Inductive vs. deductive reasoning is a choice that researchers have to make when analyzing their data. Both inductive and deductive reasoning allow researchers to come to a logical conclusion based on their data and the existing theory out there.
Let's look at the differences between the types of reasoning and examine specific examples regarding how they are used for drawing conclusions. We'll also look briefly at abductive reasoning so you can decide if it is more useful for your research. Then we can examine how you can apply inductive reasoning and deductive reasoning to your analysis in ATLAS.ti.
Inductive logic looks at observing patterns to arrive at a logical conclusion. Any analytical approach called induction starts with examining a body of data, ideally without preconceived notions, to try to reach general conclusions based solely on the data.
By identifying patterns in the data, inductive reasoning allows a researcher to form a premise, or a statement that applies to that specific set of data. As more data from other contexts or situations is incorporated into the researcher's analysis, the researcher can form what is called a major premise, or a general conclusion about the patterns observed, while a more specific conclusion regarding a particular context becomes what is known as a minor premise.
In the social sciences, major or general premises contribute to theories about human behavior and cultural patterns at a universal level.
Let's look at a basic example of inductive reasoning. Imagine that you are applying the inductive method to a basketball game you are watching for the first time. Inductive reasoning would be required in this situation since you don't have the prior knowledge or experience to make any sort of general statement about basketball.
If one knows nothing about basketball or sports, one possible inductive argument is that the players wearing shirts of the same color are on the same team working together. The general premise, at least for the moment, is that uniform colors are used to distinguish one team from another. The conclusion reached in this observation forms a working theory that can be applied to similar contexts.
Other examples of inductive reasoning generating conclusions include:
As obvious from examples such as these, the conclusions reached are based on observations of cats, cars, and pizza, but those observations may not have included exceptions (e.g., dessert pizza) to contradict the general premises generated. Qualitative research employs inductive methods iteratively to find those exceptions and further develop more universal observations and conclusions about the world.
Inductive reasoning starts from the bottom up, while deductive reasoning begins from the top down. Deductive reasoning works by applying what is already known about the world to form a hypothesis to test through research and thus reach a conclusion. As a result, the scientific method is closely associated with deductive reasoning.
Deduction starts with a first premise ("all players on the same basketball team were the same-colored uniform") and applies it to a new context or set of data. What the researcher learns about the data is known as the second premise. Finally, the researcher compares the two premises to arrive at an inference or conclusion that either affirms the first premise or warrants development of existing theory or creation of new theory.
The main difference between inductive and deductive reasoning is that deductive reasoning relies on existing knowledge independent of new data. A researcher employing deductive reasoning needs to have a valid hypothesis or preconceived notion to test against the data they are analyzing.
In the example on inductive reasoning, imagine that you are watching another basketball game, this time in a more casual setting where players don't wear uniforms. However, one set of players is wearing shirts while the other players are not. The specific conclusions about the same color uniforms are tested here but ultimately conflict with the new data.
Among other examples of deductive reasoning, imagine another simple example where you are conducting specific observations where birds lay eggs. Using previous knowledge and experience to build your premise, you are likely to conclude that the eggs will eventually hatch birds. The inference or educated guess you make about what happens to the eggs is thus tested by the actual outcome of the eggs you are observing.
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A third type of reasoning is called abduction, which relies on making an educated guess based on your observations. For example, think about how Sherlock Holmes or other famous detectives employ abductive reasoning to rule out impossible or improbable events.
As a quick example of abduction, imagine that you are observing a student who is reading a textbook. If you conclude that they are too young for elementary school or junior high school and too old for university or continuing education, the only remaining conclusion is that they belong to a secondary education context. Even if such a conclusion is not explicitly stated in the data, abduction allows you to form the kind of argument that induction or deduction might not be able to generate by building an argument from existing theory or knowledge.
The reasoning strategy you employ affects all stages of the research process, especially if you are conducting observations for qualitative research.
Observations involve the researcher directly collecting data based on what they see and sense. As a result, the reasoning that you employ will influence what you see, who you talk to, and where you point your camera.
For example, if you are conducting fieldwork with a deductive approach and a hypothesis in mind about the effects of waterfalls on local agriculture, your hypothesis might compel you to collect data primarily around the waterfalls in the area to guide your research toward a specific conclusion. On the other hand, data collection based on induction might observe a relationship between plant growth and sources of water, guiding the data collection toward waterfalls and other sources of water. The main idea is that data collection cannot capture all possible phenomena; the reasoning employed by the researcher will determine what is captured in research.
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Whether your data analysis strategy follows inductive logic or deductive logic, ATLAS.ti can help you apply your reasoning to your research data through the data analysis process.
At the core of qualitative data analysis is coding, which summarizes data into smaller segments of information that are easier to understand. These codes, when organized in a hierarchy, work inductively and deductively to help you visualize a working theory based on the data and existing knowledge.
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