Inductive and Deductive Models
Inductive and deductive models are two fundamental types of logical reasoning employed in the scientific method to advance knowledge about the world. Inductive reasoning involves drawing general principles from specific observations, allowing researchers to form hypotheses based on empirical data. For example, noticing specific patterns in behavior, such as pets preferring fish-flavored food, can lead to broader theories about preferences in pets. On the other hand, deductive reasoning works from general principles to make predictions about specific cases. For instance, knowing that certain behaviors tend to arise under specific conditions can guide predictions about individual cases, like anticipating a student's struggle in group settings based on their independent work history.
Both reasoning processes are essential for developing and testing scientific theories, yet they can be prone to errors. Inductive reasoning may lead to overgeneralizations, while deductive reasoning can sometimes result in faulty conclusions if the underlying premises are incorrect. Critical thinking and careful methodology are vital for ensuring the validity of conclusions drawn from both reasoning types. Understanding the interplay between inductive and deductive reasoning can enhance the rigor of research across various fields, including sociology and behavioral sciences, ultimately contributing to a more nuanced understanding of complex behaviors and phenomena.
Subject Terms
Inductive and Deductive Models
Abstract
Science advances through the rigorous application of the guidelines, principles, and procedures of the scientific method. This approach to studying the world around us utilizes two types of logical reasoning: inductive and deductive. Inductive reasoning occurs when one reasons from specific observations to theorize general principles. Deductive reasoning occurs when one reasons from general principles to specific applications. Neither type of logical reasoning is superior; both are necessary in order for the researcher to be able to transform observations of the real world into empirically testable hypotheses. Both types of reasoning are subject to error, however. The scientist must be careful to apply the various tools of critical thinking to the reasoning process in order to develop a theory that is both objective and verifiable.
Overview
One of the basic goals of sociology and other behavioral and social sciences is to describe, explain, and predict behavior. Before this goal can be met, however, behavior first needs to be observed in the real world. For example, I may notice that when I feed my cats "Happy Kitty" salmon dinner, they come running. Similarly, I may notice that students in my class who work well on their own tend not to work as well when required to work in a group setting. Or, I may notice that when I go to the Quick Mart for a quart of milk that people who purchase lottery tickets always appear as if they can ill afford the purchase. I may tuck any of these specific observations into the back of my mind and think about them no more. Or, I might become curious and wonder what the implication of these observations might be. For example, I may decide that my cats enjoy the flavor of "Happy Kitty" salmon dinner, and feed it to them more often. Similarly, I may decide that I need to add a lecture on team building principles to my course curriculum so that people can work more productively and effectively within small, mandated groups. Or, I may develop a theory relating socioeconomic information to the probability and consequences of lottery ticket purchase. All of these are examples of inductive reasoning: a type of logical reasoning in which inferences and general principles are drawn from specific observations or cases. Inductive reasoning is a foundation of the scientific method and enables the development of testable hypotheses from particular facts and observations.
Inductive reasoning is not the only logical process by which people reach conclusions, however. Deductive reasoning is another type of logical reasoning in which one demonstrates that a conclusion must necessarily follow from a sequence of premises, the first of which is a self-evident truth or agreed-upon data point or condition. Deductive reasoning is the foundation upon which predictions are drawn from general laws or theories. For example, if I know that cats enjoy eating fish in general, I may purchase a can of tuna the next time I need to tempt a finicky feline appetite. Similarly, if I know that people who work well independently often have difficulty working in groups, I may include a module on team building in my course curriculum. Or, based on my knowledge of the sociological research literature on lottery ticket purchasing behavior, while standing in line at the Quick Mart with my quart of milk I might look around at my fellow shoppers and predict which of these individuals will purchase a lottery ticket and which of them will not. The difference between deductive and inductive reasoning is shown in Figure 1.
Both inductive and deductive reasoning are important to the theory-building process and the scientific method. The scientific method comprises general procedures, guidelines, assumptions, and attitudes required for the organized and systematic collection, analysis, interpretation, and verification of data that can be verified and reproduced. The goal of the scientific method is to articulate or modify the laws and principles of a science. Steps in the scientific method include problem definition based on observation and review of the literature, formulation of a testable hypothesis, selection of a research design, data collection and analysis, extrapolation of conclusions, and development of ideas for further research in the area.
