad generalizations . Deductive reasoning works the other way around.

Both approaches are used in various types of research , and it’s not uncommon to combine them in your work.

Inductive-vs-deductive-reasoning

Table of contents

Inductive research approach, deductive research approach, combining inductive and deductive research, other interesting articles, frequently asked questions about inductive vs deductive reasoning.

When there is little to no existing literature on a topic, it is common to perform inductive research , because there is no theory to test. The inductive approach consists of three stages:

  • A low-cost airline flight is delayed
  • Dogs A and B have fleas
  • Elephants depend on water to exist
  • Another 20 flights from low-cost airlines are delayed
  • All observed dogs have fleas
  • All observed animals depend on water to exist
  • Low cost airlines always have delays
  • All dogs have fleas
  • All biological life depends on water to exist

Limitations of an inductive approach

A conclusion drawn on the basis of an inductive method can never be fully proven. However, it can be invalidated.

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case study research inductive or deductive

When conducting deductive research , you always start with a theory. This is usually the result of inductive research. Reasoning deductively means testing these theories. Remember that if there is no theory yet, you cannot conduct deductive research.

The deductive research approach consists of four stages:

  • If passengers fly with a low cost airline, then they will always experience delays
  • All pet dogs in my apartment building have fleas
  • All land mammals depend on water to exist
  • Collect flight data of low-cost airlines
  • Test all dogs in the building for fleas
  • Study all land mammal species to see if they depend on water
  • 5 out of 100 flights of low-cost airlines are not delayed
  • 10 out of 20 dogs didn’t have fleas
  • All land mammal species depend on water
  • 5 out of 100 flights of low-cost airlines are not delayed = reject hypothesis
  • 10 out of 20 dogs didn’t have fleas = reject hypothesis
  • All land mammal species depend on water = support hypothesis

Limitations of a deductive approach

The conclusions of deductive reasoning can only be true if all the premises set in the inductive study are true and the terms are clear.

  • All dogs have fleas (premise)
  • Benno is a dog (premise)
  • Benno has fleas (conclusion)

Many scientists conducting a larger research project begin with an inductive study. This helps them develop a relevant research topic and construct a strong working theory. The inductive study is followed up with deductive research to confirm or invalidate the conclusion. This can help you formulate a more structured project, and better mitigate the risk of research bias creeping into your work.

Remember that both inductive and deductive approaches are at risk for research biases, particularly confirmation bias and cognitive bias , so it’s important to be aware while you conduct your research.

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

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

Research bias

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

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Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Inductive Vs Deductive Research

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Inductive Vs Deductive Research

Inductive and deductive research are two different approaches to conducting a research study. While they are different in their orientation and methods, they can both be used to generate new knowledge and advance scientific understanding.

Inductive Research

Inductive research is a bottom-up approach to research where a researcher starts with specific observations and data and then works to generate general principles or theories. This approach involves collecting data and analyzing it to identify patterns, themes, or categories. From these patterns, the researcher can develop theories or concepts that can explain the observations made in the data. Inductive research is often used in qualitative research, case studies, and grounded theory research.

Deductive Research

Deductive research is a top-down approach to research where a researcher starts with a theory or hypothesis and then tests it through data collection and analysis. This approach involves testing a specific hypothesis or theory and then drawing conclusions based on the results of the analysis. Deductive research is often used in quantitative research, experimental research, and survey research.

Difference between Inductive and Deductive Research

Here are some key differences between inductive and deductive research:

Both inductive and deductive research have their strengths and weaknesses, and the choice of which to use depends on the nature of the research question, the research objectives, and the available resources. Inductive research is useful when a researcher wants to explore a new area of inquiry or when there is limited theory or previous research available, while deductive research is useful when a researcher wants to test a specific theory or hypothesis.

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  • Inductive vs Deductive Research Approach (with Examples)

Inductive vs Deductive Reasoning | Difference & Examples

Published on 4 May 2022 by Raimo Streefkerk . Revised on 10 October 2022.

The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory .

Inductive reasoning moves from specific observations to broad generalisations , and deductive reasoning the other way around.

Both approaches are used in various types of research , and it’s not uncommon to combine them in one large study.

Inductive-vs-deductive-reasoning

Table of contents

Inductive research approach, deductive research approach, combining inductive and deductive research, frequently asked questions about inductive vs deductive reasoning.

When there is little to no existing literature on a topic, it is common to perform inductive research because there is no theory to test. The inductive approach consists of three stages:

  • A low-cost airline flight is delayed
  • Dogs A and B have fleas
  • Elephants depend on water to exist
  • Another 20 flights from low-cost airlines are delayed
  • All observed dogs have fleas
  • All observed animals depend on water to exist
  • Low-cost airlines always have delays
  • All dogs have fleas
  • All biological life depends on water to exist

Limitations of an inductive approach

A conclusion drawn on the basis of an inductive method can never be proven, but it can be invalidated.

Example You observe 1,000 flights from low-cost airlines. All of them experience a delay, which is in line with your theory. However, you can never prove that flight 1,001 will also be delayed. Still, the larger your dataset, the more reliable the conclusion.

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When conducting deductive research , you always start with a theory (the result of inductive research). Reasoning deductively means testing these theories. If there is no theory yet, you cannot conduct deductive research.

The deductive research approach consists of four stages:

  • If passengers fly with a low-cost airline, then they will always experience delays
  • All pet dogs in my apartment building have fleas
  • All land mammals depend on water to exist
  • Collect flight data of low-cost airlines
  • Test all dogs in the building for fleas
  • Study all land mammal species to see if they depend on water
  • 5 out of 100 flights of low-cost airlines are not delayed
  • 10 out of 20 dogs didn’t have fleas
  • All land mammal species depend on water
  • 5 out of 100 flights of low-cost airlines are not delayed = reject hypothesis
  • 10 out of 20 dogs didn’t have fleas = reject hypothesis
  • All land mammal species depend on water = support hypothesis

Limitations of a deductive approach

The conclusions of deductive reasoning can only be true if all the premises set in the inductive study are true and the terms are clear.

  • All dogs have fleas (premise)
  • Benno is a dog (premise)
  • Benno has fleas (conclusion)

Many scientists conducting a larger research project begin with an inductive study (developing a theory). The inductive study is followed up with deductive research to confirm or invalidate the conclusion.

In the examples above, the conclusion (theory) of the inductive study is also used as a starting point for the deductive study.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Cite this Scribbr article

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Streefkerk, R. (2022, October 10). Inductive vs Deductive Reasoning | Difference & Examples. Scribbr. Retrieved 22 February 2024, from https://www.scribbr.co.uk/research-methods/inductive-vs-deductive-reasoning/

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  • Published: 15 September 2021

Comparing inductive and deductive analysis techniques to understand health service implementation problems: a case study of childhood vaccination barriers

  • Carissa Bonner   ORCID: orcid.org/0000-0002-4797-6460 1 ,
  • Jane Tuckerman 2 ,
  • Jessica Kaufman 2 ,
  • Daniel Costa 3 , 4 ,
  • David N. Durrheim 5 ,
  • Lyndal Trevena 1 ,
  • Susan Thomas 5 &
  • Margie Danchin 2 , 6 , 7  

Implementation Science Communications volume  2 , Article number:  100 ( 2021 ) Cite this article

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Effective implementation requires a comprehensive understanding of individual, organisational and system determinants. This study aimed to compare inductive and deductive analysis techniques to understand a complex implementation issue. We used childhood vaccination as a case study, an issue with wide-ranging barriers contributing to low-vaccine uptake internationally.

The study is based on the Behaviour Change Wheel framework, which was derived from several levels of theory: the 3 components of the COM-B framework (capability, opportunity and motivation) can be mapped to the 14 domains of the Theoretical Domains Framework (TDF), which is based on 84 underlying constructs. We first conducted a review of systematic reviews of parent-level barriers to childhood vaccination. Subsequently we (1) inductively coded these barriers into a data-driven framework, using thematic analysis, and (2) deductively mapped the barriers to COM-B and TDF domains and constructs. These processes were undertaken by two authors independently, and discrepancies were resolved through discussion. Inductive and deductive results were compared.

The inductive process coded 583 descriptions of barriers identified from the literature into a framework of 74 barriers in 7 categories. The initial definitions used to map the barriers to deductive domains/constructs led to 89% agreement at the domain level. Resolving discrepancies required further definitions at the construct level. Of the 14 TDF domains, 10 were clearly identified in the data from the barrier reviews. Some domains were not specific enough to differentiate between types of barriers (e.g. Environmental Context and Resources), while other domains were not represented in the review data (e.g. Behavioural Regulation).

Conclusions

Using both inductive and deductive analysis techniques can help achieve a more comprehensive understanding of barriers to health service implementation. The inductive categories represented the review data in a clearer way than the theoretical domains, with better differentiation; but the missing deductive domains were useful as a way to identify additional issues to investigate further. Both analysis techniques resulted in a comprehensive list of barriers to vaccination that would not have been achieved using either approach alone. We recommend a hybrid approach combining TDF with broader frameworks, for future researchers conducting evidence syntheses.

