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Deductive reasoning happens when a researcher works from the more general information to the more specific. Sometimes this is called the “top-down” approach because the researcher starts at the top with a very broad spectrum of information and they work their way down to a specific conclusion. For instance, a researcher might begin with a theory about his or her topic of interest. From there, he or she would narrow that down into more specific hypotheses that can be tested. The hypotheses are then narrowed down even further when observations are collected to test the hypotheses.
This ultimately leads the researcher to be able to test the hypotheses with specific data, leading to a confirmation (or not) of the original theory and arriving at a conclusion. An example of deductive reasoning can be seen in this set of statements: Every day, I leave for work in my car at eight o’clock. Every day, the drive to work takes 45 minutes I arrive to work on time.
Therefore, if I leave for work at eight o’clock today, I will be on time. The deductive statement above is a perfect logical statement, but it does rely on the initial premise being correct.
Perhaps today there is construction on the way to work and you will end up being late. This is why any hypothesis can never be completely proved, because there is always the possibility for the initial premise to be wrong. Deductive reasoning is one of the two basic forms of valid reasoning.
It begins with a general hypothesis or known fact and creates a specific conclusion from that generalization. This is the opposite of inductive reasoning, which involves creating broad generalizations from specific observations.
The basic idea of deductive reasoning is that if something is true of a class of things in general, this truth applies to all members of that class. One of the keys for sound deductive reasoning, then, is to be able to properly identify members of the class, because incorrect categorizations will result in unsound conclusions. Truth and Validity For deductive reasoning to be sound, the original hypothesis or generalization also must be correct. A logical deduction can be made from any generalization, even if it is not true. If the generalization is wrong, hough, the specific conclusion can be logical and valid but still can be incorrect. Examples One can better understand deductive reasoning by looking at examples.
A generalization might be something such as, "All wasps have stingers. " The logical conclusion of a specific instance would then be, "That is a wasp, so it has a stinger. " This is a valid deduction. The truth of the deduction, however, depends on whether the observed insect is, indeed, a wasp. People often use deductive reasoning without even knowing it. For example, a parent might say to a child, "Be careful of that wasp — it might sting you. The parent says this because he or she knows that wasps have stingers and, therefore, that the observed wasp has a stinger and might sting the child. Inductive Reasoning Inductive reasoning works the opposite way, moving from specific observations to broader generalizations and theories. This is sometimes called a “bottom up” approach. The researcher begins with specific observations and measures, begins to then detect patterns and regularities, formulate some tentative hypotheses to explore, and finally ends up developing some general conclusions or theories.
An example of inductive reasoning can be seen in this set of statements: Today, I left for work at eight o’clock and I arrived on time. Therefore, every day that I leave the house at eight o’clock, I will arrive to work on time. While inductive reasoning is commonly used in science, it is not always logically valid because it is not always accurate to assume that a general principle is correct. In the example above, perhaps ‘today’ is a weekend with less traffic, so if you left the house at eight o’clock on a Monday, it would take longer and you would be late for work.
It is illogical to assume an entire premise just because one specific data set seems to suggest it. Inductive reasoning would work in the opposite order. The specific observation would be that a particular wasp has a stinger. One could then induce that all wasps have stingers. Many scientific tests involve proving whether a deduction or induction is, in fact, true. Inducing that all cats have orange fur because one cat has orange fur, for example, could be easily disproved by observing cats that do not have orange fur.
One of the most common and useful forms of deductive reasoning is the syllogism. A syllogism is a specific form of argument that has three easy steps: a major premise, a minor premise and a logical conclusion. For example, the premise "Every X has the characteristic Y" could be followed by the premise "This thing is X," which would yield the conclusion "This thing has the characteristic Y. " The first wasp example could be broken up into the major premise "Every wasp has a stinger," the minor premise "This insect is a wasp" and the conclusion "This insect has a stinger. Creating a syllogism is considered a good way for deductive reasoning to be tested to ensure that it is valid. Actual Practice By nature, inductive reasoning is more open-ended and exploratory, especially during the early stages. Deductive reasoning is narrower and is generally used to test or confirm hypotheses. Most social research, however, involves both inductive and deductive reasoning throughout the research process. The scientific norm of logical reasoning provides a two-way bridge between theory and research.
In practice, this typically involves alternating between deduction and induction. A good example of this is the classic work of Emile Durkheim on suicide. When Durkheim pored over tables of official statistics on suicide rates in different areas, he noticed that Protestant countries consistently had higher suicide rates than Catholic ones. His initial observations led him to inductively create a theory of religion, social integration, anomie, and suicide. His theoretical interpretations in turn led him to deductively create more hypotheses and collect more observations.
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