As shown in Figure 2, both inductive and deductive reasoning are essential to the theory-building process. Empirical research is an important part of this process. The design of a research study starts with a theory based on real-world observation. For example, from personal experience with the types of cat food my cats like to eat, I may develop a preliminary theory that cat food containing some type of fish as its main ingredient is more likely to tempt their appetites than are other kinds of cat food. This preliminary theory is based on the inductive reasoning process that I used to extrapolate my observations of what my cats have liked to eat in the past to a prediction of what my cats might like to eat in the future. Based on my observations and my preliminary theory, I could then develop a hypothesis about the relationship between my cats' behavior and the type of food that I offer them. This hypothesis would be an empirically testable declaration that certain variables and their corresponding measure are related in a specific way proposed by a theory. In this example, my hypothesis might be that my cats prefer food that contains fish as the primary ingredient. To assist in the development of a testable hypothesis that would lead to generalizable and repeatable results, I may want to further refine the operational definitions in the hypothesis. For example, based on other previous observations I may have noticed that my cats prefer canned food to kibble, or that they prefer the "Happy Kitty" brand of canned food to the "Sour Puss" brand. Based on these observations and the concomitant inductive reasoning process, I might choose to limit my hypothesis to deal only with "Happy Kitty" brand cat food. Therefore, my tentative hypothesis might be that my cats prefer fish-flavored varieties of "Happy Kitty" cat food to other varieties of "Happy Kitty." This hypothesis could be empirically tested by simultaneously offering my cats a bowl of "Happy Kitty" and salmon and a bowl of "Happy Kitty" beef stew. If the results of my experiment indicate that my hypothesis is correct, I might extrapolate these results deductively and purchase a can of "Happy Kitty" shrimp-and-gravy cat food.
As can be seen in Figure 2, the advancement of science typically depends on both the inductive and deductive reasoning processes. My personal observations—whether they be about my cats' preferences and food, my students' ability to work well in groups, or the general characteristics of people who purchase lottery tickets—and the concomitant application of the inductive reasoning process give me little more than empirically based personal opinion in the form of a preliminary theory. It is not until I articulate a testable hypothesis and subject my theory to the rigors of the scientific method, that I can apply the deductive reasoning process and determine whether or not my theory is likely to be true. However, without my personal observations and the concomitant inductive process, I would never have been able to form a testable hypothesis, apply the scientific method, and use deductive reasoning to be able to predict the future behavior of cats, students, or lottery ticket purchasers. Both types of reasoning are essential and interrelated.
Applications
The Scientific Method. Although both inductive and deductive reasoning are essential to the scientific method and the advancement of science, neither of these tools is without its pitfalls. Caution must be used in the application of either reasoning method. For example, based on my observations of the food preferences of my cats, I might apply inductive reasoning and conclude that they would find any type of fish-flavored cat food preferable to other flavors. However, if my empirical observations are based only on my cats' responses to the salmon and beef stew varieties of "Happy Kitty" brand cat food, my extrapolation of these observations to include other brands of cat food or even other varieties within the "Happy Kitty" brand might be erroneous. It may be that my cats just do not like "Happy Kitty" beef stew. Or, it may be that my cats like "Happy Kitty" beef stew better than all fish-flavored varieties except for salmon. Similarly, it is easy to misuse the deductive reasoning process by generalizing the results inappropriately. For example, if the results of my cat food research study concluded that my cats are more likely to eat fish-flavored cat food than beef stew, I cannot necessarily extrapolate those results to include all felines or even all house cats. Although some cats prefer fish, others prefer beef, and even others prefer cantaloupe. My research study did not cover these contingencies. Therefore, I cannot use the deductive reasoning process to generalize to conclusions about these things.
Morse and Mitcham (2002) explored a number of the possible pitfalls that can be encountered in the application of the inductive and deductive reasoning processes. Documented concern over reasoning processes and their proper application goes back as far as Aristotle. In the end, however, most philosophers and scientists conclude that both inductive and deductive reasoning have their place in the scientific method. However, one needs to be careful to appropriately apply these tools if one desires to reach a logical conclusion and meaningful result.