Peer Review reports

Contributions to the literature

Deductive theoretical analysis techniques to understand implementation problems, such as the TDF and COM-B, may raise different issues compared to inductive data-driven analysis techniques

This paper describes a process for comparing inductive and deductive analysis techniques to understand an implementation challenge of global significance

We describe an analysis process using several levels of framework development (84 constructs underlying the 14 TDF domains, which link to the 3 COM-B components) and identify new directions to improve the specificity of theoretical behavioural constructs in future research

The paper illustrates how inductive and deductive analysis techniques synergise to produce a more comprehensive understanding of health service barriers than using either approach alone

Effective implementation of a health service programme, guideline or treatment requires understanding a wide range of system, organisational and individual determinants of uptake [ 1 ]. This may involve reviewing existing literature for well-established problems or conducting original research if the issue is new. Incorporating theoretical frameworks can ensure all possible drivers are considered [ 2 ].

The use of theoretical frameworks enables an understanding of the mechanisms of change from individual to system levels, which can then be targeted in interventions. Multiple theories are used in healthcare, from simple models of individual health behaviour change like the Theory of Planned Behaviour [ 3 ], to broader systems thinking approaches to map the complexity of policy drivers [ 4 ]. The Behaviour Change Wheel (BCW) is one approach that attempts to bring individual and system level factors together [ 5 ], based on the COM-B (capability, opportunity, motivation—behaviour) framework that synthesises 14 behavioural constructs in the Theoretical Domains Framework (TDF) [ 6 ] into broader categories.

The TDF summarises the many overlapping constructs in the behaviour change literature and was developed through expert consensus from 128 theoretical constructs in 33 theoretical models of behaviour [ 7 ]. It provides an overview of 14 key theoretical constructs that explain health behaviour and is a descriptive framework rather than a theory of causality. A separate systematic review of 19 frameworks for behaviour change interventions led to the BCW, which aims to guide the development of interventions by connecting the determinants of behaviour with behaviour change techniques [ 5 ]. Developed in conjunction with the BCW, and at its central core, is the COM-B framework which proposes that behaviour is a product of the interaction between capability (psychological or physical), opportunity (social or physical) and motivation (automatic or reflective) [ 5 , 7 ].

The COM-B and TDF have been mapped to each other, but there is some duplication of the current 14 TDF domains across the COM-B components. Table  1 summarises this theoretical relationship.

Primary research is often used to identify barriers to implementation in different health service contexts, and this is the approach generally used with the TDF [ 7 ]. Some issues have been well researched, but this evidence must be synthesised in order to inform comprehensive intervention design [ 8 ]. Previous reviews have applied theoretical frameworks to help with this. For example, the BCW can be used to describe interventions in terms of broader functions [ 9 ], and the COM-B can be used to display barriers and facilitators at multiple levels (patient, provider, system) [ 9 ]. The TDF can be used together with the COM-B to group barriers and facilitators of health outcomes [ 10 ], or as a stand alone framework [ 11 ].

A deductive analysis technique using theory-driven constructs may identify different implementation issues compared to inductive techniques that are data-driven. A deductive application of theory ensures that all psychological constructs relevant to behaviour are considered, even if research has not identified every construct. However, since these theoretical frameworks are based heavily on psychological theory, the internal ‘motivation’ aspect is more clearly defined than the more external ‘opportunity’ aspect. This imbalance does not necessarily align with the prevalence and significance of practical issues in health service implementation, which might be defined as ‘physical opportunity’. A hybrid approach can be used to address this [ 12 , 13 ], but the extra time and expertise required need to be weighed against the potential benefits.

The aim of this paper is to compare inductive and deductive analysis techniques applied to the same implementation issue and illustrate how these processes can complement each other. We use parent uptake of childhood vaccination as an example of an international issue with wide ranging barriers identified in multiple reviews.

Theoretical approach

The study was based on the BCW framework because it incorporates both individual and system level barriers to behaviour and is based on several levels of theory: the 3 components of the COM-B framework can be mapped to the 14 domains of the TDF, which is based on 84 underlying constructs [ 5 ].

Context: The Vaccine Barriers Assessment Tool (VBAT) project

This analysis is based on data gathered for the Vaccine Barriers Assessment Tool (VBAT) project, which aims to design and validate a survey tool to diagnose the causes of under-vaccination in children under 5 years. Developed in Australia and New Zealand, VBAT aims to incorporate both access and acceptance barriers in a comprehensive tool which will include both short and long form versions, for different uses. An overview of systematic reviews of primary studies on barriers to childhood vaccination was conducted, and 583 descriptions of parental barriers to childhood vaccination uptake were extracted and inductively grouped into categories [ 14 ]. Barriers were extracted if they were reported from or relevant to the specific perspective of parents of children under 5 years; barriers from the perspective of health professionals or the health system alone were not included. The findings of the review were thematically organised into a framework of barriers. In a separate deductive process, the 583 barrier descriptions were mapped to the 14 domain version of the TDF, to check whether any theoretical determinants of childhood vaccine uptake were missing in the systematic review data. The purpose of this exercise for the VBAT project was to ensure that a comprehensive pool of potential survey questions could be generated that captured both access and psychological or acceptance barriers. The inductive review and development of the VBAT items will be reported separately (manuscript in preparation [ 15 ]). In the results of this article, we describe the utility of using both inductive inductive and deductive analysis techniques to identifying drivers of vaccination. Specific terms are used as outlined in Table  2 .

Figure  1 illustrates the inductive and deductive processes, supported by regular group meetings with all authors to discuss each step. We used the perspective of parents (not health professionals or health systems), which affected the way the deductive categories were applied. The prevalence of domains was examined to determine missing theoretical constructs in the data.

figure 1

Inductive and deductive processes

Mapping inductive barriers to deductive domains

The initial definitions used to compare inductive barriers with theoretical domains/constructs led to 89% agreement at the domain level. For example, we specified that all barriers relating to the clinic setting will be under the domain of Environmental Context and Resources. Resolving disagreements for the domains and subsequent constructs required further definitions at the construct level before 100% agreement was reached. Table  3 illustrates this for the domain of Environmental Context and Resources, where we decided that issues relating to how appointment times are managed will be under the construct of Organisational culture/climate, while issues relating to inconvenient access for the parent will be under the construct of Person x Environment Interaction. The full list of definitions in available in Appendix .

Figure  2 shows the number of barriers represented in each theoretical domain. Table  4 shows the relationship between deductive COM-B components and TDF domains, and inductive barriers identified in systematic reviews of primary research. Of the 14 TDF domains, 10 were definitively present in inductive data while 4 domains were not covered in the initial coding: Optimism, Intentions, Goals and Behavioural Regulation (with the exception of two very general barriers for Intentions and Goals with no further explanation). Two domains grouped many different concepts under generic terms (Beliefs within Beliefs about Consequences, Barriers and Facilitators within Environmental Context/Resources). Of the 84 constructs within the 14 TDF domains, many were not identified in the inductive data. This is shown in yellow in Appendix .

figure 2

Number of barriers in each TDF domain from inductive data-driven process

Overall, we found it useful to synthesise health service implementation barriers using both inductive and deductive analysis techniques to gain a comprehensive understanding of the barriers to childhood vaccination. The inductive data-driven categories represented the primary research data in a clearer way than the deductive theoretical domains, with better differentiation; but the four missing theoretical domains were useful as a way to identify key gaps to be addressed in the item pool for developing a new tool to diagnose the causes of childhood under-vaccination.

Resolving conflicts at the domain level was relatively straighforward, with 100% agreement reached quickly. However, there were some barriers that could have been placed in several domains. For example, previous experience of vaccine side effects could be framed as knowledge, beliefs or salient events. Resolving conflicts at the construct level was more difficult because many constructs within a domain were very similar when applied to the brief barrier descriptions extracted from reviews, for example the influence of family member opinions could fit within group identity, social norm or social pressure. The decisions made at construct level were arguably more subjective than the domain level, but both needed to be considered to make sense of many barriers that could be framed in different ways.

For this study, it was necessary to go into more theoretical detail than the commonly used frameworks: the COM-B and TDF. Importantly, the gaps identified in our inductive review would not have been found if the analysis had only been done at the COM-B level, as all six components were addressed by the 10 inductive barrier categories. In addition, the 14 TDF domains were still not specific enough for two coders to reliably map the barrier data so we were required to go back a step to the 84 theoretical constructs that informed the TDF development. We found it helpful to use a combination of domain and construct level to map the data. A previous review using the TDF identified some issues that could not be mapped to the TDF, including clinician and patient characteristics. However, some of these could be mapped at the construct level depending on the framing, such as under professional identity, skills, environment x person and resources constructs [ 16 ].

Practical implications

This paper provides analysis techniques for anyone seeking to understand an implementation issue that already has a large amount of qualitative and/or quantitative research—complementing an earlier paper that focuses on how to apply the TDF in primary qualitative research [ 7 ]. There are several practical implications for other researchers seeking to comprehensively understand implementation barriers using theoretical frameworks in this way. Firstly, researchers need to decide on very specific framing for a health situation. In our case, we decided we would only consider the parent perspective on vaccinating their child, which determined how we framed barriers relating to the doctors’ knowledge. Conducting this process from the health professional perspective would produce different results in terms of the theoretical constructs identified in the literature. We included both barriers to the intervention and barriers to implementation but other projects may need to distinguish between these. Secondly, the COM-B framework was not specific enough with uneven explanation of different barrier types, so researchers may need to go into more detail at domain and construct level to interpret the data. Thirdly, theory was useful for identifying gaps in an inductive review of literature, but inductive categories made more sense for the specific implementation topic. The value of using deductive theory-driven analysis techniques may depend on available resources, given this process took 2 authors with prior knowledge of behavioural frameworks around 2 weeks for coding and discussion. For our purposes, this review will inform the development of a diagnostic tool to measure the causes of under-vaccination, requiring us to include the widest possible range of behavioural drivers. For other projects, it may be more prudent to focus only on the theoretical drivers that are within an organisation’s control to address or to identify inductive issues from the perspective of key stakeholders to ensure their interest and support. Future questionnaire developers may benefit from reviewing existing validated survey items prior to a literature review, so that barriers can be linked to established items at the same time.

