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❶Premises about the correlation of two things can indicate a causal relationship between them, but additional factors must be confirmed to establish the exact form of the causal relationship. Qualitative and Quantative approaches You gave clear differences in a balance, simple to understand, I suppose you are a teacher by profession.

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.

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. 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. Which would be more appropriate qualitative or quantitative?

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. 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?

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. Points of distinction top notch.

Straight to the point. Keep up the good work. I found this site while searching the difference between the two on Google. Thank you for the input. 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.

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. 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.

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

Could you please kindly let me know what is my reasearch method Thanks. Dr, your explanation about inductive research and deductive, is meaningful to postgraduate students. What is your suggestion on my research topic: Today the same question appeared and I used your explanation as my response to the question. May I ask you question? Focus on what your research objectives are and then choose the approach that will be most efefctive in meeting these objectives. Thanks Deborah for the explanation but, i want to ask if descriptive is inductive or deductive approach?

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: Thank you so much Deborah.

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.

Deborah, your work is precise,well organized and relevant. Hi Doc, thank you for making things simpler for me. I will always be incontact with your website. Hi Deborah, i just went through the abductive approach which is combination of inductive and deductive Approach. But after going through the conversation in this page helped me a lot. Thank u very much. Title is Knowledge and Learning Model among effective repatriation.

Thank u all again. Hi Deborah Thank you very much for the article. 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? 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. Your explanation of concepts is succint and easily conceivable. No problem — you can use the contact form and your message will go directly to my email address. 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. Inductive and deductive approaches to research.

However it seems short. Dear Almaz, thank you for your feedback. Therefore, I am taking the Western literature outcomes and applying these in UAE context to see the results. Will this research be treated as "Deductive' or "Inductive"? Should I choose 'Quantitative" or "Qualitative' approach? Wishing you all the best. So what is grounded theory?

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?

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. Thank You so very much Deborah. I really got to uncover what puzzled me on deductive versus inductive approaches. It was very simple and useful. It is very useful for my dissertation. If I want to conduct a research to find those elements in a bank operation. 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: In answer to your question, I think maybe you are confusing research questions with hypotheses. 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.

Hi Deborah thank you for a great article. 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: Hi Deborah, Thank you for the precise and helpful information.. I need your help as I feel a little bit confused.

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? What do you think? It may require a little bit of referencing.

Furthermore, adding citation line below will be useful for academic use. 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.

Hi, It is really helpful me to get sorted these concepts in research field in simple manner. Thanks for that and really appreciate it. That was just perfect. Now it is completely clear. 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?

You cannot start with inductive and then switch to deductive — it must be one or the other. Great work and explanation and also the researcher herself is very energetic and motivated to help others… world is because of people like you.

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. 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. Your email address will not be published. This site uses Akismet to reduce spam. Learn how your comment data is processed. Deborah Gabriel Research Guides March 17, July 5, deductive research , doctoral research , grounded theory , inductive research , qualitative research , research approcahes , research methods Comments.

Has this post helped you? If so then please leave a comment! Methods and methodology Deborah Gabriel Pingback: Thanks — Chantal Reply thank you so much for the information. Reply Deborah, thanks for this elaboration. Thanks Reply Deductive research is more aimed towards testing a hypothesis and therefore is an approach more suited to working with quantitative data. Reply Dear Deborah, it has been very long time since you posted this article. Reply Thanks so much Reply Thank u for the information, it really helps me.

Reply Exactly, your work is simple and clear, that there are two research approaches, Inductive and deductive. This is how we share knowledge,and you become more knowledgable Reply Thank you Lambawi, I am glad that these posts are proving useful.

Reply This has been helpful. Reply Thank you ever so much for making it simple and easily understandable. Best wishes Molla Reply The explanation is simple and easy to understand it has helped to a lot thank you Reply very helpful and explained simply. Reply Excellent presentation please! Thank you Reply Thanks; this has been helpful in preparation for my forthcoming exams Reply This is fantastic, I have greatly beneffitted from this straight forward illustration Reply Thanks…i will benifited to read this Reply Thanks for your help.

Keep it like that so that will be our guide towards our destinations. Reply Thank-you for your academic insights. Reply Thank you for your clarification. Comte was confident to lay laws as irrefutable foundation of other knowledge , and the churches, honoring eminent scientists, sought to focus public mindset on altruism —a term Comte coined—to apply science for humankind's social welfare via Comte's spearheaded science, sociology.

During the s and s, while Comte and Mill were the leading philosophers of science, William Whewell found enumerative induction not nearly so simple, but, amid the dominance of inductivism, described "superinduction".

Rarely spotted by Whewell's predecessors, such mental inventions rapidly evade notice. Having once had the phenomena bound together in their minds in virtue of the Conception, men can no longer easily restore them back to detached and incoherent condition in which they were before they were thus combined".

These "superinduced" explanations may well be flawed, but their accuracy is suggested when they exhibit what Whewell termed consilience —that is, simultaneously predicting the inductive generalizations in multiple areas—a feat that, according to Whewell, can establish their truth.