Inductive Reasoning Pitfalls: The Pink Elephant Paradox. Two classes of potential pitfalls in inductive reasoning are worthy of particular consideration. The "pink elephant paradox" is a situation that occurs in the research equivalent of being told not to think about pink elephants: once the idea of pink elephants enters into one's thought process it is virtually impossible to exorcise them. A similar problem is the "experimenter effect." This is a phenomenon in which the expectations of a researcher lead to unintentional errors in the conduct or design of an experiment which, in turn, lead to the results of the experiment confirming the researcher's original hypothesis. Most experts, therefore, agree on the importance of a researcher taking time for self-examination to consider the ways in which they could potentially influence the reasoning behind and outcomes of a study before beginning (Teusner, 2016).
Conceptual Tunnel Vision. Another pitfall that can be encountered in inductive reasoning is sometimes referred to as "conceptual tunnel vision." This concept refers to a situation in which a researcher erroneously excludes data from consideration because s/he does not believe that it is relevant. The results of many research studies are somewhat ambiguous and open to different interpretations. More than one well-meaning researcher has interpreted ambiguous results in a way that confirms his/her original hypothesis by unintentionally excluding alternative explanations from his/her conclusions. For example, a behavioral scientist may have a theory concerning the factors influencing the development of a strong, effective team. In an effort to test his/her theory, the researcher may be very careful to include operationally well-defined variables related to the theory being investigated. However, the researcher's focus on the inclusion of all appropriate variables to the theory being investigated may also lead to the exclusion of other variables related to other theories. This is one of many reasons that research results reported in the literature cannot always be replicated by other researchers. This type of error in inductive reasoning processes is also reflected in both the professional literature and the popular media with the inappropriate recommendation of various palliatives for physical and psychological problems that later research finds to be in error (e.g., when a drug once considered to be a miracle cure for a specific disease or disorder is later found to have no impact or have negative side effects that require its recall). Similarly, the development of theoretical constructs itself can be an error-prone process, particularly in the social and behavioral sciences where there may be no widely agreed upon definition for many of the terms being tested.
Qualitative Research Errors. Errors in logical reasoning are particularly of concern in qualitative research. One way to help lessen the probability of logical errors is to apply the principles of critical thinking. This involves the application of intellectually disciplined processes in which one actively conceptualizes, applies, analyzes, synthesizes, and evaluates information. For example, to lessen the possibility of one's theory becoming a self-fulfilling prophecy, the scientific method requires one to first review the scientific literature on the subject. This is done in part to determine alternative hypotheses that should be considered and tested. Examining competing theories will also enable one to better think through the logic of the experimental hypothesis to be tested and to design a research paradigm that will yield less ambiguous results.
Deconstruction is another useful tool for minimizing the probability of logical errors. In deconstruction, one critically analyzes scientific literature with the assumption that there is no external referent for language and that "truth" cannot be proven. This enables one to better examine the literature and past research with an objective and critical eye in order to determine where other interpretations are possible and where holes exist in current theories. Researchers should critically examine and analyze the literature as a whole and evaluate individual theories in greater context to better understand them. Deconstruction in this context includes the identification of attributes, assumptions, gaps, limitations, and other perspectives that may affect the veracity of a theory.
After an objective examination of the concepts in the scientific literature, the researcher next proceeds to focus his/her inquiry by developing a skeletal framework about the concept under investigation, using data and prior knowledge to inform the way the theory is shaped. This framework should be based on the concept analysis done as part of the literature review. At this point in the logical process in qualitative research, the scope of data collection needs to remain somewhat broad in order to guard against prematurely eliminating data or concepts that are essential to the theory. The skeletal framework helps the researcher focus the investigation by providing a structure for the inquiry. The concepts within the framework are then expanded and verified using the process of inductive reasoning. This allows the development of a theory that is empirically verifiable using the process of deductive reasoning and quantitative research. Quantitative research depends on more objective examination of the variables and their relationships in a theory and is typically conducted on more well-defined phenomena than is qualitative research. In quantitative research, the earlier empirical observations of the researcher are stated as a testable hypothesis about the causal relationships between variables (i.e., the hypothesized effect of the independent variable on the dependent variable) and the development of a research paradigm that controls for the influence of other variables that are extraneous to the hypothesis under investigation. The results of inferential statistical tests are used to inform the deductive reasoning process in confirming or refining the hypothesis. In some cases, researchers have studied the best methodologies to use in order to combine results across multiple studies, both qualitative and quantitative, to better inform analysis and the deductive reasoning process (Kachroo, Krishen, & Agarwal, 2017).