Theoretical implications

More generally, this study has implications for theoretical frameworks commonly used in implementation science. Some constructs are vague and became catch alls, such as barriers and facilitators. Others are too specific and hard to distinguish, particularly group vs social norms, which could be combined into one category. In our experience, the decision was often between constructs in different domains, rather than constructs within a domain, suggesting that there are some issues with the way the TDF domains are differentiated. On the other hand, the construct level was often too subjective and detailed to identify clear gaps in data. This suggests that overarching frameworks like the COM-B and TDF need to be supplemented with more context-specific frameworks for different health areas (e.g. prevention versus treatment of infectious disease), targets of behaviour change (e.g. parents versus doctors), and the context (e.g. higher resource settings where psychological barriers may be more important, versus lower resource settings where practical access issues require greater differentiation). Another option would be to use broad implementation frameworks that include practical issues like cost, such as the Consolidated Framework for Implementation Research (CFIR) [ 17 ]. Other researchers have found it helpful to combine the TDF and CFIR for a more comprehensive approach [ 1 ]. A third option would be to add more specific domains to the next version of the TDF to better differentiate between issues relating to ‘Environmental Context and Resources’. In our review, this covered a very wide range of issues: socio-economic issues such as having low income, societal issues like the influence of media, health system issues like vaccine supply and cost, and individual access issues like distance and time. This was found to be a catch all category in many previous reviews of clinicians and patients using the TDF [ 16 , 18 , 19 , 20 , 21 , 22 ], so is not limited to the issue of vaccination barriers. For example, a review of barriers to low back pain guidelines found this domain was common to 4/5 clinician behaviours while many other domains were not covered at all [ 20 ]. Another review on diabetic screening identified 17 barriers in this domain versus 6 for the next most common domain [ 18 ]. Further development of this construct may need to be specific to different health topics.

For the purpose of the VBAT study, we aimed to identify the widest possible range of behavioural barriers documented in the literature, not the relationships between them, so a framework approach was appropriate. We framed all concepts as ‘barriers’ by reversing concepts framed as facilitators where required, for consistency. VBAT will be used to identify the presence of key access and/or acceptance barriers in specific populations. Once identified, the key barriers would require more specific models or theories to guide intervention development, which may frame the same construct as either facilitator or barrier.

Strengths and limitations

This study involved independent coding for both inductive and deductive analysis techniques. Our team included a wide variety of expertise to help contextual framing for theoretical constructs as applied to inductive barriers. The limitations include restricting our review data to parent barriers only, which affected the way that health professionals’ and heatlh system barriers were conceptualised. We also applied only one overarching framework based on behaviour change models and acknowledge that there are many other approaches to this theoretical issue.

In conclusion, using both inductive and deductive analysis techniques can help achieve a more comprehensive understanding of health service implementation problems, but the TDF approach needs to be refined in the context of vaccination. We recommend a hybrid approach combining TDF with frameworks such as CFIR, for future researchers conducting evidence syntheses using a theoretical approach. The process is subjective so requires a wide range of expertise to reduce biased interpretation and to maximise utility of the identified barriers for the specified purpose.

Availability of data and materials

Data available on request from Carissa Bonner ( [email protected] ).

Abbreviations

Behaviour Change Wheel

Theoretical Domains Framework

Capability-Opportunity-Motivation-Behaviour Model

Vaccine Barriers Assessment Tool

Consolidated Framework for Implementation Research

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Acknowledgements

We thank Michael Fajardo for assistance with searching for the original systematic review, and Carys Batcup for assistance finding other reviews that have used the COM-B and TDF frameworks and managing references.

The project was funded by a National Health and Medical Research Council grant from the Australian government (NHMRC Project Grant #1164200).

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CB conceived/designed the study, conducted the analysis, and drafted the paper. JT and JK conducted the analysis and were major contributors in writing the manuscript. DC, DD, LT, ST and MD contributed to group discussions to design the analysis approach and interpret the results and revised the manuscript. The authors read and approved the final manuscript and are accountable for the work.

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Bonner, C., Tuckerman, J., Kaufman, J. et al. Comparing inductive and deductive analysis techniques to understand health service implementation problems: a case study of childhood vaccination barriers. Implement Sci Commun 2 , 100 (2021). https://doi.org/10.1186/s43058-021-00202-0

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case study research inductive or deductive

Inductive and Deductive Theory in Case Studies

David takeuchi’s survey at the university of hawaii.

The case study describes a survey performed by David Takeuchi and his team in 1974 which aimed at explaining the reasons for different treatment of marijuana by the students of the University of Hawaii (Babbie, 2016). Various explanations for this issue were offered. Some said that marijuana smokers had problems with the studies; others considered that the students were looking for original values (Babbie, 2016). However, data analysis performed by Takeuchi showed that both opinions were wrong.

Takeuchi discovered that men were more likely to smoke than women; non-Asians were more likely to smoke than Asians; and students living in apartments were more inclined to smoke than those who stayed at home (Babbie, 2016).

The researchers investigated that each of the variables impacted the probability of the student’s being a marijuana smoker. For instance, eighty percent of non-Asian males staying in apartments smoked; while so did only ten percent of Asian females who stayed at home (Babbie, 2016). In this case, the analysis led to an interesting result. Rather than investigating why some students smoked, the researchers investigated why others did not. Having supposed that every student had an impulse to try marijuana, the scholars assumed that the students had various social restraints. These restrictions averted the students from being influenced by those impulses (Babbie, 2016).

As a result of the social constraint theory, Takeuchi made three reasons. According to the first one, women had more restrictions on smoking than men. According to the second, students staying at home had more restraints than those living in rented apartments. The third reasoning was concerned with the subculture: Asian students had more restrictions than non-Asian ones (Babbie, 2016.

In this case, the researchers managed to find a crucial pattern of drug use earlier than they found an explanation for that pattern. Therefore, instead of analyzing the reasons why some students were smokers, the scholars examined the reasons why others weren’t (Babbie, 2016).

Definition of Inductive Theory

The inductive theory presupposes the analysis from the investigation of knowledge to the general development of theory: “data to theory” (Cargan, 2007, p. 31). For instance, the theoretical principles of some issues are only possible after the compilation of the statistical evidence. The collected data is called “grounded theory” (Cargan, 2007, p. 31). Several common mistakes are possible when applying inductive logic: oversimplification, overgeneralization, and tautological reasoning (Cargan, 2007).

Specific Aspects of the Study that Make It Inductive

The inductiveness of the study indicated that the theory appeared as a result of data analysis. At the beginning of the research, the researchers did not think of such a theory (Babbie, 2016).

Guillermina Jasso’s Theory of Distributive Justice

The case study analyzes Guillermina Jasso’s theory of distributive justice. According to Jasso, the theory presents a mathematical explanation of the process in which people examine themselves in contrast with others based on their values (Babbie, 2016). Thus, the participants are evaluating whether they are being treated justly or unjustly.

To support the mathematical inclination of her theory, Jasso marks her key variable – the justice evaluation – as J. One of the assumptions of Jasso’s theory defines the basic axiom of comparison delineating the theory’s substantive issue of departure (Babbie, 2016). Jasso remarks that the people’s sense of receiving fair treatment results from their comparison of themselves to the others. Jasso suggests that people’s impression of distributive justice is the function of comparison holdings (C) and actual holding (A). Hence, the sense of justice is the comparison of one’s possessions to the possessions of other people. These two components are used as variables in Jasso’s study (Babbie, 2016).

The further stage where Jasso proposes a rule of measurement is necessary as some of the investigated goods are concrete and others are abstract (Babbie, 2016). The concrete goods are analyzed conventionally and the abstract ones – relatively. Therefore, the theory shall present a formula for carrying out that measurement (Babbie, 2016).

Jasso’s theorizing allows her to conclude that a person will likely steal from his/her group member than from a stranger. Here, Jasso points out that A will increase in both cases (stealing from a group member or an outsider), while C will be different.

Definition of Deductive Theory

The deductive theory is based on the “theory to data” approach (Cargan, 2007, p. 31). The theory involves reasoning from collective theoretical explanations established separately to the collected data. As a rule, deductive theories are evolved via literature research. They often begin with the reconsideration of other analyses that have examined the analogous issues. Such an approach allows combining previous achievements in the field with the current study’s outcomes. Thus, the major function of deductive theory is to present a possibility of making predictions based on past observations (Cargan, 2007).

Specific Aspects of the Study that Make It Deductive

Jasso’s derivations prove that the theory is deductive. She has tested the predictions to see whether her reasonable assumptions happen in practice.

Babbie, E. (2016). The basics of social research (7th ed.). Belmont, CA: Cengage.

Cargan, L. (2007). Doing social research . Plymouth, UK: Rowman & Littlefield.

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Principles of Social Research Methodology pp 59–71 Cite as

Inductive and/or Deductive Research Designs

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This chapter aims to introduce the readers, especially the Bangladeshi undergraduate and postgraduate students to some fundamental considerations of inductive and deductive research designs. The deductive approach refers to testing a theory, where the researcher builds up a theory or hypotheses and plans a research stratagem to examine the formulated theory. On the contrary, the inductive approach intends to construct a theory, where the researcher begins by gathering data to establish a theory. In the beginning, a researcher must clarify which approach he/she will follow in his/her research work. The chapter discusses basic concepts, characteristics, steps and examples of inductive and deductive research designs. Here, also a comparison between inductive and deductive research designs is shown. It concludes with a look at how both inductive and deductive designs are used comprehensively to constitute a clearer image of research work.