Perhaps to accommodate prevailing view of science as inductivist method, Whewell devoted several chapters to "methods of induction" and sometimes said "logic of induction"—and yet stressed it lacks rules and cannot be trained. Originator of pragmatism , C S Peirce who, as did Gottlob Frege independently, in the s performed vast investigations that clarified the basis of deductive inference as mathematical proof, recognized induction but continuously insisted on a third type of inference that Peirce variously termed abduction or retroduction or hypothesis or presumption.

Having highlighted Hume's problem of induction , John Maynard Keynes posed logical probability as its answer—but then figured not quite. The principle of induction, as applied to causation, says that, if A has been found very often accompanied or followed by B , then it is probable that on the next occasion on which A is observed, it will be accompanied or followed by B.

If the principle is to be adequate, a sufficient number of instances must make the probability not far short of certainty. If this principle, or any other from which it can be deduced, is true, then the casual inferences which Hume rejects are valid, not indeed as giving certainty, but as giving a sufficient probability for practical purposes.

If this principle is not true, every attempt to arrive at general scientific laws from particular observations is fallacious, and Hume's skepticism is inescapable for an empiricist. The principle itself cannot, of course, without circularity, be inferred from observed uniformities, since it is required to justify any such inference. It must therefore be, or be deduced from, an independent principle not based on experience. To this extent, Hume has proved that pure empiricism is not a sufficient basis for science.

But if this one principle is admitted, everything else can proceed in accordance with the theory that all our knowledge is based on experience. It must be granted that this is a serious departure from pure empiricism, and that those who are not empiricists may ask why, if one departure is allowed, others are forbidden. These, however, are not questions directly raised by Hume's arguments.

What these arguments prove—and I do not think the proof can be controverted—is that the induction is an independent logical principle, incapable of being inferred either from experience or from other logical principles, and that without this principle, science is impossible".

In a paper, Gilbert Harman explained that enumerative induction is not an autonomous phenomenon, but is simply a masked consequence of inference to the best explanation IBE. Inductive reasoning has been criticized by thinkers as far back as Sextus Empiricus. Although the use of inductive reasoning demonstrates considerable success, its application has been questionable.

Recognizing this, Hume highlighted the fact that our mind draws uncertain conclusions from relatively limited experiences. In deduction, the truth value of the conclusion is based on the truth of the premise. In induction, however, the dependence on the premise is always uncertain. As an example, let's assume "all ravens are black. However, the assumption becomes inconsistent with the fact that there are white ravens.

Therefore, the general rule of "all ravens are black" is inconsistent with the existence of the white raven. Hume further argued that it is impossible to justify inductive reasoning: Since this is circular he concluded that our use of induction is unjustifiable with the help of Hume's Fork.

However, Hume then stated that even if induction were proved unreliable, we would still have to rely on it. So instead of a position of severe skepticism , Hume advocated a practical skepticism based on common sense , where the inevitability of induction is accepted. It is neither a psychological fact, nor a fact of ordinary life, nor one of scientific procedure".

By now, inductive inference has been shown to exist, but is found rarely, as in programs of machine learning in Artificial Intelligence AI. Inductive reasoning is also known as hypothesis construction because any conclusions made are based on current knowledge and predictions. Examples of these biases include the availability heuristic , confirmation bias , and the predictable-world bias. The availability heuristic causes the reasoner to depend primarily upon information that is readily available to them.

People have a tendency to rely on information that is easily accessible in the world around them. For example, in surveys, when people are asked to estimate the percentage of people who died from various causes, most respondents would choose the causes that have been most prevalent in the media such as terrorism, and murders, and airplane accidents rather than causes such as disease and traffic accidents, which have been technically "less accessible" to the individual since they are not emphasized as heavily in the world around them.

The confirmation bias is based on the natural tendency to confirm rather than to deny a current hypothesis. Research has demonstrated that people are inclined to seek solutions to problems that are more consistent with known hypotheses rather than attempt to refute those hypotheses. Often, in experiments, subjects will ask questions that seek answers that fit established hypotheses, thus confirming these hypotheses.

For example, if it is hypothesized that Sally is a sociable individual, subjects will naturally seek to confirm the premise by asking questions that would produce answers confirming that Sally is in fact a sociable individual. The predictable-world bias revolves around the inclination to perceive order where it has not been proved to exist, either at all or at a particular level of abstraction.

Gambling, for example, is one of the most popular examples of predictable-world bias. Gamblers often begin to think that they see simple and obvious patterns in the outcomes and, therefore, believe that they are able to predict outcomes based upon what they have witnessed. In reality, however, the outcomes of these games are difficult to predict and highly complex in nature. However, in general, people tend to seek some type of simplistic order to explain or justify their beliefs and experiences, and it is often difficult for them to realise that their perceptions of order may be entirely different from the truth.