Conclusion
Advancements in science depend on the rigorous application of the scientific method to the observations of phenomena, behavior, and other data in the real world. The scientific method requires the use of two types of logical reasoning: inductive reasoning in which one reasons from specific observations to a general principle and deductive reasoning in which one reasons from general principles to predict specific situations. One type of logical reasoning is not superior to another; both are essential for understanding and predicting behavior. However, the implementation of logical reasoning processes alone does not guarantee that the results of the reasoning process will, indeed, be logical. Errors can unintentionally affect the validity of the reasoning process as well as the validity of the outcome of the process. Therefore, it is important to carefully consider the objectivity with which inductive or deductive reasoning are applied to a problem in order to help ensure objective and usable results. Methods to aid in the logical process include critical thinking, deconstruction, development of a skeletal framework of data based on a concept analysis of the scientific literature, verification of the framework, and the development of theoretical frameworks that can be empirically tested and validated using deductive reasoning.
Terms & Concepts
Data: (sing. datum) In statistics, data are quantifiable observations or measurements that are used as the basis of scientific research.
Deductive Reasoning: A type of logical reasoning in which it is demonstrated that a conclusion must necessarily follow from a sequence of premises, the first of which is a self-evident truth or agreed-upon data point or condition. Deductive reasoning is the foundation upon which predictions are drawn from general laws or theories.
Empirical: Theories or evidence that are derived from or based on observation or experiment.
Hypothesis: An empirically testable declaration that certain variables and their corresponding measures are related in a specific way proposed by a theory.
Inductive Reasoning: A type of logical reasoning in which inferences and general principles are drawn from specific observations or cases. Inductive reasoning is a foundation of the scientific method that enables the development of testable hypotheses from particular facts and observations.
Inferential Statistics: A subset of mathematical statistics used in the analysis and interpretation of data. Inferential statistics are used to make inferences such as drawing conclusions about a population from a sample and in decision making.
Operational Definition: A definition that is stated in terms that can be observed and measured.
Qualitative Research: Scientific research in which observations cannot be or are not quantified (i.e., expressed in numerical form).
Quantitative Research: Scientific research in which observations are measured and expressed in numerical form (e.g., physical dimensions, rating scales).
Scientific Method: General procedures, guidelines, assumptions, and attitudes required for the organized and systematic collection, analysis, interpretation, and verification of data that can be verified and reproduced. The goal of the scientific method is to articulate or modify the laws and principles of a science. Steps in the scientific method include problem definition based on observation and review of the literature, formulation of a testable hypothesis, selection of a research design, data collection and analysis, extrapolation of conclusions, and development of ideas for further research in the area.
Self-Fulfilling Prophecy: A situation in which one's belief or expectation sets up a condition where the belief or expectation is met. For example, a student who thinks that s/he will not do well on an examination even if s/he studies will end up not studying and, therefore, will not do well on the examination.
Socioeconomic Status (SES): The position of an individual or group on the two vectors of social and economic status and their combination. Factors contributing to socioeconomic status include (but are not limited to) income, type and prestige of occupation, place of residence, and educational attainment.
Variable: An object in a research study that can have more than one value. Independent variables are stimuli that are manipulated in order to determine their effect on the dependent variables (response). Extraneous variables are variables that affect the response but that are not related to the question under investigation in the study.
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Suggested Reading
Anderson, M. L. & Taylor, H. F. (2002). Sociology: Understanding a diverse society (2nd ed.). Belmont, CA: Wadsworth/Thomson Learning.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: SAGE.
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