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case study research inductive or deductive

2.3 Inductive or Deductive? Two Different Approaches

Learning objectives.

  • Describe the inductive approach to research, and provide examples of inductive research.
  • Describe the deductive approach to research, and provide examples of deductive research.
  • Describe the ways that inductive and deductive approaches may be complementary.

Theories structure and inform sociological research. So, too, does research structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students new to these topics 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. Inductive and deductive approaches to research are quite different, but 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 Approaches and Some Examples

In an inductive approach Collect data, analyze patterns in the data, and then theorize from the data. to research, a researcher begins by collecting data that is relevant to his or her topic of interest. Once a substantial amount of data have been collected, the researcher will then take a breather from data collection, stepping back to get a bird’s eye view of her 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 set of observations and then they move from those particular experiences 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 2.5 "Inductive Research" outlines the steps involved with an inductive approach to research.

Figure 2.5 Inductive Research

case study research inductive or deductive

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating recent study in which the researchers took an inductive approach was Katherine Allen, Christine Kaestle, and Abbie Goldberg’s study (2011) 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. of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young 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 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.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011) 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. analyzed empirical data to better understand how best to meet the needs of young people who are homeless. The authors analyzed data from focus groups with 20 young people 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 people who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test the hypotheses that they developed from their analysis, their study ends where most deductive investigations begin: with a set of testable hypotheses.

Deductive Approaches and Some Examples

Researchers taking a deductive approach Develop hypotheses based on some theory or theories, collect data that can be used to test the hypotheses, and assess whether the data collected support the hypotheses. take the steps described earlier for inductive research and reverse their order. They start with a social theory that they find compelling and then test its implications with data. That is, they move from a more general level to a more specific one. A deductive approach to research is the one that people typically associate with scientific investigation. The researcher studies what others have done, reads existing theories of whatever phenomenon he or she is studying, and then tests hypotheses that emerge from those theories. Figure 2.6 "Deductive Research" outlines the steps involved with a deductive approach to research.

Figure 2.6 Deductive Research

case study research inductive or deductive

While not all researchers follow a deductive approach, as you have seen in the preceding discussion, many do, and there are a number of excellent recent examples of deductive research. We’ll take a look at a couple of those next.

In a study of US law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009) 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. 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 their reading of prior research and theories on the topic. Next, they tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011) 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. 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 even heat, would be associated with emotional and behavioral problems in children. The researchers found support for their hypothesis, demonstrating that policymakers should probably be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011). 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 http://asanet.org/press/Negative_Classroom_Environment_Adversely_Affects_Childs_Mental_Health.cfm

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 research to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to only conduct either inductive or deductive research, but then he or she discovers along the way that the other approach is needed to help illuminate findings. Here is an example of each such case.

In the case of my collaborative 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), Uggen, C., & Blackstone, A. (2004). Sexual harassment as a gendered expression of power. American Sociological Review, 69 , 64–92. 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), 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. Currently under review. we did not hypothesize about what we might find but instead inductively analyzed the 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.

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). Schutt, R. K. (2006). Investigating the social world: The process and practice of research . Thousand Oaks, CA: Pine Forge Press. As Schutt describes, researchers Lawrence Sherman and Richard Berk (1984) Sherman, L. W., & Berk, R. A. (1984). The specific deterrent effects of arrest for domestic assault. American Sociological Review, 49 , 261–272. 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 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. Figure 2.7 "Predicting the Effects of Arrest on Future Spouse Battery" summarizes the two competing theories and the predictions that Sherman and Berk set out to test.

Figure 2.7 Predicting the Effects of Arrest on Future Spouse Battery

case study research inductive or deductive

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 The researchers did what’s called replication. We’ll learn more about replication in Chapter 3 "Research Ethics" . in six additional cities (Berk, Campbell, Klap, & Western, 1992; Pate & Hamilton, 1992; Sherman & Smith, 1992). 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. Results 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 predicts that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation.

Figure 2.8 Predicting the Effects of Arrest on Future Spouse Battery: A New Theory

case study research inductive or deductive

What the Sherman and Berk research, along with the follow-up studies, shows us is that we might start with a deductive approach to research, but then, if confronted by new data that we must make sense of, we may move to an inductive approach. Russell Schutt depicts this process quite nicely in his text, and I’ve adapted his depiction here, in Figure 2.9 "The Research Process: Moving From Deductive to Inductive in a Study of Domestic Violence Recidivism" .

Key Takeaways

  • The inductive approach involves beginning with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
  • The deductive approach involves beginning with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test those hypotheses.
  • Inductive and deductive approaches to research can be employed together for a more complete understanding of the topic that a researcher is studying.
  • Though researchers don’t always set out to use both inductive and deductive strategies 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.

For a hilarious example of logic gone awry, check out the following clip from

Monty Python and Holy Grail :

Do the townspeople take an inductive or deductive approach to determine whether the woman in question is a witch? What are some of the different sources of knowledge (recall Chapter 1 "Introduction" ) they rely on?

  • Think about how you could approach a study of the relationship between gender and driving over the speed limit. How could you learn about this relationship using an inductive approach? What would a study of the same relationship look like if examined using a deductive approach? Try the same thing with any topic of your choice. How might you study the topic inductively? Deductively?
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case study research inductive or deductive

Home Market Research

Inductive vs Deductive Research: Difference of Approaches

Inductive vs deductive research: Understand the differences between these two approaches to thinking to guide your research. Learn more.

The terms “inductive” and “deductive” are often used in logic, reasoning, and science. Scientists use both inductive and deductive research methods as part of the scientific method.

Famous fictional detectives like Sherlock Holmes are often associated with deduction, even though that’s not always what Holmes does (more on that later). Some writing classes include both inductive and deductive essays.

But what’s the difference between inductive vs deductive research? The difference often lies in whether the argument proceeds from the general to the specific or the specific to the general. 

Both methods are used in different types of research, and it’s not unusual to use both in one project. In this article, we’ll describe each in simple yet defined terms.

Content Index: 

What is inductive research, stages of inductive research process, what is deductive research, stages of deductive research process, difference between inductive vs deductive research.

Inductive research is a method in which the researcher collects and analyzes data to develop theories, concepts, or hypotheses based on patterns and observations seen in the data. 

It uses a “bottom-up” method in which the researcher starts with specific observations and then moves on to more general theories or ideas. Inductive research is often used in exploratory studies or when not much research has been done on a topic before.

LEARN ABOUT: Research Process Steps

The three steps of the inductive research process are:

  • Observation: 

The first step of inductive research is to make detailed observations of the studied phenomenon. This can be done in many ways, such as through surveys, interviews, or direct observation.

  • Pattern Recognition: 

The next step is to look at the data in detail once the data has been collected. This means looking at the data for patterns, themes, and relationships. The goal is to find insights and trends that can be used to make the first categories and ideas.

  • Theory Development: 

At this stage, the researcher will start to create initial categories or concepts based on the patterns and themes from the data analysis. This means putting the data into groups based on their similarities and differences to make a framework for understanding the thing being studied.

LEARN ABOUT: Data Management Framework

These three steps are often repeated in a cycle, so the researcher can improve their analysis and understand the phenomenon over time. Inductive research aims to develop new theories and ideas based on the data rather than testing existing theories, as in deductive research.

Deductive research is a type of research in which the researcher starts with a theory, hypothesis, or generalization and then tests it through observations and data collection.

It uses a top-down method in which the researcher starts with a general idea and then tests it through specific observations. Deductive research is often used to confirm a theory or test a well-known hypothesis.

The five steps in the process of deductive research are:

  • Formulation of a hypothesis: 

The first step in deductive research is to develop a hypothesis and guess how the variables are related. Most of the time, the hypothesis is built on theories or research that have already been done.

  • Design of a research study: 

The next step is designing a research study to test the hypothesis. This means choosing a research method, figuring out what needs to be measured, and figuring out how to collect and look at the data.

  • Collecting data: 

Once the research design is set, different methods, such as surveys, experiments, or observational studies, are used to gather data. Usually, a standard protocol is used to collect the data to ensure it is correct and consistent.

  • Analysis of data: 

In this step, the collected data are looked at to see if they support or disprove the hypothesis. The goal is to see if the data supports or refutes the hypothesis. You need to use statistical methods to find patterns and links between the variables to do this.

  • Drawing conclusions: 

The last step is drawing conclusions from the analysis of the data. If the hypothesis is supported, it can be used to make generalizations about the population being studied. If the hypothesis is wrong, the researcher may need to develop a new one and start the process again.

The five steps of deductive research are repeated, and researchers may need to return to earlier steps if they find new information or new ways of looking at things. In contrast to inductive research, deductive research aims to test theories or hypotheses that have already been made.

The main differences between inductive and deductive research are how the research is done, the goal, and how the data is analyzed. Inductive research is exploratory, flexible, and based on qualitative observation analysis. Deductive research, on the other hand, is about proving something and is structured and based on quantitative analysis .

Here are the main differences between inductive vs deductive research in more detail:

case study research inductive or deductive

LEARN ABOUT: Theoretical Research

Inductive research and deductive research are two different types of research with different starting points, goals, methods, and ways of looking at the data.