A generalization more accurately, an inductive generalization proceeds from a premise about a sample to a conclusion about the population. There are 20 balls—either black or white—in an urn. To estimate their respective numbers, you draw a sample of four balls and find that three are black and one is white. A good inductive generalization would be that there are 15 black and five white balls in the urn.

How much the premises support the conclusion depends upon a the number in the sample group, b the number in the population, and c the degree to which the sample represents the population which may be achieved by taking a random sample. The hasty generalization and the biased sample are generalization fallacies. Two dicto simpliciter fallacies can occur in statistical syllogisms: Simple induction proceeds from a premise about a sample group to a conclusion about another individual.

This is a combination of a generalization and a statistical syllogism, where the conclusion of the generalization is also the first premise of the statistical syllogism. The basic form of inductive inference , simply induction , reasons from particular instances to all instances, and is thus an unrestricted generalization.

As this reasoning form 's premises, even if true, do not entail the conclusion's truth, this is a form of inductive inference. The conclusion might be true, and might be thought probably true, yet it can be false.

Questions regarding the justification and form of enumerative inductions have been central in philosophy of science , as enumerative induction has a pivotal role in the traditional model of the scientific method. The process of analogical inference involves noting the shared properties of two or more things, and from this basis inferring that they also share some further property: Analogical reasoning is very frequent in common sense , science , philosophy and the humanities , but sometimes it is accepted only as an auxiliary method.

A refined approach is case-based reasoning. A causal inference draws a conclusion about a causal connection based on the conditions of the occurrence of an effect. Premises about the correlation of two things can indicate a causal relationship between them, but additional factors must be confirmed to establish the exact form of the causal relationship. As a logic of induction rather than a theory of belief, Bayesian inference does not determine which beliefs are a priori rational, but rather determines how we should rationally change the beliefs we have when presented with evidence.

We begin by committing to a prior probability for a hypothesis based on logic or previous experience, and when faced with evidence, we adjust the strength of our belief in that hypothesis in a precise manner using Bayesian logic.

Around , Ray Solomonoff founded the theory of universal inductive inference , the theory of prediction based on observations; for example, predicting the next symbol based upon a given series of symbols. This is a formal inductive framework that combines algorithmic information theory with the Bayesian framework.

Universal inductive inference is based on solid philosophical foundations, [31] and can be considered as a mathematically formalized Occam's razor. Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity. From Wikipedia, the free encyclopedia. For the technique in mathematical proof, see Mathematical induction. Thinking portal Logic portal. Abductive reasoning Algorithmic probability Analogy Bayesian probability Counterinduction Deductive reasoning Explanation Failure mode and effects analysis Falsifiability Grammar induction Inductive inference Inductive logic programming Inductive probability Inductive programming Inductive reasoning aptitude Inductivism Inquiry Kolmogorov complexity Lateral thinking Laurence Jonathan Cohen Logic Logical positivism Machine learning Mathematical induction Mill's Methods Minimum description length Minimum message length New riddle of induction Open world assumption Raven paradox Recursive Bayesian estimation Retroduction Solomonoff's theory of inductive inference Statistical inference Stephen Toulmin Marcus Hutter.

Essentials of Logic Second ed. Upper Saddle River, NJ: While this usage is still sometimes found even in philosophical and mathematical contexts, for the most part, it is outdated.

International Journal of General Systems. The Problem of Induction. The Stanford Encyclopedia of Philosophy. Kant's account of reason". Fundamentals of Discrete Mathematical Structures 3rd ed. Retrieved 1 December Rationality without Foundations New York: Routledge , , ch.

Cambridge University Press , , pp , — A fairly recent debate has arisen over the merits of strict inductivism. Some philosophers have argued that there are other forms of nondeductive inference that do not fit the model of enumerative induction. Peirce describes a form of inference called ' abduction ' or ' inference to the best explanation '.

This form of inference appeals to explanatory considerations to justify belief. One infers, for example, that two students copied answers from a third because this is the best explanation of the available data—they each make the same mistakes and the two sat in view of the third.

Alternatively, in a more theoretical context, one infers that there are very small unobservable particles because this is the best explanation of Brownian motion.

Let us call 'liberal inductivism' any view that accepts the legitimacy of a form of inference to the best explanation that is distinct from enumerative induction. For a defense of liberal inductivism, see Gilbert Harman 's classic paper. Harman defends a strong version of liberal inductivism according to which enumerative induction is just a disguised form of inference to the best explanation ".

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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.

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In logic, we often refer to the two broad methods of reasoning as the deductive and inductive approaches. Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a "top-down" approach.

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Inductive approach, also known in inductive reasoning, starts with the observations and theories are proposed towards the end of the research . 3 Research Methods Research Types Deductive Approach Inductive Approach In research, we often refer to the two broad methods of reasoning as the deductive and inductive approaches.

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While inductive reasoning is commonly used in scientific research, it is not without its weaknesses. For example, it is not always logically valid to assume that a general principle is correct simply because it is supported by a limited number of cases.