Inductive research uses specific observations and patterns to come up with new theories. On the other hand, deductive research starts with a theory or hypothesis and tests it through observations.

Both approaches have advantages as well as disadvantages and can be used in different types of research depending on the question and goals.

QuestionPro is a responsive online platform for surveys and research that can be used for both inductive and deductive research. It has many tools and features to help you collect and analyze data, such as customizable survey templates, advanced survey logic, and real-time reporting.

With QuestionPro, researchers can do surveys, send them out, analyze the results, and draw conclusions that help them make decisions and learn more about their fields.

The platform has advanced data analysis and reporting tools that can be used with both qualitative and quantitative methods of data analysis.

Whether researchers do inductive or deductive research, QuestionPro can help them design, run, and analyze their projects completely and powerfully. So sign up now for a free trial! 

LEARN MORE         FREE TRIAL

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Dr Deborah Gabriel

Dr Deborah Gabriel

Inductive and deductive approaches to research

case study research inductive or deductive

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/

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200 thoughts on “ Inductive and deductive approaches to research ”

  • Pingback: Methods and methodology | Deborah Gabriel
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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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Quantitative and Qualitative Approaches to Generalization and Replication–A Representationalist View

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se , but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

Introduction

Qualitative and quantitative research strategies have long been treated as opposing paradigms. In recent years, there have been attempts to integrate both strategies. These “mixed methods” approaches treat qualitative and quantitative methodologies as complementary, rather than opposing, strategies (Creswell, 2015 ). However, whilst acknowledging that both strategies have their benefits, this “integration” remains purely pragmatic. Hence, mixed methods methodology does not provide a conceptual unification of the two approaches.

Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007 ). These different standards affect the way researchers handle contradictory empirical findings. For example, many empirical findings in psychology have failed to replicate in recent years (Klein et al., 2014 ; Open Science, Collaboration, 2015 ). This “replication crisis” has been discussed on statistical, theoretical and social grounds and continues to have a wide impact on quantitative research practices like, for example, open science initiatives, pre-registered studies and a re-evaluation of statistical significance testing (Everett and Earp, 2015 ; Maxwell et al., 2015 ; Shrout and Rodgers, 2018 ; Trafimow, 2018 ; Wiggins and Chrisopherson, 2019 ).

However, qualitative research seems to be hardly affected by this discussion. In this paper, we argue that the latter is a direct consequence of how the concept of generalizability is conceived in the two approaches. Whereas most of quantitative psychology is committed to a top-down strategy of generalization based on the idea of random sampling from an abstract population, qualitative studies usually rely on a bottom-up strategy of generalization that is grounded in the successive exploration of the field by means of theoretically sampled cases.

Here, we show that a common methodological framework for qualitative and quantitative research methodologies is possible. We accomplish this by introducing a formal description of quantitative and qualitative models from a representationalist perspective: both approaches can be reconstructed as special kinds of representations for empirical relational structures. We then use this framework to analyze the generalization strategies used in the two approaches. These turn out to be logically independent of the type of model. This has wide implications for psychological research. First, a top-down generalization strategy is compatible with a qualitative modeling approach. This implies that mainstream psychology may benefit from qualitative methods when a numerical representation turns out to be difficult or impossible, without the need to commit to a “qualitative” philosophy of science. Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory.

The Quantitative Strategy–Numbers and Functions

Quantitative science is about finding valid mathematical representations for empirical phenomena. In most cases, these mathematical representations have the form of functional relations between a set of variables. One major challenge of quantitative modeling consists in constructing valid measures for these variables. Formally, to measure a variable means to construct a numerical representation of the underlying empirical relational structure (Krantz et al., 1971 ). For example, take the behaviors of a group of students in a classroom: “to listen,” “to take notes,” and “to ask critical questions.” One may now ask whether is possible to assign numbers to the students, such that the relations between the assigned numbers are of the same kind as the relations between the values of an underlying variable, like e.g., “engagement.” The observed behaviors in the classroom constitute an empirical relational structure, in the sense that for every student-behavior tuple, one can observe whether it is true or not. These observations can be represented in a person × behavior matrix 1 (compare Figure 1 ). Given this relational structure satisfies certain conditions (i.e., the axioms of a measurement model), one can assign numbers to the students and the behaviors, such that the relations between the numbers resemble the corresponding numerical relations. For example, if there is a unique ordering in the empirical observations with regard to which person shows which behavior, the assigned numbers have to constitute a corresponding unique ordering, as well. Such an ordering coincides with the person × behavior matrix forming a triangle shaped relation and is formally represented by a Guttman scale (Guttman, 1944 ). There are various measurement models available for different empirical structures (Suppes et al., 1971 ). In the case of probabilistic relations, Item-Response models may be considered as a special kind of measurement model (Borsboom, 2005 ).

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Constructing a numerical representation from an empirical relational structure; Due to the unique ordering of persons with regard to behaviors (indicated by the triangular shape of the relation), it is possible to construct a Guttman scale by assigning a number to each of the individuals, representing the number of relevant behaviors shown by the individual. The resulting variable (“engagement”) can then be described by means of statistical analyses, like, e.g., plotting the frequency distribution.

Although essential, measurement is only the first step of quantitative modeling. Consider a slightly richer empirical structure, where we observe three additional behaviors: “to doodle,” “to chat,” and “to play.” Like above, one may ask, whether there is a unique ordering of the students with regard to these behaviors that can be represented by an underlying variable (i.e., whether the matrix forms a Guttman scale). If this is the case, we may assign corresponding numbers to the students and call this variable “distraction.” In our example, such a representation is possible. We can thus assign two numbers to each student, one representing his or her “engagement” and one representing his or her “distraction” (compare Figure 2 ). These measurements can now be used to construct a quantitative model by relating the two variables by a mathematical function. In the simplest case, this may be a linear function. This functional relation constitutes a quantitative model of the empirical relational structure under study (like, e.g., linear regression). Given the model equation and the rules for assigning the numbers (i.e., the instrumentations of the two variables), the set of admissible empirical structures is limited from all possible structures to a rather small subset. This constitutes the empirical content of the model 2 (Popper, 1935 ).

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Constructing a numerical model from an empirical relational structure; Since there are two distinct classes of behaviors that each form a Guttman scale, it is possible to assign two numbers to each individual, correspondingly. The resulting variables (“engagement” and “distraction”) can then be related by a mathematical function, which is indicated by the scatterplot and red line on the right hand side.

The Qualitative Strategy–Categories and Typologies

The predominant type of analysis in qualitative research consists in category formation. By constructing descriptive systems for empirical phenomena, it is possible to analyze the underlying empirical structure at a higher level of abstraction. The resulting categories (or types) constitute a conceptual frame for the interpretation of the observations. Qualitative researchers differ considerably in the way they collect and analyze data (Miles et al., 2014 ). However, despite the diverse research strategies followed by different qualitative methodologies, from a formal perspective, most approaches build on some kind of categorization of cases that share some common features. The process of category formation is essential in many qualitative methodologies, like, for example, qualitative content analysis, thematic analysis, grounded theory (see Flick, 2014 for an overview). Sometimes these features are directly observable (like in our classroom example), sometimes they are themselves the result of an interpretative process (e.g., Scheunpflug et al., 2016 ).

In contrast to quantitative methodologies, there have been little attempts to formalize qualitative research strategies (compare, however, Rihoux and Ragin, 2009 ). However, there are several statistical approaches to non-numerical data that deal with constructing abstract categories and establishing relations between these categories (Agresti, 2013 ). Some of these methods are very similar to qualitative category formation on a conceptual level. For example, cluster analysis groups cases into homogenous categories (clusters) based on their similarity on a distance metric.

Although category formation can be formalized in a mathematically rigorous way (Ganter and Wille, 1999 ), qualitative research hardly acknowledges these approaches. 3 However, in order to find a common ground with quantitative science, it is certainly helpful to provide a formal interpretation of category systems.

Let us reconsider the above example of students in a classroom. The quantitative strategy was to assign numbers to the students with regard to variables and to relate these variables via a mathematical function. We can analyze the same empirical structure by grouping the behaviors to form abstract categories. If the aim is to construct an empirically valid category system, this grouping is subject to constraints, analogous to those used to specify a measurement model. The first and most important constraint is that the behaviors must form equivalence classes, i.e., within categories, behaviors need to be equivalent, and across categories, they need to be distinct (formally, the relational structure must obey the axioms of an equivalence relation). When objects are grouped into equivalence classes, it is essential to specify the criterion for empirical equivalence. In qualitative methodology, this is sometimes referred to as the tertium comparationis (Flick, 2014 ). One possible criterion is to group behaviors such that they constitute a set of specific common attributes of a group of people. In our example, we might group the behaviors “to listen,” “to take notes,” and “to doodle,” because these behaviors are common to the cases B, C, and D, and they are also specific for these cases, because no other person shows this particular combination of behaviors. The set of common behaviors then forms an abstract concept (e.g., “moderate distraction”), while the set of persons that show this configuration form a type (e.g., “the silent dreamer”). Formally, this means to identify the maximal rectangles in the underlying empirical relational structure (see Figure 3 ). This procedure is very similar to the way we constructed a Guttman scale, the only difference being that we now use different aspects of the empirical relational structure. 4 In fact, the set of maximal rectangles can be determined by an automated algorithm (Ganter, 2010 ), just like the dimensionality of an empirical structure can be explored by psychometric scaling methods. Consequently, we can identify the empirical content of a category system or a typology as the set of empirical structures that conforms to it. 5 Whereas the quantitative strategy was to search for scalable sub-matrices and then relate the constructed variables by a mathematical function, the qualitative strategy is to construct an empirical typology by grouping cases based on their specific similarities. These types can then be related to one another by a conceptual model that describes their semantic and empirical overlap (see Figure 3 , right hand side).

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Constructing a conceptual model from an empirical relational structure; Individual behaviors are grouped to form abstract types based on them being shared among a specific subset of the cases. Each type constitutes a set of specific commonalities of a class of individuals (this is indicated by the rectangles on the left hand side). The resulting types (“active learner,” “silent dreamer,” “distracted listener,” and “troublemaker”) can then be related to one another to explicate their semantic and empirical overlap, as indicated by the Venn-diagram on the right hand side.

Variable-Based Models and Case-Based Models

In the previous section, we have argued that qualitative category formation and quantitative measurement can both be characterized as methods to construct abstract representations of empirical relational structures. Instead of focusing on different philosophical approaches to empirical science, we tried to stress the formal similarities between both approaches. However, it is worth also exploring the dissimilarities from a formal perspective.

Following the above analysis, the quantitative approach can be characterized by the use of variable-based models, whereas the qualitative approach is characterized by case-based models (Ragin, 1987 ). Formally, we can identify the rows of an empirical person × behavior matrix with a person-space, and the columns with a corresponding behavior-space. A variable-based model abstracts from the single individuals in a person-space to describe the structure of behaviors on a population level. A case-based model, on the contrary, abstracts from the single behaviors in a behavior-space to describe individual case configurations on the level of abstract categories (see Table 1 ).

Variable-based models and case-based models.

From a representational perspective, there is no a priori reason to favor one type of model over the other. Both approaches provide different analytical tools to construct an abstract representation of an empirical relational structure. However, since the two modeling approaches make use of different information (person-space vs. behavior-space), this comes with some important implications for the researcher employing one of the two strategies. These are concerned with the role of deductive and inductive reasoning.

In variable-based models, empirical structures are represented by functional relations between variables. These are usually stated as scientific laws (Carnap, 1928 ). Formally, these laws correspond to logical expressions of the form

In plain text, this means that y is a function of x for all objects i in the relational structure under consideration. For example, in the above example, one may formulate the following law: for all students in the classroom it holds that “distraction” is a monotone decreasing function of “engagement.” Such a law can be used to derive predictions for single individuals by means of logical deduction: if the above law applies to all students in the classroom, it is possible to calculate the expected distraction from a student's engagement. An empirical observation can now be evaluated against this prediction. If the prediction turns out to be false, the law can be refuted based on the principle of falsification (Popper, 1935 ). If a scientific law repeatedly withstands such empirical tests, it may be considered to be valid with regard to the relational structure under consideration.

In case-based models, there are no laws about a population, because the model does not abstract from the cases but from the observed behaviors. A case-based model describes the underlying structure in terms of existential sentences. Formally, this corresponds to a logical expression of the form

In plain text, this means that there is at least one case i for which the condition XYZ holds. For example, the above category system implies that there is at least one active learner. This is a statement about a singular observation. It is impossible to deduce a statement about another person from an existential sentence like this. Therefore, the strategy of falsification cannot be applied to test the model's validity in a specific context. If one wishes to generalize to other cases, this is accomplished by inductive reasoning, instead. If we observed one person that fulfills the criteria of calling him or her an active learner, we can hypothesize that there may be other persons that are identical to the observed case in this respect. However, we do not arrive at this conclusion by logical deduction, but by induction.

Despite this important distinction, it would be wrong to conclude that variable-based models are intrinsically deductive and case-based models are intrinsically inductive. 6 Both types of reasoning apply to both types of models, but on different levels. Based on a person-space, in a variable-based model one can use deduction to derive statements about individual persons from abstract population laws. There is an analogous way of reasoning for case-based models: because they are based on a behavior space, it is possible to deduce statements about singular behaviors. For example, if we know that Peter is an active learner, we can deduce that he takes notes in the classroom. This kind of deductive reasoning can also be applied on a higher level of abstraction to deduce thematic categories from theoretical assumptions (Braun and Clarke, 2006 ). Similarly, there is an analog for inductive generalization from the perspective of variable-based modeling: since the laws are only quantified over the person-space, generalizations to other behaviors rely on inductive reasoning. For example, it is plausible to assume that highly engaged students tend to do their homework properly–however, in our example this behavior has never been observed. Hence, in variable-based models we usually generalize to other behaviors by means of induction. This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains.

Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases. A variable-based approach implies to draw conclusions about cases by means of logical deduction; a case-based approach implies to draw conclusions about cases by means of inductive reasoning. In the following, we build on this distinction to differentiate between qualitative (bottom-up) and quantitative (top-down) strategies of generalization.

Generalization and the Problem of Replication

We will now extend the formal analysis of quantitative and qualitative approaches to the question of generalization and replicability of empirical findings. For this sake, we have to introduce some concepts of formal logic. Formal logic is concerned with the validity of arguments. It provides conditions to evaluate whether certain sentences (conclusions) can be derived from other sentences (premises). In this context, a theory is nothing but a set of sentences (also called axioms). Formal logic provides tools to derive new sentences that must be true, given the axioms are true (Smith, 2020 ). These derived sentences are called theorems or, in the context of empirical science, predictions or hypotheses . On the syntactic level, the rules of logic only state how to evaluate the truth of a sentence relative to its premises. Whether or not sentences are actually true, is formally specified by logical semantics.

On the semantic level, formal logic is intrinsically linked to set-theory. For example, a logical statement like “all dogs are mammals,” is true if and only if the set of dogs is a subset of the set of mammals. Similarly, the sentence “all chatting students doodle” is true if and only if the set of chatting students is a subset of the set of doodling students (compare Figure 3 ). Whereas, the first sentence is analytically true due to the way we define the words “dog” and “mammal,” the latter can be either true or false, depending on the relational structure we actually observe. We can thus interpret an empirical relational structure as the truth criterion of a scientific theory. From a logical point of view, this corresponds to the semantics of a theory. As shown above, variable-based and case-based models both give a formal representation of the same kinds of empirical structures. Accordingly, both types of models can be stated as formal theories. In the variable-based approach, this corresponds to a set of scientific laws that are quantified over the members of an abstract population (these are the axioms of the theory). In the case-based approach, this corresponds to a set of abstract existential statements about a specific class of individuals.

In contrast to mathematical axiom systems, empirical theories are usually not considered to be necessarily true. This means that even if we find no evidence against a theory, it is still possible that it is actually wrong. We may know that a theory is valid in some contexts, yet it may fail when applied to a new set of behaviors (e.g., if we use a different instrumentation to measure a variable) or a new population (e.g., if we draw a new sample).

From a logical perspective, the possibility that a theory may turn out to be false stems from the problem of contingency . A statement is contingent, if it is both, possibly true and possibly false. Formally, we introduce two modal operators: □ to designate logical necessity, and ◇ to designate logical possibility. Semantically, these operators are very similar to the existential quantifier, ∃, and the universal quantifier, ∀. Whereas ∃ and ∀ refer to the individual objects within one relational structure, the modal operators □ and ◇ range over so-called possible worlds : a statement is possibly true, if and only if it is true in at least one accessible possible world, and a statement is necessarily true if and only if it is true in every accessible possible world (Hughes and Cresswell, 1996 ). Logically, possible worlds are mathematical abstractions, each consisting of a relational structure. Taken together, the relational structures of all accessible possible worlds constitute the formal semantics of necessity, possibility and contingency. 7

In the context of an empirical theory, each possible world may be identified with an empirical relational structure like the above classroom example. Given the set of intended applications of a theory (the scope of the theory, one may say), we can now construct possible world semantics for an empirical theory: each intended application of the theory corresponds to a possible world. For example, a quantified sentence like “all chatting students doodle” may be true in one classroom and false in another one. In terms of possible worlds, this would correspond to a statement of contingency: “it is possible that all chatting students doodle in one classroom, and it is possible that they don't in another classroom.” Note that in the above expression, “all students” refers to the students in only one possible world, whereas “it is possible” refers to the fact that there is at least one possible world for each of the specified cases.

To apply these possible world semantics to quantitative research, let us reconsider how generalization to other cases works in variable-based models. Due to the syntactic structure of quantitative laws, we can deduce predictions for singular observations from an expression of the form ∀ i : y i = f ( x i ). Formally, the logical quantifier ∀ ranges only over the objects of the corresponding empirical relational structure (in our example this would refer to the students in the observed classroom). But what if we want to generalize beyond the empirical structure we actually observed? The standard procedure is to assume an infinitely large, abstract population from which a random sample is drawn. Given the truth of the theory, we can deduce predictions about what we may observe in the sample. Since usually we deal with probabilistic models, we can evaluate our theory by means of the conditional probability of the observations, given the theory holds. This concept of conditional probability is the foundation of statistical significance tests (Hogg et al., 2013 ), as well as Bayesian estimation (Watanabe, 2018 ). In terms of possible world semantics, the random sampling model implies that all possible worlds (i.e., all intended applications) can be conceived as empirical sub-structures from a greater population structure. For example, the empirical relational structure constituted by the observed behaviors in a classroom would be conceived as a sub-matrix of the population person × behavior matrix. It follows that, if a scientific law is true in the population, it will be true in all possible worlds, i.e., it will be necessarily true. Formally, this corresponds to an expression of the form

The statistical generalization model thus constitutes a top-down strategy for dealing with individual contexts that is analogous to the way variable-based models are applied to individual cases (compare Table 1 ). Consequently, if we apply a variable-based model to a new context and find out that it does not fit the data (i.e., there is a statistically significant deviation from the model predictions), we have reason to doubt the validity of the theory. This is what makes the problem of low replicability so important: we observe that the predictions are wrong in a new study; and because we apply a top-down strategy of generalization to contexts beyond the ones we observed, we see our whole theory at stake.

Qualitative research, on the contrary, follows a different strategy of generalization. Since case-based models are formulated by a set of context-specific existential sentences, there is no need for universal truth or necessity. In contrast to statistical generalization to other cases by means of random sampling from an abstract population, the usual strategy in case-based modeling is to employ a bottom-up strategy of generalization that is analogous to the way case-based models are applied to individual cases. Formally, this may be expressed by stating that the observed qualia exist in at least one possible world, i.e., the theory is possibly true:

This statement is analogous to the way we apply case-based models to individual cases (compare Table 1 ). Consequently, the set of intended applications of the theory does not follow from a sampling model, but from theoretical assumptions about which cases may be similar to the observed cases with respect to certain relevant characteristics. For example, if we observe that certain behaviors occur together in one classroom, following a bottom-up strategy of generalization, we will hypothesize why this might be the case. If we do not replicate this finding in another context, this does not question the model itself, since it was a context-specific theory all along. Instead, we will revise our hypothetical assumptions about why the new context is apparently less similar to the first one than we originally thought. Therefore, if an empirical finding does not replicate, we are more concerned about our understanding of the cases than about the validity of our theory.

Whereas statistical generalization provides us with a formal (and thus somehow more objective) apparatus to evaluate the universal validity of our theories, the bottom-up strategy forces us to think about the class of intended applications on theoretical grounds. This means that we have to ask: what are the boundary conditions of our theory? In the above classroom example, following a bottom-up strategy, we would build on our preliminary understanding of the cases in one context (e.g., a public school) to search for similar and contrasting cases in other contexts (e.g., a private school). We would then re-evaluate our theoretical description of the data and explore what makes cases similar or dissimilar with regard to our theory. This enables us to expand the class of intended applications alongside with the theory.

Of course, none of these strategies is superior per se . Nevertheless, they rely on different assumptions and may thus be more or less adequate in different contexts. The statistical strategy relies on the assumption of a universal population and invariant measurements. This means, we assume that (a) all samples are drawn from the same population and (b) all variables refer to the same behavioral classes. If these assumptions are true, statistical generalization is valid and therefore provides a valuable tool for the testing of empirical theories. The bottom-up strategy of generalization relies on the idea that contexts may be classified as being more or less similar based on characteristics that are not part of the model being evaluated. If such a similarity relation across contexts is feasible, the bottom-up strategy is valid, as well. Depending on the strategy of generalization, replication of empirical research serves two very different purposes. Following the (top-down) principle of generalization by deduction from scientific laws, replications are empirical tests of the theory itself, and failed replications question the theory on a fundamental level. Following the (bottom-up) principle of generalization by induction to similar contexts, replications are a means to explore the boundary conditions of a theory. Consequently, failed replications question the scope of the theory and help to shape the set of intended applications.

We have argued that quantitative and qualitative research are best understood by means of the structure of the employed models. Quantitative science mainly relies on variable-based models and usually employs a top-down strategy of generalization from an abstract population to individual cases. Qualitative science prefers case-based models and usually employs a bottom-up strategy of generalization. We further showed that failed replications have very different implications depending on the underlying strategy of generalization. Whereas in the top-down strategy, replications are used to test the universal validity of a model, in the bottom-up strategy, replications are used to explore the scope of a model. We will now address the implications of this analysis for psychological research with regard to the problem of replicability.

Modern day psychology almost exclusively follows a top-down strategy of generalization. Given the quantitative background of most psychological theories, this is hardly surprising. Following the general structure of variable-based models, the individual case is not the focus of the analysis. Instead, scientific laws are stated on the level of an abstract population. Therefore, when applying the theory to a new context, a statistical sampling model seems to be the natural consequence. However, this is not the only possible strategy. From a logical point of view, there is no reason to assume that a quantitative law like ∀ i : y i = f ( x i ) implies that the law is necessarily true, i.e.,: □(∀ i : y i = f ( x i )). Instead, one might just as well define the scope of the theory following an inductive strategy. 8 Formally, this would correspond to the assumption that the observed law is possibly true, i.e.,: ◇(∀ i : y i = f ( x i )). For example, we may discover a functional relation between “engagement” and “distraction” without referring to an abstract universal population of students. Instead, we may hypothesize under which conditions this functional relation may be valid and use these assumptions to inductively generalize to other cases.

If we take this seriously, this would require us to specify the intended applications of the theory: in which contexts do we expect the theory to hold? Or, equivalently, what are the boundary conditions of the theory? These boundary conditions may be specified either intensionally, i.e., by giving external criteria for contexts being similar enough to the ones already studied to expect a successful application of the theory. Or they may be specified extensionally, by enumerating the contexts where the theory has already been shown to be valid. These boundary conditions need not be restricted to the population we refer to, but include all kinds of contextual factors. Therefore, adopting a bottom-up strategy, we are forced to think about these factors and make them an integral part of our theories.

In fact, there is good reason to believe that bottom-up generalization may be more adequate in many psychological studies. Apart from the pitfalls associated with statistical generalization that have been extensively discussed in recent years (e.g., p-hacking, underpowered studies, publication bias), it is worth reflecting on whether the underlying assumptions are met in a particular context. For example, many samples used in experimental psychology are not randomly drawn from a large population, but are convenience samples. If we use statistical models with non-random samples, we have to assume that the observations vary as if drawn from a random sample. This may indeed be the case for randomized experiments, because all variation between the experimental conditions apart from the independent variable will be random due to the randomization procedure. In this case, a classical significance test may be regarded as an approximation to a randomization test (Edgington and Onghena, 2007 ). However, if we interpret a significance test as an approximate randomization test, we test not for generalization but for internal validity. Hence, even if we use statistical significance tests when assumptions about random sampling are violated, we still have to use a different strategy of generalization. This issue has been discussed in the context of small-N studies, where variable-based models are applied to very small samples, sometimes consisting of only one individual (Dugard et al., 2012 ). The bottom-up strategy of generalization that is employed by qualitative researchers, provides such an alternative.

Another important issue in this context is the question of measurement invariance. If we construct a variable-based model in one context, the variables refer to those behaviors that constitute the underlying empirical relational structure. For example, we may construct an abstract measure of “distraction” using the observed behaviors in a certain context. We will then use the term “distraction” as a theoretical term referring to the variable we have just constructed to represent the underlying empirical relational structure. Let us now imagine we apply this theory to a new context. Even if the individuals in our new context are part of the same population, we may still get into trouble if the observed behaviors differ from those used in the original study. How do we know whether these behaviors constitute the same variable? We have to ensure that in any new context, our measures are valid for the variables in our theory. Without a proper measurement model, this will be hard to achieve (Buntins et al., 2017 ). Again, we are faced with the necessity to think of the boundary conditions of our theories. In which contexts (i.e., for which sets of individuals and behaviors) do we expect our theory to work?

If we follow the rationale of inductive generalization, we can explore the boundary conditions of a theory with every new empirical study. We thus widen the scope of our theory by comparing successful applications in different contexts and unsuccessful applications in similar contexts. This may ultimately lead to a more general theory, maybe even one of universal scope. However, unless we have such a general theory, we might be better off, if we treat unsuccessful replications not as a sign of failure, but as a chance to learn.

Author Contributions

MB conceived the original idea and wrote the first draft of the paper. MS helped to further elaborate and scrutinize the arguments. All authors contributed to the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Annette Scheunpflug for helpful comments on an earlier version of the manuscript.

1 A person × behavior matrix constitutes a very simple relational structure that is common in psychological research. This is why it is chosen here as a minimal example. However, more complex structures are possible, e.g., by relating individuals to behaviors over time, with individuals nested within groups etc. For a systematic overview, compare Coombs ( 1964 ).

2 This notion of empirical content applies only to deterministic models. The empirical content of a probabilistic model consists in the probability distribution over all possible empirical structures.

3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick ( 2014 ) nor the Oxford Handbook of Qualitative Research edited by Leavy ( 2014 ) mention formal approaches to category formation.

4 Note also that the described structure is empirically richer than a nominal scale. Therefore, a reduction of qualitative category formation to be a special (and somehow trivial) kind of measurement is not adequate.

5 It is possible to extend this notion of empirical content to the probabilistic case (this would correspond to applying a latent class analysis). But, since qualitative research usually does not rely on formal algorithms (neither deterministic nor probabilistic), there is currently little practical use of such a concept.

6 We do not elaborate on abductive reasoning here, since, given an empirical relational structure, the concept can be applied to both types of models in the same way (Schurz, 2008 ). One could argue that the underlying relational structure is not given a priori but has to be constructed by the researcher and will itself be influenced by theoretical expectations. Therefore, abductive reasoning may be necessary to establish an empirical relational structure in the first place.

7 We shall not elaborate on the metaphysical meaning of possible worlds here, since we are only concerned with empirical theories [but see Tooley ( 1999 ), for an overview].

8 Of course, this also means that it would be equally reasonable to employ a top-down strategy of generalization using a case-based model by postulating that □(∃ i : XYZ i ). The implications for case-based models are certainly worth exploring, but lie beyond the scope of this article.

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2.5.1: Inductive or Deductive? Two Different Approaches

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Learning Objectives

  • Describe the inductive approach to research, and provide examples of inductive research.
  • Describe the deductive approach to research, and provide examples of deductive research.
  • Describe the ways that inductive and deductive approaches may be complementary.

Theories structure and inform sociological research. So, too, does research structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students new to these topics 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. Inductive and deductive approaches to research are quite different, but 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 Approaches and Some Examples

In an inductive approach to research, a researcher begins by collecting data that is relevant to his or her topic of interest. Once a substantial amount of data have been collected, the researcher will then take a breather from data collection, stepping back to get a bird’s eye view of her 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 set of observations and then they move from those particular experiences 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 2.5 outlines the steps involved with an inductive approach to research.

Figure 2.5 Inductive Research

case study research inductive or deductive

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating recent study in which the researchers took an inductive approach was Katherine Allen, Christine Kaestle, and Abbie Goldberg’s study (2011)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. of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young 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 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.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011)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. analyzed empirical data to better understand how best to meet the needs of young people who are homeless. The authors analyzed data from focus groups with 20 young people 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 people who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test the hypotheses that they developed from their analysis, their study ends where most deductive investigations begin: with a set of testable hypotheses.

Deductive Approaches and Some Examples

Researchers taking a deductive approach take the steps described earlier for inductive research and reverse their order. They start with a social theory that they find compelling and then test its implications with data. That is, they move from a more general level to a more specific one. A deductive approach to research is the one that people typically associate with scientific investigation. The researcher studies what others have done, reads existing theories of whatever phenomenon he or she is studying, and then tests hypotheses that emerge from those theories. Figure 2.6 outlines the steps involved with a deductive approach to research.

Figure 2.6 Deductive Research

case study research inductive or deductive

While not all researchers follow a deductive approach, as you have seen in the preceding discussion, many do, and there are a number of excellent recent examples of deductive research. We’ll take a look at a couple of those next.

In a study of US law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009)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.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 their reading of prior research and theories on the topic. Next, they tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011)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. 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 even heat, would be associated with emotional and behavioral problems in children. The researchers found support for their hypothesis, demonstrating that policymakers should probably be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011).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 asanet.org/press/Negative_Cla...tal_Health.cfm

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 research to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to only conduct either inductive or deductive research, but then he or she discovers along the way that the other approach is needed to help illuminate findings. Here is an example of each such case.

In the case of my collaborative 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),Uggen, C., & Blackstone, A. (2004). Sexual harassment as a gendered expression of power. American Sociological Review, 69 , 64–92. 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),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. Currently under review. we did not hypothesize about what we might find but instead inductively analyzed the 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.

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).Schutt, R. K. (2006). Investigating the social world: The process and practice of research . Thousand Oaks, CA: Pine Forge Press. As Schutt describes, researchers Lawrence Sherman and Richard Berk (1984)Sherman, L. W., & Berk, R. A. (1984). The specific deterrent effects of arrest for domestic assault. American Sociological Review, 49 , 261–272. 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 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. Figure 2.7 summarizes the two competing theories and the predictions that Sherman and Berk set out to test.

Figure 2.7 Predicting the Effects of Arrest on Future Spouse Battery

case study research inductive or deductive

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 experimentsThe researchers did what’s called replication. We’ll learn more about replication in Chapter 3. in six additional cities (Berk, Campbell, Klap, & Western, 1992; Pate & Hamilton, 1992; Sherman & Smith, 1992).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. Results 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 predicts that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation.

Figure 2.8 Predicting the Effects of Arrest on Future Spouse Battery: A New Theory

case study research inductive or deductive

What the Sherman and Berk research, along with the follow-up studies, shows us is that we might start with a deductive approach to research, but then, if confronted by new data that we must make sense of, we may move to an inductive approach. Russell Schutt depicts this process quite nicely in his text, and I’ve adapted his depiction here, in Figure 2.9.

KEY TAKEAWAYS

  • The inductive approach involves beginning with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
  • The deductive approach involves beginning with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test those hypotheses.
  • Inductive and deductive approaches to research can be employed together for a more complete understanding of the topic that a researcher is studying.
  • Though researchers don’t always set out to use both inductive and deductive strategies 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.

Monty Python and Holy Grail :

(click to see video)

Do the townspeople take an inductive or deductive approach to determine whether the woman in question is a witch? What are some of the different sources of knowledge (recall Chapter 1) they rely on?

  • Think about how you could approach a study of the relationship between gender and driving over the speed limit. How could you learn about this relationship using an inductive approach? What would a study of the same relationship look like if examined using a deductive approach? Try the same thing with any topic of your choice. How might you study the topic inductively? Deductively?

IMAGES

  1. Inductive vs Deductive Research: Difference of Approaches

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  2. Difference between Inductive and Deductive Approach?

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  3. Inductive Vs Deductive Research Approach

    case study research inductive or deductive

  4. Types of inductive and deductive research strategy

    case study research inductive or deductive

  5. Inductive vs Deductive Reasoning (With Definitions & Examples)

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  6. Inductive and Deductive Reasoning

    case study research inductive or deductive

VIDEO

  1. Types of Research Part 3

  2. Inductive and Deductive Reasoning

  3. INDUCTIVE AND DEDUCTIVE REASONING

  4. CH 2

  5. June 2, 2023

  6. Differences Between Deductive Research and Inductive Research

COMMENTS

  1. Inductive vs. Deductive Research Approach

    The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory. In other words, inductive reasoning moves from specific observations to broad generalizations. Deductive reasoning works the other way around.

  2. 2.3: Inductive or Deductive? Two Different Approaches

    In the case of my collaborative 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 ...

  3. Inductive Vs Deductive Research

    Compare and Contrast Inductive Vs Deductive Research January 5, 2024 by Muhammad Hassan Table of Contents Inductive Vs Deductive Research Inductive and deductive research are two different approaches to conducting a research study.

  4. Case Study Method: A Step-by-Step Guide for Business Researchers

    Deductive research logic begins with theory and is aimed at testing arguments, whereas relativist start with subjective accounts of lived experiences on which theory is built inductively. ... outcome of those case studies will be used as a starting point of deductive research that can then be followed by an inductive research study in different ...

  5. Case Study Methodology of Qualitative Research: Key Attributes and

    A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...

  6. Inductive vs Deductive Reasoning

    The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory. Inductive reasoning moves from specific observations to broad generalisations, and deductive reasoning the other way around.

  7. Qualitative Research Design and Data Analysis: Deductive and Inductive

    Deductive , or a priori, analysis generally means applying theory to the data to test the theory. It's a kind of "top-down" approach to data analysis. In qualitative analysis, this often means applying predetermined codes to the data.

  8. Comparing inductive and deductive analysis techniques to understand

    This study aimed to compare inductive and deductive analysis techniques to understand a complex implementation issue. We used childhood vaccination as a case study, an issue with wide-ranging barriers contributing to low-vaccine uptake internationally. Methods

  9. Inductive and Deductive Theory in Case Studies

    Summary The case study describes a survey performed by David Takeuchi and his team in 1974 which aimed at explaining the reasons for different treatment of marijuana by the students of the University of Hawaii (Babbie, 2016). Various explanations for this issue were offered.

  10. The potential of working hypotheses for deductive exploratory research

    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 wh...

  11. Inductive and/or Deductive Research Designs

    Introduction In social research, two research designs may be followed; one is inductive, and another is deductive. Strauss and Corbin ( 1998) described the inductive analysis as, "the researcher begins with an area of study and allows the theory to emerge from the data" (p. 12).

  12. Inductive or Deductive? Two Different Approaches

    Two Different Approaches Learning Objectives Describe the inductive approach to research, and provide examples of inductive research. Describe the deductive approach to research, and provide examples of deductive research. Describe the ways that inductive and deductive approaches may be complementary.

  13. Inductive vs Deductive Research: Difference of Approaches

    Inductive research is a method in which the researcher collects and analyzes data to develop theories, concepts, or hypotheses based on patterns and observations seen in the data. It uses a "bottom-up" method in which the researcher starts with specific observations and then moves on to more general theories or ideas.

  14. 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.

  15. Qualitative analysis: Deductive and inductive approaches

    Written By Andrea Bingham How you analyze qualitative data depends largely on your methodology, your personal organizational and analytic preferences, and what kind of data you have. That being said, all qualitative data analysis processes are going to fall into one of two categories: deductive or inductive.

  16. Quantitative and Qualitative Approaches to Generalization and

    This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains. Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases.

  17. Evaluating Inductive versus Deductive Research in Management Studies

    Purpose The purpose of this paper is to address the imbalance between inductive and deductive research in management and organizational studies and to suggest changes in the journal review...

  18. Can deductive approach be used in a qualitative case study?

    The deductive researcher "works from the 'top down', from a theory to hypotheses to data to add to or contradict the theory": In contrast, they define the inductive researcher as someone who...

  19. 8.1: Inductive and 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.

  20. A Step-by-Step Process of Thematic Analysis to Develop a Conceptual

    Methodologies like experimental research and case study research, which aim to verify or validate preexisting hypotheses, frequently take this approach. Naeem and Ozuem (2022a) used TORT and PMT to do a deductive thematic analysis of their data. Methodologies that want to both discover new phenomena and validate or develop current theories may ...

  21. 2.5.1: Inductive or Deductive? Two Different Approaches

    In the case of my collaborative 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 ...